The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes: Volume II: Neuroanatomical and Neuroimaging Endophenotypes and Biomarkers

The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes

THE HANDBOOK OF NEUROPSYCHIATRIC BIOMARKERS, ENDOPHENOTYPES AND GENES

Volume 1: Neuropsychological Endophenotypes and Biomarkers Volume 2: Neuroanatomical and Neuroimaging Endophenotypes and Biomarkers Volume 3: Metabolic and Peripheral Biomarkers Volume 4: Molecular Genetic and Genomic Markers

Michael S. Ritsner Editor

The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes Volume 2

Neuroanatomical and Neuroimaging Endophenotypes and Biomarkers

Editor Michael S. Ritsner, M.D., Ph.D. Associate Professor of Psychiatry, the Rappaport Faculty of Medicine Technion - Israel Institute of Technology, Haifa and Sha’ar Menashe Mental Health Center, Hadera, Israel

ISBN 978-1-4020-9830-7

e-ISBN 978-1-4020-9831-4

Library of Congress Control Number: 2008942052 © Springer Science + Business Media B.V. 2009 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper springer.com

Foreword

Common genetically influenced neuropsychiatric disorders such as schizophrenia spectrum disorders, major depression, bipolar and anxiety disorders, epilepsy, neurodegenerative and demyelinating disorders, Parkinson and Alzheimer’s diseases, alcoholism, substance abuse, and drug dependence are the most debilitating illnesses worldwide. They are characterized by their complexity of causes and by their lack of pathognomonic laboratory diagnostic tests. During the past decade many researchers around the world have explored the neuropsychiatric biomarkers and endophenotypes implicated, not only in order to understand the genetic basis of these disorders but also from diagnostic, prognostic, and pharmacological perspectives. These fields have therefore, witnessed enormous expansion in new findings obtained by neuropsychological, neurophysiological, neuroimaging, neuroanatomical, neurochemical, molecular genetic, genomic and proteomic analyses, which have generated a necessity for syntheses across the main neuropsychiatric disorders. The challenge now is to translate these findings into meaningful etiologic, diagnostic and therapeutic advances. This four volume collection of Handbooks offers a broad synthesis of current knowledge about biomarker and endophenotype approaches in neuropsychiatry. Since many of the contributors are internationally known experts, they not only provide up-to-date state of the art overviews, but also clarify some of the ongoing controversies, future challenges and proposing new insights for future researches. The contents of the volumes have been carefully planned, organized, and edited in close collaboration with the chapter authors. Of course, despite all the assistance provided by contributors and others, I alone remain responsible for the content of these Handbooks including any errors or omissions, which may remain. The Handbook is organized into four interconnected volumes covering five major sections. Volume 1 “Neuropsychological Endophenotypes and Biomarkers” contains 17 chapters composed of two parts emphasizing schizophrenia as a prototype. The first section serves as an introduction and overview of methodological issues of the biomarker and endophenotype approaches in neuropsychiatry and some technological advances. Chapters review definitions, perspectives, and issues that provide a conceptual base for the rest of the collection. The second section comprises chapters in v

vi

Foreword

which the authors present and discuss the neuropsychological, neurocognitive and neurophysiological candidate biomarkers and endophenotypes. Volume 2 “Neuroanatomical and Neuroimaging Endophenotypes and Biomarkers”, focuses on neuroanatomical and neuroimaging findings obtained for wide spectra of neuropsychiatric disorders. Volume 3 “Metabolic and Peripheral Biomarkers”, explores several specific metabolic and peripheral biomarkers, such as neuroactive steroid biomarkers, cortisol to DHEA molar ratio, mitochondrial complex, biomarkers of excitotoxicity, melatonin, retinoic acid, abnormalities of inositol metabolism in lymphocytes, and others. Volume 4 “Molecular Genetic and Genomic Markers” contains chapters devoted to searching for novel molecular genetic and genomic markers in less explored areas. This volume includes an Afterword written by Professor Robert H. Belmaker. Similarly to other publications contributed to by diverse scholars from diverse orientations and academic backgrounds, differences in approaches and opinions, as well as some overlap, are unavoidable. I believe that this collection is probably the first of its kind to go beyond the neuropsychiatric disorders and delve into the neurobiological basis for diagnosis, treatment, and prevention. The take-home message is that principles of the biomarker-endophenotype approach may be applied no matter what kind of neuropsychiatric disorder afflicts our patients. The Handbook is designed for use by a broad spectrum of readers including neuroscientists, psychiatrists, neurologists, endocrinologists, pharmacologists, psychologists, general practitioners, geriatricians, graduate students, health care providers in the fields of neurology and mental health, and others interested in trends that have crystallized in the last decade, and trends that can be expected to evolve in the coming years. It is hoped that this collection will also be a useful resource for the teaching of psychiatry, neurology, psychology and mental health. With much gratitude, I would like to acknowledge the contributors from 16 countries for their excellent cooperation. In particular, I am most grateful to Professor Irving Gottesman for his support of this project. His unending drive and dedication to the field of psychiatric genetics never ceases to amaze me. I wish to acknowledge Professor Robert H. Belmaker, distinguished biological psychiatrist, who was very willing to write the afterword for these volumes. I also wish to take this opportunity to thank my close co-workers and colleagues Drs. Anatoly Gibel, Yael Ratner, Ehud Susser, Stella Lulinski, Rachel Mayan, Professor Vladimir Lerner and Professor Abraham Weizman for their support and cooperation. Finally, I am forever indebted to my wife Galina Ritsner, sons Edward and Yisrael for their understanding, endless patience and encouragement when it was most required. I sincerely hope that these four interconnected volumes of the Handbook will further knowledge in the complex field of neuropsychiatric disorders. February, 2009

Michael S. Ritsner Editor

Contents to Volume 2

Foreword ........................................................................................................... Michael S. Ritsner

v

Contributors to Volume 2 ................................................................................

ix

Part III Neuroanatomical and Neuroimaging Findings 18 Neuroimaging Biomarkers in Alzheimer’s Disease .............................. M.S. Chong and W.S. Lim

3

19 Role of Imaging Techniques in Discerning Neurobehavioral Changes in Ischemic, Neurodegenerative and Demyelinating Disorders ................................................................................................... Turi O. Dalaker, Mona K. Beyer, Milena Stosic, and Robert Zivadinov

17

20 Towards a Functional Neuroanatomy of Symptoms and Cognitive Deficits of Schizophrenia ........................................................ David Linden

55

21 Functional and Structural Endophenotypes in Schizophrenia ....................................................................................... Stephan Bender, Matthias Weisbrod, and Franz Resch

67

22 Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders.................................................... Daniel Mamah, Deanna M. Barch, and John G. Csernansky

87

23 Magnetic Resonance Imaging Biomarkers in Schizophrenia Research .......................................................................... Heike Tost, Shabnam Hakimi, and Andreas Meyer-Lindenberg

123

24 Neurostructural Endophenotypes In Autism Spectrum Disorder ................................................................................... Armin Raznahan, Jay N. Giedd, and Patrick F. Bolton

145

vii

viii

25

26

Contents to Volume 2

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan .................................................................................. Nick C. Patel, Michael A. Cerullo, David E. Fleck, Jayasree J. Nandagopal, Caleb M. Adler, Stephen M. Strakowski, and Melissa P. DelBello

171

Neuroimaging Studies of Pediatric Obsessive-Compulsive Disorder: Special Emphasis on Genetics and Biomarkers ................... Frank P. MacMaster and David R. Rosenberg

201

Structural Brain Alterations in Cannabis Users: Association with Cognitive Deficits and Psychiatric Symptoms .............................. Nadia Solowij, Murat Yücel, Valentina Lorenzetti, and Dan I. Lubman

215

Contents to Volumes 1, 3, and 4 ......................................................................

227

Contributors to Volumes 1, 3, and 4 ...............................................................

233

Index ..................................................................................................................

241

27

Contributors to Volume 2

Caleb M. Adler, M.D., Associate Professor of Psychiatry, Co-Director, Division of Bipolar Disorders Research, Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, USA E-mail: [email protected] Deanna M. Barch, Ph.D., Professor, Departments of Psychology, Psychiatry and Radiology, Washington University, St. Louis, MO, USA E-mail: [email protected] Stephan Bender Senior scientist and commissionary Head of the joint Neurophysiological Laboratory of the Psychiatric, Psychosomatic and Child and Adolescent Psychiatric Hospital of the University of Heidelberg, Germany E-mail: [email protected] Mona K. Beyer, M.D., Ph.D., Department of Radiology, Stavanger University Hospital, Stavanger, Norway; The Norwegian Centre for Movement Disorders, Stavanger University Hospital, Stavanger, Norway Michael A. Cerullo, M.D., Assistant Professor of Psychiatry,Division of Bipolar Disorders Research, Department of Psychiatry,University of Cincinnati College of Medicine, Cincinnati, OH, USA E-mail: [email protected] John G. Csernansky, M.D., Lizzie Gilman Professor and Chairman, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA E-mail: [email protected] Turi O. Dalaker, M.D., Buffalo Neuroimaging Analysis Center, Department of Neurology, State University of New York at Buffalo, Buffalo, NY, USA; Department of Radiology, Stavanger University Hospital, Stavanger, Norway; The Norwegian Centre for Movement Disorders, Stavanger University Hospital, Stavanger, Norway E-mail: [email protected] Melissa P. DelBello, M.D., M.S., Vice-Chair for Clinical Research, Department of Psychiatry; Associate Professor of Psychiatry and Pediatrics, Division of Bipolar Disorders Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA E-mail: [email protected]

ix

x

David E. Fleck, Ph.D., Assistant Professor of Psychiatry, Division of Bipolar Disorders Research, Department of Psychiatry,University of Cincinnati College of Medicine, Cincinnati, OH, USA E-mail: [email protected] Shabnam Hakimi, B.A., Clinical Brain Disorders Branch, Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA David Linden Professor of Biological Psychiatry, Wales Institute of Cognitive Neuroscience and North Wales Clinical School, School of Psychology, University of Wales Bangor, Bangor, UK E-mail: [email protected] Valentina Lorenzetti Ph.D. candidate, Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Australia E-mail: [email protected] Dan I. Lubman, Ph.D., FRANZCP, FAChAM; Associate Professor, ORYGEN Research Centre, Department of Psychiatry, University of Melbourne, Victoria, Australia E-mail: [email protected] Frank P. MacMaster, Ph.D., Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine Detroit, MI, USA E-mail: [email protected] Daniel Mamah, M.D., M.P.E., Instructor, Department of psychiatry, Washington University School of Medicine St. Louis; President, Eastern Missouri Psychiatric Society, USA E-mail: [email protected] Chong Mei Sian Consultant, Department of Geriatric Medicine, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore E-mail: [email protected] Andreas Meyer-Lindenberg, M.D., Ph.D., Director of the Central Institute of Mental Health, Professor of Psychiatry and Psychotherapy, Faculty of Clinical Medicine Mannheim, University of Heidelberg, Germany E-mail: [email protected] Jayasree J. Nandagopal, MD, Assistant Professor of Psychiatry, Division of Bipolar Disorders Research, Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, USA E-mail: [email protected] Nick C. Patel, Pharm.D., Ph.D., Clinical Pharmacist, Lifesynch; and Clinical Assistant Professor & Health Behavior, Medical College of Georgia; USA E-mail: [email protected] Armin Raznahan, MBBS, MRCPCH, MRCPsych., Medical Research Council Clinical Research Training Fellow, Institute of Psychiatry, King’s College London, UK E-mail: [email protected]

Contributors to Volume 2

Contributors to Volume 2

xi

Franz Resch Professor, Director of the Child and Adolescent Psychiatric Hospital of the University of Heidelberg, Germany E-mail: [email protected] David R. Rosenberg, M.D., Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Children’s Hospital of Michigan, Detroit, MI, USA E-mail: [email protected] Lim Wee Shiong Consultant, Department of Geriatric Medicine, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore Nadia Solowij, Ph.D., Senior Lecturer, School of Psychology and Illawarra Institute for Mental Health, University of Wollongong, Australia, Affiliated Scientist, Schizophrenia Research Institute, Sydney, Australia E-mail: [email protected] Milena Stosic, M.D., Buffalo Neuroimaging Analysis Center, Department of Neurology, State University of New York at Buffalo, Buffalo, NY, USA Stephen M. Strakowski, MD, The Stanley and Mickey Kaplan Professor and Chair of Psychiatry Professor of Psychology and Biomedical Engineering Director, Center for Imaging Research University of Cincinnati College of Medicine, Cincinnati, OH, USA E-mail: [email protected] Heike Tost, M.D., Ph.D., Post-Doctoral Research Fellow, Clinical Brain Disorders Branch, Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA E-mail: [email protected] Matthias Weisbrod, Professor, Director of the SRH Psychiatric Hospital Karlsbad-Langensteinbach; Head of the Section for Experimental Psychopathology of the University of Heidelberg, Germany E-mail: [email protected] Murat Yücel, Ph.D., MAPS; Senior Lecturer and Clinical Neuropsychologist, Melbourne Neuropsychiatry Centre and ORYGEN Research Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, National Neuroscience Facility, Melbourne, Australia E-mail: [email protected] Robert Zivadinov, M.D., Ph.D., Buffalo Neuroimaging Analysis Center, Department of Neurology, State University of New York at Buffalo, Buffalo, NY, USA E-mail: [email protected]

Chapter 18

Neuroimaging Biomarkers in Alzheimer’s Disease M.S. Chong and W.S. Lim

Abstract With the recent advances in treatment of Alzheimer’s disease (AD) in the last decade, focus has shifted increasingly to accurate detection of earliest phase of the illness. This includes early Alzheimer’s disease (AD) as well as the intermediate state between normal aging and established AD, is commonly known as mild cognitive impairment (MCI). Clinical criteria alone are insufficient to accurately identify this at risk group of subjects and hence, biomarkers have been an area of intense research to see if they can supplement the clinical approaches. In recent years, neuroimaging has emerged as a useful biomarker in the diagnostic armamentarium of AD that serves the triple roles of early diagnosis, prediction of progression, and monitoring of disease progression. In this chapter, we review the body of evidence on the use of neuroimaging biomarkers, alone and in combination, from the standpoints of diagnosis of early AD, predicting MCI conversion to AD and monitoring subsequent disease progression. We conclude with a discussion on the implications of these findings to the application of neuroimaging biomarkers in clinical and therapeutic trials. Keywords Alzheimer’s disease • early diagnosis • biological markers • magnetic resonance imaging • positronemission tomography Abbreviations AD: Alzheimer’s disease; ADC: Apparent diffusion coefficient; APOE ε4: Apolipoprotein ε4; ASL-MRI: Arterial spin labeling; BOLD: Bloodoxygen-level-dependent; CDR: Clinical dementia rating; M.S. Chong and W.S. Lim Department of Geriatric Medicine, Tan Tock Seng Hospital, Singapore

CBF: Cerebral blood flow; 11C-PIB: 11C-Pittsburg compound B; DBM: Deformation-based morphometry; DTI: Diffusion tensor imaging; DWI: Diffusion-weighted imaging; ERC: Entorhinal cortex; fMRI: Functional MRI; 18 F-FDDNP: 2-(1-{6-[(2-[18F]fluroethyl)(methyl)amino]2-napthyl}ethylidene)malononitrile; HC: Hippocampal; MCI: Mild cognitive impairment; MRI: Magnetic resonance imaging; MRS: Magnetic resonance spectroscopy; MTL: Medial temporal lobe; PET: 2-[18F]fluoro-2-deoxyD-glucose (FDG)-Positron Emission Tomograhy; SPECT: Single-photon emission computerized tomography; VBM: Voxel-based morphometry

Introduction Given the rapid ageing of the population worldwide, global estimates of AD – generally considered to be the commonest subtype of dementia – are expected to increase from the current estimated 25–63 million in 2030, and by 2050, a staggering 114 million.1 Over the last 2 decades in particular, significant but modest breakthroughs in pharmacological treatment of this devastating condition have occurred and presently, there is increasing conviction that intervention (especially disease modifying therapy) will have to be instituted at the earliest possible stage of the illness to confer the greatest benefit. Currently, the diagnosis of prodromal AD is made using criteria which support a probabilistic diagnosis within a clinical context without added information from diagnostic biomarkers. Two commonly quoted approaches that have been validated and employed in interventional studies are the CDR2 and MCI3 (Fig. 18.1). MCI subjects have subjective features and objective evidence of cognitive impairment but of

M.S. Ritsner (ed.), The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, © Springer Science + Business Media B.V. 2009

3

4 Fig. 18.1 Stages of cognitive impairment and neuroimaging methods (present and future)

M.S. Chong and W.S. Lim Normal aging

|

Preclinical AD

CDR 0

| Prodromal AD | Mild AD | Moderate AD | Severe AD (MCI)

| CDR 0.5

|

| CDR 1

|

CDR 2

| CDR 3

Histopathological AD changes (amyloid plaques & neurofibrillary tangles)

|

-------------- Current neuroimaging techniques ---------------------------- Future neuroimaging techniques ? MCI = Mild cognitive impairment AD = Alzheimer’s disease CDR = Clinical Dementia Rating

insufficient degree to constitute dementia; they are at increased risk of progression to dementia, with conversion rates to clinical AD of approximately 12% annually and up to 80% at 6 years of follow-up.4 However, this is an unstable construct where some MCI subjects will convert to clinical AD (MCI-converters) while others will not (MCI non-converters).5 Prevailing clinical criteria for MCI have low to moderate diagnostic accuracy in identifying MCI and in predicting progression to dementia.6 The observation from neuropathological studies that the accumulation of AD pathology (β.-amyloid plaques and neurofibrillary tangles) precedes the onset of clinical disease by as long as 20–30 years,7 suggests that functional and structural brain changes may occur prior to apparent clinical manifestations of cognitive impairment. This provides the impetus for the development of reliable biomarkers such as neuroimaging to complement clinical approaches in early diagnosis and predicting progression. Whereas previously the primary purpose of neuroimaging was to rule out potentially reversible causes of cognitive impairment (such as space-occupying lesion or hydrocephalus), recent advances in the field of structural and functional neuroimaging have rendered neuroimaging as an important part of the diagnostic armamentarium of biomarkers for AD. This is reflected in the revised NINCDS-ADRDA criteria for diagnosis of AD,8 which stipulates the need for at least one abnormal biomarker (which may include structural imaging with MRI or molecular imaging with PET) in the diagnosis of AD and its prodromal stages.

In our review, we will review evidence regarding the utility of neuroimaging biomarkers from the standpoints of diagnosis of early AD, predicting MCI conversion to AD and monitoring subsequent disease progression.

Structural Neuroimaging Structural MRI Medial Temporal Lobe Volumetry MRI studies have documented that cortical atrophy occurs in a defined sequence with disease progression, in line with the predictable spatial pattern of neurofibrillary tangle accumulation seen at autopsy.9 Atrophy of medial temporal structures, namely entorhinal cortex and hippocampus has been reported in mild AD patients10,11 with subsequent volume reductions in other cortical regions with AD disease progression. Likewise, in MCI subjects, MTL atrophy has been consistently observed (Table 18.1).12 Longitudinal studies have shown decreased ERC13–15 and HC volumes16,17 at baseline to be predictive of MCI-converters. It has been argued that ERC atrophy might be a better predictor of AD progression than HC volume loss14,17 while other studies have shown less clear-cut results. In a more qualitative manner, assessing MTL atrophy using a standardized visual rating scale18–20 has also been shown to be predictive

18

Neuroimaging Biomarkers in Alzheimer’s Disease

5

Table 18.1 Neuroimaging biomarkers in predicting AD conversion in MCI patients Sensitivity/ Specificity

Accuracy

STRUCTURAL NEUROIMAGING Structural MRI Sn 50%, Sp 90%, • MRI volumetry13–17 (ERC and HC atrophy) • Visual rating scale (medial temporal lobe atrophy)18–20 Sn 70–78%, Sp 68–90%19 • Brain atrophy rates (HC, ERC and Ventricular volume, whole brain)21–23,102 Voxel-based morphometry • Voxel-based morphometry30–33 Deformation-based morphometry 35 • Multivariate deformation-based brain analysis FUNCTIONAL NEUROIMAGING SPECT • SPECT43–45 (↓ blood flow at cingulate, left frontal, inferior parietal, angular gyrus and precuneus regions) FDG-PET • PET 49–51 (↓ glucose metabolism at 84%49,51,99 temporoparietal region) 1 H MRS • Brain magnetic resonance spectroscopy (occipital cortex N-acetylaspartate/creatine ratio)

Sn,Sp approx 80%44

Ac81–85%14,16,17 Ac 60.4%102

Ac 80%(CSF maps)

Ac 84.4%43

Sn 96.8%, Sp 58.8%, PPV48.1%, Ac 75 – NPV 95.2% 51 Sn 100%, Sp 75%, PPV 83%, NPV 100%,

Ac 88.7%57

fMRI • fMRI (↑recruitment of larger extent of right parahippocampal gyrus during encoding)66 DWI • DWI (Apparent diffusion coefficient)71 DTI • Elevated mean diffusivity in MCI-converters81 MOLECULAR ADVANCES Amyloid imaging • Significantly higher 11C-PIB retention92 COMBINATION BIOMARKERS • SPECT and MRI volumetry99 • Neuropsychological testing and PET • APO-E and PET • Neuropsychological testing and MRI volumetry • CSF-tau and PET102

Sn 100%, Sp 90%

Ac 90–92.3%49–51 Ac 94%99–100 Ac 78.8%101

Sn = Sensitivity; Sp = Specificity; Ac = Accuracy; ERC = entorhinal cortex; HC = hippocampal; DWI = diffusion-weighted imaging; SPECT = Single photon emission tomography; PET = Positron emission tomography; fMRI = functional Magnetic Resonance Imaging; DTI = Diffusion tensor imaging; APOE = apoliprotein E-4

of MCI-converters. From the standpoint of MRI brain atrophy rates, those of HC, ERC and whole brain were found to be greater among MCI-converters (3–7% change per year from baseline values compared to 0.4–3.7% change per year in non-converters).21–23 Differences in longitudinal studies of brain atrophy rates between MCI-converters and normal aging over a period of up to 5 years have been demonstrated (Table 18.2).21,22

Automated Data-Driven Methods With advances in technology, the focus has shifted in recent years from manual volumetric methods of regions of interest to automated data-driven methods, such as automated measurement of whole-brain volume over time,24,25 as well as novel techniques such as voxelbased volumetry, deformation-based morphometry and analysis of cortical thickness.

6

M.S. Chong and W.S. Lim

Table 18.2 Longitudinal neuroimaging studies Neuroimaging method

Years of follow-up

Results

MRI volumetry - Whole brain volume 24

1.8 years

Normal control = −0.45% change/year Mild DAT = −0.98%/year Normal control = −0.47%/year

- Serial brain registered brain MRI 25 (Brain atrophy rate) - Hippocampal volume 11

1 year

Entorhinal cortex colume 11

1–5 years

Whole brain volume 11

1–5 years

Ventricular volume 11

1–5 years

Voxel-based morphometry - Medial occipitoparietal area 33

3 years

1–5 years

Positron Emission Tomography (PET) - Regional cerebral glucose metabolism (parietal, temporal, 1 year occipital, frontal, Posterior cingulate region) 53 Amyloid imaging (11C-PIB) - PIB retention 94

2 years

Normal control = −1.4%/year Normal converter = −3.3%/year MCI-stable = −1.8%/year MCI-converters = −3.3%/year AD-fast progressor = −3.0%/year AD-slow progressor = −3.6%/year Normal control = −2.9%/year Normal converter = −5.1%/year MCI-stable = −3.7%/year MCI-converters = −6.8%/year AD-fast progressor = −8.0%/year AD-slow progressor = −8.4%/year Normal control = −0.4%/year Normal converter = −0.8%/year MCI-stable = −0.4%/year MCI-converters = −6.8%/year AD-fast progressor = −0.6%/year AD-slow progressor = −1.4%/year Normal control = 1.7%/year Normal converter = 3.4%/year MCI-stable = 2.6%/year MCI-converters = 3.4%/year AD-fast progressor = 4.3%/year AD-slow progressor = 6.4%/year

Differences comparing healthy controls with mild-moderate AD patients z-score = 3.82 – 6.61 Relatively stable PIB retention in mild AD subjects

DAT = Dementia of Alzheimer type; MCI = Mild cognitive impairment; AD = Alzheimer’s disease

(i) VBM VBM is based on a low-dimensional spatial transformation of brain scans into a common reference space to get rid of global differences in brain size and shape; the remaining gray matter volume differences are parameters then driven into a voxel-based univariate statistic. In both AD and MCI subjects, VBM consistently shows atrophy in the cortical grey matter in the MTL and lateral

temporal and parietal association areas.26–29 VBM has also been shown to have good predictive ability for MCIconverters with reduced gray matter density in the medial temporal, hippocampal, posterior cingulate and precuneus regions compared to non-converters.30–32 A recent longitudinal study in mild AD subjects showed that VBM-derived medial occipitoparietal atrophy at baseline better anticipated the rate of progression over 3 years, compared with clinical and neuropsychological assessment.33

18

Neuroimaging Biomarkers in Alzheimer’s Disease

7

(ii) DBM

Functional Neuroimaging

DBM transforms brain volumes at high resolution to a standard template to completely eliminate the anatomical differences between brains; the deformation fields then offer a multivariate vector field of localization information from which regional volume effects can then be extrapolated. Based on the pattern of spatial distribution involving HC, bilateral temporal, (L) fusiform gyri and posterior cingulate regions, Davatzikos et al.34 reported good accuracy in differentiating MCI individuals from controls. Another study by Teipel et al.35 also demonstrated good discrimination between MCI-converters and nonconverters using multivariate deformation-based CSF and brain maps.

Functional Imaging

Reduced CBF in the parietal, posterior cingulate and precuneus have been observed in early AD39,40 and MCI subjects.41,42 Using a combination of regional CBF at the cingulate, hippocampal-amygdaloid complex and the thalamus on SPECT, 84.8% of MCI-converters were identified.43 Other studies showed decreased rCBF in the left frontal region,44 left posterior cingulate gyrus,42 inferior parietal lobe, angular gyrus and precuneus45 to be similarly predictive.

(iii) Other methods

FDG-PET

By determining the thickness of the entire cortical mantle36 automated measurements of cortical thickness have shown a high accuracy (>90%) in differentiating AD from controls. However, no data is available with regards to the use of cortical thickness in predicting AD progression in MCI subjects. Hippocampal radial atrophy mapping technique by Thompson et al.37 showed differences between AD and normal controls. Smaller HC and specifically CA1 and subicular involvement was associated with increased risk of AD progression in MCI subjects.38

An AD-like pattern of cerebral glucose hypometabolism has been observed in MCI subjects,46,47 and this is associated with elevated cerebrospinal fluid p-tau.48 FDG-PET studies also reveal regional cerebral hypometabolism in the left temporo-parietal region,49 right superior temporal region,50 inferior parietal, posterior cingulate and medial temporal cortices51 to be predictive of MCI-converters. Longitudinal FDG-PET studies show serial decline in glucose metabolism in the temporal, parietal, frontal and posterior cingulate regions. Using left frontal regions,52,53 it is estimated that only 36 subjects per group would be required to show a 33% treatment effect in an adequately powered (80%) 1 year placebocontrolled trial.53

Summary MRI volumetry and brain atrophy rates have fairly good diagnostic and predictive value in MCI subjects. Longitudinal data on brain atrophy rates with disease progression are available and hence, can be used for monitoring disease progression in clinical trials. The limitations of structural neuroimaging as a biomarker include problems with the accurate delineation of regions of interest and lack of standardization of imaging and measurement techniques, making it difficult to compare data across the different institutions. The advent of automated data-driven innovations for structural imaging holds promise, although longitudinal data are still required.

SPECT

Proton MRS MRS is a diagnostic technique measuring neuroaxonal injury by quantification of N-acetylaspartate/ creatine (NAA/Cr) ratio. There is evidence of differences in neuronal damage between AD, MCI and controls in a decremental manner in the whole brain, posterior cingulate and hippocampus.54–56 The NAA/Cr ratio in the occipital cortex has been shown to reasonably predict MCI-conversion.57 Currently there are no longitudinal data for MRS.

8

fMRI Functional MRI studies that have been conducted in early cognitive impairment subjects (namely early AD and MCI subjects) show altered resting state networks58,59 as well as decreased or delayed activations during task performances using BOLD response. However, the pattern is inconsistent and range from a decremental response from AD through MCI to normal controls,60–62 to a compensatory increased activation in hippocampus63–65 in MCI subjects. A study using fMRI showed that the MCI-converters recruited a larger extent of the right parahippocampal gyrus upon the encoding phase of memory testing,66 reflecting a compensatory response to accumulating AD pathology. Currently, there are no longitudinal fMRI data available

ASL-MRI Perfusion MRI using ASL-MRI uses magnetically labeled water protons as an endogenous tracer to denote an absolute temporal change in CBF. Its utility lies in the fact that it is able to obtain CBF maps repeatedly in short succession, thus enabling dynamic measurements of CBF. Resting ASL-MRI has shown decreased CBF in AD patients in the temporal, lateral and medial aspects of the frontal and parietal cortex compared to controls.67 A study reported attenuated CBF in posterior cingulate, precuneus, bilateral inferior parietal gyri in AD compared to MCI subjects.68 A recent ASL-MRI study of amnestic MCI subjects performing memory-encoding tasks reported significant regional cerebral hypoperfusion in the right precuneus and cuneus and an inability to modulate CBF in response to the functional task at hand.69 There is currently no evidence with regards to prediction of AD progression in MCI subjects and longitudinal data.

DWI Using DWI, HC ADC has been shown to be higher in AD and MCI subjects compared to controls.70 The measurement of HC ADC improved the ability of HC measurements to predict MCI-converters.71 There are

M.S. Chong and W.S. Lim

currently no longitudinal data on serial progression for DWI.

DTI DTI is an extended form of diffusion-weighted imaging of brain matter. Diffusion gradients are applied in several spatial directions to determine a multidimensional diffusion tensor. From these diffusion tensor measures of movement, directionality can then be derived. Fractional anisotropy measuring directionality of fibre tracts and mean diffusivity determining overall diffusivity are frequently employed parameters. The observation of widely distributed disintegration of white matter with a different pattern of degeneration from grey matter suggests that it might be an independent factor in AD progression.72 Comparing AD with normal subjects, DTI demonstrated white matter changes in the anterior temporal lobe,73 uncinate fasciculus,74 corpus callosum75 as well as corticothalamic and thalamocortical radiations.76 White matter changes are also seen in MCI subjects77–79; a study by Mueller et al,77 reported superior accuracy compared to volumetric measurements in differentiating MCI subjects from normals. However, further studies are needed to determine the utility of white matter changes detected using DTI.80 Fellgiebel et al.81 demonstrated elevated left HC mean diffusivity at baselines in MCI-converters compared to MCI non-converters despite no differences hippocampal volumes and clinical performance. No data are currently available with regards to longitudinal progression.

Summary FDG-PET appears to be the leading candidate among the functional neuroimaging modalities, with available evidence for MCI diagnosis, prediction of MCIconverters and longitudinal data in monitoring serial progression. Among the newer MRI-based techniques, DTI appears to hold great promise as theoretically, microstructural alterations of the cerebral fibre system would predate volumetric changes. However, more data (especially on longitudinal progression) are needed before definitive recommendations can be made.

18

Neuroimaging Biomarkers in Alzheimer’s Disease

Molecular Advances Amyloid Imaging Advances in molecular imaging techniques have made it possible to visualize ß-amyloid in-vivo in Alzheimer patients by the use of small molecular ligands that bind with nanomolar affinity to amyloid and that enter the brain in amounts sufficient for imaging with PET.82 Taking the cerebellum as the reference region, quantitative measures are used to analyze the generated PET images using either region-of-interest or voxel-based analysis to derive region-specific and global values of distribution volume ratio or binding potential.83–85 Because positive scans can be seen in other forms of cerebral Aβ (e.g. cerebral amyloid angiopathy), concomitant AD pathology (e.g. dementia of Lewy body with amyloid pathology), and preclinical AD pathology (i.e. asymptomatic healthy control with cortical amyloid deposition), it is best not to equate amyloid deposition to clinical diagnosis from the outset but to think of PET amyloid tracer scans more fundamentally as a method to detect and quantify cerebral β-amyloidosis.86 PET amyloid ligands can be broadly divided into two groups: 11C-based and 18F- based. Table 18.3 summarizes the characteristics of two of the more widely studied compounds.11C-PIB binds specifically to fibrillar Aβ with no demonstrable binding to neurofibrillary

9

tangles, unlike 18F-FDDNP which binds to both amyloid and tangles.82 11C-PIB shows a greater magnitude of cortical binding, which allows PIB images to be visually read without quantification (κ = 0.90) and acquired with a shorter scanning time.87 However, the short radioactive decay half-life of 11C limits the use of 11C-PIB to centers with an on-site cyclotron and 11C radiochemistry expertise.88 Rowe et al.89 recently reported the results of a novel PET tracer, 18F-BAY94–9172, which combines the characteristics of 11C-PIB with the advantages of 18F- based compounds. Amyloid imaging studies in AD revealed increased cortical retention in the frontal, parietal and lateral temporal cortices, striatum and posterior cingulate, in accordance with the distribution of amyloid pathology previously documented in postmortem studies.83,84,86 Recent studies in MCI subjects showed intermediate cortical binding compared with AD patients and controls.90–91 Small et al.91 reported that 18F-FDDNP had better discriminatory ability for MCI compared with controls with FDG-PET metabolism and MRI medial temporal lobe atrophy (AUC: 0.95 vs 0.77 vs 0.64 respectively). MCI-converters had higher PIB retention in brain at baseline compared to MCI non-converters92 and one study showing elevated PIB values in AD subjects compared to nondemented controls.93 Intriguingly, there are consistent reports of positive scans in up to 23% of healthy elderly controls, with some of these subjects demonstrating cortical binding

Table 18.3 Differential properties of amyloid imaging modalities 18

11

Aβ 40, NFT 110 min 120 min 9%

Aβ 40, Aβ 42 (fibrillar) 20 min 60–90 min 40 min if visual analysis 40–80%

Yes Yes No Limited 2-year data in MCI/healthy controls

Yes Yes Yes Unchanged PIB retention after 2 years in mild AD

F-FDDNP

Properties Binding affinity Radioactive decay T1/2 Scanning time Increase in cortical Aβ binding (AD vs controls) Available Evidence Diagnosis AD MCI Prediction of AD conversion in MCI Longitudinal course

C-PIB

Aβ: β-amyloid; AD: Alzheimer’s disease; 11C-PIB: 11C-Pittsburg compound B; 18F-FDDNP: 2-(1-{6-[(2-[18F]fluroethyl)(methyl) amino]-2-napthyl}ethylidene)malononitrile; MCI: Mild cognitive impairment; NFT: Neurofibrillary tangles

10

M.S. Chong and W.S. Lim

that was indistinguishable from AD.84,90,91 Longitudinal follow-up is required to determine whether these asymptomatic controls with positive scans truly turn out to be preclinical AD cases. In a 2-year longitudinal study using 11C-PIB, there was no significant change in PIB retention compared to baseline despite a decline in cerebral glucose metabolism and cognition.94 Stable PIB retention suggests that amyloid levels in the brain may reach a plateau early in the course of disease that precedes a decline in cerebral glucose metabolism and cognition.82 Although there was an increase in 18F-FDDNP binding at 2 years in three subjects who progressed, it is plausible that this may reflect binding to non-amyloid elements such as tau.82,94

and AD from normal controls compared to HC volumetry alone (63–74% and 78–91% for MCI and AD compared to controls respectively).95 Kawachi et al.96 reported that the accuracy of FDG-PET diagnosis of very mild AD was 89% and that of VBM-MRI was 83%, but in combination, the accuracy improved to 94%. A recent study noted the improved diagnostic classification using both 11C-PiB and structural MRI (statistical parametric mapping and VBM) compared to either imaging methods in isolation.97 For the identification of MCIconverters, a combination of both SPECT and MRI volumetry showed better discriminative performance than either used alone in predicting AD conversion.98

Summary

Combination of Neuroimaging with Other Biomarkers

To date, 11C-PIB is the most extensively studied PET amyloid tracer. There is emerging evidence for amyloid imaging in the diagnosis of prodromal AD as well as predicting AD progression in MCI subjects. From the standpoint of clinical trials of anti-amyloid therapy, in-vivo amyloid imaging pre-treatment allows selection of patients with demonstrable cerebral Aβ loads; repeated imaging during ongoing treatment allows detection of decrease in insoluble Aβ load in response to amyloid-clearing drugs such as immunotherapy. However, the lack of serial change of 11C-PIB with disease progression implies a limited role in monitoring the response to disease modifying drugs that act by halting amyloid deposition. Amyloid imaging needs to be more practically accessible and affordable before it can be transferable to the clinical diagnostic routine.

Combination Biomarkers Recent studies have combined biomarkers to ascertain whether there is any added advantage in diagnostic and predictive performance compared with a single modality. We review the evidence for combination biomarker studies that involved neuroimaging.

Combination Neuroimaging Biomarkers The addition of DTI fractional anisotropy and MRI HC volumetry improved the accuracy of diagnosing MCI

In a study of MCI subjects, it was observed that the combination of impaired delayed recall and FDG-PET cerebral hypometabolism improved classification accuracy of MCI converters to 92.3% and MCI non-converters to 92.8%.51 Various longitudinal studies involving MCI subjects also reported improved predictive accuracy with the combination of neuroimaging and other biomarkers: APOE ε4 genotype and FDG-PET99,100; episodic memory testing and MRI measures of ventricular and HC volumes101; cerebrospinal fluid tau and posterior cingulate hypoperfusion on SPECT.102

Conclusions and Future Directions Recent unprecedented advances in the area of neuroimaging biomarkers in prodromal AD are in tandem with the growing emphasis on early diagnosis of the condition where disease-modifying therapeutic strategies are very likely to have a greater impact. Of particular relevance to the area of clinical trials of disease modifying therapy would be the availability of neuroimaging biomarkers with the discriminatory capacity to accurately diagnose MCI subjects and identify those at greatest risk of advancing to clinical disease; the ability to clearly indicate disease progression would enable the monitoring of treatment response. In order for a diagnostic biomarker to be useful, certain criteria need to be met (see chapter 1 of this book, Ritsner, Gottesman). There is evidence

18

Neuroimaging Biomarkers in Alzheimer’s Disease

to support the use of MRI volumetry and FDG-PET biomarkers in the diagnosis of early cognitive impairment (MCI and early AD) with good sensitivity and specificity in differentiating pathological states (MCI and early AD) from normals as well as in predicting AD progression in at-risk MCI individuals. With regards to monitoring disease progression, the availability of reasonably good longitudinal normative data in age-matched controls supports the use of MRI volumetry and FDG-PET imaging. Using various techniques, brain atrophy rates and PET hypometabolism with disease progression exhibit a clinical effect of sufficient magnitude that can permit the use of fewer subjects in clinical trials of disease modification compared to using only anticipated changes on cognitive test scores. Recent advances in the various automated data-driven methods in structural neuroimaging can hopefully help to further improve the inter-rater reliability of volumetric data in multi-centre studies. The most exciting development among the novel techniques is arguably the emergence of amyloidspecific imaging, which opens up new avenues for the evaluation of anti-amyloid therapy. Pre-treatment identification of scan-positive MCI subjects with demonstrable Ab loads would permit the recruitment of smaller number of subjects and shorter observational periods. Comparison of pre-post treatment scans could provide an important surrogate outcome of the effectiveness of amyloid-clearing therapy. Mattis et al.86 suggested that a twofold decrease in the test-retest variability, corresponding to 10–20% reduction in PIB retention post-treatment, should be sufficient to detect a reduced Ab load. The diagnostic utility can potentially be extended to the presymptomatic histopathological AD group and allow the initiation of disease modifying therapy before extensive irreversible neuronal damage occurs (Fig. 18.1). However, practical issues relating to scan time, radioactive decay half-day, false positivity (e.g. cerebral amyloid angiopathy) and lack of longitudinal change (in the case of PIB imaging), need to be addressed. Neuroimaging biomarkers should be used in combination with other biomarkers to produce the highest diagnostic and prognostic power necessary for accurate characterization of AD at its earliest stages. In addition, we strongly recommend the use of neuroimaging and other biomarkers to be supplemented by comprehensive clinical and neuropsychological

11

assessment. The unexpected finding of greater brain volume loss despite better cognitive function among antibody responders in the phase II Aβ immunization trial103 is a reminder that any treatment-related changes in biomarker levels should always be anchored to a comprehensive clinical evaluation that additionally incorporates cognitive, behavioral and functional measures. A new multicenter AD research project called the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was launched in 2004 to identify neuroimaging measures and biomarkers associated with cognitive and functional changes in healthy elderly, MCI and AD subjects, encompassing clinical sites in United States and Canada.104,105 This would hopefully address the issue of measurement variability via the development of optimized and standardized measurement protocols. It would also enable adequately powered trials to be conducted using the newer neuroimaging modalities which hold much promise such as functional imaging techniques (e.g. BOLD fMRI/ ASLMRI), diffusion tensor imaging and amyloid imaging. With the newer neuroimaging techniques, it is foreseeable that the frontiers of diagnostic ability would move from established AD towards prodromal AD, and eventually even to preclinical AD (Fig. 18.1) where disease-modifying therapeutics would be able to target the disease at its earliest stage.

References 1. Wimo A, Winbald B, Aguero Torres H, von Strauss E. The magnitude of dementia occurrence in the world. Alz Dis Assoc Disord 2003;17:63–67. 2. Morris JC, Storandt M, Miller JP, McKeet DW, Price JL, Rubin EH, Berg L. Mild cognitive impairment represents early stage Alzheimer’s disease. Arch Neurol 2001;58:397–405. 3. Peterson RC. Mild cognitive impairment as a diagnostic entity. J Int Med 2004;256:183–194. 4. Peterson RC, Smith GE, Waring SC, Ivnik RJ, Tangalos E, Kokmen E. Mild cognitive impairment. Clinical characterization and outcome. Arch Neurol 1999;56:303–308. 5. Ritchie K, Touchon J. Mild cognitive impairment: conceptual basis and current nosological status. Lancet 2000; 355:225–228. 6. Visser PJ, Scheltens P, Verhey FR. Do MCI criteria in drug trials accurately identify subjects with predementia Alzheimer’s disease? J Neurol Neurosurg Psychiatry 2005; 76:1348–1354. 7. Price JL, Morris JC. Tangles and plaques in nondemented aging and preclinical Alzheimer disease. Ann Neurol 1999; 45:358–368. 8. Dubois B, Feldman HH, Jacova C, Dekosky ST, BarbergerGateau P, Cummings J, Delacourte A, Galasko D, Gauthier

12 S, Jicha G, Meguro K, O’brien J et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDSADRDA criteria. Lancet Neurol 2007;6(8): 734–746. 9. Thompson PM, Hayashi KM, de Zubicaray et al. Dynamics of gray matter loss in Alzheimer’s disease. J Neurosci 2003; 23:994–1005. 10. Jack CR, Petersen RC, O-Brien PC et al. MR-based hippocampal volumetry in the diagnosis of Alzheimer’s disease. Neurology 1992; 42:183–188. 11. Killiany RJ, Moss MB, Albert MS et al. Temporal lobe regions on magnetic resonance imaging identify patients with early Alzheimer’s disease. Arch Neurol 1993: 50:949–954. 12. Jack CR, Peterson RC, Xu YC et al. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 1999; 52:1397–1403. 13. L de Poledo-Morrell, TR Stoub, M Bulgakova, RS Wilson et al. MRI-derived entorhinal volume is a good predictor of conversion from MCI to AD. Neurobiol Aging 2004; 25:1197–1203. 14. BC Dickerson, I Goncharova, MP Sullivan et al. MRIderived entorhinal and hippocampal atrophy in incipient and very mild AD. Neurobiol Aging 2001; 22:747–754. 15. Killiany RJ, Gomez-Isla T, Moss M, Kikinis R, Sandor T, Jolesz F, Tanzi R, Jones K, Hyman BT, Albert MS. Use of structural magnetic resonance imaging to predict who will get Alzheimer’s disease. Ann Neurol 2000; 47:430–439. 16. CR Jack, RC Peterson, YC Xu et al. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 1999; 52:1397–1403. 17. Killiany RJ, Hyman BT, Gomez-Isla T, Moss MB, Kikinis R, Jolesz F, Tanzi R, Jones K, Albert MS. MRI measures of entorhinal cortex vs hippocampus in preclinical AD. Neurology 2002; 58:1188–1196. 18. PJ Visser, FRJ Verhey, PAM Hofman et al. Medial temporal lobe atrophy predicts Alzheimer’s disease in patients with minor cognitive impairment. J Neurol Neurosurg Psychiatry 2002; 72: 491–497. 19. ESC Korf, L-O Wahlund, PJ Visser et al. Medial Temporal Lobe Atrophy on MRI predicts dementia in patients with mild cognitive impairment. Neurology 2004;63:94–100. 20. PJ Visser, P Scheltens, FRJ Verhey et al. Medial temporal lobe atrophy and memory dysfunction as predictors for dementia in subjects with mild cognitive impairment. J Neurol 1999; 246:477–485. 21. CR Jack, MM Shiung, JL Gunter et al. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology 2004; 62:591–600. 22. Jack CR Jr, Shiung MM, Weigand SD, O’Brien PC, Gunter JL, Boeve BF, Knopman DS, Smith GE, Ivnik RJ, Tangalos EG, Petersen RC. Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology 2005;65:1227–1231. 23. Sluimer JD, Vrenken H, Blankenstein MA, Fox NC, Shceltens P et al. Whole-brain atrophy rate in Alzheimer disease. Identifying fast progressors. Neurology 2008;70: 1836–1841. 24. Fotenos AF, Snyder AZ, Girton LE, Morris JC, Buckner RL. Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 2005;64:1032–1039.

M.S. Chong and W.S. Lim 25. Fox NC, Cousens S, Scahill R et al. Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer Disease: power calculations and estimates of sample size to detect treatment effects. Arch Neurol 2000; 57:339–344. 26. Baron JC, Chetelat G, Desgrages B, Perchey G, Landeau B et al. In-vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer’s disease. Neuroimage 2001; 14:298–309. 27. Bussato GF, Garrido GE, Almeida OP, Castro CC, Camargo CH, Cid CG et al. A voxel-abased morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease. Neurobiol Aging 2003; 24:221–31. 28. Chetelat G, Desgranges B, De La Sayatte V, Viader F et al. Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. Neuroreport 2002; 13:1939–1943. 29. Pennanen C, Testa C, Laakso MP et al. A voxel-based morphometry study on mild cognitive impairment. J Neurol Neurosurg Psychiatry 2005; 76: 11–14. 30. Chetelat G, Landeau B, Eustache F, Mezenge F, Viader F, de la Sayette V, Desgranges B, Baron JC.Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. Neuroimage 2005; 27:934–946. 31. Hamalainen A, Tervo S, Grau-Olivares M, Niskanen E, Pennanen C et al. Voxel-based morphometry to detect brain atrophy in progressive mild cognitive impairment. Neuroimage 2007; 37:1122–1131. 32. Whitewell JL, Shiung MM, Przybelski SA, WEigand WS, Knopman DS et al. MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment. Neurology 2008;70:512–520. 33. Kinkingnéhun, M. Sarazin, S. Lehéricy, E. Guichart-Gomez, T. Hergueta, B. Dubois. VBM anticipates the rate of progression of Alzheimer disease: A 3-year longitudinal study. Neurology 2008; 70: 2201–2211. 34. Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM. Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobil Aging 2008;29: 514–523. 35. Teipel SJ, Bron C, Ewers M, Bokde ALW, Reiser MF et al. Multivariate deformation-based analysis of brain atrophy to predict Alzheimer’s disease in mild cognitive impairment. Neuroimage 2007; 38:13–24. 36. Lerch JP, Pruessner J, Zijdenbos AP, Collns DL, Teipel SJ et al. Automated cortical thickness measurements from MRI can accurately separate Alzheimer’s patients from normal elderly controls. Neurobiol Aging 2008;29: 23–30. 37. Thompson PM, Hayashi KM, de Zubicaray GI et a. Mapping hippocampal and ventricular change in Alzheimer disease. Neuroimage 2004; 22:1754–1766. 38. Apostolova LG, Dutton RA, Dinov ID, Hayashi KM, Toga AW et al. Conversion of Mild Cognitive Impairment to Alzheimer Disease Predicted by Hippocampal Atrophy Maps. Arch Neurol 2006; 63:693–699. 39. Johnson KA, Jones K, Holman BL, Becker JA, Spiers PA, Satlin A et al. Preclinical prediction of Alzheimer’s disease using SPECT. Neurology 1998; 50: 1563–1571. 40. Huang C, Wahlund LO, Almkvist O, Elehu D, Svensson L, Jonsson T et al. Voxel- and VOI-based analysis of SPECT CBF in relation to clinical and psychological heterogeneity of mild cognitive impairment. Neuroimage 2003; 19:1137–1144.

18

Neuroimaging Biomarkers in Alzheimer’s Disease

41. Kogure D, Matsuda H, Oshnishi T, Asada T, Uno M, Kunihiro T et al. Longitudinal evaluation of early Alzheimer’s disease using brain perfusion SPECT. J Neul Med 2000; 41:1155–1162. 42. Huang C, Wahlund LO, Svensson L, Winblad B, Julin P. Cingulate cortex hypoperfusion predicts Alzheimer’s disease in mild cognitive impairment. BMC Neurol 2002; 2:9 43. KA Johnson, K Jones, BL Holman et al. Preclinical prediction of Alzheimer’s disease using SPECT. Neurology 1998; 50:1563–1571. 44. Cabranes JA, De Juan R, Encinas M, Marcos A, Gil P, Fernandez C, De Ugarte C, Barabash A. Relevance of functional neuroimaging in the progression of mild cognitive impairment. Neurol Research 2004; 26:496–501. 45. Hirao K, Ohnishi T, Hirata Y, Yamashita F, Mori T, Moriguchi Y, Matsuda H, Nemoto K, Imabayashi E, Yamada M, Iwamoto T et al. The prediction of rapid conversion to Alzheimer’s disease in mild cognitive impairment using regional cerebral blood flow SPECT. Neuroimage 2005; 28(4):1014–1021. 46. Nestor PJ, Fryer TD, Smielewski P, Hodges JR. Limbic hypometabolism in alzheimer’s disease and mild cognitive impairment. Annals of Neurology 2003; 54:343–351 47. Mosconi L, Tsui WH, De Santi S, Li J, Rusinek H et al. Reduced hippocampal metabolism in MCI and AD. Automated FDG-PET image analysis. Neurology 2005; 64:1860–1867. 48. Fellgiebel A, Siessmeier T, Scheurich A, Winterer G, Bartenstein P et al. Association of elevated phospho-tau levels with Alzheimer-typical 18F-Fluro-2-Deoxy-D-Glucose Positron Emission Tomography Findings in Patients with Mild Cognitive Impairment. Biol Psychiatry 2004 Aug 15;56(4): 279–283. 49. Arnaiz E, Jelic V, Almkvist O, Wahlund LO, Winblad B, Valind S, Nordberg A. Impaired cerebral glucose metabolism and cognitive functioning predict deterioration in mild cognitive impairment. Neuroreport 2001; 12:851–855. 50. Chetelat G, Desgranges B, de la Sayette V, Viader F, Eustache F, Baron JC. Mild cognitive impairment: Can FDG-PET predict who is to rapidly convert to Alzheimer’s disease? Neurology 2003; 60:1374–1377. 51. Anchisi D, Borroni B, Francheschi M, Kerrouche N, Kalbe E, Beuthien-Beumann B, Cappa S, Lenz O, Ludecke S, Marcone A, Mielke R et al. Heterogeneity of Brain Glucose Metabolism in mild cognitive impairment and clinical progression to Alzheimer disease. Arch Neurol 2005; 62:1728–1733. 52. Hirono N, Hashimoto M, Ishii K, Kzui H, Mori E. One year change in cerebral glucose metabolism in patients with Alzheimer’s disease. J Neuropsychiatry Clin Neurosci 2004; 16:488–492. 53. Alexander GE, Chen K, PietriniP, Rapoport SI, Reiman EM. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer’s disease treatment studies. Am J Psychiatry 2002; 159:738–745. 54. Falini A, Bozzali M, Magnani F, Pero G, Gambini A, Benedeti B, Mossini R et al. A whole brain MR spectroscopy study from patients with Alzheimer’s disease and mild cognitive impairment. NeuroImage 2005; 26:1159–1163. 55. Martinez-Bisbal MC, Arana E, Marti-Bonmati L, Molla E, Celda B. Cognitive impairment: classification by 1H magnetic resonance spectroscopy. Eur J Neurol 2004; 11:187–193.

13 56. Ackl N, Ising M, Schreiber YA, Atiya M et al. Hippocampal metabolic abnormalities in mild cognitive impairment and Alzheimer’s disease. Neurosci Letters 2005; 384:23–28. 57. Modrego PJ, Fayed N, Pina MA. Conversion from mild cognitive impairment to probable Alzheimer’s disease predicted by brain magnetic resonance spectroscopy. Am J Psychiatry 2005; 162(4):667–675. 58. Sorg C, Riedl V, Muhlau M, Calhoun VD, Eichele T et al. Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proc Natl Acad Sci 2007;104(47)18760–18765. 59. Rombouts SARB, Barkhof F, Goekoop R, Stam CJ, Scheltens P. Altered Resting State Networks in Mild Cognitive Impairment and Mild Alzheimer’s Disease: An fMRI Study. Human Brain Mapping 2005; 26:231–239. 60. Xu G, Anouono PG, Jones J, Xu Y et al. Perfusion fMRI detects deficits in regional CBF during memoryencoding tasks in MCI subjects. Neurology 2007; 69: 1650–1656. 61. Rombouts SARB, Goekoop R, Stam CJ, Barkhof F, Scheltens P. Delayed rather than decreased BOLD response as a marker for early Alzheimer’s disease. Neuroimage 2005; 26:1078–1085. 62. Rombouts SARB, Damoiseaux JS, Goekoop R, Barkof F, Scheltens P, Smith SM, Beckmann CF. Model-free group analysis shows altered BOLD FMRI networks in dementia. Human Brain Mapping 2007. Published online 27 Nov 2007. 63. Dickerson BC, Salat DH, Greve DN, Chua EF, RandGiovannetti E, Rentz DM et al. Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD. Neurology 2005; 65:404–411. 64. Kircher TT, Weis S, Freymann K, Erb M et al. Hippocampal activation in patients with mild cognitive impairment is necessary for successful memory encoding. J Neurol Neurosurg Psychiatry 2007; 78:812–818. 65. Hämäläinen A, Pihlajamäki M, Tanila H, Hänninen T, Niskanen E, Tervo S, Karjalainen PA, Vanninen RL, Soininen H. Increased fMRI responses during encoding in mild cognitive impairment. Neurobiol Aging. 2007; 28(12):1889–1903. 66. Dickerson BC, Salat BC, Bates JF, Atiya M, Killany RJ, Greve DN, Dale AM, Stera CE, Blacker D, Albert MS, Sperling RA. Medial temporal lobe function and structure in mild cognitive impairment. Ann Neurol 2004; 56:27–35. 67. Alsop DC, Detre JA, Grossman M. Assessment of cerebral blood flow in Alzheimer’s disease by spin-labeled magnetic resonance imaging. Ann Neurol 2000; 47:93–100. 68. Johnson NA, Jahng GH, Weiner MW et al. Pattern of cerebral hypoperfusion in Alzheimer’s disease and mild cognitive impairment measure with arterial spin-labeling magnetic resonance imaging. Radiology 2005; 234:851–859. 69. Xu G, Antuono PG, Jone J, Xu Y, Wu G et al. Perfusion fMRI detects deficits in regional CBF during memory-encoding tasks in MCI subjects. Neurology 2007;69:1650–1656. 70. Kantarci K, Jack CR, Xu YC et al. Regional diffusivity of water in mild cognitive impairment and Alzheimer’s disease. Radiology 2001; 219: 101–107. 71. Kantarci K, Peterson RC, Boeve BC, Knopman DS, Weigand SD, O’Brien PC, Shiung MM, Smith GE, Ivnik RJ, Tangalos EG, Jack CR Jr. DWI predicts future progression to Alzheimer disease in amnestic mild cognitive impairment. Neurology 2005; 64:902–904.

14 72. Xie S, Xiao JX, Gong GL, Zang YF, Wang YH et al.Voxelbased detection of white matter abnormalities in mild Alzheimer disease. Neurology 2006;66:1845–1849. 73. Damoiseaux JS, Smith SM, Witter MP, Arigita EJS, Brakof F, Scheltens P et al. White matter tract integrity in aging and Alzheimer’s disease. Human Brain Mapping early view. 10.1002/hbm.20563 74. Yasmin H, Nakata Y, Aoki S, Abe O, Sato N, Nemoto K et sl. Diffusion abnormalities of the uncinate fasciculus in Alzheimer’s disease: diffusion tensor tract-specific analysis using a new method to measure the core of the tract. Neuroradiology 2008;50:293–299. 75. Sydykova D, Stahl R, Dietrich O, Ewers M, Reiser MF et al. Fiber connections between the cerebral cortex and the corpus callosum in alzheimer’s disease: a diffusion tensor imaging and voxel-based morphometry study. Cerebral Cortex 2007; 17:2276–2282. 76. Rose SE, Janke AL, Chalk JB. Gray and white matter changes in alzheimer’s disease: a diffusion tensor imaging study. J Magn Reson Imaging 2008;27:20–26. 77. Muller MJ, Greverus D, Weibrich C, Dellani PR, Scheurich A, Stoeter P et al. Diagnostic utility of hippocampal size and mean diffusivity in amnestic MCI. Neurobiol Aging 2007;28: 398–403. 78. Medina D, deToledo-Morrell L, Urresta F, Gabrieli JDE, Moseley M et al. White matter changes in mild cognitive impairment and AD: a diffusion tensor imaging study. Neurobiol Aging 2006;27;663–672. 79. Rose SE, McMahon KL, Janke Al, O’Dowd B, de Zubicaray G, Strudwick MW et al. Diffusion indices on magnetic resonance imaging and neuropsychological performance in mild cognitive impairment. J Neurol Neurosurg Psychiatry 2006; 77:1122–1128. 80. Stahl R, Dietrich O, Teipel SJ, Hampel H, Reiser MF et al. White matter damage in alzheimer disease and mild cognitive impairment: assessment with diffusion-tensor MR imaging and parallel imaging techniques. Neuroradiology 2007;243:483–492. 81. Fellgiebel A, Dellani PR, Greverus D, Scheurich A, Stoeter P et al. Predicting conversion to dementia in mild cognitive impairment by volumetric and diffusitivity measurements of the hippocampus. Psychiatry Res. 2006 Apr 30;146(3): 283–287 82. Nordberg A. Amyloid plaque imaging in vivo: current achievement and future prospects. Eur J Nucl Med Mol Imaging 2008;35(Suppl 1):S46–S50. 83. Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, Bergstrom M, Savitcheva I, Huang G, Estrada S, Ausen B et al. Imaging brain amyloid in alzheimer’s disease with Pittsburgh compound-B. Ann Neurol 2004; 55: 306–319. 84. Rowe CC, Ng S, Ackerman U, Gong SG et al. Imaging β-amyloid burden in aging and dementia. Neurology 2007;68:1718–1725. 85. Kemppainen NM, Aalto S, Wilson IA, Nagren K, Hlin S et al. Voxel-based analysis of PET amyloid ligand [11C]PIB uptake in Alzheimer disease. Neurology 2006; 67: 1575–1580. 86. Mattis CA, Lopresti BJ, Klunk WE. Impact of amyloid imaging on drug development in Alzheimer’s disease. Nucl Med Biol 2007;34:809–822.

M.S. Chong and W.S. Lim 87. Ng S, Villemagne VL, Berlangieri S, Lee ST, Cherk M, et al. Visual assessment versus quantitative assessment of 11 C-PIB and 18F-FDG PET for detection of Alzheimer’s disease. J Nucl Med 2007;48:547–552. 88. Frisoni GB. Imaging of amyloid comes of age. Lancet Neurol 2008;7(2):114–115. 89. Rowe CC, Ackerman U, Browne W, Mulligan R, Pike KL, O’Keefe G et al. Imaging of amyloid β in Alzheimer’s disease with 18F-BAY94–9172, a novel PET tracer: proof of mechanism. Lancet Neurol 2008;7:129–135. 90. Pike KE, Savage G, Villemagne VL, Ng S, Moss SA, Marfa P et al. β-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer’s disease. Brain 2007;130:2837–2844. 91. Small G, Keep V, Recoil LM et al. PET of brain amyloid and tau in mild cognitive impairment. N Engle J Med 2006;355:2652–2663. 92. Forsberg A, Engler E, Almkvist O, Bouquets G, Hangman G, Wall A, Ingham A et al. PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiol Aging 2007, doi:10.1016/j. neurobiolaging.2007.03.029 93. Minton MA, Larissa GN, She line YI, Dance CS, Lee SY et al. [11C}PIB in a nondemented population. Potential antecedent marker of Alzheimer disease. Neurology 2006; 67:446–452. 94. Engler H, Forsberg A, Amorist O et al. Two-year follow-up of amyloid deposition in patients with Alzheimer’s disease. Brain 2006; 129:2856–2866. 95. Zhang Y, Schiff N, John GH, Bayne W, Mori S, Shad L et al. Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology 2007; 68(1):13–19. 96. Kawachi T, Sheik, Sakamoto S, Sasaki M, Mori T, Yamashita F et al. Comparison of the diagnostic performance of FDGPET and VBM-MRI in very mild Alzheimer disease. Eur J Muck Mol Imaging 2006; 801–809. 97. Jack CR, Low VJ, Singe ML, Weigand Kemp BJ et al.11C Pub and structural MRI provide complementary information in imaging of Alzheimer’s disease and amnestic mild cognitive impairment. Brain 2008; 131:665–680. 98. El Fakhri G, Kijewski MF, Johnson KA, Syrkin G, Killany RJ, Becker JA, Zimmerman RE, Albert MS. MRI-guided SPECT perfusion measures and volumetric MRI in prodromal alzheimer disease. Arch Neurol 2003; 60: 1066–1072. 99. Mosconi L, Perani D, Sorbi S, Herholz K, Macmias B et al. MCI conversion to dementia and the APOE genotype. Neurology 2004; 63:2332–2340 100. Drzezga A, Grimmer T, Riemenschneider M, Lautenschlager N et al. Prediction of Individual Clinical Outcome in MCI by means of genetic assessment and 18F-FDG PET. J Nucl Med 2005; 46:1625–1632. 101. Fleisher AS, Sun S, Taylor C, Ward CP, Gamst AC et al. Volumetric MRI vs clinical predictors of Alzheimer disease in mild cognitive impairment. Neurology 2008; 70:191–199. 102. Okamura N, Arai H, Maruyama M et al. Combined analysis of CSF tau levels and [(123)I]Iodoamphetamine SPECT in mild cognitive impairment: implications for a novel predictor of Alzheimer’s disease. Am J Psychiatry 2002; 159:474–476.

18

Neuroimaging Biomarkers in Alzheimer’s Disease

103. Fox NC, Black RS, Gilman S, Rossor MN, Griffith SG, Jenkins L, Koller M. Effects of Aβ immunization (AN1792) on MRI measures of cerebral volume in Alzheimer disease. Neurology 2005; 64:1563–1572. 104. Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR, Jagust W, Trojanowski JQ, Toga AW, Beckett L. Ways toward an early diagnosis in Alzheimer’s disease: the

15 alzheimer’s disease neuroimaging initiative (ADNI). Alzheimer’s Dement 2005; 1(1):55–66. 105. Jack CR Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, Borowski B, Britson PJ, L Whitwell J, Ward C, Dale AM, Felmlee JP, Gunter JL et al. The alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging 2008; 27(4):685–691.

Chapter 19

Role of Imaging Techniques in Discerning Neurobehavioral Changes in Ischemic, Neurodegenerative and Demyelinating Disorders Turi O. Dalaker, Mona K. Beyer, Milena Stosic, and Robert Zivadinov

Abstract Magnetic resonance imaging (MRI) is the most important paraclinical measure for assessing and monitoring the pathologic changes implicated in the onset and progression of demyelinating and ischemic disorders. Conventional MRI sequences, such as T2-weighted imaging are unable to provide full details about the degree of inflammation and underlying neurodegenerative changes. Newer non-conventional MRI techniques have the potential to detect clinical impairment, disease progression, accumulation of disability, and the neuroprotective effects of treatment. The measurement of brain atrophy seems to be of growing clinical relevance as a biomarker of the disease process. Atrophy should now be included as a secondary endpoint in trials of therapies aimed at limiting disease progression. Magnetization transfer imaging is increasingly used to characterize the evolution of lesions and normal-appearing brain tissue. Magnetic resonance spectroscopy, which provides details on tissue biochemistry, metabolism, and function, also has the capacity to reveal neurodegeneration and neuroprotective mechanisms. By measuring the motion of water, diffusion imaging can provide information about the orientation, T. O. Dalaker Buffalo Neuroimaging Analysis Center, Department of Neurology, State University of New York at Buffalo, Buffalo, NY, USA; Department of Radiology, Stavanger University Hospital, Stavanger, Norway; The Norwegian Centre for Movement Disorders, Stavanger University Hospital, Stavanger, Norway M. K. Beyer Department of Radiology, Stavanger University Hospital, Stavanger, Norway; The Norwegian Centre for Movement Disorders, Stavanger University Hospital, Stavanger, Norway M. Stosic and R. Zivadinov Buffalo Neuroimaging Analysis Center, Department of Neurology, State University of New York at Buffalo, Buffalo, NY, USA

size, and geometry of tissue damage in white and gray matter. Functional MRI and other nuclear functional techniques may help clarify the brain’s plasticity- and receptor-dependent compensatory mechanisms in patients with a variety of neurologic disorders. New techniques that might bring new information to the field include studies of microglial activation and studies using multiple single photon emission computed tomography (SPECT) and positron emission tomography (PET) tracers. All these techniques are useful in establishing diagnosis, monitoring disease activity, measuring therapeutic effect and explaining the development of disability in the short-and long-term. The role of these techniques in discerning neurobehavioral and neuropsychiatric symptoms in neurodegenerative (emphasizing Parkinson’s Disease and Dementia with Lewy Bodies), ischemic and demyelinating disorders will be discussed. Keywords Magnetic resonance imaging • nuclear medical imaging • ischemic disorder • vascular dementia • neurodegenerative disorder • Parkinson’s disease • dementia with lewy bodies • demyelinating disease Abbreviations AchE: Acetylcholine esterase; AD: Alzheimer’s disease; ADC: Apparent diffusion coefficient; ADDTC: State of California Alzheimer’s Disease Diagnostic and Treatment Centers; ADEM: Acute disseminated encephalomyelitis; AIREN: Association Internationale pour la Recherche et l’Enseignement en Neurosciences; BCR: Bicaudate ratio; BOLD: Blood oxygen level dependent; CAA: Cerebral Amyloid Angiopathies; CADASIL: Cerebral Autosomal Dominant Arteriopathy with subcortical infarcts and leucoencephalopathy; Cho: Choline; CIND: Vascular cognitive impairment no dementia; CNS: Central nervous

M.S. Ritsner (ed.), The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, © Springer Science + Business Media B.V. 2009

17

18

system; Cr: Creatinine; CSE: Conventional spin echo; CSF: Cerebrospinal fluid; CT: Computed tomography; CVD: Cerebrovascular disease; DA: Dopamine; DAT: Dopamine reuptake transporter; DLB: Dementia with Lewy Bodies; DTI: Diffusion tensor imaging; DTBZ: 11 C-dihydrotetrabenazine; DWI: Diffusion weighted imaging; FA: Fractional anisotropy; FDG: 18F-2-fluoro2-deoxyglucose; FDOPA: Fluorodopa; FLAIR: FluidAttenuated Inversion Recovery; fMRI: Functional magnetic resonance imaging; FSE: Fast-spin echo; GM: Gray matter; LB: Lewy Bodies; LBD: Lewy Body Disorder; LN: Lewy neuritis or neurites; MCI: Mild cognitive impairment; MD: Mean diffusivity; MMSE: Mini-Mental State Examination; MR: Magnetic resonance; MRI: Magnetic resonance imaging; MRS: Magnetic resonance spectroscopy; MS: Multiple sclerosis; MTA: Medial temporal lobe atrophy; MTI: Magnetization transfer imaging; MTR: Magnetization transfer ratio; NAA: N-acetyl aspartate; NABT: Normal appearing brain tissue; NAWM: Normal appearing white matter; NINDS: National Institute of Neurological Disorders and Stroke; NP: Neuropsychology; PCr: Phosphocreatinine; PD: Parkinson’s disease; PDD: Parkinson’s disease with dementia; PET: Positron emission tomography; RBD: REM sleep behavior disorder; rCBF: Regional cerebral blood flow; REM: Rapid eye movement; RF: Radio frequency; RR: Relapsing remitting; SABRE: Semi-automated brain region extraction; SIVD: Subcortical ischemic vascular dementia; SLE: Systemic lupus erythematosus; SP: Secondary progressive; SPECT: Single photon emission computed tomography; TSE: Turbo spin echo; VaD: Vascular dementia; VaMCI: Vascular mild cognitive impairment; VBM: Voxel based morphometry; VCD: Vascular cognitive disorder; VCI: Vascular cognitive impairment; VH: Visual hallucinations; WI: Weighted imaging; WM: White matter; WMH: White matter hyperintensities

Imaging Techniques Magnetic Resonance Imaging (MRI) Conventional MRI Techniques T2-weighted imaging (WI) is highly sensitive in detecting hyperintense lesions in the white matter

T. O. Dalaker et al.

(WM) and, less commonly, the gray matter (GM). The most typical sites are in the WM: the periventricular region corpus callosum, posterior fossa and cortical regions. There are a number of techniques for identifying T2 hyperintense lesions; among them, the most often recommended are conventional spin echo (CSE), fast spin echo (FSE), and fluid-attenuated inversion recovery (FLAIR).1 FLAIR is particularly helpful in the evaluation of periventricular and cortical/juxtacortical lesions, where CSF (cerebrospinal fluid) may mask the visualization of these plaques on T2-WI.1 In the last decade, continuous technical improvements in MRI hardware and software have led to the development of new pulse sequences that are more efficient and sensitive. Among them, turbo spin echo (TSE), FSE, proton density and fast-FLAIR have already demonstrated their usefulness in a wide variety of neurologic diseases, including multiple sclerosis (MS).1,2 Better lesion-to-CSF contrast is achieved with proton density WI because of the relatively lower signal intensity of CSF on this sequence and improved lesion-totissue contrast. FSE showed greater sensitivity than CSE in detecting areas of T2 prolongation. Fast FLAIR sequences are especially helpful in evaluating periventricular and cortical/juxtacortical lesions, as the CSF signal may mask these plaques on T2-WI.1,2 On noncontrast T1-WI, most lesions are isointense in WM. Some are hypointense, and these are known as black holes.1,2 T1-weighted black holes are an important MRI metric of neurodegeneration in patients with different neurologic disorders. They represent a more advanced pathological substrate of the lesions – mainly axonal loss, Wallerian degeneration, and gliotic changes.1,2 They can predict development of disability better than other lesion-based inflammatory MRI measures.3 Another very important measure of neurodegeneration in neurologic diseases is brain atrophy. Mounting evidence supports the concept that global or regional brain atrophy is an important biomarker of the disease process.4 A large number of studies underscores the usefulness of MRI in assessing central nervous system (CNS) atrophy and its relationship to long-term neurodegeneration and disability progression. It has been established that CNS atrophy is a moderate but significant predictor of neurological impairment in several neurologic diseases. The association between atrophy and disability is independent of the effect of conventional MRI lesions.

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

Non-conventional MRI Techniques Magnetization Transfer Imaging Magnetization transfer imaging (MTI) is an advanced MRI technique that has been widely used to evaluate characteristics and evolution of lesions and normal appearing brain tissue (NABT). It is based on the interactions and exchange between protons that are unbound in a free water pool with those where motion is restricted due to binding with macromolecules.5,6 Magnetization transfer (MT) contrast is achieved by applying radio frequency (RF) power only to the proton magnetization of the macromolecules. Tissue damage is usually reflected by a reduction in this exchange of protons and thus a decrease in the magnetization transfer ratio (MTR). Decreases in MTR indicate a reduced capacity of free water to exchange magnetization with the brain tissue matrix with which the water comes into intimate contact and is not specific to MS pathologic substrates. Although MTR decreases are not specific to any of the various pathologic substrates, a relationship has been shown between MTR and the percentage of residual axons and the degree of demyelination.7 The most compelling evidence to support this hypothesis comes from a postmortem study showing a strong correlation between MTR values from MS lesions and NABT with the percentage of residual axons and the degree of demyelination.8 MTI can be used to assess tissue injury in lesions, in the whole brain and in specific brain structures.

19

best available ones that have been studied are N-acetyl aspartate (NAA), choline (Cho), creatinine(Cr)/phosphocreatinine (PCr), myoinositol and lactate11 NAA is considered an in vivo marker of neuronal integrity and is often measured in relation to Cr (which is thought to be unaffected by neurodegeneration). The NAA peak in an MR (magnetic resonance) spectrum is a putative marker of neuronal and axonal integrity, and the Cho peak appears to reflect cell-membrane metabolism. While inflammation and demyelination are represented by increases in Cho, lactate and lipids, axonal and neuronal injury can be quantified through decreases in NAA. On this basis, a diminished NAA peak is interpreted as representing neuronal/axonal dysfunction.12 On the other hand, an elevated Cho peak represents heightened cell membrane turnover, as seen in demyelination, remyelination, inflammation, or gliosis. Therefore, MRS may provide unique information regarding metabolic, and not just structural, changes in the CNS. It evaluates the severity of the pathological processes, may follow disease evolution, and provides insight into its pathogenesis. Other metabolic peaks are of increasing interest in the study of neurologic disorders. Among these, the glutamate/glutamine peak represents a mixture of amino acids and bioamines used throughout the CNS as excitatory and inhibitory neurotransmitters,13 and the myoinositol peak represents a sugar-like molecule thought to be a marker of glial proliferation and now recognized for its importance in osmotic regulation of brain-tissue volume.12

Diffusion Imaging Magnetic Resonance Spectroscopy In addition to providing information on tissue structure, magnetic resonance spectroscopy (MRS) offers the potential to investigate tissue metabolism and function. MRS offers a mass of data on the biochemistry of a selected brain tissue volume, which represents potential surrogate markers for the pathology underlying the pathological process.9 It also provides a quantitative assessment of disease involvement related primarily to two major pathologic aspects of neurologic diseases, active inflammatory demyelination and axonal/neuronal injury.10 MRS provides insights into metabolic conditions within the brain. A commonly used technique is proton (1H) spectroscopy, which can measure concentration of common cerebral components. The

Diffusion-weighted imaging (DWI) and diffusion-tensor imaging (DTI) are unique MRI techniques that allow the measurement of tissue water diffusional motion and, as a consequence, provide information about orientation, size, and geometry of the tissue.14,15 The mobility of water molecules is reduced in highly organized tissue like WM and GM because of the interactions with cellular and tissue structures. In the CNS, diffusion is influenced by the microstructural components of tissue, including cell membranes and organelles. As a result, the apparent diffusion coefficient (ADC) is lower in those tissues than in pure water. The ADC values depend on the orientation of the tissue relative to the measurement. Conventionally, the average ADC is calculated from three (DWI) or

20

more (DTI) orthogonal directions that provide information on the overall diffusivity in the tissue. Pathological processes that modify tissue organization can cause abnormal water motion, thereby altering ADC values. Usually, the two main pathological processes affecting the brain are demyelination and neurodegeneration. They can alter the geometry of brain tissue orientation, resulting in an increase of water diffusivity measurable with different DWI and DTI indices. These measures include mean diffusivity (MD), fractional anisotropy (FA), entropy and tractography.

Functional MRI Functional MRI (fMRI) is a unique MRI technique, an indirect measure of blood flow and neuronal activity based on changes in the local magnetic field (T2*). FMRI can non-invasively detect activation of brain areas during the performance of tasks. There is increased blood flow to the region during the neural activation which in turn increases the amount of oxygenated hemoglobin in the capillary beds. The amount of oxygen delivered by the hemodynamic response to neuronal activity exceeds the amount required by the tissue, thus increasing the ratio of oxygenated to deoxygenated hemoglobin in the venous beds compared with the resting state. The signal change is very small, but is reliably measured by subtracting images collected at rest from images collected during activity. FMRI is not yet used clinically for diagnosis or monitoring of different neurologic disorders, but has been extensively used in research settings to examine the neural correlates of known motor, visual and neuropsychologic deficits in normal subjects to define abnormal patterns of brain activations resulting from disease, and patients with ischemic, neurodegenerative and demyelinating disorders.

Nuclear Medical Imaging The general principle behind nuclear medicine studies is assessment of the distribution of a tissue/functional specific radionuclide in the organ of interest, by detection of emitted rays from the nuclide. The radionuclide is attached a specific molecule found in the various tissues or used in physiological processes. The radiopharmaceutical

T. O. Dalaker et al.

is administrated in vivo ahead of the investigation and, based upon distribution in the examined body part, one can distinguish between normal and pathological tissue. The radionuclides are unstable nuclei (atomic species with a definite number arrangement of protons and neutrons) that decay by emission of electromagnetic radiation (most common γ-rays), fusion or loss of particles. This process can be detected by instruments known as gamma cameras. The detected signal is processed, transformed and digitalized to the images we know as the end result of nuclear imaging. For an introduction to physics and technical issues regarding this subject other literature is advised.16,17 Radionuclides for imaging are mostly artificially produced in cyclotrons or in nuclear reactors. The nuclides are attached to ligands (also called traces) that are integrated in the biological process to be examined. The choice of nuclide for a certain imaging study depends on what question will be addressed. For assessment of tissue metabolism, glucose ligands are commonly used. For estimation of certain tumors, one uses tumor-specific tracers, and various neurotransmitter processes demand process- specific ligands. In neuroimaging there are several important criteria that need to be met when using radiolabeled ligands18: (1) the metabolism of the used compounds must not interfere with the detection of the ligand, (2) the ligand must be lipophilic to cross the blood-brain barrier, (3) the halflife of the radionuclides must be long enough to be sufficiently detected and, finally, (4) the administered compound must provide a high signal-to-noise ratio. We refer to the functional imaging sections below for details on tracers used in neurobehavioral aspects of neurodegenerative and vascular diseases. The currently used nuclear neuroimaging techniques are SPECT and PET. They are both variations on the principle of gamma camera detection of emitted γ-rays. Tomographic reconstruction is used to construct three-dimensional images. By the use of the radiolabeled ligands, one can measure metabolic activity, perform receptor-binding studies, study regional blood flow and explore drug treatment response. Both techniques can detect pathology before it is visible on structural imaging. The drawback of both SPECT and PET is poor spatial resolution. In general PET is better than SPECT, but resolution is still about 3–5 mm for PET and 10 mm for SPECT.18 A way of solving the limited spatial resolution, and thereby improve

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

diagnostic accuracy, is to combine the SPECT and PET scanners with a conventional computed tomography (CT) scanner. SPECT/CT and PET/CT combine a nuclear image with high-resolution structural CT images for accurate localization of lesions and physiological processes.17 Recently, combinations with MRI scanners also have been introduced to the radiological market. Currently, SPECT is more available commercially than PET because use of radionuclides with a short half-life in PET demands an expensive on-site production of the ligands. For a general review on the use of SPECT and PET in various clinical settings, a review of Bybel et al. is recommended.19

21

Traditional CVDs are not the only cause of ischemic brain lesions leading to neurobehavioral changes and neuropsychiatric symptoms. According to Mendez and Cummings28 as referred to in Roman et al.,29 there is a long list of possible, less common, vascular etiologies that may cause vascular cognitive symptoms. This includes, among others, various inflammatory and hematological disorders, infections and toxic causes. A further description of these disorders, with their neuroimaging and neurobehavioral correlates, is beyond our intentions in this chapter.

Vascular Cognitive Impairment Imaging of Neurobehavioural and Neuropsychiatric Symptoms in Ischemic Disorders Vascular dementia (VaD) is the second most common cause of dementia after neurodegenerative disorders.20,21 Some claim that VaD might even be the leading cause of dementia in the elderly, considering the impact of vascular pathology in Alzheimer’s Disease (AD).22 Vascular Cognitive Impairment (VCI) is a broad term proposed to be used for all forms of mild to severe cognitive impairment associated with and presumed to be caused by cerebrovascular disease (CVD).23,24 VCI may be regarded as a continuum, with VaD being the most severe form and vascular cognitive impairment no dementia (CIND) a milder form of the same disease process.25 Vascular cerebral changes may lead to somatic neurological deficits, but an important possible sequel consists of neurobehavioral and neuropsychiatric changes. In addition to cognitive decline and dementia, vascular pathology in the brain may contribute to mood disorders, but also to psychosis and anxiety.26 VCI is believed to be caused in part by potentially preventable/reversible conditions, and at the present time much effort is therefore being put into this field of neuroscience. Neuroimaging is of special importance when trying to establish the relationship between vascular brain pathology and cognitive deficits. In addition to its role as an aid in establishing the diagnosis and ruling out differential diagnoses, neuroimaging may also be used to evaluate treatment effects and serve as a possible outcome measurement in clinical trials.24,27

Subclassifications O’Brien et al.23,24 classifies VCI as an umbrella term applied to all forms of cognitive impairment associated with vascular pathology. Two main categories include sporadic and hereditary disorders. The sporadic VCI is further divided into several clinical subtypes: post-stroke dementia, VaD, Mixed AD and VaD (mixed dementia) and, finally, vascular mild cognitive impairment (VaMCI). The term VaD is again subclassified into multi-infarct dementia, subcortical ischemic vascular dementia (SIVD), strategic-infarct dementia, hypoperfusion dementia, hemorrhagic dementia and dementia caused by specific arteriopathies. This proposed classification will be used in the current text, and the focus will be limited to ischemic pathology. Despite recent efforts, there is still no established consensus about what to include under the term VCI, and the above described classification is one of a few available. Rockwood et al.30 proposed that VCI include the main categories of VaD, VCI, no dementia (CIND) and mixed VCI. Another definition later proposed by Roman et al.29 restricted VCI to cases without dementia, and the term vascular cognitive disorder (VCD) included both VaD and VCI.

Cognitive Changes The cognitive changes associated with VCI are highly variable depending on location and extent of the brain lesions. Despite this, the majority of cognitive deficits

22

can be seen in executive functions, and the attention and speed of information processing may also be affected, especially in subjects with SIVD and dementia caused by small vessel subcortical ischemic changes (lacunar infarcts or ischemic WM lesions).31

T. O. Dalaker et al.

sented VaD, and that prevalence of VaMCI, cognitive impairment not qualifying for dementia diagnosis, was 36.7%. A European collaboration study showed an agestandardized prevalence of VaD of 1.6% increasing with age.20 Regarding VCI, one study estimated that about 5% of people over 65 years old had VCI.42

Diagnostic Criteria Risk Factors VaD is, as reflected by the many subtypes and classifications, a disorder with heterogeneous clinical findings and pathology. Of many available diagnostic criteria,32–38 the ones currently used most commonly both for clinical and research purposes are the criteria from the National Institute of Neurological Disorders and Stroke (NINDS) and the Association Internationale pour la Recherche et l’Enseignement en Neurosciences (AIREN).36 Those criteria require: (1) that the patient is demented; (2) evidence of CVD demonstrated by history, clinical examination or brain imaging and (3) a temporal relationship between the two disorders. Modified NINDS-AIREN criteria have been proposed for subcortical vascular dementia by Erkinjuntti et al.32

Risk factors associated with VCI can be categorized into four classes; demographic (age, male sex and lower education), genetic (various familial vascular encephalopathies), atherosclerotic (hypertension, cigarette smoking, myocardial infarction, diabetes mellitus and hyperlipidemia) and stroke-related (volume of tissue loss, evidence of bilateral cerebral infarction, strategic infarction, white matter disease, silent cerebral infarcts and cerebral atrophy).43 Vasculopathies sometimes have established genetic links. The most common is Cerebral Autosomal Dominant Arteriopathy with subcortical infarcts and leucoencephalopathy (CADASIL) with NOTCH3 gene mutation,44 and the heterogeneous group of Cerebral Amyloid Angiopathies (CAA).45

Diagnostic Neuroimaging Criteria in VCI Structural, conventional imaging plays a central role in the diagnostic work-up in most ischemic vascular disorders. It is suggested that MRI should be incorporated as an essential part of clinical trials involving VCI,24,27 but the sequences, techniques and clinical outcome to be used remain a matter of debate.39,40 Of the available diagnostic criteria for VaD, only two of them, the NINDS-AIREN36 and the State of California Alzheimer’s disease Diagnostic and Treatment Centers (ADDTC) criteria,34 require radiological findings of CVD in the form of infarcts or WM lesions.

Pathology There is a large amount of research on pathological findings in the various CVDs, but despite this there are as yet no validated pathologic criteria for VaD. Several studies show an association between cerebral infarcts and dementia, but there is still debate about the importance of vascular risk factors, size and location of infarcts, and vascular risk factors for cognitive impairment and dementia.46 White Matter Hyperintense Lesions

Epidemiology In community-based studies, the prevalence of dementia is about 30% after a stroke. The relative incidence of new-onset dementia increases from 7% after 1 year to 48% after 25 years.41 A case-control study found that 3–6 months after a stroke event, 21.3% of subjects pre-

WM hyperintensities (WMH) are common neuroimaging findings on T2- and proton density weighted brain MRI scans in normal aging, but they can also be a sign of ischemic brain disorders. In pathological exams, they are associated with evidence of hypoxic tissue injury,47 with reduced capillary density not only in the lesions but also in normal-appearing WM.48

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

Smooth periventricular lesions are believed by some to be of non-ischemic origin and consist of areas of demyelination, subependymal gliosis and discontinuity of ependymal lining.49

Macroscopic Infarcts Infarctions involving areas of major cerebral arteries may result in large territorial lesions with a variable amount of tissue loss. The infarcts develop in typical stages of gliosis with increased water content. The intensity of gliotic scarring is used in pathological studies to judge the degree and age of the infarction.50

Lacunar Infarcts Another neuroimaging finding, lacunes (small infarcts in WM and deep GM structures with diameters ranging from 3 to 15 mm),50 represent different stages of ischemic injury exhibiting loss of axons and myelin together with reactive changes.51 In pathological studies, lacunes have been associated with cognitive decline independent of other dementia pathology such as AD.52 Some studies have reported that subcortical lacunes and multiple widespread infarcts are the most common morphologic substrates of VaD.50

Microscopic Infarcts Recently there has been increasing focus on the contribution of cortical micro infarcts (not visualized by current available neuroimaging techniques) to cognitive decline both in normal aging,53 VaD and mixed dementia.54 Cortical microinfarcts might be as strong a predictor of cognitive decline as the presence of neurofibrillary tangles in AD.55

23

tissue water content, will be detected as a similar hyperintensity in MRI. Consequently, conventional MRI findings are unspecific. In addition, conventional MRI shows the extent and location of lesions, but might not differentiate between various grades of histopathological damage.56 Regarding ischemic changes in WM, lesion size is found to correlate with severity of pathologically confirmed tissue damage.49 A recent combined neuropathological and MRI study found that both lacunes and WMH identified by imaging showed good correlation with CVD. The pathological correlates of GM atrophy in brain MRI were more complex, with several processes in addition to ischemic changes, such as AD pathology.57 More recent studies have shown that non-conventional MRI techniques might increase both the sensitivity and specificity of imaging detectable histopathological changes that are not visible with conventional MRI sequences. DWI,58 MRS,59 and MTI60 have shown promising results in detection of more subtle brain changes (see below).

Non-Cognitive Neuropsychiatric Changes in Ischemic Brain Disorders In addition to cognitive impairment, other conditions such as depression, apathy and psychosis have also been linked to vascular brain lesions.26 Most studies have focused on mood disorders.61 The clearest association between ischemic brain disorder and depression is found after stroke, but the relationship between these conditions is very complex.62 Pohjasvaara et al.63 found a frequency of depressive disorder in about 40% of stroke survivors in a communitybased study. Predisposing factors for post-stroke depression are age, infarct site, female gender, decrease in social activities, living alone, severity of stroke, functional dependence and language alteration.64

Correlations Between MRI Findings and Pathology

Neuroimaging Findings and Neuropsychiatric Changes in Pathologic ischemic tissue contains more water, replac- Ischemic Brain Disorders ing neurons, myelin and axons. Higher water content is seen as a bright signal on T2-WI. However, not only ischemic lesions, but any pathology resulting in higher

The heterogeneity of the described entities is also reflected in the imaging findings of ischemic brain

24

disorders. The many conditions share a lot of common imaging characteristics, but both location and extent of the lesions can vary dramatically. This complicates the diagnostic process and represents a challenge for neuroradiologists and clinicians to solve. Traditionally, conventional MRI in particular is of major importance in the diagnostic follow-up in many ischemic brain disorders. Even though imaging findings like WMH are unspecific and also common in normal aging, they can be considered a result of ischemic brain diseases when clinical history and other diagnostic tools are taken into consideration.

Conventional Techniques In routine clinical practice, neuroimaging is essential to demonstrate CVD. In VCI and VaD, it is therefore necessary not only to exclude differential diagnosis, but also to diagnose the various conditions.65 Although CT is still important in many clinical settings, MRI is becoming the method of choice because, among other factors, it offers better tissue specificity and sensitivity with respect to pathology. Therefore, in this section regarding structural imaging, we refer only to MRI studies.

T. O. Dalaker et al.

lower education and ethnicity.67 Hypertension has been found to be a risk factor for both WMH and development of brain atrophy,73 although the blood pressure can be reduced by use of antihypertensive therapy,74 thereby reducing the risk of both WMH progression and atrophy. The main predictor for progression of WMH in healthy individuals has been found to be high lesion volume at baseline, especially in deep WM and frontal regions.75

Sequences Accumulation of WMH around the ventricles, also called leukoaraiosis, has a bright signal on T2–WI, indicating pathology involving increased water content and gliotic scarring in the WM of the brain. The FLAIR sequence is commonly used for detection of these WMH. This sequence suppresses the signal from CSF, thereby allowing only signals from tissue pathology to show-up as hyperintense on the scan. Typical WMH in subcortical ischemic disease include extensive periventricular and deep WM signal abnormalities that especially affect the genu and anterior limb of the internal capsule, anterior corona radiate and anterior centrum semiovale.24

White Matter Hyperintensities Methods for assessment Epidemiology Epidemiological imaging studies show that WMH are common in the elderly.66–68 In a population based study, de Leeuw et al.66 showed that, of 1077 subjects between 60–90 years of age, only 5% had no WMH detectable on MRI. Most studies show that women have more WMH changes than men,66,69,70 but there are others reporting that there is no such difference.68

Risk Factors The most evident risk factor for WMH is increasing age, but other known risk factors are hypertension and decreased peak expiratory flow, elevated levels of glycated hemoglobin,71 type 2 diabetes,72 smoking,

Earlier work estimated the amount of WMH using various visual qualitative rating scales.76–78 Theses scales are easy to implement and relative insensitive to artefacts, but have limitations regarding objectivity and ceiling effects.79 Recently, computer assisted volumetric analyses have become more widely used.68,80 Comparison of different methods has shown that visual scoring is less sensitive than volumetric assessment,79 and that automated quantitative segmentation methods are suitable for assessing impact on cognitive function.81 Others have found that visual rating is as good as the more complex methods in routine clinical practice, but that volumetric assessment should be used in research settings.82 A review of current concepts regarding WMH analysis is described in detail elsewhere.83

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

Cognitive Impact Normal Aging WMH have been associated with reduced cognitive skills in non-disabled independently living elderly.84 A recent study85 reported that age-dependent WMH are related to both global cognitive dysfunction and various specific cognitive performances such as executive function, attention and speed processing. Despite evidence that age-related WMH influence cognitive status, some studies report that there is no such relationship. These suggest that MRI might be oversensitive when it comes to WMH, and that higher water content in the brain does not necessarily result in loss of function.40 The association between WMH (especially low grade) and cognition in non-demented

Fig. 19.1 Axial FLAIR MRI sequence showing periventricular and deep white matter hyperintensities in non-demented 82 year old male. Total WMH volume in this subject was about 55 ml. Courtesy of Turi O. Dalaker, MD. Department of Radiology, Stavanger University Hospital, Stavanger, Norway; The Norwegian Centre for Movement Disorders, Stavanger University Hospital, Stavanger, Norway

25

individuals is at the present time complex and not fully understood. Figure 19.1 shows WMH on a FLAIR sequence in a non-demented elderly individual.

Vascular Dementia The NINDS-AIREN criteria for VaD36 require that WMH involve at least 25% of total brain WM. In order to be significant, WMH must be diffuse and characterized by irregular periventricular lesions extending into deep WM, sparing areas thought to be protected from perfusion insufficiency (e.g., subcortical U-fibers and external capsule-claustrum-extreme capsule). A combined neuropathological and MRI population based study found that severe WMH were independent risk factors for dementia.86 Cohen et al.87 found that, in

26

VaD, WMH were strongly associated with reduced executive-psychomotor performance, but not with other domains and global cognitive function. This study found that whole brain volume was a better predictor of global cognitive status, indicating that WMH are only one part of the pathological process in VaD. The relationship between WMH and dementia might be driven by a threshold effect.88,89 This implies that a certain amount of WMH is needed to have clinical consequence. Once this threshold is reached, other factors might contribute more to cognitive impairment than WMH volume.90 This can explain that some studies fail to detect a relationship between grades of WMH and cognitive function in established dementia.56 Vascular Mild Cognitive Impairment O’Brien et al.23 define cognitive impairment resulting from CVD that is not severe enough to qualify for VaD as VaMCI. VaMCI is currently investigated as a possible predictor of future VaD.91 Studies have found an association between WMH volume and deficits on cognitive tests in VaMCI,92–94 especially for executive dysfunction.95 However, Sachdev et al.93 found excess WMH and infarcts in post-stroke patients with VaD and VaMCI, compared to subjects with no cognitive impairment. In that study, significant predictors of cognitive impairment were stroke volume and premorbid function, but not WMH volume. A recent study showed that WMH, especially those that are periventricular, in MCI patients predicted conversion to VaD and mixed dementia (VaD in combination with AD), but did not increase the risk of developing other dementias like AD, DLB and frontotemporal dementia.96

White Matter Hyperintensities and Depression The relationship between ischemic WMH and late-life depression is currently debated. Several studies have reported that WMH, especially in deep WM, are strongly associated with depressive symptoms.26,97–99 This correlation is found in both non-demented 97 and demented100 subjects. O’Brien et al.100 reported that frontal WMH especially were related to depressive symptoms in their study sample of demented subjects, and that the same relationship was found irrespective

T. O. Dalaker et al.

of subjects diagnosed as AD, VaD or DLB. In another study, WMH rather than lacunar infarcts were found to predict depression in the elderly.99 On the other hand, other studies find no support for a vascular hypothesis of depression.101 This was also the conclusion of a recent review.102 Integrated neuroimaging findings of CVD, such as WMH with clinical data, are proposed in order to better diagnose vascular depression.61 At the present time, further studies are warranted to validate the proposed criteria.

Cerebral Infarcts Cortical, large vessel infarcts The neurobehavioral and neuropsychiatric impact of large complete infarcts depends on localization and the extent of the lesion. Multi-infarct dementia is a traditional term for a clinical dementia syndrome believed to be caused by multiple cortical infarcts (see Fig. 19.2). It includes an acute onset of symptoms in relation with clinical stroke, stepwise deterioration and focal neurologic and cognitive deficits.103 Up until the early 1990s, the majority of cerebrovascular pathology leading to dementia was believed to be caused by large cortical and subcortical infarcts.104

Lacunar infarcts Lacunes in subcortical ischemic vascular disorder are typically located in the caudate, globus pallidus, thalamus, internal capsule, corona radiata and in frontal WM.24 The amount of lacunes has been found to be a significant predictor of cognitive status in the elderly.84 Vataja et al.105 showed that lacunar infarcts (especially those affecting frontal-subcortical circuits), in addition to temporal lobe atrophy, WMH volume, education and Mini-Mental State Examination (MMSE) score, predicted executive dysfunction in patients three months after an ischemic stroke. The role of lacunar infarcts is currently being investigated as the possible main substrate for neuropsychiatric dysfunction in SIVD. A study106 found that the volume of subcortical lacunes, regardless of location, was associated with executive dysfunction in non-demented patients. This was recently confirmed.107 In CADASIL, a ‘true’ small vessel disease,

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

27

Fig. 19.2 Patient with VaD; (a) 68-year old male patient presenting symptoms of subcortical type dementia. The axial FLAIR image shows multiple lacunar infarcts and confluent white matter hyperintensities. (b) Seventy-eight-year old patient with vascular dementia. The axial FLAIR image shows a cortical infarct in the temporo-parietal part of his right hemisphere. In

addition, he has some periventricular white matter hyperintensities. He also had a small lacunar infarct in the frontal white matter on the right side (not shown). (Courtesy of Dagne Hoprekstad MD, Department of Geriatric Medicine, Stavanger University Hospital, and Department of Radiology, Stavanger University Hospital, Stavanger, Norway)

lacunar lesions have been found as major predictors of reduced cognitive skills.108,109 This finding is contrary to an earlier study finding no association between either volume or localization of lacunes and cognition in SIVD.110 In that study, dementia correlated best with cortical and hippocampal atrophy. The exact impact of lacunes on cognitive decline in SIVD is therefore not yet established.

Brain Atrophy

Silent infarcts With the introduction and frequent use of MRI, not only age-dependent, incidental WMH, but also clinically silent brain infarcts are frequently observed. Such asymptomatic infarcts have been shown to be associated with increased dementia risk as well as with steeper cognitive decline.41,111 Vermeer et al.111 found in a longitudinal study that silent thalamic infarcts at baseline were related with reduced memory, whereas non-thalamic infarcts were related with decline in psychomotoric speed. Subjects with multiple infarcts performed worse than subjects with single infarcts. Over time (about three years), a steep reduction in cognitive function was found only in cases that developed new infarcts.

Analysis estimating brain volumes has been used not only in the neuropsychiatric aspects of neurodegenerative disorders, but also with respect to ischemic pathology. In normal aging, a large epidemiological study found that high WMH volume correlated with GM atrophy.112 One study113 found that baseline medial temporal lobe atrophy (MTA), rather than WMH, predicted cognitive status after stroke. Pohjasvaara et al.114 addressed the relationship between vascular lesions and atrophy in post-stroke dementia and concluded that the disease resulted from a combination between the infarct feature, WMH volume, MTA and the subject’s educational level. In CADASIL, atrophy is currently investigated as an additional important characteristic of the disease.115,116 Cerebral volumetric assessment might thus be an additional aspect in understanding the pathogenesis behind neuropsychiatric and neurobehavioral changes in ischemic brain diseases. A limitation to keep in mind is that brain atrophy might develop rather late in the course of some diseases and for the time being serves mostly to characterize the natural development of the pathology.

28

Summary of Conventional MRI Findings Conventional MRI techniques play an important role both in diagnosis and estimating prognosis of neuropsychiatric aspects in the heterogenous group of VaD. In recent years, we have increased our knowledge on both incidental and disease related findings. Still, many questions remain and problems need to be solved in the future. These challenges may be addressed to some extent by using new non-conventional imaging techniques.

Non-conventional Techniques Rovaris et al.117 investigated the impact of age on conventional MRI findings such as WMH and normalized brain volume, but also on non-conventional techniques such as magnetization transfer ratio (MTR) and DWI. They found that normalized brain volume and WMH were significant predictors of subject age, and both measurements correlated with non-conventional measurements. The authors recommend DWI methods as an important supplementary tool in the investigation of age-related brain tissue changes. In the past few years, non-conventional techniques have been increasingly used not only in the detection of age-related brain changes, but also in discerning the association between neurobehavioral changes and ischemic vascular disorders. Diffusion-Weighted Imaging and Diffusion Tensor Imaging In order to find a better correlation with cognitive impairment than WMH in cerebral small vessel disease, O’Sullivan et al.58 used both DTI and conventional MRI in their evaluation of correlations between neuroimaging and neuropsychological performance. They found that mean diffusivity was pathologically increased both in WMH and in normal appearing white matter (NAWM). The DTI changes in NAWM, as opposed to WMH volume, correlated with cognitive scores. Regarding conventional measurements, brain volume predicted cognitive impairment best. Of special interest is the finding that DWI/DTI were able to detect specific pathology in the NAWM. This is in line with a pathological study combining post mortem MRI and histology,48 which showed the great potential of

T. O. Dalaker et al.

newer imaging techniques. DWI/DTI might thus serve as promising imaging techniques for further knowledge about vascular changes in the brain not visible on traditional MRI scans.

Magnetization Transfer Imaging Split et al.60 found that age-related WMH with the same appearance on T2-WI had a heterogeneous appearance using MTI, probably reflecting histological differences in these lesions. This might explain previous contradictory findings on the clinical impact of WMH, since all WMH traditionally have been regarded as being the same.

Magnetic Resonance Spectroscopy A longitudinal study showed that use of MRS was indeed superior to conventional MRI measurements in predicting cognitive decline after stroke.59 Baseline frontal NAA/Cr ratio predicted reduced cognitive performance over both 1 and 3 years. The MRS results were better predictors than hippocampal, whole brain or WMH volumes. The authors suggested that assessment of the frontal NAA/Cr ratio can serve as a possible biomarker for identifying patients at risk for cognitive decline after stroke.

Functional Imaging Contrary to its activity in neurodegenerative disorders, functional imaging has played a minor role compared to structural imaging in the diagnostic work-up of neurobehavioral aspects in CVD. Despite this, it has evolved substantially in recent years, and the implication of modalities such as SPECT is one of the reasons why the old VaD criteria are in need of revision.118 The principles for techniques and available post-processing data are similar to those described below under the section about Lewy Body disorders.

SPECT and PET Functional imaging studies in VCI have mainly focused on cerebral metabolism and regional cerebral blood

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

29

flow (rCBF) using techniques such as 18F-2-fluoro-2deoxyglucose (FDG) - PET and perfusion SPECT. The pattern of altered brain metabolism is more variable in vascular disorders than in neurodegenerative, reflecting the heterogeneity of the lesions involved. Still, a few studies have found that functional nuclear imaging can distinguish VaD from differential diagnoses such as AD119–121 and frontotemporal dementia.122 Functional imaging can be used to see the remote effect of different ischemic lesions on cerebral higher functions. In the past few years, radiotracer imaging has been used in many studies to address this issue. For lacunar infarcts, Clarke et al.123 found that an isolated infarct in the left anterior nuclei of the thalamus in a patient with clinical amnesia was associated with reduced metabolism in the thalamus, amygdala and posterior cingulate cortex. These are all regions known to be involved in memory functions. Another study found that not only symptomatic but also silent lacunes were associated with global reduction of brain glucose metabolism.124 PET and SPECT have also evaluated the effect of WMH on cerebral metabolism.123,125,126 In a FDG-PET study of subjects without signs of neurological disorders, Takahasi et al.126 found that increasing severity of incidental WMH, particularly periventricular lesions, was associated with reduced cerebral metabolism.

Furthermore, the periventricular lesions were associated with reduced performance on tests for attention and speed. Figure 19.3 shows an example of MRI and corresponding SPECT examination in a patient with small vessel disease and ischemic lesions. An FDG-PET study125 investigated the effect of various ischemic subcortical lesions on cortical metabolism in patients with VaD. This study, with a relatively small number of subjects and heterogeneity in lesion types, found that the type of subcortical pathology had a different influence on the amount of reduced metabolism in the cortex. In general, reduced metabolism was associated with the presence of WMH and lacunes. In addition, the severity of WM lesions correlated with anxiety, depression and overall severity of neuropsychiatric symptoms. Reed et al.127 performed a PET study investigating the impact of subcortical ischemic lesions on neuropsychological status in ischemic VaD, taking cerebral glucose metabolism, and both hippocampal and cortical atrophy into consideration. The results showed that the functional impact of subcortical lesions, both lacunes and WMH, was strongest in the dorsolateral frontal cortex. The MRI pathology correlated with hypometabolism here and also with reduced executive function on neuropsychological tests. Hypometabolism and dysexecutive function was also associated with cortical

Fig. 19.3 FLAIR MRI (left) and SPECT (right) images of 67 year old female with small vessel disease. Axial FLAIR shows WMH, both periventricular and in deep white matter, and a small lacunar infarction (arrow) in the left capsula interna. Axial SPECT shows multifocal hypoperfusion in white matter corresponding to WMH,

but also in normal appearing white matter (Courtesy of John Baker, Ph.D., David Wack, M.A., and Robert Miletich, M.D., Ph.D., Dept. of Nuclear Medicine, University at Buffalo. Work supported by in part by a grant from the Gustavus and Louise Pfeiffer Research Foundation)

30

atrophy, suggesting that this contributed to the clinical and imaging findings. In SIVD, Yang et al.128 showed that, rCBF measured by SPECT showed a characteristic pattern with reduced metabolism in deep GM structures, superior temporal and ventral subcallosal gyri. This pattern correlated with cognitive dysfunction. Taken together, the above studies show that vascular pathology seems to affect cerebral metabolism in a way that might contribute to the cognitive dysfunction observed in VCI.

Conclusion and Future Directions Neuroimaging plays an important part in the assessment of neuropsychiatric symptoms related to CVD. Conventional MRI findings have been investigated extensively, but certain findings that are age-related (WMH) need further study. Functional imaging provides important knowledge about how ischemic lesions affect brain activity. Non-conventional imaging increases specificity beyond MRI visible pathology. This is important for ischemic disorders with subtle and unspecific findings using conventional methods. Most of the work has been done on VCI and VaD. Future studies need to extend this work, especially in finding distinctive features for the various VCI subgroups as well as in defining the relationship between CVD and other neurobehavioral aspects. By increasing our knowledge about the impact of vascular pathology on various neurobehavioral and neuropsychiatric conditions, we might identify preventable and treatable contributing factors such as hypertension. Neuroimaging is a unique tool for in vivo assessment of CVD and might provide answers to many unanswered questions.

Imaging of Neurobehavioral and Neuropsychiatric Symptoms in Lewy Body Disorders A neurodegenerative disorder is a term used for hereditary and sporadic conditions which are characterized by progressive nervous system dysfunction. These disorders are often associated with atrophy of the affected

T. O. Dalaker et al.

central or peripheral nervous system structures. (http:// www.ncbi.nlm.nih.gov/sites/entrez) Often in degenerative brain disorders the neuronal loss is a result of protein dysmetabolism with deposits of abnormal protein aggregates.129 The neuronal loss can lead to many symptoms depending upon where the pathology is located and can lead to motor disability, such as tremor and/or cognitive deficits. The most common neurodegenerative dementia in the elderly is AD affecting about 65% of dementia cases.130 In AD an abnormal production and accumulation of β-amyloid protein results in plaques and neurofibrillary tangle deposits in the brain tissue.131 DLB, which accounts for about 20% of late-onset dementias, is considered by many to be the second most common subgroup of degenerative dementias.132 There is a considerable overlap in pathological characteristics in AD and DLB with cortical amyloid plaques and cholinergic deficits in both of them, but the presence of widespread, especially cortical, α-synulein aggregates called Lewy Bodies (LB) and Lewy neurites (LN) differentiates DLB from AD.133 134 DLB is a “Lewy Body Disorder” (LBD) along with PD and PD with dementia (PDD). All of them are neurodegenerative disorders associated with a defect α-synuclein metabolism.135 The results of this neurodegeneration are shown in characteristic motor impairments along with various neurobehavioral/neuropsychiatric symptoms. There is a continuing debate on whether these three diagnoses represent different disorders or are just different stages in a spectrum of LBD.136,137 So far, differences in the temporal sequence of symptoms and somewhat different clinical features are used to justify the distinction between these disorders.138 The degenerative dementias are associated with a significant burden upon patients, caregivers and society.139,140 Much research effort in past years has therefore been put into developing specific biomarkers, in imaging as well as genetics, pathology and biochemistry, to understand the disease processes and aid development of effective drugs. Imaging of AD is described in a previous chapter of this book. Our focus in the following sections will be on imaging techniques used in discerning neurobehavioral and neuropsychiatric impairment in LBD. The spectrum of non-motor symptoms in LBD is broad, including cognitive impairment, depression, rapid eye movement (REM) sleep behavior disorder (RBD) and psychosis. Imaging has been used in characterization

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

of all these aspects of LBD, but the majority of work has been done on impairment of cognitive functions in PD, PDD and DLB.

31

learning are common, together with rapid forgetting and language deficits.151

Mild Cognitive Impairment Parkinson’s Disease Idiopathic PD is the most common neurodegenerative movement disorder and affects about 1–2% of the population over the age of 65. Its prevalence increases with higher age and about 3–5% of people over 85 years suffer from PD.141 The etiology is multifactorial, with clear genetic findings only in the early onset variant of the disorder.142 Parkinsonism is a clinical diagnosis defined by the presence of rigidity, bradykinesia, tremor and postural abnormalities.143 For a diagnosis of Parkinsonism, at least two of the four motor signs must be present. The diagnosis of PD is a clinical challenge because the symptoms may be subtle at the onset. A neuropathological study confirmed idiopathic PD in only 76% of a cohort of 100 patients with clinical PD.144 The use of strict diagnostic criteria has increased the accuracy of clinical diagnosis in Parkinsonian syndromes.145 The neuropathological hallmarks of PD are dopamine depletion and cell loss in the nigrostriatal tract and the presence of Lewy bodies. The classic motor symptoms of PD are thought to be mainly due to neuronal loss in substantia nigra, probably due to α-synuleinopathy and dopaminergic pathology. The non-motor symptoms associated with PD have previously been thought to be caused by changes in nondopaminergic transmitter systems.146

Cognitive Impairment and Dementia In addition to motor symptoms, cognitive impairment is common in PD both as dementia147 and, in nondemented patients, in the form of MCI.148 The postural instability and gait difficulty motor subtype of PD have been found to be associated with a faster rate of cognitive decline.149,150 Dementia in PD is characterized by slowing of cognitive and motor skills, evidence of memory retrieval deficits, executive dysfunction and mood disturbances. This is different from AD in which memory deficits with impaired

MCI is the term generally used for describing the transitional stage between normal cognitive functioning and AD.152 The term MCI is also suggested and used for other types of disorders, e.g., PD, although clinical criteria for MCI in PD have not yet been established. MCI in PD typically implies reduced visuospatial and executive functions contributing to impaired working memory, but deficits in long-term memory have also been reported.148,153,154 The presence of MCI in PD seems to identify patients with a high risk of developing dementia155 The pathology behind cognitive deficits in PD is not yet completely understood, but the hypotheses include both neurotransmitter deficits (dopamine, serotonin, acetylcholine and noradrenalin) and cortical LB deposits.156,157

Other Neurobehavioral and Neuropsychiatric Symptoms Other neurobehavioral and neuropsychiatric symptoms associated with idiopathic PD are depression,158 RBD, where the subjects appear to ‘act out their dreams’,159 fatigue160,161 and psychosis, especially in the form of visual hallucinations (VH).162 The diversity of these various conditions reflects the fact that PD is a multisystem brain disorder affecting more than the nigrostriatal dopaminergic system.163 Both clinical and academic efforts are important in diagnosing, treating and understanding these non-motor aspects of PD.

Dementia with Lewy Bodies To the best of our knowledge there are no epidemiological studies investigating specific risk factors for DLB. DLB is defined clinically as dementia accompanied by the following core features: fluctuating cognition and consciousness, spontaneous features of Parkinsonism and visual hallucinations.132

32

In DLB the described Parkinsonism motor features are present, and only in début with pure dementia can it be distinguished from idiopathic PD in early stages. What differs from PD in the clinical picture is a rapid onset of dementia within 12 months after onset of Parkinsonism. This feature is used in the diagnostic criteria for the disorder.164 The cognitive impairment in DLB differs from that in PDD in that DLB subjects tend to have more severe executive dysfunction.137 As for other psychiatric symptoms, DLB patients experience more hallucinations and psychosis than those with PDD.165 The VH as well as visuospatial impairment can also be used to discriminate DLB from AD.166 For further reading on the differences between DLB and PDD, a review by Aarsland et al. is advised,137 whereas for DLB versus both PDD and AD, a recent review by Metzler-Baddeley is recommended.167

Pathology in Lewy Body Disorders Clear neuropathological differences between DLB and PDD are not yet established. The distribution of LB in the two disorders overlaps,136,138,168 and one study did not identify any significant differences in cortical LB or Alzheimer type of pathology between the two conditions.169 This was confirmed by another study that found the pathological findings in LBD and AD to be similar to some extent.170 Recently, Mrak et al.134 reviewed the neuroinflammatory process involving activated microglia, known to be part of both DLB and AD. This common link might explain some of the clinical overlaps.

Role of Neuroimaging in Lewy Body Disorders There is a clear need for tools that can aid in the difficult diagnostic work-up in these common and disabling neurodegenerative disorders. A correct diagnosis is especially important since the disorders have different responses to medication with little or no effect of levodopa and a hypersensitivity to neuroleptics in DLB,171,172 and also may have different disease progression.173 It is of major importance to get accurate and comprehensive descriptions for all aspects of the disorders, especially in early stages. Knowledge about

T. O. Dalaker et al.

the natural history of each disorder is essential for the individual patient, but also for the design of clinical trials aiming for development of neuroprotective therapy. Clinical progression may often differ on an individual basis174 and, considering this, neuroimaging might contribute to clarifying the differences in vivo and provide possible biomarkers based upon both functionality and morphology. Different neuroimaging modalities are therefore currently being investigated as possible improved outcome markers in various neurodegenerative disorders.175

Functional Imaging Functional imaging modalities such as PET, SPECT and fMRI provide valuable information on brain function in the neuropsychiatric aspects of PD, PDD and DLB. Using the different approaches available, one can investigate brain metabolism and rCBF (perfusion) in addition to pre- and post-synaptic function of various neurotransmitter systems. Functional imaging can evaluate subjects of interest in a neutral resting state, as well as while performing specific tasks. This provides a unique insight into the function of normal and pathological cerebral processes. Another advantage of functional imaging is that, by manipulating administration of relevant drugs, one can see how therapy influences cerebral function. Functional imaging can utilize radioactive nuclides (PET and SPECT) or be based upon natural characteristics of blood flow changes accompanying neural activity in the brain (fMRI). Different methods of image analysis can be regionof-interest based or voxel-based. Region-of-interest methods have been traditionally used, but tend to be both subjective and time-consuming. Development, of automated techniques like voxel-based morphometry (VBM)176,177 and Freesurfer178 in recent years has introduced unbiased techniques that are not dependent on regional a priori hypotheses.

SPECT and PET Both SPECT and PET are nuclear imaging techniques involving radioactive isotopes linked to so-called radiotracers which estimate CBF and the function of various neurotransmitters, enzymes and receptors in the brain.

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

Radiotracer imaging is currently evaluated for use as a promising biomarker in the diagnostic process of all LBD, for assessment of prognosis and for drug development.179–183 Despite all the advantages of radiotracer imaging, recent developments and extensive use, there is still a controversy about the role of this imaging modality as a valid biomarker.184 Ravina et al.182 discussed in a review on the subject that information provided by radiotracer imaging in clinical trials in PD is not valid for reasons other than exploring disease biology, and that one needs to be careful about the use of such imaging in evaluating other aspects of a study such as diagnosis, prognosis and therapy monitoring. Consequently, for the time being, none of the techniques can serve as a useful surrogate endpoint. This issue must be kept in mind when evaluating the role of SPECT and PET in both neuropsychiatric and motor symptoms of the disorders.

33

addition to their impact on motor functions, also considered to play a role, albeit controversial, in the neurobehavioral dysfunction found in the disorders.187 Thus, both SPECT and PET with DA relevant tracers can be used to extend our knowledge in this field. Diagnostic Role of Dopamine Tracers The diagnosis of DLB still relies on clinical assessment as the main component. In the revised criteria for clinical and pathological diagnosis of DLB, reduced striatal DAT activity in functional imaging (DATimaging) with PET or SPECT is introduced as a feature suggesting the diagnosis of DLB.164 These imaging techniques are also promising as markers for preclinical PD diagnosis.179 SPECT is now relatively widely available and routinely used. Dopamine and Cognition

Dopamine-related Tracers One of the main advantages of nuclear imaging is that one can track in vivo specific molecules such as the different neurotransmitters. Depletion of dopamine has been known to be related to Parkinsonism since the 1960s.185 Therefore, an obvious neurotransmitter of interest for LBD is dopamine (DA). There are several neuroimaging radiotracers available for detection of cerebral DA functions.180 The most commonly used tracer is 18F-6-Fluorodopa (FDOPA) which acts pre-synaptic in parallel with DA and serves as an indicator of nerve terminal activity and dopaminergic cell count. Other pre-synaptic tracers assess synaptic vesicle transport (most commonly used 11C-dihydrotetrabenazine (DTBZ) or they can act as plasmalemmal reuptake site ligands (e.g., dopamine reuptake transporter [DAT]). One of the commonly used techniques is DAT imaging by SPECT using the tracer 123I-β-CIT. The other group consists of ligands with postsynaptic binding sites on D1- or D2-receptors. For more reading on the role of this in routine clinical practice, a recent review by Scherfler et al. is recommended.186 Functional imaging with SPECT and PET is used in research with the aim of understanding the biological basis for neuropsychiatric symptoms in PD, PDD and DLB.180 Abnormal metabolic networks caused by nigro-striatal degeneration and DA deficits are, in

Cognitive decline in PD has generally been regarded as a frontal lobe dysfunction based upon loss of input from basal ganglia to the frontal cortex.188 Reduced FDOPA uptake in the right caudate nucleus in PET has been associated with reduced performance in cognitive tests in early PD.189 DA depletion has been found to affect frontal lobe functions in PD patients.190 The effect of DA loss in the caudate nucleus on cognitive functions is debated, with some studies claiming that DAT binding in the caudate nucleus affects metabolic activity in the frontal lobes,191 while others argue against this.192,193 In advanced PD, PET studies have shown loss of striatal D2 receptors.194,195 A study that did not focus on neuropsychiatric symptoms found reduced D2 receptors in the extrastrial, dorsolateral prefrontal cortex, and thalamus.196 A recently published study of patients with DLB found a reduction in temporal D2 receptors in the temporal cortex that correlated inversely with LB pathology in the neocortex and with greater cognitive decline, but not with psychotic symptoms.197 In summary, these functional nuclear imaging studies indicate that loss of DA plays a role in the cognitive dysfunction found in both PD and DLB. Dopamine and Sleep Disturbances DA depletion is also thought to play a role in sleep abnormalities associated with PD and DLB, and may reflect the underlying α-synuclein pathology. For the

34

time being, a possible explanation for RBD seems to be disturbed DA function.198 Subjective daytime sleepiness also seems to be associated with dopaminergic nigrostriatal degeneration in early PD.199,200 Nuclear imaging studies have investigated dopaminergic function in relation to nocturnal leg movements,201 sleep microstructure 202 and REM sleep duration 203 in PD. Together, these results indicate that disturbed dopamine function might be involved in the various sleep disorders in PD.

T. O. Dalaker et al.

between these LBDs, which may explain not only the subtle clinical differences between them but also confirm that differences between the disorders exist. In addition to the correlation between cholinergic deficits and cognition, another study by Bohnen et al.211 found that depressive symptoms were associated with cholinergic denervation in PD and that this was increased in PDD compared to PD. This adds valuable information to the discussion about the pathophysiologic mechanism behind depression in PD.158 Seretonin and Nor-adrenalin

Other Transmitter Systems Although the main body of functional imaging studies in LBD has focused on DA, other neurotransmitter systems have increasingly been the focus for SPECT and PET studies in PD, PDD and DLB.204

Acetylcholine Cholinergic function, like dopaminergic, can be measured in various ways. A well recognized marker is acetylcholine esterase (AChE) which reflects both pre -and post-synaptic cholinergic transmission. Functional nuclear imaging has found widespread evidence that cholinergic dysfunction plays a major role in development of dementia in PD.192,205,206 Previous work by Bohnen et al. has found even more extensive acetylcholine loss in PDD than in AD,207 and that loss correlated with poorer results on neuropsychological tests for attention and executive functioning.208 This study also found cholinergic deficits in non-demented PD. A recent study209 on DLB and PDD investigating muscarine M1 and M4 receptors found significant upregulation in occipital regions in both conditions. More studies are needed to see if this is associated with non-motor symptoms such as visuospatial dysfunction and visual hallucinations. Another finding in this study was reduction in muscarinic receptors in frontal and temporal regions in PDD, but not DLB. This is in line with previous pathological and clinical studies indicating that cholinergic dysfunction plays a part in the cognitive dysfunction in LBD.210 It also shows that the pattern of neurotransmitter affectedness may be different

Serotonergic and noradrenergic dysfunction have also been shown in LBD.212 Reduced midbrain raphe 5-HT1A (receptors regulating serotonine release) binding in PET, indicating loss of serotonin receptors, have been found to be correlated with tremor in PD.213 One study has also investigated whether such loss could be associated with depression scores in PD, since serotonin function is known to be affected in idiopathic depression. Doder et al.213 did not confirm this in their PET study of normal controls and euthymic and depressive PD patients. But they found a difference in postsynaptic 5-HT1A binding in the frontal and anterior cingulate cortex in depressed PD versus non-depressed PD. Another SPECT study investigating midbrain 5-HT transporter binding did not find any reduction in PD compared to controls; thus, there was no evidence for correlation with depression scores.214 A recent study found preferential loss of serotonin markers in the caudate compared to the putamen, but future studies needs to confirm whether the reduced serotonin in the caudate is associated with cognitive impairment in PD.215 Thus, the role of serotonergic dysfunction on neuropsychiatric and neurobehavioral symptoms in LBD is unclear and more studies are needed. There are currently no commercially available specific PET or SPECT markers for the evaluation of noradrenergic function.138 11C-RTI 32 PET is a combined DAT and noradrenergic transporter ligand. An association with depression in PD and reduced noradrenergic transporter ligand uptake in the locus ceruleus, thalamus and several areas of the limbic system has been shown, indicating that noradrenergic dysfunction is involved in mood disturbance in PD.216

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

Activated Microglia and a-synuclein Tracers

35

our understanding of LBD. At the present time much effort is being expended into finding such a tracer.219

Pathological studies have found activated microglia in PD,217 DLB and AD.134 Microglial cells constitute about 10–20% of cerebral WM cells and are part of a natural defense mechanism. Activation of microglia can be marked in vivo using the PET ligand, 11 C-PK11195. Ouchi et al.218 showed increased microglial activation that corresponded to the loss of a DAT marker in early PD. To the best of our knowledge, no current nuclear imaging studies have investigated microglial activation with respect to neuropsychiatric or neurobehavioral symptoms in any of the LBDs. In addition to the developing field of microglial imaging, recent studies with multiple PET radiotracers, including tracers for abnormal protein aggregates, are of interest.219 The ability to detect α-synuclein pathology in vivo would contribute tremendously to

SPECT and PET can not only assess specific molecular functions, but also regional metabolism in the brain. An extensively used tool is 18FDG-PET. Recently, FDG-PET together with DAT studies has been used for multivariate network analyses of cognitive networks in PD.220,221 Using rapid and automated voxelbased algorithms, both motor and cognitive diseasespecific metabolic networks in PD were found (Fig. 19.4). These networks are shown to be influenced by treatment, 222 and advancing disease is associated with progressive increases and decreases in regional metabolism in both

Fig. 19.4 Parkinson’s disease-related spatial covariance patterns. (a) Parkinson’s Disease-Related Pattern (PDRP). The motor-related pattern was characterized by relative increases in pallidothalamic, pontocerebellar and motor cortical/supplementary motor area (SMA) metabolic activity (top), associated with reductions in lateral premotor and posterior parietal areas

(bottom). (b) Parkinson’s Disease-Related Cognitive Pattern (PDCP). This pattern included a relative hypometabolism of the dorsolateral prefrontal cortex, rostral supplementary motor area (preSMA) and superior parietal regions, associated with relative cerebellar/dentate nucleus metabolic increases (Reproduced from Brain, Huang et al.221 With permission)

Cerebral Metabolism

36

motor and cognitive patterns. Changes associated with the cognitive metabolic network appeared early in the disorder, but developed at a slower rate than the motorrelated pattern. In addition, the motor associated network, but not the cognitive network, was correlated with reduced DAT binding in the striatum.221 The PD-related cognitive pattern was characterized by reduced metabolic activity in the prefrontal and parietal cortex and with a relative increase in the cerebellum and dentate nuclei. The pattern correlated with performance on tests for memory and executive functioning.220 A recently published review by Eckert et al. elaborates on this issue.219 Regarding neuropsychiatric symptoms, a FDG-PET study found a network pattern in the lateral frontal cortex and anterior limbic cortex that corresponded to dysphoria in non-demented PD.223 This was in contrast to another pattern in the parieto-occipital-temporal cortex and medial temporal regions, which was associated with visuospatial and mnemonic functioning.

Perfusion Studies One of the mentioned presynaptic DA PET tracers, C-DTBZ, along with other ligands, has the ability to assess both cortical perfusion metabolism in addition to disease-specific neurochemistry.224 Another more common way of cerebral metabolic assessment is use of non-transmitter-specific H2O15 PET to look indirectly at the rCBF. Such studies have found reduced basal ganglia blood flow in PD compared to controls when performing planning225 and mnemonic tasks,190 as well as when testing cognitive speed.226 Frontal lobe blood flow is not altered,225,226 but one of the studies found higher activity in the hippocampus in PD, indicating a shift in the cognitive network.225 In a H215O PET study, Cools et al.227 showed that the administration of L-dopa reduced pathological blood flow changes in PD during cognitive tasks involving fronto-striatal circuits. Later, learning-related increases in the hippocampal blood flow longitudinally, in combination with reduction in parietal region, were shown.228 Marie et al.,229 also using H2O15 PET, showed that in early idiopathic PD there is a working memory deficit especially when performing a task with long delays. This was associated with prefrontal, parietal and posterior cingulate reduced blood flow and could imply a functional disconnection between prefrontal and paralimbic areas. 11

T. O. Dalaker et al.

RCBF has also been used in a longitudinal SPECT study together with cognitive assessment, where one found that cognitive testing predicted, better than SPECT findings, the progression of newly diagnosed PD patients.230 As mentioned previously, the role of rCBF measurements using PET and SPECT as biomarkers for cognitive prognosis in PD is thus not straightforward. RCBF has also been used to investigate other neuropsychiatric aspects of LBD. VH are common in both disorders. Using SPECT, Oishi et al.231 found hyperperfusion in various temporal regions in addition to hypoperfusion in the right fusiform gyrus in PD patients with non-psychotic VH. They concluded that this might be a result of inappropriate visual processing. Even though there are many advantages and interesting aspects with functional imaging, there are also some limitations to consider when using these methods.175 The nuclear imaging methods involve exposure to radioactive substances and can thus be a potential health risk due to radiation. In many places these modalities are not available, specifically PET, and they tend to be more expensive. Many areas of normal activation, metabolism and perfusion are not fully understood and specific changes due to degeneration are yet to be established. For further reading on use of nuclear imaging in DLB, an extensive review by Kemp and Holmes is recommended,232 and for PD a review by Brooks and Piccini is advised.212

fMRI Another technique for estimating cerebral metabolism where rCBF is detected based on the blood oxygen level dependent (BOLD) effect as applied. In a recent study, Monchi et al.233 used fMRI to examine the roles of specific basal ganglia structures in various cognitive functions. They suggest that the striatum is important in planning and execution of self-generated novel action and that the subthalamic nucleus is involved when a new motor program is solicited independent of strategy choice. In a fMRI study using three different visual tasks, differences between AD and DLB were found, some unrelated to task performance and some related to differences in behavioral performance. These results were thought to be a result of different functional pathologies in AD and DLB.234 As in metabolic PET and SPECT studies, fMRI also has the advantage of the ability to see immediate

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

effects of drug therapy. Mattay et al.235 found that in PD, dopaminergic therapy improved not only motor activation, but also prefrontal activation involved in a working memory task. This adds to the already mentioned studies showing the importance of DA to cognitive function. FMRI has also been used for evaluation of pathologic rCBF in visual hallucinations associated with PD. Stebbins et al.236 found different activation patterns in PD, with chronic VH compared to PD patients who had never had VH. This included a shifting of activations from posterior to anterior regions in the brain in tests demanding attention.

Structural Imaging Conventional Techniques In LBD, CT and conventional MRI with standard clinical sequences is so far mostly used to exclude various differential diagnoses.237 Many studies have tried to detect basal ganglia pathology or other possible disease-specific findings in PD using MRI, but so far the results have been inconsistent and not of clinical use.238–240

Volumetric Analysis Volumetric MRI on 3D-T1-WI is currently used at many scientific centers as a possible biomarker for measuring the degenerative process in PD, PDD and DLB. MRI is widely available and relatively inexpensive compared to PET. Still, its value is mostly for research purposes. Mueller et al.175 refer in a recent review to volumetric MRI as the most promising marker for detecting disease-modifying effects of neuroprotective drugs in neurodegenerative disorders, especially in AD. This is based upon the well documented relationship between neuronal loss and atrophy. In PD, the atrophy seems to develop at a slower rate than that reported in AD, and in cognitively intact PD patients the rate of atrophy per year has not been shown to be different from that of healthy controls. In PDD the rate of atrophy was increased.241 For the PDD group, longitudinal MRI may thus be used for detection of effects of disease-modifying treatment.

37

Studies investigating longitudinal evolution of brain atrophy in PD are needed for better understanding of the natural course of the pathological processes and also for improved understanding of patient subgroups in which different rates of atrophy may predict clinical phenotypes. Brain Atrophy and Cognition In PD, a few studies242–247 have investigated the pattern of cerebral atrophy in patients with PD with/without associated dementia, compared to normal controls. These studies have been based on relatively small numbers of subjects, often with long disease duration. They have mostly used VBM and looked for regional atrophy in GM and how it correlated with dementia in PD. Both Beyer et al. (Fig. 19.5)242 and Burton et al.243 found widespread areas of atrophy in their population of PDD and non-demented PD patients. In general, PDD patients presented more atrophy, and the atrophy was observed in the limbic, temporal, parietal, frontal and occipital regions as well in some subcortical areas. Other studies have found atrophy in the hippocampus,247 thalamus and anterior cingulate,246 in the hippocampus and amygdalae,244 and in limbic/paralimbic and prefrontal areas in PD/PDD.245 A specific pattern of GM atrophy in PDD is yet to be established. A pattern of focal atrophy in DLB was recently published.248 DLB patients were compared with gender- and age-matched AD patients and normal controls. Using VBM, a pattern of relative focused atrophy differentiating DLB from AD was found in the midbrain, hypothalamus and substantia innominata, with relative sparing of the hippocampus and temporal cortex. This confirmed results from previous studies that showed less atrophy of the hippocampus in DLB than in AD.247,249 MCI in PD is currently being investigated as a prodromal stage for PDD.155,250 Volumetric MRI studies have also looked into this interesting field. A study of older, more advanced MCI PD patients242 found GM atrophy in temporal, precentral and frontal regions compared with non-demented patients with PD. A recent VBM study on early MCI PD showed atrophy in the posterior cingulate and precuneus versus cognitively intact PD patients.251 The posterior cingulate and precunes are known to be affected in MCI as in prodromal AD,252 and future studies are needed to establish its role in predicting the development of dementia in PD.

38

T. O. Dalaker et al.

Fig. 19.5 PDD versus PD: Dementia in PD is associated with gray matter atrophy Areas of reduced gray matter volume in patients with PDD compared to PD without dementia. Areas of atrophy in the PDD group are shown on the glass brain, where significant areas of atrophy are shown as gray and black clusters. Courtesy of M. K. Beyer MD, PhD Department of Radiology, Stavanger University Hospital, Stavanger, Norway; The Norwegian Centre for Movement Diosorders, Stavanger University Hospital, Stavanger, Norway

Meyer et al.253 compared what they called neurodegenerative MCI (prodromal AD), Parkinson-Lewy Body MCI and vascular MCI. They found more hippocampal atrophy in the neurodegenerative MCI, and more WMH and lacunes in vascular MCI than in other MCI subtypes. Parkinson-Lewy Body MCI showed more pronounced third ventricular enlargement, hippocampal atrophy (though less than neurodegenerative cases) and less vascular pathology than vascular MCI. The authors reported that the various MCI subtypes show specific MRI abnormalities. Volumetric MRI studies have also been used to discern possible different atrophy patterns between PDD and DLB.243,254,255 Burton et al.243 found no significant differences, but Beyer et al.255 recently found more cortical atrophy in DLB than PDD (Fig. 19.6). Both studies compared healthy controls and AD. Compared to AD, both DLB and PDD had less temporal lobe atrophy. Volumetric MRI needs to be used in larger

studies to see whether it can serve as a tool to differentiate between DLB and PD.256 Brain Atrophy and Other Neuropsychiatric and Neurobehavioral Symptoms Few studies have looked at the relation between neuropsychiatric/neurobehavioral symptoms and patterns of atrophy in MRI. A study of non-demented patients with PD and visual hallucinations257 showed atrophy in the lingual gyrus and superior parietal lobe compared to a non-hallucinating PD group, but also compared to healthy controls. These regions are involved in higher visual processing, and the observed atrophy might provide a possible explanation for the hallucinations found in some PD patients. Recently published work on GM atrophy, in association with depression in PD, revealed that depression in PD might be related to loss of GM in the bilateral

Fig. 19.6 PDD versus DLB: Areas of reduced gray matter in patients with dementia with Lewy bodies compared with Parkinson’s disease with dementia. Age as covariant in the analysis. Results shown on axial slices on T1-WI, where significant areas of atrophy are shown as yellow clusters. Atrophy was found bilaterally in the inferior parietal lobule and in the precu-

neus, and on the right side: insula, inferior temporal gyrus, and in the lentiform nucleus. Left side: angular gyrus, cuneus, and in the superior occipital gyrus. Courtesy of M. K. Beyer MD, PhD Department of Radiology, Stavanger University Hospital, Stavanger, Norway; The Norwegian Centre for Movement Diosorders, Stavanger University Hospital, Stavanger, Norway

40

orbitofrontal and right temporal regions in addition to the limbic system.258

White Matter Hyperintensities The influence of WMH on cognition in PD is an interesting field to study. So far, a few studies have been published on WMH in PD. One study concluded that frontal WMH might contribute to dementia in PD,259 although PDD patients had no more WMH than controls.259,260 In the Beyer study, there was a difference in WMH between the PD and the PDD group, where PDD patients had more WMH than patients who were cognitively intact. In newly diagnosed PD patients, a recent study261 found that the total volume of WMH correlated with performance on the MMSE.262 The relationship between WMH and cortical AChE activity using PET in PDD was studied by Marshall et al.263 They found no correlation between the two parameters. There is a clear need in the future for more regional studies on WMH distribution and cognitive function/ non-motor symptoms in the LBD.

T. O. Dalaker et al.

tests assessing language function. In general, few studies have used MRS to investigate the biochemical basis for neurobehavioral changes in PD, PDD and DLB. Further reading on use of MRS for this and other aspects of the disorders can be found in Rango et al.269

Diffusion-weighted and Tensor Imaging Considering cognition, Firbank et al.270 recently published a DTI study where they found that whole brain atrophy in DLB and AD was correlated with reduced FA in the bilateral posterior cingulate regions. The reduced FA was considered a sign of disruption of the cingulum. The cingulum is the WM bundle that connects the hippocampus and posterior cingulate. Hippocampal atrophy and posterior cingulate hypometabolism are, as mentioned, a common feature of both disorders, and hippocampal atrophy was also associated with reduced FA. The authors concluded that there is a disruption of WM tracts connecting the regions, but that it is not known whether this precedes, or is a result of, atrophy or hypometabolism. Reduced FA in the bilateral posterior cingulate regions in PDD was also shown in another study.271

Non-conventional Techniques Magnetic Resonance Spectroscopy

Other Techniques

It is yet to be established in PD whether MRS of the basal ganglia can serve as a marker for the disorder.264 Camicoli et al.151 found evidence that metabolic changes in the posterior cingulate region correlated with memory performance in non-demented PD. An earlier study 265 found evidence for reduction of NAA levels in occipital regions in PDD compared to PD, and they also found that this correlated with cognitive measurements. Proton MRS has also been utilized in mild to moderate DLB, where one showed metabolic changes in the WM266 and centrum semiovale when comparing to healthy controls, indicating axonal damage. In addition to proton MRS, another technique is phosphorus (31P) MRS, which can be used to assess in vivo the bioenergetic status of tissues.267 One of the issues to address with phosphorus MRS is mitochondrial impairment. Hu et al.268 combined this type of MRS with FDG-PET on non-demented PD and found temporoparietal abnormalities with both techniques. The MRS changes correlated with global cognitive impairment and performance on neuropsychological

Other structural imaging techniques, such as ultrasonography of the brain stem,272,273 have been studied in PD, mostly to see if they can be used as an aid in the diagnostic process of PD.

Conclusion and Future Directions At present, nuclear imaging serves as the best, although limited, radiological tool in assessing neuropsychiatric symptoms in various LBD. Complementary knowledge is provided by especially volumetric techniques and fMRI. Despite this, imaging is only in the phase of investigating the biological basis for such dysfunction, and future work is needed to clear its role as a biomarker for prognostic and diagnostic purposes.184 There is a clear need for tools that can aid in the difficult diagnostic work-up in LBD. Different imaging modalities add new pieces of the puzzle for understanding of all aspects of the disorders, especially in

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

early stages. Knowledge about the natural history of each disorder is essential for the individual patient, and neuroimaging might contribute to clarifying the differences in vivo and provide possible biomarkers based upon both functionality and morphology. Through functional imaging, subjects can be studied both at rest and while performing tasks. This provides unique insight into the function of normal and pathological cerebral processes. Another advantage of functional imaging is that, by manipulating administration of relevant drugs, one can investigate whether therapy influences cerebral function. Functional imaging with SPECT and PET is used in research that aims to understand the biological basis for neuropsychiatric symptoms in PD, PDD and DLB. DA deficits are considered to play a role, albeit controversial, in the cognitive dysfunction found in both PD and DLB. Functional imaging has found widespread evidence that cholinergic dysfunction plays a major role in the development of dementia in PD. Studies have also shown that the pattern of neurotransmitter affectedness may be different between LBD subtypes. In addition, depressive symptoms were associated with cholinergic denervation in PD. Loss of serotonin may also be associated with cognitive impairment. An association between reduced noradrenergic transporter ligand uptake and depression in PD has been found. New techniques that might bring new information to the field include studies of microglial activation and studies using multiple PET tracers. Through metabolic PET studies, both motor and cognitive disease-specific metabolic networks in PD have been found. Perfusion studies have shown that perfusion patterns can be altered by treatment. Together with fMRI, these techniques can be used for the study of neuropsychiatric and neurobehavioral symptoms in LBD. In the future, they can also probably be used to assess the effect of new treatment on neuropsychiatric symptoms. These techniques will be important for research in LBD in the years to come. The role of structural imaging in the future might be to investigate how different functional imaging changes affect the brain, as assessed by measurement of brain atrophy and diffusion. Large longitudinal studies in early LBD, as in PD, may show us atrophy patterns that may aid in the understanding of MCI in PD and may have prognostic value for more rapid disease progression. Being the most available method, structural MRI will thus also play an important role along with the functional imaging methods in the years to come.

41

Imaging of Neurobehavioral and Neuropsychiatric Symptoms in Demyelinating Disorders Table 19.1 lists the most common conditions to be considered in differential diagnosis when confronted with multi-focal lesions on MRI scans suggestive of the most prevalent demyelinating disease, i.e., MS. In particular, lesions in systemic lupus erythematosus (SLE) and acute disseminated encephalomyelitis

Table 19.1 Principle conditions mimicking multiple sclerosis on MRI scans or considered in the clinical differential diagnosis of MS • MS variants - Acute malignant MS (Marburg variant) - Charcot type - Clinically isolated syndromes - Neuromyelitis Optica (Devic’s disease) - Schilder’s disease - Solitary inflammatory masses (leukoencephalitis) • Normal aging • Migraine • Cerebrovascular diseases - Diabetes - Collagen vascular disease - Hypertension - Periventricular leukomalacia - Primary CNS Vasculitis - Subcortical atherosclerotic encephalopathy (Binswanger’s disease) - Susac syndrome • Infectious and inflammatory diseases - Abscesses - Acute disseminated encephalomyelitis - Human immunodeficiency virus encephalitis - Lyme disease - Progressive multifocal leucoencephalopathy - Sarcoidosis - Subacute sclerosing panencephalitis - Syphilis - Tuberculosis • Toxic/metabolic diseases - Chemotherapy or radiotherapy effects - Leucodystrophies - Mitochondrial diseases - Osmotic myelinolysis - Toluene toxicity - Vitamin B12 deficiency • Neoplastic disease - Metastases - Primary brain or intravascular lymphoma Source: Reproduced by Zivadinov & Bakshi.304 With permission.

42

(ADEM) may resemble those of early MS on MRI. SLE neurological manifestations include seizures, vascular events, movement disorders, and optic nerve involvement. SLE can also produce relapsing, multifocal neurological manifestations similar to those of MS. ADEM shows multi-focal WM lesions involving the cerebral hemispheres, cerebellum, and brain stem that may or may not be distinguishable from MS, and is typically triggered by a viral infection or vaccination, although idiopathic cases may also occur. Involvement of the subcortical GM nuclei, and large size and early confluence of lesions favors a diagnosis of ADEM,274 although deep GM lesions are also frequently present in MS, as recently identified using new myelin basic protein-based pathologic staining methods. The presence of acute spinal cord symptoms is not a diagnostic dilemma for MS, when MRI of the brain shows typical WM lesions and the CSF demonstrates intrathecal IgG synthesis and oligoclonal bands. However, when MRI of the brain is normal or atypical, or the CSF is normal or other concomitant factors are present (e.g., cord compressive lesion), alternative causes must be considered. MRI is sensitive in detecting focal demyelinating syndromes such as myelitis,275 leukoencephalitis and optic neuritis.276

Neuropsychological Impairment in MS Neuropsychological defects are observed in approximately half of MS patients.277 Most often, deficits are found on tests measuring new learning and memory, as well as speed of information processing.278 Cognitive impairment is a significant source of unemployment,279 social skills problems, and poor quality of life.280 Although approximately half of patients diagnosed with MS are cognitively impaired, neuropsychology (NP) profiles vary for reasons that are not well understood. Some recent research suggests that the NP presentation in MS is related to clinical course.281 It remains very difficult to predict which patients will develop NP impairment. For these reasons, routine cognitive screening and NP testing have been advocated in the clinical care of MS patients. Information processing speed and working memory are two interrelated cognitive domains that are frequently defective in MS. Processing speed is the pace at which mental operations are performed.

T. O. Dalaker et al.

Working memory is defined as a dynamic and multicomponent limited capacity system for storing and manipulating information in the execution of complex cognitive tasks. It is well established that deficits on tests measuring episodic memory for recently acquired information are common in MS, especially when more taxing retrieval strategies such as free recall are required.282 MS patients usually perform similarly to normal controls in recognition tasks, suggesting that memory problems in MS patients are primarily due to defective retrieval rather than to encoding of the information. Other domains of cognitive function that are defective in at least some MS patients are the spatial and executive functions. Research shows that impairment in these domains occurs with considerable frequency in MS, although less commonly than in the domains of processing speed and memory.283 As a further result, research indicates that cognitive deficits are common in MS patients, especially in the domains of processing speed and memory, and perhaps higher executive function. Work has begun recently to disentangle the relative impact of information processing speed and working memory on other cognitive areas. The majority of findings indicate that encoding and retrieval are compromised in MS. Better understanding of cognitive dysfunction in patients with MS can inform clinicians about the nature of these problems and contribute to more selective employment potential.

Correlation Between NP Dysfunction and Brain Imaging The extensive recent review from our group on correlation between NP dysfunction and brain imaging in MS is recommended for more detail reading.284 As MS is defined in part by the presence of demyelinating cerebral lesions (Fig. 19.7), it has been of interest to correlate the degree of such pathology with NP morbidity in MS. Such efforts date to the early work of Rao et al.279 nearly 15 years ago. With the advent of new techniques in both MRI research and neuropsychology, there continues to be interest in delineating more precisely the relationship between such brain pathology and NP test performance. This section reviews recent findings pertaining to various MRI measures of brain pathology, and their correlations with NP testing.

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

43

Fig. 19.7 (a) Axial T2-PD weighted image in a 44 year-old woman with secondary-progressive MS showing multiple hyperintense lesions in the periventricular white matter (arrows). The lesions are characteristic for MS, including a size of generally

≥5 mm, oval/ovoid morphology and with many directly contacting the ventricular ependyma; (b) Sagittal FLAIR image in a 44 yearold man with RR MS showing multiple pericallosal lesions (arrows) with a classic perivenular orientation (Dawson’s fingers)

White Matter Hyperintensities

speed and attention in MS patients. Our group reported that linear measures of brain atrophy like the bicaudate ratio (BCR), which is the minimum intercaudate distance divided by brain width along the same line, are abnormal in MS and strongly correlated with cognitive impairment.289 We recently endeavored to determine whether lesion burden or atrophy accounts for the most variance in predicting the presence of NP impairment in MS.290 We studied 37 MS patients and 27 matched healthy controls. Several conventional MRI measures of lesion burden or atrophy were obtained, including T1 hypointense lesion volume, T2 lesion volume, third ventricle width, BCR and whole brain atrophy. The results revealed that all cognitive variables were predicted by third ventricle width and, when this effect was removed from consideration, whole brain atrophy accounted for the most variance. Therefore, central and whole brain atrophy (Fig. 19.8) account for more variance in MS cognition than does lesion burden. More recent work has made possible the examination of regional atrophy using reliable, quantitative measures of normalized regional brain atrophy.291 Regional parenchyma brain atrophy is an important topic of discussion in the literature and many different segmentation methods have been used for tissue parcellation in MS studies.292 Manual tracing of lobes or structures is a labor intensive process and prone to poor reproducibility. To increase reliability of manual parcellation of different brain structures, our group

Measures of lesion burden, mostly acquired on T2-WI, significantly correlate with impairment on NP testing (Fig. 19.7). Rao et al.,285 for example, reported that the total lesion area best predicted performance deficits in recent memory, abstract/conceptual reasoning, language, and visuospatial problem solving, while the size of the corpus callosum was a good predictor of information processing speed and problem solving. Recently, it has been worthwhile to investigate the relative contributions of regional versus total lesion burden as related to NP dysfunction. In more recent work, Sperling et al.286 conducted a 4-year longitudinal study to investigate the relationship between regional lesion WMH volume and cognitive performance. On each of three evaluations, deficits in processing speed and memory significantly correlated with frontal and parietal regional lesion volume as well as total lesion volume.

Brain Atrophy Brain atrophy contributes to cognitive impairment in patients with MS.287 Approximately 50% of brain atrophy in MS patients occurs in various cortical and subcortical regions.4 Hohol et al.288 reported a significant relationship between brain-to-intracranial-volume ratio and performance in nonverbal memory, processing

44

T. O. Dalaker et al.

Fig. 19.8 Representative images of normalized neocortical volumes (NCV) segmented with SIENAX automated method in a 62 year-old patient with secondary-progressive (SP)

MS. From left to right are shown the original 3D-T1-SPGR image, cortical gray matter mask and cortical white matter mask

previously used a digital 3D version of the Harvard Medical School brain atlas as a reference.291 We also demonstrated that normalized regional brain parenchyma measures correlated better with different MRI and clinical outcomes than the absolute ones.293 Further, we recently used the semi-automated brain region extraction (SABRE) technique to parcellate the brain into 26 regions (divided into GM/WM) and showed that GM atrophy was prominent in the superior frontal/ parietal lobes and deep brain structures.294 Using the same technique, we showed an independent relationship between cortical atrophy and cognitive impairment after accounting for the effects of central atrophy.295 Other studies that assessed brain atrophy in specific regions confirmed a diminished corpus callosum area296 and lower volume of the thalamus297and caudate.298 Locatelli et al.293 examined the relationships between these measures and cognitive performance in patients with relapsing remitting (RR) MS. Normalized regional frontal volumes correlated significantly with tests measuring attention, processing speed and executive function. These studies suggest that regional atrophy accounts for more variance than lesion burden, whole brain atrophy, or lateral ventricle volume in predicting MS–associated memory dysfunction. Mounting evidence supports the premise that disease involvement of GM structures may significantly contribute to clinical disability and cognitive dysfunction in MS patients.299 Amato et al.300 showed that cortical atrophy was found only in cognitively impaired patients and significantly correlated with poorer performance on tests of verbal memory, attention/

concentration, and verbal fluency. Therefore, cortical atrophy may contribute to the development of cognitive impairment in MS from the earliest stages of the disease.

Non-conventional MRI Measures: MTI, MRS and Diffusion Imaging Zivadinov et al.301 examined whether cognitive impairment is dependent on the extent and severity of the burden of disease, diffuse microscopic brain damage or both in 63 RR MS patients. Multiple regression analysis models demonstrated that the only variables to correlate independently with cognitive impairment were atrophy and MTR. Christodoulou et al.302 investigated neuropsychological performance in 37 individuals with RR and secondary progressive (SP) MS. They related the cognitive outcome to neuroimaging measures, including central atrophy, T2-lesion volume, and ratios of NAA/ Cr and Cho. They found that central atrophy accounted for the highest correlations with cognition, followed by MRS measures and T2-lesion volume. In particular, correlations for MRS measures with cognitive functioning tended to be stronger in right hemisphere locations. Furthermore, these authors made an early attempt to assess multiple MR techniques in a single sample, finding that a combination of techniques correlated better with cognition than any single measure, accounting for well over half the variance in overall cognitive performance.

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

Benedict et al.303 investigated the correlation between DWI and cognitive dysfunction in RR and SP MS patients. They employed DWI and more traditional, quantitative MRI measures to investigate which of these measures accounted for greater variance in different NP domains. A new quantitative DWI measure called entropy was used to explore a potential relationship between diffusion-related systematic disarray and cognitive function in MS. The results showed significant correlations between DWI measures and measures of auditory/verbal learning memory, visual/ spatial learning and memory, speed of information processing, working memory, and concept formation, with the strongest association being observed between DWI entropy and performance on the memory tests.

Conclusion and Future Directions For almost two decades, structural neuroimaging methods have been used to investigate brain pathology with regard to various cognitive problems in demyelinating disorders. By applying these methods, the effects of MS neuropathology in cognitive processes have been shown using various conventional and nonconventional MRI measures. Where different brain measures are concerned, recent findings show that brain atrophy compared to lesion burden accounts for the majority of the variance in neuropsychological performance in MS. Advances in non-conventional MRI techniques, such as MTI, MRS and DTI that reveal microscopic pathological changes within the NAWM have also shown considerable promise. Acknowledgements Dr. Dalaker was supported by the Dr. Larry D. Jacobs Fellowship of the Buffalo Neuroimaging Analysis Center, the Research Council of Norway (grant# 186966), a research grant from Biogen Idecs MS fund 2007 and from Halvor Hoies foundation. The authors thank Prof. Dag Aarsland for critical review of the manuscript. The authors also thank Eve Salczynski for technical support in the preparation of this manuscript.

References 1. Zivadinov R, Bakshi R. Role of MRI in multiple sclerosis I: inflammation and lesions. Front Biosci 2004;9:665–683. 2. Zivadinov R, Cox JL. Neuroimaging in multiple sclerosis. Int Rev Neurobiol 2007;79:449–474.

45

3. Zivadinov R, Leist TP. Clinical-Magnetic Resonance Imaging Correlations in Multiple Sclerosis. J Neuroimaging 2005;15(Suppl 4):10S–21S. 4. Zivadinov R, Bakshi R. Role of MRI in multiple sclerosis II: brain and spinal cord atrophy. Front Biosci 2004;9:647–664. 5. Filippi M, Rocca MA. Magnetization transfer magnetic resonance imaging in the assessment of neurological diseases. J Neuroimaging 2004;14(4):303–313. 6. Horsfield MA, Barker GJ, Barkhof F, Miller DH, Thompson AJ, Filippi M. Guidelines for using quantitative magnetization transfer magnetic resonance imaging for monitoring treatment of multiple sclerosis. J Magn Reson Imaging 2003;17(4):389–397. 7. van Buchem MA, McGowan JC, Kolson DL, Polansky M, Grossman RI. Quantitative volumetric magnetization transfer analysis in multiple sclerosis: estimation of macroscopic and microscopic disease burden. Magn Reson Med 1996;36(4):632–636. 8. Schmierer K, Scaravilli F, Altmann DR, Barker GJ, Miller DH. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann Neurol 2004;56(3): 407–415. 9. Narayanan S, Francis SJ, Sled JG, Santos AC, Antel S, Levesque I, et al. Axonal injury in the cerebral normalappearing white matter of patients with multiple sclerosis is related to concurrent demyelination in lesions but not to concurrent demyelination in normal-appearing white matter. Neuroimage 2006;29(2):637–642. 10. Inglese M, Grossman RI, Filippi M. Magnetic resonance imaging monitoring of multiple sclerosis lesion evolution. J Neuroimaging 2005;15(4 Suppl):22S–29S. 11. Camicioli RM, Hanstock CC, Bouchard TP, Gee M, Fisher NJ, Martin WR. Magnetic resonance spectroscopic evidence for presupplementary motor area neuronal dysfunction in Parkinson’s disease. Mov Disord 2007;22(3):382–386. 12. Narayana PA. Magnetic resonance spectroscopy in the monitoring of multiple sclerosis. J Neuroimaging 2005;15(4 Suppl):46S–57S. 13. Vrenken H, Barkhof F, Uitdehaag BM, Castelijns JA, Polman CH, Pouwels PJ. MR spectroscopic evidence for glial increase but not for neuro-axonal damage in MS normal-appearing white matter. Magn Reson Med 2005;53(2):256–266. 14. Filippi M, Cercignani M, Inglese M, Horsfield MA, Comi G. Diffusion tensor magnetic resonance imaging in multiple sclerosis. Neurology 2001;56(3):304–311. 15. Rovaris M, Gass A, Bammer R, Hickman SJ, Ciccarelli O, Miller DH, et al. Diffusion MRI in multiple sclerosis. Neurology 2005;65(10):1526–1532. 16. Frankle WG, Slifstein M, Talbot PS, Laruelle M. Neuroreceptor imaging in psychiatry: theory and applications. Int Rev Neurobiol 2005;67:385–440. 17. Saha G. Physics and Radiobiology of Nuclear Medicine. 3rd ed. New York: Springer, 2006. 18. Garcia-Alloza M, Bacskai BJ. Techniques for brain imaging in vivo. Neuromolecular Med 2004;6(1):65–78. 19. Bybel B, Brunken RC, Shah SN, Wu G, Turbiner E, Neumann DR. PET and PET/CT imaging: what clinicians need to know. Cleve Clin J Med 2006;73(12):1075–1087. 20. Lobo A, Launer LJ, Fratiglioni L, Andersen K, Di Carlo A, Breteler MM, et al. Prevalence of dementia and major subtypes in Europe: A collaborative study of population-based

46 cohorts. Neurologic Diseases in the Elderly Research Group. Neurology 2000;54(11 Suppl 5):S4–9. 21. Qiu C, De Ronchi D, Fratiglioni L. The epidemiology of the dementias: an update. Curr Opin Psychiatry 2007;20(4):380–385. 22. Roman GC. Vascular dementia may be the most common form of dementia in the elderly. J Neurol Sci 2002;203–204:7–10. 23. O’Brien JT. Vascular cognitive impairment. Am J Geriatr Psychiatry 2006;14(9):724–733. 24. O’Brien JT, Erkinjuntti T, Reisberg B, Roman G, Sawada T, Pantoni L, et al. Vascular cognitive impairment. Lancet Neurol 2003;2(2):89–98. 25. Williamson JB, Nyenhuis DL, Pedelty L, Byrd S, Jhaveri M, Wang C, et al. Baseline differences between Vascular Cognitive Impairment No Dementia reverters and nonreverters. J Neurol Neurosurg Psychiatry 2008. 26. O’Brien J. Behavioral symptoms in vascular cognitive impairment and vascular dementia. Int Psychogeriatr 2003;15(Suppl 1):133–138. 27. Schmidt R, Petrovic K, Ropele S, Enzinger C, Fazekas F. Progression of leukoaraiosis and cognition. Stroke 2007;38(9):2619–2625. 28. Mendez M, Cummings JL. Dementia: A Clinical Approach. 3rd ed. Philadelphia: Butterworth-Heinemann, 2003. 29. Roman GC, Sachdev P, Royall DR, Bullock RA, Orgogozo JM, Lopez-Pousa S, et al. Vascular cognitive disorder: a new diagnostic category updating vascular cognitive impairment and vascular dementia. J Neurol Sci 2004;226(1–2):81–87. 30. Rockwood K, Howard K, MacKnight C, Darvesh S. Spectrum of disease in vascular cognitive impairment. Neuroepidemiology 1999;18(5):248–254. 31. Chui HC. Subcortical ischemic vascular dementia. Neurol Clin 2007;25(3):717–740, vi. 32. Erkinjuntti T, Inzitari D, Pantoni L, Wallin A, Scheltens P, Rockwood K, et al. Research criteria for subcortical vascular dementia in clinical trials. J Neural Transm Suppl 2000;59:23–30. 33. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. Washington, DC, 1994. 34. Chui HC, Victoroff JI, Margolin D, Jagust W, Shankle R, Katzman R. Criteria for the diagnosis of ischemic vascular dementia proposed by the State of California Alzheimer’s Disease Diagnostic and Treatment Centers. Neurology 1992;42(3 Pt 1):473–480. 35. Hachinski VC, Iliff LD, Zilhka E, Du Boulay GH, McAllister VL, Marshall J, et al. Cerebral blood flow in dementia. Arch Neurol 1975;32(9):632–637. 36. Roman GC, Tatemichi TK, Erkinjuntti T, Cummings JL, Masdeu JC, Garcia JH, et al. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology 1993;43(2):250–260. 37. Pohjasvaara T, Mantyla R, Ylikoski R, Kaste M, Erkinjuntti T. Comparison of different clinical criteria (DSM-III, ADDTC, ICD-10, NINDS-AIREN, DSM-IV) for the diagnosis of vascular dementia. National Institute of Neurological Disorders and Stroke-Association Internationale pour la Recherche et l’Enseignement en Neurosciences. Stroke 2000;31(12):2952–2957.

T. O. Dalaker et al. 38. World Health Organization. The ICD-10 classification of mental and behavioral disorders, 1993. 39. Enzinger C, Fazekas F, Matthews PM, Ropele S, Schmidt H, Smith S, et al. Risk factors for progression of brain atrophy in aging: six-year follow-up of normal subjects. Neurology 2005;64(10):1704–1711. 40. Schmidtke K, Hull M. Cerebral small vessel disease: how does it progress? J Neurol Sci 2005;229–230:13–20. 41. Leys D, Henon H, Mackowiak-Cordoliani MA, Pasquier F. Poststroke dementia. Lancet Neurol 2005;4(11):752–759. 42. Rockwood K, Wentzel C, Hachinski V, Hogan DB, MacKnight C, McDowell I. Prevalence and outcomes of vascular cognitive impairment. Vascular Cognitive Impairment Investigators of the Canadian Study of Health and Aging. Neurology 2000;54(2):447–451. 43. Gorelick PB. William M. Feinberg Lecture: Cognitive vitality and the role of stroke and cardiovascular disease risk factors. Stroke 2005;36(4):875–879. 44. Kalaria RN, Viitanen M, Kalimo H, Dichgans M, Tabira T. The pathogenesis of CADASIL: an update. J Neurol Sci 2004;226(1–2):35–39. 45. Maia LF, Mackenzie IR, Feldman HH. Clinical phenotypes of Cerebral Amyloid Angiopathy. J Neurol Sci 2007;257(1–2):23–30. 46. Troncoso JC, Zonderman AB, Resnick SM, Crain B, Pletnikova O, O’Brien RJ. Effect of infarcts on dementia in the Baltimore longitudinal study of aging. Ann Neurol 2008 64(2):168–176. 47. Fernando MS, Simpson JE, Matthews F, Brayne C, Lewis CE, Barber R, et al. White matter lesions in an unselected cohort of the elderly: molecular pathology suggests origin from chronic hypoperfusion injury. Stroke 2006;37(6):1391–1398. 48. Brown WR, Moody DM, Thore CR, Challa VR, Anstrom JA. Vascular dementia in leukoaraiosis may be a consequence of capillary loss not only in the lesions, but in normal-appearing white matter and cortex as well. J Neurol Sci 2007;257(1–2):62–66. 49. Fazekas F, Kleinert R, Offenbacher H, Schmidt R, Kleinert G, Payer F, et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology 1993;43(9):1683–1689. 50. Jellinger KA. The enigma of vascular cognitive disorder and vascular dementia. Acta Neuropathol 2007;113(4):349–388. 51. Ishii N, Nishihara Y, Imamura T. Why do frontal lobe symptoms predominate in vascular dementia with lacunes? Neurology 1986;36(3):340–345. 52. Gold G, Kovari E, Herrmann FR, Canuto A, Hof PR, Michel JP, et al. Cognitive consequences of thalamic, basal ganglia, and deep white matter lacunes in brain aging and dementia. Stroke 2005;36(6):1184–1188. 53. Kovari E, Gold G, Herrmann FR, Canuto A, Hof PR, Michel JP, et al. Cortical microinfarcts and demyelination significantly affect cognition in brain aging. Stroke 2004;35(2):410–414. 54. Gold G, Giannakopoulos P, Herrmann FR, Bouras C, Kovari E. Identification of Alzheimer and vascular lesion thresholds for mixed dementia. Brain 2007;130(Pt 11):2830–2836. 55. Gold G, Kovari E, Hof PR, Bouras C, Giannakopoulos P. Sorting out the clinical consequences of ischemic lesions in

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

brain aging: a clinicopathological approach. J Neurol Sci 2007;257(1–2):17–22. 56. Bronge L, Wahlund LO. White matter changes in dementia: does radiology matter? Br J Radiol 2007;80 Spec No 2:S115–120. 57. Jagust WJ, Zheng L, Harvey DJ, Mack WJ, Vinters HV, Weiner MW, et al. Neuropathological basis of magnetic resonance images in aging and dementia. Ann Neurol 2008;63(1):72–80. 58. O’Sullivan M, Morris RG, Huckstep B, Jones DK, Williams SC, Markus HS. Diffusion tensor MRI correlates with executive dysfunction in patients with ischaemic leukoaraiosis. J Neurol Neurosurg Psychiatry 2004;75(3):441–447. 59. Ross AJ, Sachdev PS, Wen W, Brodaty H, Joscelyne A, Lorentz LM. Prediction of cognitive decline after stroke using proton magnetic resonance spectroscopy. J Neurol Sci 2006;251(1–2):62–69. 60. Spilt A, Goekoop R, Westendorp RG, Blauw GJ, de Craen AJ, van Buchem MA. Not all age-related white matter hyperintensities are the same: a magnetization transfer imaging study. AJNR Am J Neuroradiol 2006;27(9):1964–1968. 61. Steffens DC. Establishing diagnostic criteria for vascular depression. J Neurol Sci 2004;226(1–2):59–62. 62. Dieguez S, Staub F, Bruggimann L, Bogousslavsky J. Is poststroke depression a vascular depression? J Neurol Sci 2004;226(1–2):53–58. 63. Pohjasvaara T, Leppavuori A, Siira I, Vataja R, Kaste M, Erkinjuntti T. Frequency and clinical determinants of poststroke depression. Stroke 1998;29(11):2311–2317. 64. Gabaldon L, Fuentes B, Frank-Garcia A, Diez-Tejedor E. Poststroke depression: importance of its detection and treatment. Cerebrovasc Dis 2007;24(Suppl 1):181–188. 65. van Straaten EC, Scheltens P, Barkhof F. MRI and CT in the diagnosis of vascular dementia. J Neurol Sci 2004;226(1–2):9–12. 66. de Leeuw FE, de Groot JC, Achten E, Oudkerk M, Ramos LM, Heijboer R, et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry 2001;70(1):9–14. 67. Liao D, Cooper L, Cai J, Toole J, Bryan N, Burke G, et al. The prevalence and severity of white matter lesions, their relationship with age, ethnicity, gender, and cardiovascular disease risk factors: the ARIC Study. Neuroepidemiology 1997;16(3):149–162. 68. Wen W, Sachdev P. The topography of white matter hyperintensities on brain MRI in healthy 60- to 64-year-old individuals. Neuroimage 2004;22(1):144–154. 69. Sachdev PS, Parslow R, Wen W, Anstey KJ, Easteal S. Sex differences in the causes and consequences of white matter hyperintensities. Neurobiol Aging 2007. 70. van den Heuvel DM, Admiraal-Behloul F, ten Dam VH, Olofsen H, Bollen EL, Murray HM, et al. Different progression rates for deep white matter hyperintensities in elderly men and women. Neurology 2004;63(9): 1699–1701. 71. Murray AD, Staff RT, Shenkin SD, Deary IJ, Starr JM, Whalley LJ. Brain white matter hyperintensities: relative importance of vascular risk factors in nondemented elderly people. Radiology 2005;237(1):251–257.

47

72. Korf ES, White LR, Scheltens P, Launer LJ. Brain aging in very old men with type 2 diabetes: the Honolulu-Asia Aging Study. Diabetes Care 2006;29(10):2268–2274. 73. Wiseman RM, Saxby BK, Burton EJ, Barber R, Ford GA, O’Brien JT. Hippocampal atrophy, whole brain volume, and white matter lesions in older hypertensive subjects. Neurology 2004;63(10):1892–1897. 74. Firbank MJ, Wiseman RM, Burton EJ, Saxby BK, O’Brien J T, Ford GA. Brain atrophy and white matter hyperintensity change in older adults and relationship to blood pressure: Brain atrophy, WMH change and blood pressure. J Neurol 2007;254(6):713–721. 75. Sachdev P, Wen W, Chen X, Brodaty H. Progression of white matter hyperintensities in elderly individuals over 3 years. Neurology 2007;68(3):214–222. 76. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol 1987;149(2): 351–356. 77. Scheltens P, Barkhof F, Leys D, Pruvo JP, Nauta JJ, Vermersch P, et al. A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. J Neurol Sci 1993;114(1):7–12. 78. Wahlund LO, Barkhof F, Fazekas F, Bronge L, Augustin M, Sjogren M, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke 2001;32(6):1318–1322. 79. van Straaten EC, Fazekas F, Rostrup E, Scheltens P, Schmidt R, Pantoni L, et al. Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke 2006;37(3):836–840. 80. Benson RR, Guttmann CR, Wei X, Warfield SK, Hall C, Schmidt JA, et al. Older people with impaired mobility have specific loci of periventricular abnormality on MRI. Neurology 2002;58(1):48–55. 81. Tiehuis AM, Vincken KL, Mali WP, Kappelle LJ, Anbeek P, Algra A, et al. Automated and Visual Scoring Methods of Cerebral White Matter Hyperintensities: Relation with Age and Cognitive Function. Cerebrovasc Dis 2007;25(1–2):59–66. 82. Gouw AA, Van der Flier WM, van Straaten EC, Barkhof F, Ferro JM, Baezner H, et al. Simple versus complex assessment of white matter hyperintensities in relation to physical performance and cognition: the LADIS study. J Neurol 2006;253(9):1189–1196. 83. Yoshita M, Fletcher E, DeCarli C. Current concepts of analysis of cerebral white matter hyperintensities on magnetic resonance imaging. Top Magn Reson Imaging 2005;16(6):399–407. 84. van der Flier WM, van Straaten EC, Barkhof F, Verdelho A, Madureira S, Pantoni L, et al. Small vessel disease and general cognitive function in nondisabled elderly: the LADIS study. Stroke 2005;36(10):2116–2120. 85. Verdelho A, Madureira S, Ferro JM, Basile AM, Chabriat H, Erkinjuntti T, et al. Differential impact of cerebral white matter changes, diabetes, hypertension and stroke on cognitive performance among non-disabled elderly. The LADIS study. J Neurol Neurosurg Psychiatry 2007;78(12):1325–1330. 86. Fernando MS, Ince PG. Vascular pathologies and cognition in a population-based cohort of elderly people. J Neurol Sci 2004;226(1–2):13–17.

48 87. Cohen RA, Paul RH, Ott BR, Moser DJ, Zawacki TM, Stone W, et al. The relationship of subcortical MRI hyperintensities and brain volume to cognitive function in vascular dementia. J Int Neuropsychol Soc 2002;8(6):743–752. 88. Libon DJ, Price CC, Giovannetti T, Swenson R, Bettcher BM, Heilman KM, et al. Linking MRI hyperintensities with patterns of neuropsychological impairment: evidence for a threshold effect. Stroke 2008;39(3):806–813. 89. Wright CB, Festa JR, Paik MC, Schmiedigen A, Brown TR, Yoshita M, et al. White matter hyperintensities and subclinical infarction: associations with psychomotor speed and cognitive flexibility. Stroke 2008;39(3):800–805. 90. Sweet LH, Paul RH, Cohen RA, Moser D, Ott BR, Gordon N, et al. Neuroimaging correlates of dementia rating scale performance at baseline and 12-month follow-up among patients with vascular dementia. J Geriatr Psychiatry Neurol 2003;16(4):240–244. 91. Meyer JS, Xu G, Thornby J, Chowdhury MH, Quach M. Is mild cognitive impairment prodromal for vascular dementia like Alzheimer’s disease? Stroke 2002;33(8):1981–1985. 92. Debette S, Bombois S, Bruandet A, Delbeuck X, Lepoittevin S, Delmaire C, et al. Subcortical hyperintensities are associated with cognitive decline in patients with mild cognitive impairment. Stroke 2007;38(11):2924–2930. 93. Sachdev PS, Brodaty H, Valenzuela MJ, Lorentz L, Looi JC, Berman K, et al. Clinical determinants of dementia and mild cognitive impairment following ischaemic stroke: the Sydney Stroke Study. Dement Geriatr Cogn Disord 2006;21(5–6):275–283. 94. Yoshita M, Fletcher E, Harvey D, Ortega M, Martinez O, Mungas DM, et al. Extent and distribution of white matter hyperintensities in normal aging, MCI, and AD. Neurology 2006;67(12):2192–2198. 95. Bombois S, Debette S, Delbeuck X, Bruandet A, Lepoittevin S, Delmaire C, et al. Prevalence of subcortical vascular lesions and association with executive function in mild cognitive impairment subtypes. Stroke 2007;38(9):2595–2597. 96. Bombois S, Debette S, Bruandet A, Delbeuck X, Delmaire C, Leys D, et al. Vascular subcortical hyperintensities predict conversion to vascular and mixed dementia in MCI patients. Stroke 2008;39(7):2046–2051. 97. de Groot JC, de Leeuw FE, Oudkerk M, Hofman A, Jolles J, Breteler MM. Cerebral white matter lesions and depressive symptoms in elderly adults. Arch Gen Psychiatry 2000;57(11):1071–1076. 98. Krishnan MS, O’Brien JT, Firbank MJ, Pantoni L, Carlucci G, Erkinjuntti T, et al. Relationship between periventricular and deep white matter lesions and depressive symptoms in older people. The LADIS Study. Int J Geriatr Psychiatry 2006;21(10):983–989. 99. O’Brien JT, Firbank MJ, Krishnan MS, van Straaten EC, van der Flier WM, Petrovic K, et al. White matter hyperintensities rather than lacunar infarcts are associated with depressive symptoms in older people: the LADIS study. Am J Geriatr Psychiatry 2006;14(10):834–841. 100. O’Brien J, Perry R, Barber R, Gholkar A, Thomas A. The association between white matter lesions on magnetic resonance imaging and noncognitive symptoms. Ann N Y Acad Sci 2000;903:482–489. 101. Rainer MK, Mucke HA, Zehetmayer S, Krampla W, Kuselbauer T, Weissgram S, et al. Data from the VITA

T. O. Dalaker et al. Study do not support the concept of vascular depression. Am J Geriatr Psychiatry 2006;14(6):531–537. 102. Herrmann LL, Le Masurier M, Ebmeier KP. White matter hyperintensities in late life depression: a systematic review. J Neurol Neurosurg Psychiatry 2008;79(6):619–624. 103. Hachinski VC, Lassen NA, Marshall J. Multi-infarct dementia. A cause of mental deterioration in the elderly. Lancet 1974;2(7874):207–210. 104. Erkinjuntti T. Vascular cognitive deterioration and stroke. Cerebrovasc Dis 2007;24 Suppl 1:189–194. 105. Vataja R, Pohjasvaara T, Mantyla R, Ylikoski R, Leppavuori A, Leskela M, et al. MRI correlates of executive dysfunction in patients with ischaemic stroke. Eur J Neurol 2003;10(6):625–631. 106. Kramer JH, Reed BR, Mungas D, Weiner MW, Chui HC. Executive dysfunction in subcortical ischaemic vascular disease. J Neurol Neurosurg Psychiatry 2002;72(2):217–220. 107. Carey CL, Kramer JH, Josephson SA, Mungas D, Reed BR, Schuff N, et al. Subcortical lacunes are associated with executive dysfunction in cognitively normal elderly. Stroke 2008;39(2):397–402. 108. Liem MK, van der Grond J, Haan J, van den Boom R, Ferrari MD, Knaap YM, et al. Lacunar infarcts are the main correlate with cognitive dysfunction in CADASIL. Stroke 2007;38(3):923–928. 109. Viswanathan A, Gschwendtner A, Guichard JP, Buffon F, Cumurciuc R, O’Sullivan M, et al. Lacunar lesions are independently associated with disability and cognitive impairment in CADASIL. Neurology 2007;69(2):172–179. 110. Fein G, Di Sclafani V, Tanabe J, Cardenas V, Weiner MW, Jagust WJ, et al. Hippocampal and cortical atrophy predict dementia in subcortical ischemic vascular disease. Neurology 2000;55(11):1626–1635. 111. Vermeer SE, Prins ND, den Heijer T, Hofman A, Koudstaal PJ, Breteler MM. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med 2003;348(13):1215–1222. 112. Wen W, Sachdev PS, Chen X, Anstey K. Gray matter reduction is correlated with white matter hyperintensity volume: a voxel-based morphometric study in a large epidemiological sample. Neuroimage 2006;29(4):1031–1039. 113. Firbank MJ, Burton EJ, Barber R, Stephens S, Kenny RA, Ballard C, et al. Medial temporal atrophy rather than white matter hyperintensities predict cognitive decline in stroke survivors. Neurobiol Aging 2007;28(11):1664–1669. 114. Pohjasvaara T, Mantyla R, Salonen O, Aronen HJ, Ylikoski R, Hietanen M, et al. How complex interactions of ischemic brain infarcts, white matter lesions, and atrophy relate to poststroke dementia. Arch Neurol 2000;57(9):1295–1300. 115. O’Sullivan M, Ngo E, Viswanathan A, Jouvent E, Gschwendtner A, Saemann PG, et al. Hippocampal volume is an independent predictor of cognitive performance in CADASIL. Neurobiol Aging 2007 Epub ahead of print, In Press. 116. Peters N, Holtmannspotter M, Opherk C, Gschwendtner A, Herzog J, Samann P, et al. Brain volume changes in CADASIL: a serial MRI study in pure subcortical ischemic vascular disease. Neurology 2006;66(10):1517–1522. 117. Rovaris M, Iannucci G, Cercignani M, Sormani MP, De Stefano N, Gerevini S, et al. Age-related changes in conventional, magnetization transfer, and diffusion-tensor MR imaging findings: study with whole-brain tissue histogram analysis. Radiology 2003;227(3):731–738.

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

118. Nagata K, Saito H, Ueno T, Sato M, Nakase T, Maeda T, et al. Clinical diagnosis of vascular dementia. J Neurol Sci 2007;257(1–2):44–48. 119. Kerrouche N, Herholz K, Mielke R, Holthoff V, Baron JC. 18FDG PET in vascular dementia: differentiation from Alzheimer’s disease using voxel-based multivariate analysis. J Cereb Blood Flow Metab 2006;26(9):1213–1221. 120. Mielke R, Pietrzyk U, Jacobs A, Fink GR, Ichimiya A, Kessler J, et al. HMPAO SPET and FDG PET in Alzheimer’s disease and vascular dementia: comparison of perfusion and metabolic pattern. Eur J Nucl Med 1994;21(10):1052–1060. 121. Nagata K, Maruya H, Yuya H, Terashi H, Mito Y, Kato H, et al. Can PET data differentiate Alzheimer’s disease from vascular dementia? Ann N Y Acad Sci 2000;903:252–261. 122. Lojkowska W, Ryglewicz D, Jedrzejczak T, SienkiewiczJarosz H, Minc S, Jakubowska T, et al. SPECT as a diagnostic test in the investigation of dementia. J Neurol Sci 2002;203–204:215–219. 123. Clarke S, Assal G, Bogousslavsky J, Regli F, Townsend DW, Leenders KL, et al. Pure amnesia after unilateral left polar thalamic infarct: topographic and sequential neuropsychological and metabolic (PET) correlations. J Neurol Neurosurg Psychiatry 1994;57(1):27–34. 124. Takahashi W, Takagi S, Ide M, Shohtsu A, Shinohara Y. Global reduction of cerebral glucose metabolism in persons with symptomatic as well as asymptomatic lacunar infarction. Keio J Med 2000;49(Suppl 1):A98–100. 125. Sultzer DL, Mahler ME, Cummings JL, Van Gorp WG, Hinkin CH, Brown C. Cortical abnormalities associated with subcortical lesions in vascular dementia. Clinical and position emission tomographic findings. Arch Neurol 1995;52(8):773–780. 126. Takahashi W, Takagi S, Ide M, Shohtsu A, Shinohara Y. Reduced cerebral glucose metabolism in subjects with incidental hyperintensities on magnetic resonance imaging. J Neurol Sci 2000;176(1):21–27. 127. Reed BR, Eberling JL, Mungas D, Weiner M, Kramer JH, Jagust WJ. Effects of white matter lesions and lacunes on cortical function. Arch Neurol 2004;61(10):1545–1550. 128. Yang DW, Kim BS, Park JK, Kim SY, Kim EN, Sohn HS. Analysis of cerebral blood flow of subcortical vascular dementia with single photon emission computed tomography: adaptation of statistical parametric mapping. J Neurol Sci 2002;203–204:199–205. 129. Cummings JL. Toward a molecular neuropsychiatry of neurodegenerative diseases. Ann Neurol 2003;54(2):147–154. 130. Doudet DJ. Neurodegenerative disease. Mol Imaging Biol 2007;9(4):159–160. 131. Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002;297(5580):353–356. 132. McKeith IG, Galasko D, Kosaka K, Perry EK, Dickson DW, Hansen LA, et al. Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the consortium on DLB international workshop. Neurology 1996;47(5):1113–1124. 133. Jellinger KA. The morphological basis of mental dysfunction in Parkinson’s disease. J Neurol Sci 2006;248(1–2):167–172. 134. Mrak RE, Griffin WS. Common inflammatory mechanisms in Lewy body disease and Alzheimer disease. J Neuropathol Exp Neurol 2007;66(8):683–686.

49

135. Cummings JL. Dementia with lewy bodies: molecular pathogenesis and implications for classification. J Geriatr Psychiatry Neurol 2004;17(3):112–119. 136. Ballard C, Ziabreva I, Perry R, Larsen JP, O’Brien J, McKeith I, et al. Differences in neuropathologic characteristics across the Lewy body dementia spectrum. Neurology 2006;67(11):1931–1934. 137. Aarsland D, Ballard CG, Halliday G. Are Parkinson’s disease with dementia and dementia with Lewy bodies the same entity? J Geriatr Psychiatry Neurol 2004;17(3):137–145. 138. Lippa CF, Duda JE, Grossman M, Hurtig HI, Aarsland D, Boeve BF, et al. DLB and PDD boundary issues: diagnosis, treatment, molecular pathology, and biomarkers. Neurology 2007;68(11):812–819. 139. Andlin-Sobocki P, Jonsson B, Wittchen HU, Olesen J. Cost of disorders of the brain in Europe. Eur J Neurol 2005;12(Suppl 1):1–27. 140. Olesen J, Leonardi M. The burden of brain diseases in Europe. Eur J Neurol 2003;10(5):471–477. 141. Fahn S. Description of Parkinson’s disease as a clinical syndrome. Ann N Y Acad Sci 2003;991:1–14. 142. Farrer MJ. Genetics of Parkinson disease: paradigm shifts and future prospects. Nat Rev Genet 2006;7(4): 306–318. 143. Sethi KD. Clinical aspects of Parkinson disease. Curr Opin Neurol 2002;15(4):457–460. 144. Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 1992;55(3):181–184. 145. Hughes AJ, Daniel SE, Lees AJ. Improved accuracy of clinical diagnosis of Lewy body Parkinson’s disease. Neurology 2001;57(8):1497–1499. 146. Lang AE, Obeso JA. Time to move beyond nigrostriatal dopamine deficiency in Parkinson’s disease. Ann Neurol 2004;55(6):761–765. 147. Aarsland D, Andersen K, Larsen JP, Lolk A, KraghSorensen P. Prevalence and characteristics of dementia in Parkinson disease: an 8-year prospective study. Arch Neurol 2003;60(3):387–392. 148. Janvin C, Aarsland D, Larsen JP, Hugdahl K. Neuropsychological profile of patients with Parkinson’s disease without dementia. Dement Geriatr Cogn Disord 2003;15(3): 126–131. 149. Alves G, Larsen JP, Emre M, Wentzel-Larsen T, Aarsland D. Changes in motor subtype and risk for incident dementia in Parkinson’s disease. Mov Disord 2006;21(8):1123–1130. 150. Williams-Gray CH, Foltynie T, Brayne CE, Robbins TW, Barker RA. Evolution of cognitive dysfunction in an incident Parkinson’s disease cohort. Brain 2007;130(Pt 7):1787–1798. 151. Camicioli R, Fisher N. Progress in clinical neurosciences: Parkinson’s disease with dementia and dementia with Lewy bodies. Can J Neurol Sci 2004;31(1):7–21. 152. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004;256(3):183–194. 153. Dubois B, Pillon B. Cognitive deficits in Parkinson’s disease. J Neurol 1997;244(1):2–8. 154. Foltynie T, Brayne CE, Robbins TW, Barker RA. The cognitive ability of an incident cohort of Parkinson’s patients in the UK. The CamPaIGN study. Brain 2004;127 (Pt 3):550–560.

50 155. Janvin CC, Larsen JP, Aarsland D, Hugdahl K. Subtypes of mild cognitive impairment in Parkinson’s disease: progression to dementia. Mov Disord 2006;21(9):1343–1349. 156. Braak H, Rub U, Jansen Steur EN, Del Tredici K, de Vos RA. Cognitive status correlates with neuropathologic stage in Parkinson disease. Neurology 2005;64(8):1404–1410. 157. Emre M. What causes mental dysfunction in Parkinson’s disease? Mov Disord 2003;18(Suppl 6):S63–71. 158. Leentjens AF. Depression in Parkinson’s disease: conceptual issues and clinical challenges. J Geriatr Psychiatry Neurol 2004;17(3):120–126. 159. Boeve BF, Silber MH, Ferman TJ. REM sleep behavior disorder in Parkinson’s disease and dementia with Lewy bodies. J Geriatr Psychiatry Neurol 2004;17(3):146–157. 160. Herlofson K, Larsen JP. The influence of fatigue on healthrelated quality of life in patients with Parkinson’s disease. Acta Neurol Scand 2003;107(1):1–6. 161. Lou JS, Kearns G, Oken B, Sexton G, Nutt J. Exacerbated physical fatigue and mental fatigue in Parkinson’s disease. Mov Disord 2001;16(2):190–196. 162. Wint DP, Okun MS, Fernandez HH. Psychosis in Parkinson’s disease. J Geriatr Psychiatry Neurol 2004;17(3):127–136. 163. Braak H, Bohl JR, Muller CM, Rub U, de Vos RA, Del Tredici K. Stanley Fahn Lecture 2005: The staging procedure for the inclusion body pathology associated with sporadic Parkinson’s disease reconsidered. Mov Disord 2006;21(12):2042–2051. 164. McKeith IG, Dickson DW, Lowe J, Emre M, O’Brien JT, Feldman H, et al. Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology 2005;65(12):1863–1872. 165. Aarsland D, Ballard C, Larsen JP, McKeith I. A comparative study of psychiatric symptoms in dementia with Lewy bodies and Parkinson’s disease with and without dementia. Int J Geriatr Psychiatry 2001;16(5):528–536. 166. Tiraboschi P, Salmon DP, Hansen LA, Hofstetter RC, Thal LJ, Corey-Bloom J. What best differentiates Lewy body from Alzheimer’s disease in early-stage dementia? Brain 2006;129(Pt 3):729–735. 167. Metzler-Baddeley C. A review of cognitive impairments in dementia with Lewy bodies relative to Alzheimer’s disease and Parkinson’s disease with dementia. Cortex 2007;43(5):583–600. 168. Colosimo C, Hughes AJ, Kilford L, Lees AJ. Lewy body cortical involvement may not always predict dementia in Parkinson’s disease. J Neurol Neurosurg Psychiatry 2003;74(7):852–856. 169. Tsuboi Y, Dickson DW. Dementia with Lewy bodies and Parkinson’s disease with dementia: are they different? Parkinsonism Relat Disord 2005;11(Suppl 1):S47–51. 170. Jellinger KA. Morphological substrates of parkinsonism with and without dementia: a retrospective clinico-pathological study. J Neural Transm Suppl 2007(72):91–104. 171. Ballard C, Grace J, McKeith I, Holmes C. Neuroleptic sensitivity in dementia with Lewy bodies and Alzheimer’s disease. Lancet 1998;351(9108):1032–1033. 172. McKeith I, Fairbairn A, Perry R, Thompson P, Perry E. Neuroleptic sensitivity in patients with senile dementia of Lewy body type. BMJ 1992;305(6855):673–678. 173. Olichney JM, Galasko D, Salmon DP, Hofstetter CR, Hansen LA, Katzman R, et al. Cognitive decline is faster in

T. O. Dalaker et al. Lewy body variant than in Alzheimer’s disease. Neurology 1998;51(2):351–357. 174. Alves G, Wentzel-Larsen T, Aarsland D, Larsen JP. Progression of motor impairment and disability in Parkinson disease: a population-based study. Neurology 2005;65(9):1436–1441. 175. Mueller SG, Schuff N, Weiner MW. Evaluation of treatment effects in Alzheimer’s and other neurodegenerative diseases by MRI and MRS. NMR Biomed 2006;19(6):655–668. 176. Ashburner J, Friston KJ. Voxel-based morphometry–the methods. Neuroimage 2000;11(6 Pt 1):805–821. 177. Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 2001;14(1 Pt 1):21–36. 178. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33(3):341–355. 179. Berg D. Marker for a preclinical diagnosis of Parkinson’s disease as a basis for neuroprotection. J Neural Transm Suppl 2006(71):123–132. 180. Colloby S, O’Brien J. Functional imaging in Parkinson’s disease and dementia with Lewy bodies. J Geriatr Psychiatry Neurol 2004;17(3):158–163. 181. Dagher A, Nagano-Saito A. Functional and anatomical magnetic resonance imaging in Parkinson’s disease. Mol Imaging Biol 2007;9(4):234–242. 182. Ravina B, Eidelberg D, Ahlskog JE, Albin RL, Brooks DJ, Carbon M, et al. The role of radiotracer imaging in Parkinson disease. Neurology 2005;64(2):208–215. 183. Tolosa E, Wenning G, Poewe W. The diagnosis of Parkinson’s disease. Lancet Neurol 2006;5(1):75–86. 184. Michell AW, Lewis SJ, Foltynie T, Barker RA. Biomarkers and Parkinson’s disease. Brain 2004;127(Pt 8):1693–1705. 185. Ehringer H, Hornykiewicz O. [Distribution of noradrenaline and dopamine (3-hydroxytyramine) in the human brain and their behavior in diseases of the extrapyramidal system.]. Klin Wochenschr 1960;38:1236–1239. 186. Scherfler C, Schwarz J, Antonini A, Grosset D, Valldeoriola F, Marek K, et al. Role of DAT-SPECT in the diagnostic work up of Parkinsonism. Mov Disord 2007;22(9):1229–1238. 187. Carbon M, Marie RM. Functional imaging of cognition in Parkinson’s disease. Curr Opin Neurol 2003;16(4): 475–480. 188. Alexander GE, DeLong MR, Strick PL. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci 1986;9:357–381. 189. Bruck A, Portin R, Lindell A, Laihinen A, Bergman J, Haaparanta M, et al. Positron emission tomography shows that impaired frontal lobe functioning in Parkinson’s disease is related to dopaminergic hypofunction in the caudate nucleus. Neurosci Lett 2001;311(2):81–84. 190. Owen AM, Doyon J, Dagher A, Sadikot A, Evans AC. Abnormal basal ganglia outflow in Parkinson’s disease identified with PET. Implications for higher cortical functions. Brain 1998;121 (Pt 5):949–965. 191. Carbon M, Ma Y, Barnes A, Dhawan V, Chaly T, Ghilardi MF, et al. Caudate nucleus: influence of dopaminergic input on sequence learning and brain activation in Parkinsonism. Neuroimage 2004;21(4):1497–1507.

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

192. Hilker R, Thomas AV, Klein JC, Weisenbach S, Kalbe E, Burghaus L, et al. Dementia in Parkinson disease: functional imaging of cholinergic and dopaminergic pathways. Neurology 2005;65(11):1716–1722. 193. Ito K, Nagano-Saito A, Kato T, Arahata Y, Nakamura A, Kawasumi Y, et al. Striatal and extrastriatal dysfunction in Parkinson’s disease with dementia: a 6-[18F]fluoro-L-dopa PET study. Brain 2002;125(Pt 6):1358–1365. 194. Antonini A, Schwarz J, Oertel WH, Pogarell O, Leenders KL. Long-term changes of striatal dopamine D2 receptors in patients with Parkinson’s disease: a study with positron emission tomography and [11C]raclopride. Mov Disord 1997;12(1):33–38. 195. Booij J, Tissingh G, Boer GJ, Speelman JD, Stoof JC, Janssen AG, et al. [123I]FP-CIT SPECT shows a pronounced decline of striatal dopamine transporter labelling in early and advanced Parkinson’s disease. J Neurol Neurosurg Psychiatry 1997;62(2):133–140. 196. Kaasinen V, Aalto S, K NA, Hietala J, Sonninen P, Rinne JO. Extrastriatal dopamine D(2) receptors in Parkinson’s disease: a longitudinal study. J Neural Transm 2003;110(6):591–601. 197. Piggott MA, Ballard CG, Dickinson HO, McKeith IG, Perry RH, Perry EK. Thalamic D2 receptors in dementia with Lewy bodies, Parkinson’s disease, and Parkinson’s disease dementia. Int J Neuropsychopharmacol 2007;10(2):231–244. 198. Hilker R, Burghaus L, Razai N, Jacobs AH, Szelies B, Heiss WD. Functional brain imaging in combined motor and sleep disorders. J Neurol Sci 2006;248(1–2):223–226. 199. Boeve BF, Silber MH, Saper CB, Ferman TJ, Dickson DW, Parisi JE, et al. Pathophysiology of REM sleep behaviour disorder and relevance to neurodegenerative disease. Brain 2007;130(Pt 11):2770–2788. 200. Happe S, Baier PC, Helmschmied K, Meller J, Tatsch K, Paulus W. Association of daytime sleepiness with nigrostriatal dopaminergic degeneration in early Parkinson’s disease. J Neurol 2007;254(8):1037–1043. 201. Happe S, Pirker W, Klosch G, Sauter C, Zeitlhofer J. Periodic leg movements in patients with Parkinson’s disease are associated with reduced striatal dopamine transporter binding. J Neurol 2003;250(1):83–86. 202. Happe S, Anderer P, Pirker W, Klosch G, Gruber G, Saletu B, et al. Sleep microstructure and neurodegeneration as measured by [123I]beta-CIT SPECT in treated patients with Parkinson’s disease. J Neurol 2004;251(12):1465–1471. 203. Hilker R, Razai N, Ghaemi M, Weisenbach S, Rudolf J, Szelies B, et al. [18F]fluorodopa uptake in the upper brainstem measured with positron emission tomography correlates with decreased REM sleep duration in early Parkinson’s disease. Clin Neurol Neurosurg 2003;105(4): 262–269. 204. Bohnen NI, Frey KA. Imaging of cholinergic and monoaminergic neurochemical changes in neurodegenerative disorders. Mol Imaging Biol 2007;9(4):243–257. 205. Fujita M, Ichise M, Zoghbi SS, Liow JS, Ghose S, Vines DC, et al. Widespread decrease of nicotinic acetyl-choline receptors in Parkinson’s disease. Ann Neurol 2006;59(1): 174–177. 206. Kuhl DE, Minoshima S, Fessler JA, Frey KA, Foster NL, Ficaro EP, et al. In vivo mapping of cholinergic terminals in normal aging, Alzheimer’s disease, and Parkinson’s disease. Ann Neurol 1996;40(3):399–410.

51

207. Bohnen NI, Kaufer DI, Ivanco LS, Lopresti B, Koeppe RA, Davis JG, et al. Cortical cholinergic function is more severely affected in parkinsonian dementia than in Alzheimer disease: an in vivo positron emission tomographic study. Arch Neurol 2003;60(12):1745–1748. 208. Bohnen NI, Kaufer DI, Hendrickson R, Ivanco LS, Lopresti BJ, Constantine GM, et al. Cognitive correlates of cortical cholinergic denervation in Parkinson’s disease and parkinsonian dementia. J Neurol 2006;253(2):242–247. 209. Colloby SJ, Pakrasi S, Firbank MJ, Perry EK, Piggott MA, Owens J, et al. In vivo SPECT imaging of muscarinic acetylcholine receptors using (R,R) 123I-QNB in dementia with Lewy bodies and Parkinson’s disease dementia. Neuroimage 2006;33(2):423–429. 210. McKeith I, Del Ser T, Spano P, Emre M, Wesnes K, Anand R, et al. Efficacy of rivastigmine in dementia with Lewy bodies: a randomised, double-blind, placebo-controlled international study. Lancet 2000;356(9247):2031–2036. 211. Bohnen NI, Kaufer DI, Hendrickson R, Constantine GM, Mathis CA, Moore RY. Cortical cholinergic denervation is associated with depressive symptoms in Parkinson’s disease and parkinsonian dementia. J Neurol Neurosurg Psychiatry 2007;78(6):641–643. 212. Brooks DJ, Piccini P. Imaging in Parkinson’s disease: the role of monoamines in behavior. Biol Psychiatry 2006;59(10):908–918. 213. Doder M, Rabiner EA, Turjanski N, Lees AJ, Brooks DJ. Tremor in Parkinson’s disease and serotonergic dysfunction: an 11C-WAY 100635 PET study. Neurology 2003;60(4):601–605. 214. Kim SE, Choi JY, Choe YS, Choi Y, Lee WY. Serotonin transporters in the midbrain of Parkinson’s disease patients: a study with 123I-beta-CIT SPECT. J Nucl Med 2003;44(6):870–876. 215. Kish SJ, Tong J, Hornykiewicz O, Rajput A, Chang LJ, Guttman M, et al. Preferential loss of serotonin markers in caudate versus putamen in Parkinson’s disease. Brain 2008;131(Pt 1):120–131. 216. Remy P, Doder M, Lees A, Turjanski N, Brooks D. Depression in Parkinson’s disease: loss of dopamine and noradrenaline innervation in the limbic system. Brain 2005;128(Pt 6):1314–1322. 217. Imamura K, Hishikawa N, Sawada M, Nagatsu T, Yoshida M, Hashizume Y. Distribution of major histocompatibility complex class II-positive microglia and cytokine profile of Parkinson’s disease brains. Acta Neuropathol 2003;106(6):518–526. 218. Ouchi Y, Yoshikawa E, Sekine Y, Futatsubashi M, Kanno T, Ogusu T, et al. Microglial activation and dopamine terminal loss in early Parkinson’s disease. Ann Neurol 2005;57(2):168–175. 219. Eckert T, Tang C, Eidelberg D. Assessment of the progression of Parkinson’s disease: a metabolic network approach. Lancet Neurol 2007;6(10):926–932. 220. Huang C, Mattis P, Tang C, Perrine K, Carbon M, Eidelberg D. Metabolic brain networks associated with cognitive function in Parkinson’s disease. Neuroimage 2007;34(2): 714–723. 221. Huang C, Tang C, Feigin A, Lesser M, Ma Y, Pourfar M, et al. Changes in network activity with the progression of Parkinson’s disease. Brain 2007;130(Pt 7):1834–1846.

52 222. Asanuma K, Tang C, Ma Y, Dhawan V, Mattis P, Edwards C, et al. Network modulation in the treatment of Parkinson’s disease. Brain 2006;129(Pt 10):2667–2678. 223. Mentis MJ, McIntosh AR, Perrine K, Dhawan V, Berlin B, Feigin A, et al. Relationships among the metabolic patterns that correlate with mnemonic, visuospatial, and mood symptoms in Parkinson’s disease. Am J Psychiatry 2002;159(5):746–754. 224. Koeppe RA, Gilman S, Joshi A, Liu S, Little R, Junck L, et al. 11C-DTBZ and 18F-FDG PET measures in differentiating dementias. J Nucl Med 2005;46(6):936–944. 225. Dagher A, Owen AM, Boecker H, Brooks DJ. The role of the striatum and hippocampus in planning: a PET activation study in Parkinson’s disease. Brain 2001;124(Pt 5):1020–1032. 226. Sawamoto N, Honda M, Hanakawa T, Aso T, Inoue M, Toyoda H, et al. Cognitive slowing in Parkinson disease is accompanied by hypofunctioning of the striatum. Neurology 2007;68(13):1062–1068. 227. Cools R, Stefanova E, Barker RA, Robbins TW, Owen AM. Dopaminergic modulation of high-level cognition in Parkinson’s disease: the role of the prefrontal cortex revealed by PET. Brain 2002;125(Pt 3):584–594. 228. Carbon M, Eidelberg D. Functional imaging of sequence learning in Parkinson’s disease. J Neurol Sci 2006; 248(1–2):72–77. 229. Marie RM, Lozza C, Chavoix C, Defer GL, Baron JC. Functional imaging of working memory in Parkinson’s disease: compensations and deficits. J Neuroimaging 2007;17(4):277–285. 230. Dujardin K, Defebvre L, Duhamel A, Lecouffe P, Rogelet P, Steinling M, et al. Cognitive and SPECT characteristics predict progression of Parkinson’s disease in newly diagnosed patients. J Neurol 2004;251(11):1383–1392. 231. Oishi N, Udaka F, Kameyama M, Sawamoto N, Hashikawa K, Fukuyama H. Regional cerebral blood flow in Parkinson disease with nonpsychotic visual hallucinations. Neurology 2005;65(11):1708–1715. 232. Kemp PM, Holmes C. Imaging in dementia with Lewy bodies: a review. Nucl Med Commun 2007;28(7):511–519. 233. Monchi O, Petrides M, Strafella AP, Worsley KJ, Doyon J. Functional role of the basal ganglia in the planning and execution of actions. Ann Neurol 2006;59(2):257–264. 234. Sauer J, ffytche DH, Ballard C, Brown RG, Howard R. Differences between Alzheimer’s disease and dementia with Lewy bodies: an fMRI study of task-related brain activity. Brain 2006;129(Pt 7):1780–1788. 235. Mattay VS, Tessitore A, Callicott JH, Bertolino A, Goldberg TE, Chase TN, et al. Dopaminergic modulation of cortical function in patients with Parkinson’s disease. Ann Neurol 2002;51(2):156–164. 236. Stebbins GT, Goetz CG, Carrillo MC, Bangen KJ, Turner DA, Glover GH, et al. Altered cortical visual processing in PD with hallucinations: an fMRI study. Neurology 2004;63(8):1409–1416. 237. Warmuth-Metz M, Naumann M, Csoti I, Solymosi L. Measurement of the midbrain diameter on routine magnetic resonance imaging: a simple and accurate method of differentiating between Parkinson disease and progressive supranuclear palsy. Arch Neurol 2001;58(7):1076–1079. 238. Dormont D, Ricciardi KG, Tande D, Parain K, Menuel C, Galanaud D, et al. Is the subthalamic nucleus hypointense

T. O. Dalaker et al. on T2-weighted images? A correlation study using MR imaging and stereotactic atlas data. AJNR Am J Neuroradiol 2004;25(9):1516–1523. 239. Minati L, Grisoli M, Carella F, De Simone T, Bruzzone MG, Savoiardo M. Imaging degeneration of the substantia nigra in Parkinson disease with inversion-recovery MR imaging. AJNR Am J Neuroradiol 2007;28(2):309–313. 240. Oikawa H, Sasaki M, Tamakawa Y, Ehara S, Tohyama K. The substantia nigra in Parkinson disease: proton densityweighted spin-echo and fast short inversion time inversionrecovery MR findings. AJNR Am J Neuroradiol 2002;23(10):1747–1756. 241. Burton EJ, McKeith IG, Burn DJ, O’Brien JT. Brain atrophy rates in Parkinson’s disease with and without dementia using serial magnetic resonance imaging. Mov Disord 2005;20(12):1571–1576. 242. Beyer MK, Janvin CC, Larsen JP, Aarsland D. A magnetic resonance imaging study of patients with Parkinson’s disease with mild cognitive impairment and dementia using voxel-based morphometry. J Neurol Neurosurg Psychiatry 2007;78(3):254–259. 243. Burton EJ, McKeith IG, Burn DJ, Williams ED, O’Brien JT. Cerebral atrophy in Parkinson’s disease with and without dementia: a comparison with Alzheimer’s disease, dementia with Lewy bodies and controls. Brain 2004;127(Pt 4):791–800. 244. Junque C, Ramirez-Ruiz B, Tolosa E, Summerfield C, Marti MJ, Pastor P, et al. Amygdalar and hippocampal MRI volumetric reductions in Parkinson’s disease with dementia. Mov Disord 2005;20(5):540–544. 245. Nagano-Saito A, Washimi Y, Arahata Y, Kachi T, Lerch JP, Evans AC, et al. Cerebral atrophy and its relation to cognitive impairment in Parkinson disease. Neurology 2005;64(2):224–229. 246. Summerfield C, Junque C, Tolosa E, Salgado-Pineda P, Gomez-Anson B, Marti MJ, et al. Structural brain changes in Parkinson disease with dementia: a voxel-based morphometry study. Arch Neurol 2005;62(2):281–285. 247. Tam CW, Burton EJ, McKeith IG, Burn DJ, O’Brien JT. Temporal lobe atrophy on MRI in Parkinson disease with dementia: a comparison with Alzheimer disease and dementia with Lewy bodies. Neurology 2005;64(5): 861–865. 248. Whitwell JL, Weigand SD, Shiung MM, Boeve BF, Ferman TJ, Smith GE, et al. Focal atrophy in dementia with Lewy bodies on MRI: a distinct pattern from Alzheimer’s disease. Brain 2007;130(Pt 3):708–719. 249. Hashimoto M, Kitagaki H, Imamura T, Hirono N, Shimomura T, Kazui H, et al. Medial temporal and wholebrain atrophy in dementia with Lewy bodies: a volumetric MRI study. Neurology 1998;51(2):357–362. 250. Janvin CC, Aarsland D, Larsen JP. Cognitive predictors of dementia in Parkinson’s disease: a community-based, 4-year longitudinal study. J Geriatr Psychiatry Neurol 2005;18(3):149–154. 251. Dalaker T, Zivadinov R, Larsen J, Beyer M, Cox J, Alves G, et al. Cingulate Atrophy Predicts Mild Cognitive Impairment in Early Parkinson Disease. A Voxel-based Morphometry Study. Neurology 2008;70(Suppl 1):P05.137. 252. Borroni B, Anchisi D, Paghera B, Vicini B, Kerrouche N, Garibotto V, et al. Combined 99mTc-ECD SPECT and

19

Role of Imaging Techniques in Discerning Neurobehavioral Changes

neuropsychological studies in MCI for the assessment of conversion to AD. Neurobiol Aging 2006;27(1):24–31. 253. Meyer JS, Huang J, Chowdhury MH. MRI confirms mild cognitive impairments prodromal for Alzheimer’s, vascular and Parkinson-Lewy body dementias. J Neurol Sci 2007;257(1–2):97–104. 254. Almeida OP, Burton EJ, McKeith I, Gholkar A, Burn D, O’Brien JT. MRI study of caudate nucleus volume in Parkinson’s disease with and without dementia with Lewy bodies and Alzheimer’s disease. Dement Geriatr Cogn Disord 2003;16(2):57–63. 255. Beyer MK, Larsen JP, Aarsland D. Gray matter atrophy in Parkinson disease with dementia and dementia with Lewy bodies. Neurology 2007;69(8):747–754. 256. Seppi K, Rascol O. Dementia with Lewy bodies and Parkinson disease with dementia: can MRI make the difference? Neurology 2007;69(8):717–718. 257. Ramirez-Ruiz B, Marti MJ, Tolosa E, Gimenez M, Bargallo N, Valldeoriola F, et al. Cerebral atrophy in Parkinson’s disease patients with visual hallucinations. Eur J Neurol 2007;14(7):750–756. 258. Feldmann A, Illes Z, Kosztolanyi P, Illes E, Mike A, Kover F, et al. Morphometric changes of gray matter in Parkinson’s disease with depression: a voxel-based morphometry study. Mov Disord 2008;23(1):42–46. 259. Beyer MK, Aarsland D, Greve OJ, Larsen JP. Visual rating of white matter hyperintensities in Parkinson’s disease. Mov Disord 2006;21(2):223–229. 260. Burton EJ, McKeith IG, Burn DJ, Firbank MJ, O’Brien JT. Progression of white matter hyperintensities in Alzheimer disease, dementia with lewy bodies, and Parkinson disease dementia: a comparison with normal aging. Am J Geriatr Psychiatry 2006;14(10):842–849. 261. Dalaker T, Larsen J, Bergsland N, Beyer M, Alves G, Dwyer M, et al. Extent of brain atrophy and white matter hyperintensities in early Parkinson Disease. A large case-control study. Neurology 2008;70(Suppl 1):P08.023,A437. 262. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12(3):189–198. 263. Marshall GA, Shchelchkov E, Kaufer DI, Ivanco LS, Bohnen NI. White matter hyperintensities and cortical acetylcholinesterase activity in parkinsonian dementia. Acta Neurol Scand 2006;113(2):87–91. 264. Clarke CE, Lowry M. Systematic review of proton magnetic resonance spectroscopy of the striatum in parkinsonian syndromes. Eur J Neurol 2001;8(6):573–577. 265. Summerfield C, Gomez-Anson B, Tolosa E, Mercader JM, Marti MJ, Pastor P, et al. Dementia in Parkinson disease: a proton magnetic resonance spectroscopy study. Arch Neurol 2002;59(9):1415–1420. 266. Molina JA, Garcia-Segura JM, Benito-Leon J, GomezEscalonilla C, del Ser T, Martinez V, et al. Proton magnetic resonance spectroscopy in dementia with Lewy bodies. Eur Neurol 2002;48(3):158–163. 267. Martin WR. MR spectroscopy in neurodegenerative disease. Mol Imaging Biol 2007;9(4):196–203. 268. Hu MT, Taylor-Robinson SD, Chaudhuri KR, Bell JD, Labbe C, Cunningham VJ, et al. Cortical dysfunction in non-demented Parkinson’s disease patients: a combined

53

(31)P-MRS and (18)FDG-PET study. Brain 2000;123 (Pt 2):340–352. 269. Rango M, Arighi A, Biondetti P, Barberis B, Bonifati C, Blandini F, et al. Magnetic resonance spectroscopy in Parkinson’s disease and parkinsonian syndromes. Funct Neurol 2007;22(2):75–79. 270. Firbank MJ, Blamire AM, Krishnan MS, Teodorczuk A, English P, Gholkar A, et al. Atrophy is associated with posterior cingulate white matter disruption in dementia with Lewy bodies and Alzheimer’s disease. Neuroimage 2007;36(1):1–7. 271. Matsui H, Nishinaka K, Oda M, Niikawa H, Kubori T, Udaka F. Dementia in Parkinson’s disease: diffusion tensor imaging. Acta Neurol Scand 2007;116(3):177–181. 272. Berg D, Merz B, Reiners K, Naumann M, Becker G. Five-year follow-up study of hyperechogenicity of the substantia nigra in Parkinson’s disease. Mov Disord 2005;20(3):383–385. 273. Ressner P, Skoloudik D, Hlustik P, Kanovsky P. Hyperechogenicity of the substantia nigra in Parkinson’s disease. J Neuroimaging 2007;17(2):164–167. 274. Murthy JM. Acute disseminated encephalomyelitis. Neurol India 2002;50(3):238–243. 275. Bakshi R, Kinkel PR, Mechtler LL, Bates VE, Lindsay BD, Esposito SE, et al. Magnetic resonance imaging findings in 22 cases of myelitis: comparison between patients with and without multiple sclerosis. Eur J Neurol 1998;5(1):35–48. 276. Wingerchuk DM, Weinshenker BG. Neuromyelitis optica: clinical predictors of a relapsing course and survival. Neurology 2003;60(5):848–853. 277. Rao SM, Leo GJ, Bernardin L, Unverzagt F. Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology 1991;41(5):685–691. 278. Benedict RH, Fischer JS, Archibald CJ, Arnett PA, Beatty WW, Bobholz J, et al. Minimal neuropsychological assessment of MS patients: a consensus approach. Clin Neuropsychol 2002;16(3):381–397. 279. Rao SM. Neuropsychology of multiple sclerosis. Curr Opin Neurol 1995;8(3):216–220. 280. Benedict RH, Wahlig E, Bakshi R, Fishman I, Munschauer F, Zivadinov R, et al. Predicting quality of life in multiple sclerosis: accounting for physical disability, fatigue, cognition, mood disorder, personality, and behavior change. J Neurol Sci 2005;231(1–2):29–34. 281. Kraus JA, Schutze C, Brokate B, Kroger B, Schwendemann G, Hildebrandt H. Discriminant analysis of the cognitive performance profile of MS patients differentiates their clinical course. J Neurol 2005;252(7):808–813. 282. DeLuca J, Gaudino EA, Diamond BJ, Christodoulou C, Engel RA. Acquisition and storage deficits in multiple sclerosis. J Clin Exp Neuropsychol 1998;20(3):376–390. 283. Foong J, Rozewicz L, Quaghebeur G, Thompson AJ, Miller DH, Ron MA. Neuropsychological deficits in multiple sclerosis after acute relapse. J Neurol Neurosurg Psychiatry 1998;64(4):529–532. 284. Tekok-Kilic A, Benedict RH, Zivadinov R. Update on the relationships between neuropsychological dysfunction and structural MRI in multiple sclerosis. Expert Rev Neurother 2006;6(3):323–331. 285. Rao SM, Leo GJ, Haughton VM, St Aubin-Faubert P, Bernardin L. Correlation of magnetic resonance imaging

54 with neuropsychological testing in multiple sclerosis. Neurology 1989;39(2 Pt 1):161–166. 286. Sperling RA, Guttmann CR, Hohol MJ, Warfield SK, Jakab M, Parente M, et al. Regional magnetic resonance imaging lesion burden and cognitive function in multiple sclerosis: a longitudinal study. Arch Neurol 2001;58(1):115–121. 287. Benedict RH, Carone DA. Brain Atrophy, Cognitive Dysfunction and Emotional Disturbances in Multiple Sclerosis. In: Zivadinov R, Bakshi R, eds. Brain and Spinal Cord Atrophy in Multiple Sclerosis. Hauppauge, NY: Nova Biomedical Books, 2004: 137–166. 288. Hohol MJ, Guttmann CR, Orav J, Mackin GA, Kikinis R, Khoury SJ, et al. Serial neuropsychological assessment and magnetic resonance imaging analysis in multiple sclerosis. Arch Neurol 1997;54(8):1018–1025. 289. Bermel RA, Bakshi R, Tjoa C, Puli SR, Jacobs L. Bicaudate ratio as a magnetic resonance imaging marker of brain atrophy in multiple sclerosis. Arch Neurol 2002;59(2):275–280. 290. Benedict RH, Weinstock-Guttman B, Fishman I, Sharma J, Tjoa CW, Bakshi R. Prediction of neuropsychological impairment in multiple sclerosis: comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Arch Neurol 2004;61(2):226–230. 291. Zivadinov R, Locatelli L, Stival B, Bratina A, Grop A, Nasuelli D, et al. Normalized regional brain atrophy measurements in multiple sclerosis. Neuroradiology 2003;45(11):793–798. 292. Simon JH. Linear and regional measures of brain atrophy in multiple sclerosis. In: Zivadinov R, Bakshi R, eds. Brain and Spinal Cord Atrophy in Multiple Sclerosis. Hauppauge, NY: Nova Biomedical Books, 2004: 15–28. 293. Locatelli L, Zivadinov R, Grop A, Zorzon M. Frontal parenchymal atrophy measures in multiple sclerosis. Mult Scler 2004;10(5):562–568. 294. Carone DA, Benedict RH, Dwyer MG, Cookfair DL, Srinivasaraghavan B, Tjoa CW, et al. Semi-automatic brain region extraction (SABRE) reveals superior cortical and deep gray matter atrophy in MS. Neuroimage 2006;29(2):505–514. 295. Tekok-Kilic A, Benedict RH, Weinstock-Guttman B, Dwyer MG, Carone D, Srinivasaraghavan B, et al. Independent contributions of cortical gray matter atrophy

T. O. Dalaker et al. and ventricle enlargement for predicting neuropsychological impairment in multiple sclerosis. Neuroimage 2007;36: 1294–1130. 296. Evangelou N, Konz D, Esiri MM, Smith S, Palace J, Matthews PM. Regional axonal loss in the corpus callosum correlates with cerebral white matter lesion volume and distribution in multiple sclerosis. Brain 2000;123 (Pt 9):1845–1849. 297. Houtchens MK, Benedict RH, Killiany R, Sharma J, Jaisani Z, Singh B, et al. Thalamic atrophy and cognition in multiple sclerosis. Neurology 2007;69(12):1213–1223. 298. Pagani E, Rocca MA, Gallo A, Rovaris M, Martinelli V, Comi G, et al. Regional brain atrophy evolves differently in patients with multiple sclerosis according to clinical phenotype. AJNR Am J Neuroradiol 2005;26(2):341–346. 299. Horakova D, Cox JL, Havrdova E, Hussein S, Dolezal O, Cookfair D, et al. Evolution of different MRI measures in patients with active relapsing-remitting multiple sclerosis over 2 and 5 years: a case-control study. J Neurol Neurosurg Psychiatry 2008;79(4):407–414. 300. Amato MP, Bartolozzi ML, Zipoli V, Portaccio E, Mortilla M, Guidi L, et al. Neocortical volume decrease in relapsing-remitting MS patients with mild cognitive impairment. Neurology 2004;63(1):89–93. 301. Zivadinov R, De Masi R, Nasuelli D, Bragadin LM, Ukmar M, Pozzi-Mucelli RS, et al. MRI techniques and cognitive impairment in the early phase of relapsing-remitting multiple sclerosis. Neuroradiology 2001;43(4):272–278. 302. Christodoulou C, Krupp LB, Liang Z, Huang W, Melville P, Roque C, et al. Cognitive performance and MR markers of cerebral injury in cognitively impaired MS patients. Neurology 2003;60(11):1793–1798. 303. Benedict RH, Bruce J, Dwyer MG, Weinstock-Guttman B, Tjoa C, Tavazzi E, et al. Diffusion-weighted imaging predicts cognitive impairment in multiple sclerosis. Mult Scler 2007;13(6):722–730. 304. Zivadinov R, Bakshi R. Role of magnetic resonance imaging in the diagnosis and prognosis of multiple sclerosis. In: Olek M, ed. Multiple Sclerosis, Etiology, Diagnosis, and New Treatment Strategies. Totowa, NJ: Humana Press, 2005: 55–90.

Chapter 20

Towards a Functional Neuroanatomy of Symptoms and Cognitive Deficits of Schizophrenia David Linden

Abstract The techniques of neuroimaging and non-invasive neurophysiology, alone or in combination, provide unprecedented access to the mechanisms of normal and pathological perception and cognition in the human brain. They are especially useful for the investigation of the neural basis of psychiatric symptoms, for which animal models cannot be obtained, for example hallucinations. Functional magnetic resonance imaging has revealed that auditory cortex, frontal language areas and parts of the limbic system are commonly active during auditory verbal hallucinations. The anatomical connectivity underlying this pathophysiological network can be studied with another magnetic resonance imaging technique, diffusion tensor imaging. A similar rationale of combining functional and structural methods can be applied to other core symptoms of schizophrenia. The study of the pathophysiology of psychotic symptoms is closely linked to that of perceptual and cognitive deficits, which probably contribute to the generation of the clinical symptoms, but often precede or outlast them, making them important trait markers. Deficits of working memory and executive function have been associated with characteristic changes in late components of the event-related potential of the electroencephalogram (EEG) and distinctive patterns of prefrontal metabolic activity. However, recent studies also showed differences at earlier stages of perceptual processing, affecting sensory cortices in both the auditory and the visual domain. In addition to overall activity, coherence within and across areas, as evidenced by synchronous oscillations of the EEG, seems to be impaired in schizophrenia during certain cognitive tasks. These findings from neuroimaging and electrophysiology are D. Linden School of Psychology and North Wales Clinical School, Bangor University

not only important for the cognitive neuroscience of schizophrenia, but can also inform models that explain schizophrenia at the molecular level. Keywords Schizophrenia • psychopathology • brain imaging • temporal lobe • frontal lobe • hallucinations • social cognition Abbreviations AVH: Auditory Verbal Hallucinations; COMT: Catechol-O-Methyltransferase; DLPFC: Dorsolateral Prefrontal Cortex; DMN: Default-Mode Network; DSM-IV: Diagnostic and Statistical Manual of Mental Disorders, 4th edition; EEG: Electroencephalography/Electroencephalogram; EOS: Early-Onset Schizophrenia; ERP: Event-Related Potential(s); FA: Fractional Anisotropy; fMRI: Functional Magnetic Resonance Imaging; GRM3: Metabotropic Glutamate Receptor 3; ICD-10: International Classification of Disease, 10th edition; L: Left hemisphere (of brain); MEG: Magnetoencephalography; PANSS: Positive and Negative Syndrome Scale; PET: Positron Emission Tomography; PT: Planum Temporale (Temporal Plane); R: Right hemisphere (of brain); SPECT: Single-Photon Emission Computed Tomography; TFA: TimeFrequency Analysis; VLPFC: Ventrolateral Prefrontal Cortex; WM: Working Memory

Introduction Schizophrenia is a fairly recent term for a very old phenomenon. Paranoia, bizarre behaviour, odd beliefs and strange perceptions and feelings have been documented in medical and literary writing since classical antiquity and were variously subsumed under the terms “phrenesis”, “melancholia” or “mania”. Emil

M.S. Ritsner (ed.), The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, © Springer Science + Business Media B.V. 2009

55

56

Kraepelin (1856–1926) is credited with the first definition of what he called “dementia praecox”. He thus described a disorder which presented with psychotic symptoms, such as auditory or tactile hallucinations or delusions of persecution, emotional blunting and stereotyped behaviour and had an unrelenting course toward deterioration, leading to early cognitive decline. In his system, documented in his Textbook of Psychiatry, the contrast was between dementia praecox and manic depressive illness, which he regarded as periodical and lacking of progressive deterioration. However, the syndrome described by Kraepelin did not always lead to rapid cognitive decline and was not confined to adolescence and early adulthood. The term “dementia praecox” therefore did not seem ideal, and Eugen Bleuler (1857–1939), in his book “Dementia Praecox or the Group of Schizophrenias” suggested the term that it still in use today. The term “schizophrenia” – Greek for “split mind” – was used to denote the patients’ failure to integrate their feelings, thoughts, memories and perceptions into a coherent whole, rather than implying a “split personality”, as sometimes assumed by popular culture.1 Bleuler believed that the more dramatic symptoms of schizophrenia that Kraepelin had described were accessory to its core features that included loosening of associations, ambivalence, inappropriate affect and autistic behaviour. Bleuler thus gave schizophrenia a much broader definition and also recognised subclinical forms of “latent” schizophrenia. His ideas can be regarded as precursors of the later concepts of schizotypy and schizotaxia. The first set of clinical symptoms that could be used by clinicians to support or reject a diagnosis of schizophrenia was provided by Kurt Schneider (1887–1967). Schneider classified highly specific symptoms that were of great utility for the differentiation of schizophrenia and other mental disorders as “first-rank symptoms”. These included hearing one’s own thoughts, hearing voices arguing or commenting on one’s actions, or experiencing one’s thoughts as being influenced or taken away by others (“Ichstoerungen”, disturbances of the self or ego). Presence of any of these first-rank symptoms, in the absence of an obvious medical condition, made the diagnosis of schizophrenia very likely. All of these milestones in the development of the definition and understanding of schizophrenia

D. Linden

influenced the systems of operationalised psychiatric diagnosis from the first version of the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association in 1951 to the current DSM-IV of 1994, and likewise the WHO’s International Classification of Diseases (10th edition, 1992). The basic idea of a separation of affective and schizophreniform psychoses goes back to Kraepelin, whereas the use of a set of clinical signs and symptoms to determine presence or absence of a specific disorder is based on the work of Bleuler and Schneider. The first rank symptoms, in particular, play a prominent role in the catalogue of criteria for schizophrenia provided by both diagnostic systems. For example, both DSM-IV and ICD-10 regard hallucination of voices that give a running commentary on the patient’s action or that engage in a dialogue as a sufficient criterion of schizophrenia. ICD-10 also puts special emphasis on Schneider’s disturbances of the ego, such as thought insertion, broadcast or withdrawal. Schizophrenia, like most mental disorders, is thus defined by a combination of symptoms. However, the very concept of schizophrenia has been challenged over several decades. Key arguments were that it did not provide a reliable diagnostic entity and artificially included patients with very different clinical presentations under the same diagnostic heading. Several researchers proposed alternative concepts based on a more dimensional view of psychopathology and bridging the divide between schizophrenia and affective psychosis.1 The proponents of the concepts availed themselves of two main arguments. Antipsychotic drugs worked for schizophrenia regardless of symptoms, although this is less true for the non-paranoid positive symptoms and doubtful for the negative symptoms, and heritability was higher for the syndrome than for individual symptoms, although these differences may be subtle.2 The key difficulty in deciding these clinically very important questions – whether schizophrenia actually represents a disease entity – resides in the absence of sufficiently sensitive and specific biological markers. It would therefore seem to be a rewarding enterprise to determine biological markers of individual symptoms or sub-syndromes of schizophrenia, and ideally investigate their specificity and heritability. These can then be used as vehicles to explore potential biomarkers of the disease itself.

20

Towards a Functional Neuroanatomy of Symptoms and Cognitive Deficits of Schizophrenia

Functional Neuroanatomy of Symptoms Structure and Function Associations between structural changes and brain function in schizophrenia have been most prominently established for the temporal lobes in association with auditory hallucinations. Patients with higher degrees of cortical atrophy also showed more severe and frequent hallucinations.3 Conversely, volume of temporal lobe white matter may be increased in patients with prominent auditory hallucinations.4 Such findings can be the starting point for an analysis of pathological networks, which can be aided by the use of diffusion tensor imaging (DTI), an MRI technique that utilises the restrictions on free movement of water molecules imposed by the myelin sheet of neurons in order to map the integrity of fibre tracts in white matter. The value most often used to characterize the integrity of fibres is fractional anisotropy (FA), which describes the degree to which displacement of water molecules varies in space. FA is decreased in schizophrenia in the frontal and occipital white matter, and in the fibre tracts of the uncinate fasciculus, cingulum and corpus callosum.5,6 These findings are commonly interpreted as evidence for anatomical dis- or hypoconnectivity. However, increased FA (and thus possibly increased connectivity) in the arcuate fasciculus, the fibre bundle linking Broca’s and Wernicke’s areas, was described for schizophrenia patients in the subsample with chronic auditory hallucinations.7 In a recent DTI study from our group on 24 schizophrenia patients with a history of auditory hallucinations and 24 matched control participants, the arcuate fasciculus was again the only area showing increased FA in the patient group. We also investigated the relationship of this potential hyperconnectivity with the severity of symptoms and found that the increased FA values in this region correlated with increased severity of auditory hallucinations.8 Hyperconnectivity between some areas is therefore a possibility in schizophrenia and may contribute to specific symptoms. Such hyperconnectivity may be the result of abnormal migration of nerve cells during the embryonic stage. Alternatively, pruning, the mechanism by which exuberant connections that may form during postnatal brain development are cut back, may not work efficiently. Either way, findings of hyperconnectivity would support a neurodevelopmental

57

model of schizophrenia. However, the DTI measures employed still await neuroanatomical validation, e.g. by post-mortem tracer studies with diffusing dyes. Because the psychopathology of schizophrenia is intricately linked with language functions, perhaps more than any other mental disorder, changes in the dominant hemisphere have long been postulated. In most right-handers, the planum temporale, which comprises Wernicke’s area in the dominant hemisphere, is larger and the Sylvian fissure longer on the left. The majority of imaging and post-mortem studies found that this L>R asymmetry was reduced in schizophrenia.9 Our own imaging study (Oertel et al., unpublished observations) comparing 15 schizophrenia patients, 11 first-degree relatives and 15 matched controls confirmed the reduction of L>R asymmetry in schizophrenia (in fact, patients showed no difference in planum temporale volume between both hemispheres). Interestingly, this reduced lateralisation of the temporal lobes correlated with more severe positive symptoms, for example hallucinations, as measured by the PANSS. This finding parallels that of the increased connectivity along the arcuate fasciculus identified with DTI because here again a structural alteration of the brain, which is probably the result of a neurodevelopmental process, was correlated with the severity of psychotic symptoms. It is particularly remarkable that both changes concern the structure and connectivity of language areas. After all, the arcuate fasciculus connects the planum temporale and thus Wernicke’s area with Broca’s area. The comparison with the relatives confirmed that neurodevelopmental mechanisms (rather than mechanisms triggered by the onset of the disease) are at play in the reduction of PT asymmetry, and that these are genetically influenced. The relatives showed intermediate values both in overall planum temporale volume reduction and in loss of lateralization. PT abnormalities may thus be a biological genetic marker of schizophrenia. This reduction of PT asymmetry may have its functional correlate in the multiple abnormalities of language, which are prominent in the characteristic symptoms of auditory verbal hallucinations, odd speech, neologisms and pervasive in formal thought disorder. Deficits in comprehension have even been suggested to have high predictive value for the conversion to psychosis in the schizophrenia prodrome. However, many patients with schizophrenia have normal language between psychotic episodes, and this might be explained by the

58

ability of the non-dominant hemisphere to take over some language functions if the dominant is impaired, as frequently observed in recovery from strokes affecting the language areas.

Functional Imaging The investigation of brain structure has yielded interesting insights in potential abnormalities of the development of both gray and white matter in schizophrenia. However, biomarkers derived from structural imaging are at best “trait markers” of the disorder and vulnerability for particular symptoms, but not markers of the symptomatic states themselves. For diagnostic purposes, trait markers are actually preferable, because they are invariant to the patients current symptomatic state – after all, the real art will be to diagnose schizophrenia in the absence of obvious symptoms. However, if we want to understand the mechanisms leading to particular symptoms (and the brain areas and possibly neurotransmitter systems involved) we will need to investigate brain activity directly during symptomatic states (“state markers”). With the development of cognitive neuroscience over the past 20 years, a new field of cognitive neuropsychiatry has therefore emerged, which aims to operationalise psychiatric symptoms in experimental paradigms and investigate the underlying brain mechanisms with functional imaging and non-invasive neurophysiology. Again, the mechanisms of AVH have been in the centre of investigation, probably owing to their nature as

D. Linden

a prominent, relatively frequent, and circumscribed symptom of schizophrenia. The most direct evidence of the neural systems that contribute to the symptoms of schizophrenia has come from fMRI. Increased activity correlated with AVH was observed in primary auditory cortex, corresponding to the subjective vividness of the experience (Fig. 20.1). The wider network of AVH comprised language areas in the frontal and temporal lobes and limbic areas, including amygdala and hippocampus.10 On this basis, we tentatively built a neuropsychological model of AVH that suggests generation in speech production areas of the frontal lobe, with material retrieved from long-term memory through the hippocampus and receiving its affective connotation from the amygdala. These self-generated sentences, however, are not uttered but transmitted through the (possibly hyperconnected) arcuate fasciculus to temporal regions, including Heschl’s gyrus, the site of primary auditory cortex. It is presumably at this point that the patient becomes aware of the AVH and attributes it to an external source. This model is still very speculative and needs corroboration from larger patient samples. However, it has already led to partly successful attempts at attenuating hallucinations with transcranial magnetic stimulation over the temporal lobe.11,12 Another key group of symptoms, again related to language (dys) functions, that have been investigated with functional imaging, were those related to thought disorder. For example, incoherent speech was found to be inversely correlated with activity in Wernicke’s area in a preliminary report on six patients with schizophrenia.13 Not all symptoms can be accessed directly by functional imaging, for example, because they do not fluctuate

Fig. 20.1 Brain activity during auditory hallucinations (a) and in response to auditory stimulation (b) in a patient with schizophrenia. Both conditions prominently activate auditory cortex in Heschl’s gyrus, as can be seen on the surface reconstructions of the temporal lobe (From ref.10. With kind permission from Elsevier)

20

Towards a Functional Neuroanatomy of Symptoms and Cognitive Deficits of Schizophrenia

over the course of seconds or minutes, as required for analysis protocols, or because their on- and offset cannot be clearly reported by patients. The preferred method to study the neural correlates of these symptoms, which include delusional beliefs, disturbances of the ego, disorganization, and affective and negative symptoms, has been to obtain measures of their frequency and severity over the lifetime or the current period (e.g., through PANSS scores) and correlate these with structural imaging parameters. In a similar vein, functional imaging trait markers can be defined, using either task-related14 or resting state activation.15 Resting state activation has attracted the particular attention of researchers recently because of its association with the so-called default-mode network (DMN). Activity in the DMN commonly becomes disrupted during cognitive task processing, which may indicate a shift from a resting-state to an active control mode. If impaired self-monitoring capabilities are a feature of schizophrenia, one might assume that deactivation of the DMN is incomplete during cognitive tasks or active mental activity, leading to intrusions of irrelevant information that contribute to perceptual or cognitive symptoms. Although this link has not yet been proven directly, dysfunctional connectivity of the DMN indeed seems to be particularly associated with the positive symptoms of schizophrenia.15

Disease versus Symptom Specificity By their very definition, trait markers are not specific for a particular disease, but rather supposed to indicate the liability to develop the disease. Their presence in genetic high-risk groups, such as those with two or more relatives with schizophrenia,14 is normally a requirement for their acceptance as biological genetic marker. Because of the genetic links between the different mental disorders, for example the overlap in genetic susceptibility to schizophrenia and mood disorders,16 any of these trait markers may indicate susceptibility to more than one disease. Indeed, many of the neurocognitive markers of schizophrenia, for example deficits of memory or social cognition, may also be present in patients with bipolar disorder or their relatives.17 Even the neuroimaging markers of clinical states are not necessarily disease-specific. We cannot exclude that the brain activation patterns during

59

hallucinations in other disorders are similar to those observed in schizophrenia. Indeed, the findings of direct electrical stimulation studies (for presurgical mapping) and psychotic experiences during seizure activity in temporal lobe epilepsy suggest at least partly overlapping networks of auditory hallucination in schizophrenia and epilepsy. We recently investigated brain activity during visual hallucinations in a patient with schizophrenia,18 which allowed us to compare the activation pattern with those reported for non-psychotic visual hallucinations in Charles Bonnet syndrome.19 In both disorders, specialised higher visual areas, corresponding to the content of the hallucinations (e.g., faces) were active during visual hallucinations. The schizophrenia patient showed additional activation in the hippocampus. This conforms to the involvement of limbic areas observed during auditory hallucinations.10 A possible neuropsychological interpretation would be that the content of the hallucinations in schizophrenia is retrieved from long-term memory, whereas in deafferentation syndromes such as Charles-Bonnet it is produced by reverberating activity in the disinhibited higher sensory areas. For further clinical applications of functional imaging of symptom correlates in schizophrenia, we would probably need to identify specific neurotransmitter systems as being affected. For example, both cholinergic and dopaminergic dysfunction has been implicated in visual hallucinations,20 but this has not received direct confirmation from molecular imaging with PET or SPECT. Functional magnetic resonance imaging on its own conveys no specific information about neurotransmitter systems, because it reflects the vasodilatation supposed to occur in response to increased synaptic activity regardless of the transmitter involved. However, combination with pharmacological challenges (pharmaco-fMRI) or genetics may provide some information about transmitter-specific responses (see chapter by Tost and Meyer-Lindenberg in this volume).

Biomarkers of Cognitive Deficits? Early functional imaging studies assessed resting blood flow and glucose metabolism with xenon-133 inhalation, SPECT and PET and found reductions in the frontal lobes, termed “hypofrontality”. Such

60

D. Linden

hypofrontality would be compatible with a model of dopamine deficiency in the mesofrontal system (in contrast with hyperactivity in the mesolimbic system), which could explain some of the cognitive and motivational deficits of schizophrenia. However, in studies of brain activation during the “resting state” the experimenter has very little control over the type of mental activity the participants engage in. Thus, any differences between patients and controls may reflect differences in mental activity during the resting state (e.g., more or less inner speech, imagery, worrying) as much as disease-specific physiological processes.

Common Patterns of Group Differences Functional imaging during perceptual or cognitive tasks where patients perform worse than controls may avoid some of the confounds of resting-state studies and identify dysfunction of neural mechanisms supporting cognition. However, interpretation of these results is rarely straightforward. Individual differences in effort, task performance and motivation may all contribute to brain activation patterns that are not exclusively attributable to the disease in question. If these potential confounds can be minimised, the following major patterns of activation changes in patients are conceivable (Table 20.1): (a) Patients show the same brain activation as controls regardless of poorer task performance (b) Patients activate areas supporting a cognitive task less than controls

(c) Patients activate such areas more than controls (d) Patients activate these areas less in some and more in other conditions (for example, high vs. low working memory load) (e) Patients activate completely different areas from healthy controls (f) Patients show similar levels of activation, but different levels of deactivation, for example less deactivation of the DMN All of these patterns (and often in combination) have been reported in studies with schizophrenia patients (and also in patients with a range of other neuropsychiatric disorders, for example dementia). Pattern b lends itself to the most straightforward (though not necessarily correct) interpretation. Similar to the hypoactivation in resting-state studies, it may indicate that fewer neural resources are available to process the task, for example as a consequence of neurodegeneration. Patterns (a), (c) and (d) can all be explained by models of processing efficiency. Patients may still activate the relevant brain areas to the same degree (a) or even more (c) than controls, but this activation does not result in equal performance. Of course, hyperactivation can also combine with preserved performance, as in several studies in relatives of schizophrenia patients,21 can be tentatively interpreted as indicating successful compensation for less efficient processing. However, until such studies are replicated in larger and more homogenous samples, no firm conclusions on the brain mechanisms of cognitive processing in those at genetic risk for schizophrenia are possible. Functional imaging, and even EEG, only provide us with global quantitative measures of brain activation

Table 20.1 Patterns of activation differences between patient and control groups and their possible interpretation (for details see text) Brain area with suprathreshold activation in controls

Activation in patients

(a) (b) (c)

Yes Yes Yes

Equal Less More

(d) (e) (e) (f)

Yes No No Deactivation

More or less, depending on load Yes Yes Less deactivation

Correlation with behaviour

No/negative

No Yes

Possible interpretation Less efficient processing Less cortical volume Less efficient processing/more noise/ unsuccessful compensation Capacity shift Aberrant activation Compensation Less efficient processing

20

Towards a Functional Neuroanatomy of Symptoms and Cognitive Deficits of Schizophrenia

and cannot differentiate between efficient and inefficient neural circuits. Along similar lines, Weinberger’s group even proposed that higher frontal fMRI signal indicates higher noise rather than higher signal in the neural networks supporting working memory.22 Pattern (d) has been taken to indicate lower cognitive capacity. Patients reach their maximum processing capacity earlier (at lower levels of cognitive load) than controls – resulting in performance levels that are similar to those achieved by controls at higher loads. Such a “left-shift” in load-response functions has been proposed for both schizophrenia23 and dementia.24 Pattern (e) may indicate aberrant activity, if it is deemed to be non-functional, or compensatory mechanisms, if the alternative brain system supports performance. For example, if patients with Alzheimer’s dementia cannot support visuospatial processing with normal levels of parietal activation (pattern (b) ), they may engage in alternative strategies such as configural analysis involving object recognition areas in the occipitotemporal stream.25 Pattern (f) can again be conceptualised as indicating aberrant activity. If suppression of the DMN is important for optimal task performance, failure to deactivate it fully may interfere with task processing or indicate lower levels of concentration on the task at hand.

Recent Directions in Studies of Working Memory Functional imaging during the performance of working memory (WM) tasks has played a prominent role in the quest for neurocognitive biomarkers of schizophrenia because of the crucial role of prefrontal cortex for WM maintenance and manipulation.26 Although early studies did find the frontal hypoactivation predicted on the basis of studies of resting state metabolism, discrepant findings soon cast doubt on the appropriateness of simple models of reduced frontal activity. Manoach23 reconciled these findings by suggesting that they reflected measures taken at different points of the curve relating activation to task difficulty and that in fact hyperactivaiton at lower loads in patients changed to hypoactivation at higher loads that exceeded capacity. Increasing activation of prefrontal cortex supports WM performance at higher loads up to and beyond capacity limits.27, 28 In our visual WM fMRI study, patients with early onset schizophrenia (EOS) did not

61

show this load-dependent increase. Patients also had a weaker DLPFC-parietal functional connectivity, replicating several earlier studies.29 Instead, their activation shifted to a network of dorsal premotor and parietal areas linked to attention.30 This attention network can support WM as well, but less efficiently.26 Reduced WM capacity in schizophrenia thus seems to be a result of the shift from WM to attention-related areas that is forced by the disturbed long-range interactions of prefrontal cortex. Another compensatory pathway seems to be in the ventrolateral prefrontal cortex (VLPFC). Patients with relatively well-preserved WM performance who showed altered activation or functional connectivity of the DLPFC showed increased activity and/or functional connectivity of VLPFC.29 Genetic imaging suggests that reduced DLPFC connectivity and compensation by the VLPFC are particularly associated with abnormalities in dopamine metabolism (variations in the COMT gene) and glutamate signaling (variations in the GRM 3 gene).29

Cognitive and Clinical Symptoms General cognitive deficits are often disabling for the patients concerned and negatively affect their psychosocial rehabilitation. They have also been recognised as a poor prognostic factor and are therefore of importance for research and cognitive intervention in schizophrenia. However, the link with the initial clinical presentation and characteristic symptoms is not always straightforward to establish. Thus, the elucidation of their neural mechanisms may provide reliable diagnostic markers, but not necessarily insight into the mechanisms of the clinically most obvious features of the disorders. This link may be closer for cognitive deficits that are theoretically and/or empirically linked with specific clinical symptoms, such as deficits in social cognition and negative symptoms.31 The neural substrates of these deficits have recently attracted growing attention. Lee et al.32 scanned schizophrenia patients while they were reasoning about empathy and forgiving. Patients were scanned on two occasions, once during an acute episode, and once after recovery. The main change in brain activation was an increase in the left medial prefrontal cortex after recovery. This brain area has been implicated in self-referential and social

62

judgements in a large number of studies. Altered neural mechanisms of social cognition have also been demonstrated for judgments about the social dispositions of others. When patients with paranoid schizophrenia had to rate the trustworthiness of faces, their activation in classical areas involved in emotional processing of faces, including the right amygdala and fusiform face area, was reduced compared to controls.33 Decoding people’s intentions through interpretation of their facial expression is a crucial component of social cognition. Less reliable decoding mechanisms may contribute to misinterpretations of the intentions of others, leading to suspiciousness or even delusions of reference and paranoia. There is some indication that patients with schizophrenia differ in their assessment of the emotionality of faces from the healthy population. However, they still seem to be able to utilise emotional cues. We recently demonstrated a benefit for angry faces in WM in healthy individuals.34 Preliminary evidence from our lab suggest that patients with schizophrenia show the same benefit, regardless of overall poorer WM performance (S. Linden, unpublished observations). Such dissociations between preserved processing of emotional stimuli and impaired cognition may open up avenues for training programmes that are aimed at improving social cognition in schizophrenia. Another example of a cognitive paradigm that may bear directly on characteristic symptoms of schizophrenia is semantic priming, which may detect the “loosening of associations” promoted by Bleuler as a core feature of psychotic thought disorder. In a recent fMRI study, patients showed different patterns of prefrontal activation to control participants. Although controls showed the expected suppression of prefrontal activity after repetition of related items, patients showed the opposite effect.35 However, these results are difficult to interpret because the two groups did not differ in their behavioural response.

Cognitive Neurophysiology of Schizophrenia The non-invasive electrophysiological techniques (EEG, MEG) have the advantage compared to fMRI of real time resolution, but at the cost of much lower spatial resolution. Several potential biological mark-

D. Linden

ers of schizophrenia have been identified using the event-related potentials (ERP) of the EEG (see chapter by van der Stelt and Belger in this volume). The classical view was that only late components of the ERP like the P3 are abnormal in schizophrenia while early visual processing is unimpaired. This has recently been challenged.36 A growing body of evidence from patients with schizophrenia indicates abnormalities of both early visual P1 and the early auditory P50 wave. However, these abnormalities appear to be task-dependent. Although standard checkerboard stimulation does not normally result in reduced evoked potentials in schizophrenia, stimulation biased for the magnocellular pathway revealed impaired P1 generation, which correlated with reduced fibre integrity of visual pathways.37 Our study with a visual working memory paradigm yielded a severely impaired P138 (Fig. 20.2). This highlighted possible links with abnormalities in thalamocortical circuits, particularly in the magnocellular system, discussed in the anatomical literature and opened up a new avenue of investigation, focusing on the contribution of perceptual systems to cognitive deficits. Compared to the long tradition of ERP studies of schizophrenia and other neuropsychiatric disorders, the investigation of the time frequency patterns of brain activity obtained from EEG data is a much more recent and smaller, but attractive and growing field. The analysis of ERP only captures a very small part of the information contained in EEG data, which is highly time-locked to the stimulus across trials and exhausts itself in a single cortical potential change. These components are very good signatures of the initial phases of sensory processing and of some clearly defined cogntive processes (e.g., the P300 for target detection, the N400 for the detection of semantic incongruencies), but cannot capture the complexity of the neural code. Conversely, time frequency analysis (TFA), dividing the EEG activity into several frequency bands and assessing the changes of relative power over time, permits the investigation of the patterns of oscillatory activity that provide potentially rich and versatile means of information storage and processing. Deficits in oscillatory activity were identified particularly in higher frequency bands (the beta band, 15–30 Hz, and the gamma band, > 30 Hz), for example during tasks of gestalt perception, such as Kanizsa

20

Towards a Functional Neuroanatomy of Symptoms and Cognitive Deficits of Schizophrenia

63

Fig. 20.2 ERPs during the encoding phase of a WM task. The ERP responses following the first sample stimulus for WM load 1 (black line), the second stimulus for load 2 (green line) and the third stimulus for load 3 (red line) are shown at the central frontal electrode (Fz), the central occipital electrode (Oz), the vertex

electrode (Cz) and the central parietal electrode (Pz) for controls (top row) and patients with schizophrenia (bottom row). Data at Oz clearly show the attenuation of the P1 component in the patient group (From ref.38. With kind permission from the American Medical Association)

objects, where the edges of a geometric object have to be extrapolated from the corners, and Mooney faces, where contours of faces have to be segregated from unstructured patterns.39, 40 Interestingly, Haenschel et al.41 found load-response functions similar to the pattern “d” described above for functional imaging studies (Table 20.1) in their time frequency data. During the maintenance phase of their WM paradigm, induced gamma activity peaked at load 2 for patients with early-onset schizophrenia, but continued to increase towards load 3 for control participants. These results may indicate that the cortical storage system reached its capacity limit at lower loads in the patient group. Deficits in maintaining oscillatory activity in specific frequency bands could thus result in the information overload that may underlie cognitive deficits and symptoms of schizophrenia. These findings are of interest in the context of the putative role of synchronised gamma activity for cognitive and perceptual integration, and communication between neuronal assemblies in general.42 Synchronised high frequency activity crucially depends on intact inhibitory interneuron circuits. If these are dysfunctional in schizophrenia, as suggested by some of the post-mortem histochemical studies, a resulting failure to integrate neuronal activity temporally within and across areas may result in some of the cognitive deficit and even psychotic symptoms.43

Conclusions and Future Directions Probing the neurobiological substrate of putative biomarkers by multi-method approaches The large bodies of structural imaging, functional imaging and electrophysiological literature have long co-existed without much mutual interaction and cross-fertilisation. In this respect, cognitive neuropsychiatry has lagged behind other areas of cognitive neuroscience where, for example, the experimental integration of fMRI and EEG has become a standard procedure.44 The interpretation of differences between patients and controls on measures of neuroimaging or neurophysiology would often benefit from the availability of data from another modality. For example, if functional activation is lower in patients than controls, the interpretation will be very different depending on whether a similar structural loss can be observed. Similarly, if an EEG study reveals lower amplitude of a particular ERP component, the combination with TFA and fMRI can resolve whether this is an effect of larger jitter in stimulus-response latencies, or whether neural responses are overall lower. Although no study so far has fully explored the potential of combining different imaging and neurophysiology methods, we have attempted to adduce information from additional modalities where needed. For example,

64

in our study of temporal lobe abnormalities in schizophrenia patients and their relatives we found parallel reductions on the functional and structural measures, indicating volume loss as the primary deficit (Oertel et al., unpublished observations). Another example of a combination of methods that may reveal the neural genesis of a biomarker is the correlation of fibre integrity deficits, as identified with DTI, with reductions in P1 amplitude.37 Investigating Disease Specificity One major shortcoming of the extant neuroimaging and neurophysiology literature on schizophrenia is that only very few studies explored the disease specificity of the identified biological markers. The most effort to this effect has so far been made in the field of structural imaging where, for example, the specificity of progressive volume loss in the superior temporal gyrus has been shown by comparison with first-episode patients suffering from affective disorder.45 However, studies with other diagnostic control groups, ideally matched for illness duration, medication and other potentially confounding factors, are difficult to conduct and have unfortunately remained rare exceptions. Most studies so far have confined themselves to proposing neural correlates of schizophrenia or a particular feature of the disorder. Yet if we ever want to utilise these measures for purposes of differential diagnosis, disease classification or treatment monitoring, we first need to investigate their sensitivity and specificity not only in discriminating patients from healthy controls but also in differentiating patients with schizophrenia from those with other mental disorders. In light of the considerable overlap in the genetic and neurocognitive risk factors of schizophrenia and bipolar disorder, it is presently open whether such a programme will be successful, but it will doubtless be a worthwhile and clinically much needed enterprise. Clinical Relevance Biomarkers that can identify mental disorders, for example schizophrenia, with high sensitivity and specificity could make a major contribution to the reliability of psychiatric diagnosis. However, they would not necessarily inform us about the neural mechanisms leading to the clinically relevant deficits. To this end, we would ideally identify intermediate phenotypes that can be linked to cognitive models of

D. Linden

symptom generation. Semantic priming in patients with thought disorder, social cognition in patients with negative symptoms, and avoidance behaviour in patients with paranoia are linked with specific symptoms both on theoretical and empirical grounds. Finding the neural substrates for these particular cognitive phenotypes (which do not necessarily constitute classical “deficits”) might provide an avenue to the neural mechanisms contributing to the generation of the actual symptoms as well. Furthermore, only those biomarkers that are closely associated with clinically relevant alterations of perception and cognition are likely to show treatment-dependent changes. This would open up an important role for biomarkers in the evaluation of the effects of pharmacological and non-pharmacological treatment in humans, in a way that is currently only possible in animal models. Furthermore, these deficits in cognitive domains that are directly relevant to clinical symptoms of schizophrenia, may be worthwhile targets for specific cognitive interventions in the service of improved psychosocial rehabilitation. Acknowledgments The author’s research was supported by grants from the Wellcome Trust (grant number 077185/Z/05/Z) and the Stanley Medical Research Institute.

References 1. Bentall RP. Madness Explained: Psychosis and Human Nature. London: Penguin Books. 2003. 2. Cardno A, Rijsdijk F, Sham P, Murray R, McGuffin P. A twin study of genetic relationships between psychotic symptoms. Am J Psychiatry. 2002;159(4):539–545. 3. O’Daly O, Frangou S, Chitnis X, Shergill S. Brain structural changes in schizophrenia patients with persistent hallucinations. Psychiatry Res. 2007;156(1):15–21. 4. Shapleske J et al. A computational morphometric MRI study of schizophrenia: effects of hallucinations. Cereb Cortex. 2002;12:1331–1341. 5. Kubicki M, Park H, Westin C, et al. DTI and MTR abnormalities in schizophrenia: analysis of white matter integrity. Neuroimage. 2005;26(4):1109–1118. 6. Rotarska-Jagiela A, Schönmeyer R, Oertel V, Haenschel C, Vogeley K, Linden D. The corpus callosum in schizophreniavolume and connectivity changes affect specific regions. Neuroimage. 2008;39(4):1522–1532. 7. Hubl D, Koenig T, Strik W, Federspiel A, Kreis R, Boesch C, Maier SE, Schroth G, Lovblad K, Dierks T. Pathways that make voices: white matter changes in auditory hallucinations. Arch. Gen. Psychiatry 2004;61:658–668. 8. Rotarska-Jagiela A, van de Ven VG, Oertel V, Haenschel C, Maurer K, Linden DEJ. Disturbed anatomical and func-

20

9. 10.

11.

12.

13.

14.

15.

16. 17.

18.

19.

20. 21.

22.

23.

24.

25.

Towards a Functional Neuroanatomy of Symptoms and Cognitive Deficits of Schizophrenia tional connectivity in schizophrenia: a combined DTI and fMRI study. Human Brain Mapping conference abs. 2006. Crow TJ. Schizophrenia as failure of hemispheric dominance for language, Trends Neurosci. 1997;20:339–343. Dierks T, Linden D, Jandl M, et al. Activation of Heschl’s gyrus during auditory hallucinations. Neuron. 1999;22(3): 615–621. Lee S, Kim W, Chung Y, et al. A double blind study showing that two weeks of daily repetitive TMS over the left or right temporoparietal cortex reduces symptoms in patients with schizophrenia who are having treatment-refractory auditory hallucinations. Neurosci Lett. 2005;376(3):177–181. Jandl M, Steyer J, Weber M, et al. Treating auditory hallucinations by transcranial magnetic stimulation: a randomized controlled cross-over trial. Neuropsychobiology. 2006;53(2):63–69. Kircher T, Liddle P, Brammer M, Williams S, Murray R, McGuire P. Neural correlates of formal thought disorder in schizophrenia: preliminary findings from a functional magnetic resonance imaging study. Arch Gen Psychiatry. 2001;58(8):769–774. Whalley H, Gountouna V, Hall J, et al. Correlations between fMRI activation and individual psychotic symptoms in unmedicated subjects at high genetic risk of schizophrenia. BMC Psychiatry. 2007;7:61. Rotarska-Jagiela A, van de Ven VG, Oertel V, Haenschel C, Linden DEJ. Altered functional connectivity in schizophrenia patients compared to controls examined with self-organizing group independent component analysis. Human Brain Mapping conference abs. 2007. Craddock N, Forty L. Genetics of affective (mood) disorders. Eur J Hum Genet. 2006;14(6):660–668. Hill S, Harris M, Herbener E, Pavuluri M, Sweeney J. Neurocognitive allied phenotypes for schizophrenia and bipolar disorder. Schizophr Bull. 2008;34(4):743–759. Oertel V, Rotarska-Jagiela A, van de Ven V, Haenschel C, Maurer K, Linden D. Visual hallucinations in schizophrenia investigated with functional magnetic resonance imaging. Psychiatry Res. 2007;156(3):269–273. Ffytche D, Howard R, Brammer M, David A, Woodruff P, Williams S. The anatomy of conscious vision: an fMRI study of visual hallucinations. Nat Neurosci. 1998;1(8): 738–742. Ffytche D. Visual hallucinations and the Charles Bonnet syndrome. Curr Psychiatry Rep. 2005;7(3):168–179. Macdonald AW 3rd, Thermenos H, Barch D, Seidman L. Imaging Genetic Liability to Schizophrenia: Systematic Review of fMRI Studies of Patients’ Nonpsychotic Relatives. Schizophr Bull. Jun 2008. Egan M, Goldberg T, Kolachana B, et al. Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci USA 2001;98(12): 6917–6922. Manoach D. Prefrontal cortex dysfunction during working memory performance in schizophrenia: reconciling discrepant findings. Schizophr Res. 2003;60(2–3):285–298. Prvulovic D, Van de Ven V, Sack A, Maurer K, Linden D. Functional activation imaging in aging and dementia. Psychiatry Res. 2005;140(2):97–113. Prvulovic D, Hubl D, Sack A, et al. Functional imaging of visuospatial processing in Alzheimer’s disease. Neuroimage. 2002;17(3):1403–1414.

65

26. Linden D. The working memory networks of the human brain. Neuroscientist. 2007;13(3):257–267. 27. Linden D, Bittner R, Muckli L, et al. Cortical capacity constraints for visual working memory: dissociation of fMRI load effects in a fronto-parietal network. Neuroimage. 2003;20(3):1518–1530. 28. Mayer J, Bittner R, Nikolic´ D, Bledowski C, Goebel R, Linden D. Common neural substrates for visual working memory and attention. Neuroimage. 2007;36(2):441–453. 29. Tan H-Y, Callicott JH, Weinberger DR. Dysfunctional and compensatory prefrontal cortical systems, genes and the pathogenesis of schizophrenia. Cer Cortex. 2007;17: i171–i181. 30. Bittner R; Disturbed Functional Connectivity During Working MemoryEncoding, Maintenance, and retrieval in adolescents with early-onset schizophrenia - an event-related functional magnetic resonance imaging study. Schizophrenia Res. 2008;102(1–3): Supplement 2 31. Ochsner K. The social-emotional processing stream: five core constructs and their translational potential for schizophrenia and beyond. Biol Psychiatry. 2008;64(1):48–61. 32. Lee K, Brown W, Egleston P, et al. A functional magnetic resonance imaging study of social cognition in schizophrenia during an acute episode and after recovery. Am J Psychiatry. 2006;163(11):1926–1933. 33. Pinkham A, Hopfinger J, Pelphrey K, Piven J, Penn D. Neural bases for impaired social cognition in schizophrenia and autism spectrum disorders. Schizophr Res. 2008;99(1–3): 164–175. 34. Jackson MC, Wu C-Y, Linden DEJ, Raymond JE. Enhanced visual short-term memory for angry faces. J Experiment Psychol - Human Percept Perform, in press. 35. Kuperberg G, Deckersbach T, Holt D, Goff D, West W. Increased temporal and prefrontal activity in response to semantic associations in schizophrenia. Arch Gen Psychiatry. 2007;64(2):138–151. 36. Butler PD, Javitt DC. Early-stage visual processing deficits in schizophrenia. Curr Opin Psychiatry. 2005;18: 151–157. 37. Butler PD, Zemon V, Schechter I, Saperstein AM, Hoptman MJ, Lim KO, Revheim N, Silipo G, Javitt DC. Early-stage visual processing and cortical amplification deficits in schizophrenia. Arch Gen Psychiatry. 2005 May;62(5): 495–504. 38. Haenschel C, Bittner R, Haertling F, et al. Contribution of impaired early-stage visual processing to working memory dysfunction in adolescents with schizophrenia: a study with event-related potentials and functional magnetic resonance imaging. Arch Gen Psychiatry. 2007;64(11): 1229–1240. 39. Spencer KM, Nestor PG, Niznikiewicz MA, Salisbury DF, Shenton ME, McCarley RW. Abnormal neural synchrony in schizophrenia. J Neurosci 2003;23:7407–7411. 40. Uhlhaas P, Linden D, Singer W, et al. Dysfunctional long-range coordination of neural activity during Gestalt perception in schizophrenia. J Neurosci. 2006;26(31): 8168–8175. 41. Haenschel C, Bittner RA, Haertling F, Rotarska-Jagiela A, Maurer K, Singer W, Linden DEJ, Rodriguez E. Disorders of work memory processes and oscillation for young patients with schizophrenia. Nervenarzt. 2007;78 (Suppl 2):90.

66 42. Uhlhaas P, Haenschel C, Nikolic D, Singer W. The role of oscillations and synchrony in cortical networks and their putative relevance for the pathophysiology of schizophrenia. Schizophr Bull. Jun 2008;34:927–943. 43. Phillips WA, Silverstein SM. Convergence of biological and psychological perspectives on cognitive coordination in schizophrenia. Behav Brain Sci 2003;26:65–138.

D. Linden 44. Linden D. What, when, where in the brain? Exploring mental chronometry with brain imaging and electrophysiology. Rev Neurosci. 2007;18(2):159–171. 45. Kasai K, Shenton M, Salisbury D, et al. Progressive decrease of left superior temporal gyrus gray matter volume in patients with first-episode schizophrenia. Am J Psychiatry. 2003;160(1):156–164.

Chapter 21

Functional and Structural Endophenotypes in Schizophrenia Stephan Bender, Matthias Weisbrod, and Franz Resch

Abstract Various studies show, that a longer duration of untreated psychosis relates to a worse prognosis. Thus the early recognition of schizophrenia seems a crucial challenge. However, many clinical symptoms of prodromal schizophrenia stages are not sufficiently specific. Here we present an overview over recent contributions of neuroimaging and electrophysiological as well as genetic studies: Which additional information offer endophenotypes (such as P300, P50 sensory gating, MMN, smooth pursuit eye movements; indicating a specific genetic vulnerability) together with a better understanding of schizophrenic pathophysiology (state-dependent biological markers, e.g. aggravated motor neurological soft signs during psychosis) in prodromal schizophrenia when still ambiguous clinical symptoms are present. Examples (e.g. from COMT polymorphisms to working memory deficits) are given to illustrate more specific underlying neuronal mechaS. Bender Department for Child and Adolescent Psychiatry, Centre for Psychosocial Medicine, University of Heidelberg, Blumenstraße 8, D-69115 Heidelberg; Section for Experimental Psychopathology, Psychiatric Hospital, Centre for Psychosocial Medicine, University of Heidelberg, Voßstraße 4, D-69115 Heidelberg; Psychosomatic Hospital, Centre for Psychosocial Medicine, University of Heidelberg, Im Neuenheimer Feld 421, D-69120 Heidelberg M. Weisbrod Section for Experimental Psychopathology, Psychiatric Hospital, Centre for Psychosocial Medicine, University of Heidelberg, Voßstraße 4, D-69115 Heidelberg; SRH Klinikum Karlsbad-Langensteinbach, Psychiatric Hospital, Guttmannstraße 1, D-76307 Karlsbad, Germany F. Resch Department for Child and Adolescent Psychiatry, Centre for Psychosocial Medicine, University of Heidelberg, Blumenstraße 8, D-69115 Heidelberg

nisms behind behavioural symptoms. This way, a characteristic pattern of disturbed cerebral maturation might be distinguished in order to complement clinical instruments of early schizophrenia detection. While today, the specificity and sensitivity of the aforementioned markers does still not allow diagnosing all the heterogeneous forms of the schizophrenic syndrome, it seems a promising approach to define specific highrisk constellations for subgroups of patients to allow timely early interventions when they are justified. The main focus of this chapter will be to connect clinical symptoms with genetic findings via endophenotypes and to give an impression how clinical early recognition attempts could be complemented by functional and structural endophenotypes that could serve as diagnostic markers. State markers and endophenotypes as a specific kind of trait marker are compared, possibilities and limitations with respect to sensitivity and specificity are discussed. Keywords Schizophrenia • early recognition • endophenotype • biological marker • review • P50, P300 • mismatch negativity • eye movements • movementrelated potentials Abbreviations BDNF: Brain derived neurotrophic factor; BLIP: Brief limited intermittent psychotic symptoms; BSABS: Bonn scale for the assessment of basic symptoms; CAARMS: Comprehensive Assessment of At-Risk Mental States; COMT: Catechol-O-Methyltransferase; CT: Computer tomogram; DAOA: D-amino-acid oxidase activator; DISC: Disrupted in schizophrenia; DNA: Deoxyribonucleic acid; DSM IV: Diagnostic and Statistical Manual of Mental Disorders, 4th edition; DTI: Diffusion tensor imaging; DUP: Duration of untreated psychosis; EEG: Electroencephalogram; fMRI: Functional magnetic

M.S. Ritsner (ed.), The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, © Springer Science + Business Media B.V. 2009

67

68

resonance imaging; GABA: Gamma aminobutyric acid; GAD: Glutamic acid decarboxylase; GAF: Global Assessment of Functioning; GRM3: Metabotropic glutamate receptor 3; Hz: Hertz; ICD-10: International Classification of Diseases (World Health Organization); IRAOS: Instrument for the Assessment of onset and early course of Schizophrenia; Met: Methionine; MMN: Mismatch negativity; MRI: Magnetic resonance imaging; ms: Milliseconds; NSS: Neurological soft signs; PPI: Pre-pulse inhibition; RNA: Ribonucleid acid; SIPS: Structured Interview for Prodromal Syndromes; TMS: Transcranial magnetic stimulation; UHR: Ultra-high-risk; Val: Valine.

Introduction Early Schizophrenia Recognition The early detection and intervention in schizophrenic patients is one of the great challenges which psychiatry faces in these days. The longer it takes until the patient is treated after the first manifestation of the illness (duration of untreated psychosis; DUP), the prognosis deteriorates according to the majority of studies.1 There seems to be at least a moderate association, even taking into account that recent results in a large sample raise new doubts again.2 Different approaches of leading international research groups have created various clinical instruments in order to describe and detect prodromal symptoms and early psychotic symptoms as sensitively and as specifically as possible: 1. A symptom cluster of the “Bonner Skala für die Beurteilung von Basissymptomen (BSABS, Bonn scale for the assessment of basic symptoms)” which refers to formal thought disorder as well as speech, perception and movement deficits, was able to differentiate especially well between at-risk-subjects (clinical population), who actually developed a psychotic episode within the 10-years of examination, and who did not.3 2. CAARMS (Comprehensive Assessment of At-Risk Mental States),4 examines perception disturbances, formal thought disorder and disorganization, abnormal thought content, motor difficulties, concentration

S. Bender et al.

and attention, emotion and affectiveness, energy and stress tolerance. It also assesses the severity, duration and frequency of these prodromal symptoms. 3. SIPS (Structured Interview for Prodromal Syndromes)5; also takes a positive family history of psychiatric illnesses into a account. 4. IRAOS (Instrument for the Assessment of onset and early course of schizophrenia)6; recently was further developed to the early recognition form ERIraos.7 It now integrates the most suitable items from the BSABS approach mentioned above. 5. Taking into account also individual longitudinal (developmental) data and (attenuated) positive symptoms, an UHR (ultra-high-risk) group8 refers to patients with  A positive family history, unspecific psychiatric symptoms and an impaired overall adaption (GAF-Score, Global Assessment of Functioning, fifth axis, DSM IV) which has deteriorated by at least 30% within the last 12 months.  Mild positive symptoms for at least 1 week.  BLIPS (brief limited intermittent psychotic symptoms) as self-limiting short psychotic episodes lasting shorter than 1 week. Patients fulfilling at least one of these criteria had a probability of 40% to develop a manifest psychosis within 1 year.8 This list is not meant to be exhaustive. But it gives a good impression about the most important assessed clinical symptoms for the early detection of schizophrenia. It illustrates that there is some converging evidence, however, a wide variety of instruments and unspecific symptoms remains. Altogether, mild positive psychotic symptoms, the genetic risk (positive family history), the global functioning as well as (as specific as possible) cognitive and motor symptoms are used to infer the individual risk of the patient. The separation of an early initial prodrome state (EIPS) and a late initial prodromal state (LIPS)9 seems useful: The latter comprises mild psychotic symptoms as well as BLIPS (brief limited intermittent psychotic symptoms) and could justify a pharmacological intervention with atypical neuroleptics.7 In contrast, the more unspecific EIPS would indicate a prophylactic psychosocial/psychoeducational/psychotherapeutic approach with medication playing a secondary role and being only employed according to prevailing target symptoms.

21

Functional and Structural Endophenotypes in Schizophrenia

Also during childhood and adolescence prodromal basic symptoms seem to be only of limited specificity.10 However, they appeared significantly more often in patients which developed schizophrenia later on than in patients with other child and adolescent psychiatric diagnoses. The same is true for adult subjects.11 First intents of a neuroleptic and/or psychotherapeutic treatment of late schizophrenic prodromal stages have yielded preliminary promising results,8,12 reporting a better control over emerging symptoms. However, critical voices prefer a strictly symptom-oriented treatment13: The diagnostic instruments which are available today still represent a dissatisfying compromise between the necessary specificity of the assessed symptoms (according to the low prevalence of schizophrenia an extremely high specificity is necessary in order to avoid too many false positive results causing unnecessary medication side effects and psychological stress) and the detection of early prodromal (non-psychotic) stages of the disease (the earlier, the more unspecific are the clinical symptoms).14 Thus the early detection of schizophrenia was so far often limited to the (also very important) immediate treatment of manifest acute schizophreniform psychosis. In contrast, patients with predominant negative symptoms (deficit schizophrenia)15 are still difficult to detect.

Towards a Complementation of Psychopathological Assessment by Biological Markers In other non-psychiatric medical disciplines, unspecific symptoms, such as chest pain, are also well known, however, specific chemical, radiological and/ or electrophysiological examinations often allow to assign unspecific symptoms to their specific pathophysiological origin. Chest pain can be further assessed e.g. by electrocardiography (myocardial infarction) or spiral CT (pulmonary embolism). According to the results of these biological markers a causal treatment becomes possible. In psychiatry, for example one could ask, whether a lack of drive in a major depressive episode results from a different neural mechanism than the lack of drive which goes along with negative symptoms of schizophrenia? With a little luck our methods could be suitable to differentiate these different neural mechanisms, e.g. in

69

a very simplified case a correlate of dopaminergic neurotransmission such as smooth eye tracking movements16 from a correlate of serotonergic neurotransmission such as the stimulus-intensity slope of auditory evoked potentials.17 Such differences could indicate the most appropriate causal pharmacological treatment. The most important problems to this approach are the establishment of suitable cut-offs (where does normal interindividual variability end and where does pathology start – this variability might be considerably higher in the plastic central nervous system than in the heart or lung) and that sometimes endophenotypes might simply illustrate the common direct cause of the behavioural symptom such as e.g. a (pre-) frontal hypoactivation which might occur in any kind of lack of drive. The future will show, whether there are diseasespecific neuronal mechanisms for neuropsychological symptoms which can be detected by neuroimaging. Or whether we can only visualize the common direct causes for behavioural symptoms (the “new phrenology”) while the specific mechanisms remain hidden in more cellular or molecular levels. An obstacle remains the use of an artificial categorical classification system as basis for psychiatric research, because the diagnosis “schizophrenia” will contain various underlying genotypes. For this reason a more symptom-oriented approach18 and the exact psychopathological characterization of patients seem to be of a decisive value. Genetics rarely behaves according to ICD-10 or DSM-IV.19 Moreover, different genetic contributions have been suggested for different forms of schizophrenia.20

Endophenotypes Versus State-Dependent Biological Markers Prodromal stages of schizophrenia are conceived as a result of the interaction between genetic vulnerability, biological influences and environmental stress. Environmental risk factors have been found to be rather unspecific so far.21 As diagnostic markers, state-dependent measures could be especially suitable when they reflect the accumulation of environmental stressors or the interaction between environmental stressors and a genetic disposition.

70

Endophenotypes One approach refers to endophenotypes, which reflect genetic vulnerability in physiological parameters. An important characteristic of endophenotypes as markers for a specific early detection of subjects at risk for schizophrenia is that as trait markers they are present before the onset of the manifest disease symptoms.22 A combination of endophenotype (increased genetic risk) and statedependent markers (psychosis imminent?) could be most appropriate for diagnostic purposes (or parameters which directly reflect the interaction between both factors). Combinations of different endophenotypes have been shown to provide better predictive values than single parameters.23 Diagnostically relevant endophenotypes require a certain degree of specificity of the respective vulnerability for a symptom or diagnosis. First results for P30024, 25 and smooth pursuit eye movements26–32 show despite some inconsistencies, that at least some endophenotypes of schizophrenia could be more specific than environmental risk factors (indicating a specific genetic vulnerability which leads to decompensation with rather unspecific environmental stressors).

Biological Markers In contrast to endophenotypes, which denominate only genetically determined characteristics, biological markers refer to any kind of diagnostically valuable parameter regardless of whether it is genetically and/or environmentally determined. Biological markers can be state or trait markers, depending on whether they are present only during the acutely ill state or whether they are always present (traits). Environmentally determined trait markers can complement endophenotypes as indicators of vulnerability. State markers could give important hints about whether the manifestation of symptoms (acute psychosis) is imminent, if they reflect pathogenetic processes which are active already during early or late prodromal states. Neurophysiological markers have been suggested to lead to improvements in the early detection of schizophrenia.33, 34 So far, we lack reliable parameters directly reflecting gene-environment interactions and imminent psychosis (which in the long run could be needed to reach a very high positive predictive value for the transition to a psychotic episode).

S. Bender et al.

At present, the most promising approach might be to assess the clinical symptoms and – if their predictive value alone is not sufficient and the patient wishes a further clarification and therapy of his problems – to combine the clinical examination with an estimation of the specific genetic vulnerability for schizophrenia (and other mental disorders) which carries the at-risk subject. And as long as we do not know sufficient details about all the involved genetic factors, the estimation of the genetic risk might be better accomplished by the assessment of an intermediate level – endophenotypes – rather than by direct genetic analysis. The inclusion of endophenotypes (genetic risk) and other biological markers (especially state-dependent, as soon as available) might represent an important next step for the early-recognition movement in schizophrenia research.

From Genes Over Endophenotypes to Neuropsychology and Psychopathology In the following, some of the most important recent genetic advances will be presented35, 36 as well as the associated endophenotypes. We try to give an impression of future possibilities with respect to the development of specific tests for the early detection of prodromal stages of schizophrenia on the basis of the underlying genetic vulnerability, age-dependent maturation of gene expression and interactions with environmental and biological factors. Often the exact links between possible neurophysiological or – psychological diagnostic markers and the respective candidate genes are only rudimentally understood, sometimes the function of the encoded proteins remains unknown. This is even more true for the developmental course of gene expression through childhood and adolescence. Many of the implicated candidate genes however are directly or indirectly linked to glutamatergic, GABAergic or dopaminergic neurotransmission or neuroprotection. There are several levels of analysis ranging from the genes and RNA over proteins (e.g. enzymatic activity) and neurotransmitters, brain activation as measured by fMRI (functional magnetic resonance imaging), PET (positron emission tomography) or EEG (e.g. evoked/event-related potentials),

21

Functional and Structural Endophenotypes in Schizophrenia

neuropsychological functions such as working memory to clinical symptoms. The most elaborated models today exist for the COMT (Catechol-O-Methyltransferase) and Dysbindin genes. We will first give an overview over different important structural and functional endophenotypes and markers for schizophrenia. We will then start from the genes relevant for a certain neurotransmitter or relevant for the volume of a certain brain region and give the known links to schizophrenic endophenotypes/symptoms.

Neurophysiological Endophenotypes and Markers

71

Inhibition Cortical inhibition can be assessed by transcranial magnetic stimulation (TMS), e.g. by the length of the cortical silent period, i.e. the duration of the disappearance of muscle activity (electromyogram) during isometric contraction after a single TMS pulse. Short term intracortical inhibition is assessed by paired pulse paradigms when a conditioning subthreshold TMS pulse affects the electromyographic response (motor evoked potential) of a second suprathreshold stimulus. A novel way to assess cortical excitability and inhibition might be the EEG-response to TMS. An example for an indirect measure of cortical inhibition is the NoGo-P300 (see below).

General Sensory Grey Matter Reduction Pre-pulse-inhibition (PPI) Morphological magnetic resonance imaging (MRI) indicates a longitudinal developmental reduction in grey matter throughout childhood and adolescence which is increased in schizophrenia.

The peripheric startle response (including an eye-blink reflex and alterations in skin conductance, heart rate and respiration) is reduced by a preceding warning stimulus.

Reduced Anisotropy (Myelination Deficits) P50 Gating By diffusion tensor imaging (DTI) the amount of anisotropy and thus myelinated fibres can be assessed. Dysconnectivity – EEG coherence, gamma-band responses, latency variability: EEG coherence indicates the similarity of the EEG between two sites on the scalp in a certain frequency band and is a good measure of how far the underlying cortical regions cooperate with each other. New approaches assess event-related changes in coherence. Especially gamma band, i.e. frequencies about 40 Hz and above, coherence is thought to reflect functional coupling of different brain areas in order to form gestalt-like representations. While alpha-frequencies indicate idling cortical occipital or sensorimotor cortex, gamma band activity represents neuronal activation. Deficits in gamma-band coupling have been described in schizophrenic patients. A larger variability of reaction time37 or various neurophysiological parameters38 could indicate a deficient, less reliable neurotransmission.

The early cortical response to auditory stimulation at a latency of about 50 ms (P50) is inhibited for the second of two paired clicks which occur shortly after each other. This sensory gating is thought to occur because the first stimulus already contains all relevant information and is reduced in schizophrenic patients, at-risk subjects and relatives of schizophrenic patients.

P300 P300 is an event-related potential which is evoked when the subject detects behaviour-relevant stimuli. Usually the oddball-paradigm is applied, where a target stimulus (20% probability) which requires a response (button press, counting) has to be distinguished from a distractor (80% probability). P300 is a large widespread positivity with a latency of about 300 ms (P300) and has been related to higher order

72

stimulus processing and context updating/memory encoding. Multiple generators of a large network have been identified, among them important sources in the temporal and parietal lobe. There are specific types of P300, such as NoGo P300, when the subject is required to suppress a response (which is performed in 80% after the distractor) when the target stimulus occurs. NoGo P300 is located more anterior on the scalp with respect to Go P300 and is thought to involve frontal/ anterior cingulate sources (response inhibition).

Mismatch Negativity (MMN) MMN is an event-related potential, which appears when sudden changes in an otherwise uniform auditory stimulation are automatically detected. Generators are supposed to be located in frontal and temporal cortex. MMN is reduced in schizophrenic patients. Other: Different kinds of sensory evoked potentials or potentials reflecting gestalt-like object recognition based on degraded stimuli39 have been found to be reduced in schizophrenia.

(Pre-) Frontal/Motor Hypofrontality Reduced prefrontal activation during cognitive tasks in functional magnetic resonance imaging (fMRI) was found in schizophrenic patients. Low levels of N-acetyl-aspartate, a correlate of glutamatergic neurotransmission, were found in the prefrontal cortex by magnetic resonance spectroscopy. Bereitschaftspotential and Movement-Related Potentials In self-paced freely selected movements a negative potential (readiness- or Bereitschaftspotential) preceding the start of muscle activity develops over the supplementary motor area. It later becomes lateralized contralateral to the movement side and extends over the premotor and primary motor cortices (negative slope and motor cortex potential). The amplitude of the Bereitschaftspotential is reduced in schizophrenics as a correlate of impaired decision making. There

S. Bender et al.

might also be differences in the lateralisation of motor cortex activation in later stages.

Smooth Pursuit Eye Tracking Subjects follow a slowly moving visual stimulus with their eyes (no head movements). Parameters are e.g. gain (ratio of eye velocity to target velocity), the number of corrective and anticipatory saccades or the amplitude of pre-saccadic position errors. Deficits in smooth eye-tracking might result from motor and/or sensory problems.

(Anti-)Saccade Tasks Subjects are requested to inhibit looking at a visual stimulus which is presented laterally to a fixation point, but to look into the opposite direction (antisaccade task) or to delay looking at the site of the visual stimulus for a certain time (delayed oculomotor response task).

From Genes to Endophenotypes In the following, we arrange detailed examples of relationships between genetic findings and endophenotypes according to the genetic findings because alterations in the same gene might affect different endophenotypes. Thus the top-down approach is easier to follow than the other way round. For a simplified overview of the relations between genetic findings and endophenotypes see Table 21.1. However, we would like to point out that recent findings from genome-wide association studies in large samples suggest that despite many replications the effects sizes of each single gene cannot explain more than up to 1% of variance with respect to manifest schizophrenia. It is therefore highly likely that most endophenotypes which are frequently found in schizophrenia result from an interaction between and/or additional effects of different genes. We want to illustrate some pathways according to the currently available data, but kindly ask the reader to keep in mind that there should be many more (and more complex interactions). 1. GAD 1/GAD 67 (glutamic acid decarboxylase) GAD 1 (2q31.1), codes for GAD 67 (glutamic acid decarboxylase), a key enzyme of GABA synthesis in

Dopaminergic NT

Glutamatergic NT (glutamate level and/or synapse stabilization)

GABA-ergic NT

NAA-level (magnetic resonance spectroscopy, MRS)

Overall grey matter loss during childhood and adolescence increased anisotropy (diffusion tensor imaging, DTI)

Hippocampus volume

Movement-related potentials P50 sensory gating

Prefrontal hypoactivation (fMRI, PET) Mismatch negativity (MMN) P50 sensory gating

Reduced intracortical and transcallosal inhibition (TMS)

Decreased coherence (EEG), e.g. gamma band; increased latency variance of e.g. P300

Hippocampus activation (fMRI, BOLD contrast) P300 lateralization, amplitude (context updating)

Functional

Physiological (non-invasively measured) endophenotype level

Molecular endopheno-type level Structural

Source: Modified according to Bender et al.33

A7-nicotine-R

COMT

GMR3

GABAR3 DAOA

GAD-1

Neurotransmission (NT):

BDNF Neuregulin

Dysbindin

DISC 1, 2

Neuronal integrity:

Gene level

Table 21.1 Simplified overview

Working memory deficits (Wisconsin Card Sorting Test) Smooth pursuit eye movement deficits Neurological soft signs

Neurological soft signs

Increased variability of reaction times

Smooth pursuit eye movement deficits

Neuropsychological endophenotype level

21 Functional and Structural Endophenotypes in Schizophrenia 73

74

inhibitory interneurons.40 Chromosome 2q was significantly associated with schizophrenia in a metaanalysis.35 Expression studies found a reduced number of GAD 67-expriming interneurons in the anterior cingulate41 and the dorsolateral prefrontal cortex of schizophrenic patients.36, 42 Inhibitory deficits in Chandelier cells could lead to less neuronal synchrony and less efficient cortical neurotransmission.43 Reduced prefrontal GABAergic inhibition could lead to impaired executive control and working memory in schizophrenic patients44 as well as qualitative abnormalities in eye tracking movements40 (see below). Moreover, a reduced intracortical and transcallosal motor cortex inhibition could be shown by transcranial magnetic stimulation.45 GAD 1-allels were found to be associated with a strong grey matter reduction in childhood onset schizophrenia indicating a dysfunctional maturation or neurodegeneration. 2a. DAOA (D-amino-acid oxidase activator) G72/G30 on chromosome 13q33.2 codes for DAOA, which plays an important role in glutamatergic excitatory neurotransmission.46 Associations of polymorphisms of this gene with schizophrenia as well as with bipolar disorders have been found, for adult subjects47, 48 as well as for children.49 A later disease onset and less autistic traits have been found in the patient sample analyzed by Addington et al. which might indicate a more “adult type” schizophrenia/psychosis. Half of all patients with unspecific psychoses in their sample later developed bipolar disorder. 2b. GRM3 (metabotropic glutamate receptor 3) is a glutamate transporter which can be found on glial cells, regulates extracellular glutamate levels and whose polymorphisms show characteristic accumulations in schizophrenic patients.50 Low levels of N-acetyl-aspartate (a parameter in magnetic resonance spectroscopy which is closely linked to synaptic activity and tissue glutamate levels) were found in the prefrontal cortex of schizophrenics.50 They could reflect problems in glutamatergic neurotransmission as a reason for worse achievements in cognitive tests. 3. Dysbindin Polymorphisms of the dysbindin gene (DTNBP1, 6p22.3) were also associated with schizophrenia.35 Dysbindin plays a role in the production of pre-synaptic proteins, influences extracellular glutamate levels

S. Bender et al.

and has neuroprotective properties.51 A relation between social isolation and a bad premorbid adaption at school (premorbid adjustment scale)52 has been found.

Smooth Pursuit Eye Movements Disturbances of eye tracking movements, when schizophrenic patients follow a slowly moving object with their eyes – smooth pursuit16 – have been described. Twin studies have shown that these abnormalities were associated with the genetic vulnerability for the disease (40–80% of schizophrenic patients and 25–40% of their first grade relatives presented irregularities in smooth pursuit eye movements by increased corrective saccades in contrast to less than 10% of normal control subjects).53–55 Finally, eye tracking movement deficits were associated with a locus on chromosome 6p.56, 57 On the other hand there are also links between smooth pursuit eye movements and COMT (Catechol-O-Methyltransferase, see below)58, 59 and other gene loci.60 Some associations might be produced indirectly by correlations between susceptibility genes of schizophrenic patients. The pathophysiological mechanisms could include a sensory deficit in motion perception and estimation/prediction.61 However, also the frontal eye field shows a decreased activation62 and motor deficits affecting the memory trace of the preceding constant eye movement (efference copy) could play an additional important role apart from the visual information.63 An exact recording of the eye movements and a good control of the influences of medication seem to be essential for future studies.64 Deficits in smooth pursuit eye movements exist already during childhood in childhood onset schizophrenia.65–67 The separation of healthy subjects and patients can only be partially accomplished as both groups show a strong overlap.68 Hints towards a specificity of eye tracking movement deficits were provided by negative results in adult patients with attention-deficit/hyperactivity disorder,69 however, certain deficits were found also in patients with affective (psychotic) disorders.31

Saccade Tasks With respect to saccade tasks, there were also similar abnormalities in children and adults with schizophrenia. However, some results question that this is due to

21

Functional and Structural Endophenotypes in Schizophrenia

genetic influences, because healthy subjects and notaffected relatives of the patients did not differ from each other.70 Antisaccade tasks might not represent a suitable type of task to assess a schizophrenic endophenotype71 though other authors contradict.72 In any case the question remains whether eye movements represent an independent construct or if they rather represent a well measurable neurological soft sign with respect to oculomotor movements. In this case they would have to be integrated within the larger frame of a general sensory-motor integration deficit.

P300 A further endophenotype, which has been related to dysbindin73 is the so called P300-component of the event-related EEG potential. Stimuli which confer relevant information for a subject’s task – in contrast to simple sensory stimulation – evoke a widespread positivity on the scalp with a latency of about 300 milliseconds (P300). P300 is thought to reflect context updating and the interpretation of the subjective meaning of a stimulus (e.g. task relevance) for the subject (higher cognitive processing). Schizophrenic patients showed a reduced P300 amplitude,74–76 which correlated with reduced temporal lobe volume,77 where important cortical generators of P300 are supposed to be located. P300 differences in schizophrenic patients present both trait and state characteristics78: Left temporal P300 amplitude reductions have been found to be statedependent and to correlate with positive symptoms.79 However, the P300 amplitude reduction has also trait characteristics,80 and at-risk subjects81 and relatives of schizophrenic patients74, 82 showed similar deficits. Especially the frontal NoGo P300 (the type of P300, which is evoked, when a target stimulus requires the subject to suppress a response which is always given to the more frequent distractor) is reduced in schizophrenics.73, 83 The anterior cingulate is thought to be involved in these NoGo-P300 differences (reflecting difficulties in response inhibition and response monitoring).73, 83 The Dysbindin-gene itself has been related especially to negative and cognitive schizophrenic symptoms as well as to a worse outcome.84 4. DISC 1, 2 (disrupted in schizophrenia) 1q41, 1q42: DISC1, DISC 2 (disrupted in schizophrenia)35 is a candidate gene of schizophrenia which is pre-

75

dominantly expressed in the hippocampus (and to some extent in prefrontal cortex) and which is thought to be responsible for hippocampus development and synaptic density.36 In schizophrenic patients with certain DISC haplotypes changes in hippocampus volume were found.85 Moreover, there were also functional alterations resulting in changes in hippocampus activation (functional magnetic resonance imaging) in working and declarative memory tasks.86 A developmental model which links hippocampal and prefrontal/basal ganglia deficits has been developed.87, 88 It is based on the fact that rats whose ventral hippocampus is lesioned as neonates later on show a hyper-reactive dopamine system. 5. COMT (Catechol-O-Methyltransferase) COMT, 22q11DS,35 is a key enzyme in dopamine degradation.89 The velocardiofacial syndrome as a model of a natural genetic disorder on 22q conveys a 25-fold increased risk for schizophrenia36 and also nonpsychotic affected subjects show a strong grey matter loss as do patients suffering from childhood onset schizophrenia.90 The Val(108/158)Met-polymorphism (Val/Met substitution) leads to a fourfold increased enzymatic activity in dopamine degradation and thus reduced available dopamine levels and increased risk for schizophrenia.91 In a COMT Val-allele mouse model schizophrenia-like symptoms could be shown.92 However, there are also negative findings from a casecontrol study showing no association between COMT polymorphisms and schizophrenia.93 COMT influences prefrontal cortex function, which can be assessed by the Wisconsin Card Sorting Test.91, 94, 95 Schizophrenic patients perseverate in this test when they are required to find out the rules according to which cards are sorted when suddenly these rules are changed.96 Yet also other prefrontal functions such as working memory are influenced by COMT.97, 98 First hints towards a successful prediction of a good response of working memory deficits to atypical neuroleptic medication were described.99 “Mismatch negativity” (MMN) is an event-related potential, which appears when sudden changes in an otherwise uniform auditory stimulation are automatically detected. MMN is reduced in schizophrenic patients.100–102 Healthy relatives show abnormalities in some though not all studies,103, 104 but a direct link between COMT and MMN could be established.105 Deficits are supposed to be specific for schizophrenia, there were none in patients with unipolar or bipolar

76

affective disorders.106 However, pharmacological influences, e. g. nicotine, seem to play a non-neglectable role.107 MMN is generated by cortical sources in the temporal auditory and possibly in the frontal cortex.108 Recent findings emphasize that it may be important which aspect of the auditory signal makes the difference (duration deviants are more effective than pitch deviants) and that chronification might play a more important role than genetic contributions.109 A prefrontal dopamine deficit (especially D1-receptor mediated, resulting in impaired executive control) due to the increased dopamine degradation (Val-allele of COMT) is thought to produce a disinhibition of the basal ganglia (reduced GABAergic tonic inhibition). Subsequently a subcortical increase in tyrosinehydroxilase messenger RNA-levels and finally an uncontrolled phasic subcortical dopamine secretion responsible for associative loosening and positive symptoms such as hallucinations.89, 110, 111 There are experimental data which show that prefrontal transcranial magnetic stimulation (TMS) leads to increased dopamine levels in the Nucleus caudatus and the Nucleus accumbens.112, 113 A cortical/ subcortical dopamine imbalance is the modern form of the dopamine hypothesis of schizophrenia/psychotic disorders. The example of 22q11DS, COMT activity, MMN, working memory, Wisconsin Card Sorting Test performance and positive symptoms shows how endophenotypes can be established at different intermediate levels between genes and clinical symptoms, starting at a molecular biological level (COMT activity) over the physiological assessment of neuronal systems (MMN) up to neuropsychological specific behavioural tasks (Wisconsin Card Sorting Test). COMT activity has also been related to power increases in the theta and delta frequency bands in the resting EEG in schizophrenic patients114 as a possible result of tonic dopamine levels. However, it was the Met-allele which has been associated with the slow frequency power increase. Studies with small sample sizes should be interpreted cautiously because many genes are involved in the complex regulation of dopamine level and the efficacy of dopaminergic neurotransmission. While the EEG shows strong genetic influences, the disease process of manifest schizophrenia diminishes the EEG similarities between monozygotic discordant twins.115

S. Bender et al.

Motor System A close connection between motor and cognitive deficits of schizophrenic patients has been repeatedly shown.116–118 It might be mediated to some extent by the Val-allele of COMT especially in deficit schizophrenia (patients suffering predominantly from negative symptoms).119 COMT was able to explain up to 15% of the variance of motor symptoms which is even a larger portion than the 10% of explained variance of cognitive deficits.

Movement-related Potentials A correlate of reduced ability of the patients to make up their mind could be the reduced readiness potential before spontaneous self-initiated movements.120–124 Also attention allocation and the intended response preparation after a warning stimulus which indicates that soon a target stimulus will require a fast motor reaction have been found impaired resulting in reduced contingent negative variation amplitudes.121–131 These alterations have been found to be more closely related to negative rather than positive symptoms. They were more pronounced for self-initiated freely selected movements than for stereotyped reaction movements.132–135 Parkinsonian side effects of neuroleptic medication is not thought to be responsible for the amplitude reductions before the start of movement execution because neuroleptics were found to rather normalise the potential amplitude.136 In contrast, elevated postimperative negative variation137–141 could not only reflect difficulties of schizophrenic patients in the establishment of the contingency between two stimuli but also motor side effects of neuroleptic medication.136 However, contingent negative variation abnormalities were not found in all studies.142 Apart from COMT also polymorphisms in the alpha-3 subunit of the GABA-A receptor have been linked to motor deficits in schizophrenia and might form part of their genetic basis.143

Changes in the Frequency Domain: Event-Related Alpha Desynchronization A reduced event-related central alpha power reduction (resting frequency of the motor cortex) before

21

Functional and Structural Endophenotypes in Schizophrenia

self-initiated movements was described for adult schizophrenic subjects.144 However, evidence with respect to alpha-power abnormalities is still limited.

Lateralization Versus Amplitude Functional magnetic resonance imaging (fMRI) showed differences in the lateralisation of sensorimotor cortex activation as well as a hypo-activation of the supplementary motor area and subcortical areas.145– 147 Hypoactivation might be state-dependent (a possible marker of imminent psychosis) and could be resolved by neuroleptic medication. In contrast, differences in the lateralisation of cortical activation (which exceed mere differences in the degree of right handedness) could be a trait-marker.148, 149

Neurological Soft Signs Neurological soft signs (NSS), i.e. non-focal somatosensory and motor coordination and integration deficits,15, 117, 150–155 are one of the best established (including prospective studies) risk factors for schizophrenia (“cognitive dysmetria hypothesis”). Structural MRI points towards a reduced cerebellar volume.156 Unfortunately, the specificity of NSS is limited and also patients with affective disorders or epilepsy show elevated NSS scores. However, the underlying neuronal mechanisms leading to similar NSS but different psychiatric and neurological illnesses should vary and might be specific for each disease. fMRI and/or electrophysiological methods might be able to unveil schizophrenia-specific defects. However, so far there are no studies examining specific NSS, such as dysdiadochokinesis, by these methods. The Val-allele of COMT and lower prefrontal dopamine levels have also been related to a sensory gating deficit of P50 as well,157 However, cholinergic neurotransmission might play an even more important role. 6. Alpha-7-nicotinic-acetylcholine receptor gene The alpha-7-nicotinic-acetylcholine receptor on chromosome 15 158,159 has been associated with schizophrenic disorders and with the so called P50 sensory gating deficit.

77

P50 Sensory Gating Sensory gating means that the early cortical responses to auditory stimulation at a latency of about 50 ms (P50) is inhibited in healthy subjects for the second of two paired clicks which occur shortly after each other. This sensory gating is thought to occur because the first stimulus already contains all relevant information and is reduced in schizophrenic patients, atrisk subjects and relatives of schizophrenic patients.160–165 P50 sensory gating was found to be reduced not only in schizophrenia but also in psychotic bipolar disorder,166 so sensory gating deficits might indicate a vulnerability towards psychotic disorders rather than towards schizophrenia alone. P50 might include both temporal as well as frontal sources.167 P50 gating is also impaired in childhood onset schizophrenia.66 There are first data which describe the physiological maturation of P50 sensory gating in infancy, childhood and adolescence.168, 169 However, developmental data are generally lacking for many endophenotypes. Recently, difficulties to replicate P50 sensory gating deficits have emerged. In a meta-analysis,170 methodological details have been pointed out which could explain the contradictory results between different groups of investigators. Future replication studies will have to show the reliability and generalizability of P50 findings. An assessment of the more pronounced later components N1/P2 might show a better reliability than P50.171

Pre-pulse Inhibition (PPI) In contrast, “pre-pulse inhibition (PPI)” refers to the phenomenon, that the peripheric startle response is reduced by a preceding warning stimulus in healthy subjects. Deficits in prepulse inhibition in schizophrenic patients seem to be independent from antisaccade tasks or P50 sensory gating.22, 172 They are also not specific for schizophrenia173 but also occur in obsessive/compulsive disorder or Huntington’s disease. 7. Neuroregulin Neuroregulin (NRG1; 8p21) is a gene, which is involved in neuronal migration, myelination and the establishment of neuronal networks.174, 175 It plays an important

78

S. Bender et al.

Influences of (Genetically Programmed) Maturation (Differential Gene Expression) and Gene–Environment 8. BDNF (brain derived neurotrophic factor) Interactions BDNF is a growth factor which protects glutamatergic role for neuronal plasticity and embryonic development36 and has been associated with schizophrenia.

pyramidal cells, facilitates the generation of new synapses and makes synaptic transmission more efficient.19, 111, 176 Grey Matter Reduction Alterations in at least some of the latter genes might lead to a common morphological endophenotype, i.e. a reduction of grey matter during childhood and adolescence177–183 which exceeds the physiological pruning in healthy subjects.184 Most brain areas are affected, yet hippocampus and the temporal lobe, the (pre-) frontal cortex and the cerebellum present the most important volumetric deficits.36, 156, 185 Relatives of schizophrenic patients seem to be less affected yet also show grey matter reductions.186 For the manifestation of acute psychosis still an interaction with triggering environmental factors seems necessary.181, 187 Dysconnection Hypothesis The reduction of brain volume could find a functional correlate in the dysconnection hypothesis – or reflect that not only grey, but also white matter changes appear relevant (DTI, diffusion tensor imaging.188 There might be a general increase in noise in the neuronal communication leading for example to an increased intraindividual variability of reaction times189, 190 and increased latency variability e.g. of P300.38 However, there might also be a specific dysconnection and dysmaturation of prefronto-temporal circuits191 or of dorsolateral prefrontal cortex with other task-relevant regions.192 The increased noise could be reflected by a lack of stability in gamma band synchronisation.193 Frequencies in the gamma band (around 40 Hz) are thought to subserve short term neuronal interactions in order to build a cognitive representation or a Gestalt phenomenon. EEG coherence (i.e. the similarity of the electrical brain activity over different sites on the scalp) is thought to measure the interaction/cooperation of the underlying cortical areas and could be affected in schizophrenia.194

There are some hints towards a disturbed cerebral maturation in schizophrenic patients. Additional valuable information apart from a mere cross-sectional assessment of schizophrenia markers might be in longitudinal changes (detecting “dysmaturation”) in the crucial parameters assessed repeatedly in at-risk subjects (eliminating also some problems of interindividual variability). The reduction of grey matter during childhood and adolescence177–183 exceeds the physiological pruning in healthy subjects.184 Neurological soft signs which decrease with increasing age still persist in schizophrenic patients. An exciting finding is that the changes in movement-related evoked potentials of schizophrenic patients resemble the pattern of healthy pre-pubertal children.195 The “pandysmaturation” hypothesis of schizophrenia, i.e. a profoundly disturbed maturation in many domains, has been opposed to Kraepelin’s neurodegenerative model of a “dementia praecox” and seems to be still up to date.196 Gene expression is no static process but changes dramatically throughout childhood and adolescence in a tissue and age-dependent way during ontogenic maturation (developmental genetics).36 Apart from biologically determined maturation even the environment and the personal history of a patient can affect gene expression: Increased pup licking and grooming of young mice by their parents affected the methylation and thus expression of the glucocorticoid-receptorpromotor DNA in the hippocampus.197 Such effects might explain why the concordance of monozygotic twins with respect to schizophrenic psychosis is far from 100%.198 Gene-environment-interactions and biologically determined age-dependent maturation of gene expression are difficult to disentangle. In any case, results from adult studies can not be transferred to children or adolescents without additional evidence that a certain parameter is already disturbed at earlier maturational stages. An advantage could be that earlyonset schizophrenia might involve a higher genetic load than schizophrenia in adults. Abnormalities in the secretion of Reelin in GABAergic cells have been related to disturbances in neuronal migration and maturation.199

21

Functional and Structural Endophenotypes in Schizophrenia

Limitations of the Approach Though there is a consistency in genetic findings,200, 201 these are far from an unanimous replication of results and the contribution of a single gene to explain the manifestation of schizophrenia is supposed to be below 1%.202, 203 Genetic studies rather indicate a great genetic heterogeneity of the polygenetic disease schizophrenia.204 This makes it extremely unlikely to find endophenotypes which might serve as diagnostic markers with a sufficient sensitivity and specificity for all patients classified as schizophrenic. The “specificity” of at least some endophenotypes (and also genes could be limited,205 for example P50 abnormalities were also found in patients and relatives in psychotic bipolar disorder206). The accuracy of diagnostic group prediction (schizophrenia/healthy) of a combination of four endophenotypes (P50, P300, MMN and antisaccades) was still limited to 80%.23 This is also reflected by the fact, that there are also some contradicting results with respect to studies concerning the neurophysiological endophenotypes e.g. P300207, MMN104 and P50.161, 208–210 Thus future research should rather not aim at finding diagnostic markers for the whole of schizophrenia. Instead, it seems likely that the other way round for certain subgroups of schizophrenic patients (carrying a certain genetic vulnerability) characteristic high-risk constellations can be determined which could justify a more frequent diagnostic psychiatric assessment and specific psychoeducational/pharmacological treatment.

Conclusions and Future Directions The replication of many findings still needs to be done and clinical applications have not yet been developed. However, this seems an important task for future schizophrenia research, because coherent relevant models for the underlying pathology seem more important than developing ever new parameters in which schizophrenic patients differ from healthy subjects. Due to the pervasive deficits in schizophrenic patients they differ from healthy subjects in many ways. It now seems important to integrate these differences to a reduced number of explaining underlying factors, which could be relevant for the clinical diagnostic process and give predictive hints towards which therapy might be most

79

effective. The wide range of possible available schizophrenia endophenotypes as well as state-markers of psychosis could in the future provide important insights into the underlying neuronal mechanisms behind rather unspecific symptoms of a patient and a suitable combination of parameters might assist the diagnostic and therapeutic decision making process despite the high inter-individual variabilities. Future publications should provide sufficient data in order to allow a meta-analytic estimation of the specificity and sensitivity of the respective parameters if they were combined in a more complex battery. Nevertheless, further studies which address the correlations between different schizophrenia markers (applying more than one parameter to the same patient sample) will be necessary in order to select the most appropriate combination.

References 1. Mossaheb N, Wiesegger G, Amminger GP, Kasper S, Tauscher J. Früherkennung und Frühintervention der Schizophrenie. Nervenarzt 2006;77:23–34. 2. Ho BC, Alicata D, Ward J, et al. Untreated initial psychosis: relation to cognitive deficits and brain morphology in firstepisode schizophrenia. Am J Psychiatry 2003;160:142–148. 3. Klosterkotter J, Hellmich M, Steinmeyer EM, SchultzeLutter F. Diagnosing schizophrenia in the initial prodromal phase. Arch Gen Psychiatry 2001;58:158–164. 4. Yung A, Yuen H, McGorry PD, et al. Mapping the onset of psychosis: the comprehensive assessment of at-risk mental states. Aust N Z J Psychiatry 2005;39:964–971. 5. Miller TJ, McGlashan TH, Rosen JL, et al. Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: predictive validity, interrater reliability and training to reliability. Schizophr Bull 2003;29:703–715. 6. Hafner H, Riecher-Rossler A, Hambrecht M, et al. IRAOS: an instrument for the assessment of onset and early course of schizophrenia. Schizophr Res 1992;6:209–223. 7. Hafner H, Maurer K, Ruhrmann S, et al. Early detection and secondary prevention of psychosis: facts and visions. Eur Arch Psychiatry Clin Neurosci 2004;254:117–128. 8. McGorry PD, Yung A, Phillips L, et al. Randomized controlled trial of interventions designed to reduce the risk of progression to first-episode psychosis in a clinical sample with subthreshold symptoms. Arch Gen Psychiatry 2002; 59:921–928. 9. Ruhrmann S, Schultze-Lutter F, Klosterkotter J. Early detection and intervention in the initial prodromal phase of schizophrenia. Pharmacopsychiatry 2003;36:S162–S167. 10. Resch F, Koch E, Mohler E, Parzer P, Brunner R. Early detection of psychotic disorders in adolescents: specificity of basic symptoms in psychiatric patient samples. Psychopathology 2002;35:259–266.

80 11. Schultze-Lutter F, Ruhrmann S, Picker H, von Reventlow HG, Brockhaus-Dumke A, Klosterkotter J. Basic symptoms in early psychotic and depressive disorders. Br J Psychiatry Suppl 2007;51:S31–37. 12. Woods SW, Breier A, Zipursky RB, et al. Randomized trial of olanzapine versus placebo in the symptomatic acute treatment of the schizophrenic prodrome. Biol Psychiatry 2003;54:453–464. 13. Cornblatt BA, Lencz T, Kane JM. Treatment of the schizophrenia prodrome: is it presently ethical? Schizophr Res 2001;51:31–38. 14. Verdoux H, Cougnard A. Schizophrenia: who is at risk? Who is a case? Int Clin Psychopharmacol 2006;21(Suppl 2):S17–19. 15. Galderisi S, Maj M, Mucci A, et al. Historical, psychopathological, neurological, and neuropsychological aspects of deficit schizophrenia: A multicenter study. Am J Psychiatry 2002;159:983–990. 16. Holzman PS, Proctor LR, Hughes DW. Eye-tracking patterns in schizophrenia. Science 1973;181:179–181. 17. Hegerl U, Juckel G. Auditory evoked dipole source activity: indicator of central serotonergic dysfunction in psychiatric patients? Pharmacopsychiatry 1994;27:75–78. 18. Berrettini WH. Genetic bases for endophenotypes in psychiatric disorders. Dialogues Clin Neurosci 2005;7:95–101. 19. Maier W, Hofgen B, Zobel A, Rietschel M. Genetic models of schizophrenia and bipolar disorder: overlapping inheritance or discrete genotypes? Eur Arch Psychiatry Clin Neurosci 2005;255:159–166. 20. Beckmann H, Franzek E. The genetic heterogeneity of “schizophrenia”. World J Biol Psychiatry 2000;1:35–41. 21. Verdoux H. Perinatal risk factors for schizophrenia: how specific are they? Curr Psychiatry Rep 2004;6:162–167. 22. Cadenhead KS. Vulnerability markers in the schizophrenia spectrum: implications for phenomenology, genetics, and the identification of the schizophrenia prodrome. Psychiatr Clin North Am 2002;25:837–853. 23. Price GW, Michie PT, Johnston J, et al. A multivariate electrophysiological endophenotype, from a unitary cohort, shows greater research utility than any single feature in the Western Australian family study of schizophrenia. Biol Psychiatry 2005;60:1–10. 24. Weir N, Fiaschi K, Machin D. The distribution and latency of the auditory P300 in schizophrenia and depression. Schizophr Res 1998;31:151–158. 25. Blackwood DH, Fordyce A, Walker MT, St Clair DM, Porteous DJ, Muir WJ. Schizophrenia and affective disorders–cosegregation with a translocation at chromosome 1q42 that directly disrupts brain-expressed genes: clinical and P300 findings in a family. Am J Hum Genet 2001;69: 428–433. 26. Flechtner KM, Steinacher B, Sauer R, Mackert A. Smooth pursuit eye movements in schizophrenia and affective disorder. Psychol Med 1997;27:1411–1419. 27. Lencer R, Trillenberg P, Trillenberg-Krecker K, et al. Smooth pursuit deficits in schizophrenia, affective disorder and obsessive-compulsive disorder. Psychol Med 2004;34:451–460. 28. Mahlberg R, Steinacher B, Mackert A, Flechtner KM. Basic parameters of saccadic eye movements–differences between unmedicated schizophrenia and affective disorder patients. Eur Arch Psychiatry Clin Neurosci 2001;251:205–210.

S. Bender et al. 29. Malaspina D, Amador XF, Coleman EA, Mayr TL, Friedman JH, Sackeim HA. Smooth pursuit eye movement abnormality in severe major depression: effects of ECT and clinical recovery. J Neuropsychiatry Clin Neurosci 1994;6:36–42. 30. Muir WJ, St Clair DM, Blackwood DH, Roxburgh HM, Marshall I. Eye-tracking dysfunction in the affective psychoses and schizophrenia. Psychol Med 1992;22:573–580. 31. Kathmann N, Hochrein A, Uwer R, Bondy B. Deficits in gain of smooth pursuit eye movements in schizophrenia and affective disorder patients and their unaffected relatives. Am J Psychiatry 2003;160:696–702. 32. Rosenberg DR, Sweeney JA, Squires-Wheeler E, Keshavan MS, Cornblatt BA, Erlenmeyer-Kimling L. Eye-tracking dysfunction in offspring from the New York High-Risk Project: diagnostic specificity and the role of attention. Psychiatry Res 1997;66:121–130. 33. Bender S, Weisbrod M, Resch F. Which perspectives can endophenotypes and biological markers offer in the early recognition of schizophrenia? J Neural Transm 2007;114:1199–1215. 34. van der Stelt O, Belger A. Application of electroencephalography to the study of cognitive and brain functions in schizophrenia. Schizophr Bull 2007;33:955–970. 35. Lewis CM, Levinson DF, Wise LH, et al. Genome scan meta-analysis of schizophrenia and bipolar disorder, part II: Schizophrenia. Am J Hum Genet 2003;73:34–48. 36. Rapoport JL, Addington AM, Frangou S, Psych MR. The neurodevelopmental model of schizophrenia: update 2005. Mol Psychiatry 2005;10:434–449. 37. Kaiser S, Roth A, Rentrop M, Friederich HC, Bender S, Weisbrod M. Intra-individual reaction time variability in schizophrenia, depression and borderline personality disorder. Brain Cogn 2008;66:73–82. 38. Roth A, Roesch-Ely D, Bender S, Weisbrod M, Kaiser S. Increased event-related potential latency and amplitude variability in schizophrenia detected through wavelet-based single trial analysis. Int J Psychophysiol 2007;66:244–254. 39. Doniger GM, Foxe JJ, Murray MM, Higgins BA, Javitt DC. Impaired visual object recognition and dorsal/ventral stream interaction in schizophrenia. Arch Gen Psychiatry 2002;59:1011–1020. 40. Addington AM, Gornick M, Duckworth J, et al. GAD1 (2q31.1), which encodes glutamic acid decarboxylase (GAD67), is associated with childhood-onset schizophrenia and cortical gray matter volume loss. Mol Psychiatry 2005;10:581–588. 41. Woo TU, Walsh JP, Benes FM. Density of glutamic acid decarboxylase 67 messenger RNA-containing neurons that express the N-methyl-D-aspartate receptor subunit NR2A in the anterior cingulate cortex in schizophrenia and bipolar disorder. Arch Gen Psychiatry 2004;61:649–657. 42. Kalkman HO, Loetscher E. GAD(67): the link between the GABA-deficit hypothesis and the dopaminergic and glutaminergic theories of psychosis. J Neural Transm 2003;110:803–812. 43. Volk DW, Lewis DA. Impaired prefrontal inhibition in schizophrenia: relevance for cognitive dysfunction. Physiol Behav 2002;77:501–505. 44. Lewis DA, Volk DW, Hashimoto T. Selective alterations in prefrontal cortical GABA neurotransmission in schizophrenia: a novel target for the treatment of working memory dysfunction. Psychopharmacology (Berl) 2004;174:143–150.

21

Functional and Structural Endophenotypes in Schizophrenia

45. Daskalakis ZJ, Christensen BK, Chen R, Fitzgerald PB, Zipursky RB, Kapur S. Evidence for impaired cortical inhibition in schizophrenia using transcranial magnetic stimulation. Arch Gen Psychiatry 2002;59:347–354. 46. Korostishevsky M, Kaganovich M, Cholostoy A, et al. Is the G72/G30 locus associated with schizophrenia? Single nucleotide polymorphisms, haplotypes, and gene expression analysis. Biol Psychiatry 2004;56:169–176. 47. Korostishevsky M, Kremer I, Kaganovich M, et al. Transmission disequilibrium and haplotype analyses of the G72/G30 locus: Suggestive linkage to schizophrenia in Palestinian Arabs living in the North of Israel. Am J Med Genet B Neuropsychiatr Genet 2006;141:91–95. 48. Badner JA, Gershon ES. Meta-analysis of whole genome linkage scans of bipolar disorder and schizophrenia. Mol Psychiatry 2002;7:405–411. 49. Addington AM, Gornick M, Sporn AL, et al. Polymorphisms in the 13q33.2 gene G72/G30 are associated with childhoodonset schizophrenia and psychosis not otherwise specified. Biol Psychiatry 2004;55:976–980. 50. Egan MF, Straub RE, Goldberg TE, et al. Variation in GRM3 affects cognition, prefrontal glutamate, and risk for schizophrenia. Proc Natl Acad Sci USA 2004;101:12604–12609. 51. Numakawa T, Yagasaki Y, Ishimoto T, et al. Evidence of novel neuronal functions of dysbindin, a susceptibility gene for schizophrenia. Hum Mol Genet 2004;13:2699–2708. 52. Gornick MC, Addington AM, Sporn A, et al. Dysbindin (DTNBP1, 6p22.3) is associated with childhood-onset psychosis and endophenotypes measured by the Premorbid Adjustment Scale (PAS). J Autism Dev Disord. 2005;10:1–8. 53. Holzman PS, Kringlen E, Levy DL, Proctor LR, Haberman SJ, Yasillo NJ. Abnormal-pursuit eye movements in schizophrenia. Evidence for a genetic indicator. Arch Gen Psychiatry 1977;34:802–805. 54. Holzman PS, Kringlen E, Levy DL, Proctor LR, Haberman S. Smooth pursuit eye movements in twins discordant for schizophrenia. J Psychiatr Res 1978;14:111–120. 55. Holzman PS, Kringlen E, Matthysse S, et al. A single dominant gene can account for eye tracking dysfunctions and schizophrenia in offspring of discordant twins. Arch Gen Psychiatry 1988;45:641–647. 56. Arolt V, Lencer R, Nolte A, et al. Eye tracking dysfunction is a putative phenotypic susceptibility marker of schizophrenia and maps to a locus on chromosome 6p in families with multiple occurrence of the disease. Am J Med Genet 1996;67:564–579. 57. Matthysse S, Holzman PS, Gusella JF, et al. Linkage of eye movement dysfunction to chromosome 6p in schizophrenia: additional evidence. Am J Med Genet B Neuropsychiatr Genet 2004;128:30–36. 58. Thaker G, Wonodi I, Avila MT, Hong LE, Stine OC. Catechol-O-methyltransferase polymorphism and eye tracking in schizophrenia: a preliminary report. Am J Psychiatry 2004;161:2320–2322. 59. Rybakowski JK, Borkowska A, Czerski PM, Hauser J. Eye movement disturbances in schizophrenia and a polymorphism of catechol-O-methyltransferase gene. Psychiatry Res 2002;113:49–57. 60. Rybakowski JK, Borkowska A, Czerski PM, DmitrzakWeglarz M, Hauser J. The study of cytosolic phospholipase A2 gene polymorphism in schizophrenia using eye move-

61. 62.

63.

64. 65.

66.

67.

68.

69.

70.

71.

72.

73.

74.

75. 76.

77.

81 ment disturbances as an endophenotypic marker. Neuropsychobiology 2003;47:115–119. Holzman PS. Eye movements and the search for the essence of schizophrenia. Brain Res Brain Res Rev 2000;31:350–356. Hong LE, Tagamets M, Avila MT, Wonodi I, Holcomb H, Thaker G. Specific motion processing pathway deficit during eye tracking in schizophrenia: a performance-matched functional magnetic resonance imaging study. Biol Psychiatry 2005;57:726–732. Thaker G, Ross DE, Cassady SL, et al. Smooth persuit eye movements to extraretinal motion signals: deficits in relatives of patients with schizophrenia. Arch Gen Psychiatry 1998;55:830–836. Hutton S, Kennard C. Oculomotor abnormalities in schizophrenia: a critical review. Neurology 1998;50:604–609. Kumra S, Sporn A, Hommer DW, et al. Smooth pursuit eyetracking impairment in childhood-onset psychotic disorders. Am J Psychiatry 2001;158:1291–1298. Ross RG, Olincy A, Harris JG, et al. Evidence for bilineal inheritance of physiological indicators of risk in childhoodonset schizophrenia. Am J Med Genet 1999;88:188–199. Ross RG, Olincy A, Mikulich SK, et al. Admixture analysis of smooth pursuit eye movements in probands with schizophrenia and their relatives suggests gain and leading saccades are potential endophenotypes. Psychophysiology 2002;39:809–819. Sporn A, Greenstein D, Gogtay N, et al. Childhood-onset schizophrenia: smooth pursuit eye-tracking dysfunction in family members. Schizophr Res 2005;73:243–252. Ross RG, Olincy A, Harris JG, Sullivan B, Radant A. Smooth pursuit eye movements in schizophrenia and attentional dysfunction: adults with schizophrenia, ADHD, and a normal comparison group. Biol Psychiatry 2000;48:197–203. Ross RG, Heinlein S, Zerbe GO, Radant A. Saccadic eye movement task identifies cognitive deficits in children with schizophrenia, but not in unaffected child relatives. J Child Psychol Psychiatry 2005;46:1354–1362. Levy DL, O’Driscoll G, Matthysse S, Cook SR, Holzman PS, Mendell NR. Antisaccade performance in biological relatives of schizophrenia patients: a meta-analysis. Schizophr Res 2004;71:113–125. Calkins ME, Curtis CE, Iacono WG, Grove WM. Antisaccade performance is impaired in medically and psychiatrically healthy biological relatives of schizophrenia patients. Schizophr Res 2004;71:167–178. Fallgatter AJ, Herrmann MJ, Hohoff C, et al. DTNBP1 (Dysbindin) Gene variants modulate prefrontal brain function in healthy individuals. Neuropsychopharmacology 2006;31:2002–2010. Bramon E, McDonald C, Croft RJ, et al. Is the P300 wave an endophenotype for schizophrenia? A meta-analysis and a family study. Neuroimage 2005;27:960–968. Winterer G, Egan MF, Raedler T, et al. P300 and genetic risk for schizophrenia. Arch Gen Psychiatry 2003;60:1158–1167. Weisbrod M, Hill H, Niethammer R, Sauer H. Genetic influence on auditory information processing in schizophrenia: P300 in monozygotic twins. Biol Psychiatry 1999;46:721–725. McCarley RW, Salisbury DF, Hirayasu Y, et al. Association between smaller left posterior superior temporal gyrus volume on magnetic resonance imaging and smaller left

82

78.

79.

80.

81.

82.

83.

84.

85.

86.

87.

88.

89.

90.

91.

92.

93.

S. Bender et al. temporal P300 amplitude in first-episode schizophrenia. Arch Gen Psychiatry 2002;59:321–331. Mathalon DH, Ford JM, Pfefferbaum A. Trait and state aspects of P300 amplitude reduction in schizophrenia: a retrospective longitudinal study. Biol Psychiatry 2000;47: 434–449. Higashima M, Nagasawa T, Kawasaki Y, et al. Auditory P300 amplitude as a state marker for positive symptoms in schizophrenia: cross-sectional and retrospective longitudinal studies. Schizophr Res 2003;59:147–157. Molina V, Munoz F, Martin-Loeches M, Casado P, Hinojosa JA, Iglesias A. Long-term olanzapine treatment and p300 parameters in schizophrenia. Neuropsychobiology 2004; 50:182–188. van der Stelt O, Lieberman JA, Belger A. Auditory P300 in high-risk, recent-onset and chronic schizophrenia. Schizophr Res 2005;77:309–320. Bharath S, Gangadhar BN, Janakiramaiah N. P300 in family studies of schizophrenia: review and critique. Int J Psychophysiol 2000;38:43–54. Weisbrod M, Kiefer M, Marzinzik F, Spitzer M. Executive control is disturbed in schizophrenia: evidence from eventrelated potentials in a Go/NoGo task. Biol Psychiatry 2000;47:51–60. Fanous AH, Kendler KS. Genetics of clinical features and subtypes of schizophrenia: a review of the recent literature. Curr Psychiatry Rep 2008;10:164–170. Seidman LJ, Faraone SV, Goldstein JM, et al. Left hippocampal volume as a vulnerability indicator for schizophrenia: a magnetic resonance imaging morphometric study of nonpsychotic first-degree relatives. Arch Gen Psychiatry 2002;59:839–849. Callicott JH, Straub RE, Pezawas L, et al. Variation in DISC1 affects hippocampal structure and function and increases risk for schizophrenia. Proc Natl Acad Sci USA 2005;102:8627–8632. Powell KJ, Binder TL, Hori S, et al. Neonatal ventral hippocampal lesions produce an elevation of DeltaFosB-like protein(s) in the rodent neocortex. Neuropsychopharmacology 2006;31:700–711. Wong AH, Lipska BK, Likhodi O, et al. Cortical gene expression in the neonatal ventral-hippocampal lesion rat model. Schizophr Res 2005;77:261–270. Akil M, Kolachana BS, Rothmond DA, et al. Catechol-Omethyltransferase genotype and dopamine regulation in the human brain. J Neurosci 2003;23:2008–2013. Eliez S, Schmitt JE, White CD, Reiss AL. Children and adolescents with velocardiofacial syndrome: a volumetric MRI study. Am J Psychiatry 2000;157:409–415. Egan MF, Goldberg TE, Kolachana BS, et al. Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci USA 2001;98:6917–6922. Paterlini M, Zakharenko SS, Lai WS, et al. Transcriptional and behavioral interaction between 22q11.2 orthologs modulates schizophrenia-related phenotypes in mice. Nat Neurosci 2005;8:1586–1594. Galderisi S, Maj M, Kirkpatrick B, et al. COMT Val(158)Met and BDNF C(270)T polymorphisms in schizophrenia: a casecontrol study. Schizophr Res 2005;73:27–30.

94. Bilder RM, Volavka J, Czobor P, et al. Neurocognitive correlates of the COMT Val(158)Met polymorphism in chronic schizophrenia. Biol Psychiatry 2002;52:701–707. 95. Nolan KA, Bilder RM, Lachman HM, Volavka J. Catechol O-methyltransferase Val158Met polymorphism in schizophrenia: differential effects of Val and Met alleles on cognitive stability and flexibility. Am J Psychiatry 2004;161:359–361. 96. Franke P, Maier W, Hain C, Klingler T. Wisconsin Card Sorting Test: an indicator of vulnerability to schizophrenia? Schizophr Res 1992;6:243–249. 97. Goldberg TE, Egan MF, Gscheidle T, et al. Executive subprocesses in working memory: relationship to catechol-Omethyltransferase Val158Met genotype and schizophrenia. Arch Gen Psychiatry 2003;60:889–896. 98. Ho BC, Wassink TH, O’Leary DS, Sheffield VC, Andreasen NC. Catechol-O-methyl transferase Val158Met gene polymorphism in schizophrenia: working memory, frontal lobe MRI morphology and frontal cerebral blood flow. Mol Psychiatry 2005;10:229, 287–298. 99. Weickert TW, Goldberg TE, Mishara A, et al. Catechol-Omethyltransferase val108/158met genotype predicts working memory response to antipsychotic medications. Biol Psychiatry 2004;56:677–682. 100. Baldeweg T, Klugman A, Gruzelier J, Hirsch SR. Mismatch negativity potentials and cognitive impairment in schizophrenia. Schizophr Res 2004;69:203–217. 101. Umbricht D, Krljes S. Mismatch negativity in schizophrenia: a meta-analysis. Schizophr Res 2005;76:1–23. 102. Todd J, Michie PT, Budd TW, Rock D, Jablensky AV. Auditory sensory memory in schizophrenia: inadequate trace formation? Psychiatry Res 2000;96:99–115. 103. Michie PT, Innes-Brown H, Todd J, Jablensky AV. Duration mismatch negativity in biological relatives of patients with schizophrenia spectrum disorders. Biol Psychiatry 2002;52:749–758. 104. Bramon E, Croft RJ, McDonald C, et al. Mismatch negativity in schizophrenia: a family study. Schizophr Res 2004;67:1–10. 105. Baker K, Baldeweg T, Sivagnanasundaram S, Scambler P, Skuse D. COMT Val108/158 Met modifies mismatch negativity and cognitive function in 22q11 deletion syndrome. Biol Psychiatry 2005;58:23–31. 106. Umbricht D, Koller R, Schmid L, et al. How specific are deficits in mismatch negativity generation to schizophrenia? Biol Psychiatry 2003;53:1120–1131. 107. Baldeweg T, Wong D, Stephan KE. Nicotinic modulation of human auditory sensory memory: Evidence from mismatch negativity potentials. Int J Psychophysiol 2006;59:49–58. 108. Oknina LB, Wild-Wall N, Oades RD, et al. Frontal and temporal sources of mismatch negativity in healthy controls, patients at onset of schizophrenia in adolescence and others at 15 years after onset. Schizophr Res 2005;76:25–41. 109. Magno E, Yeap S, Thakore JH, Garavan H, De Sanctis P, Foxe JJ. Are auditory-evoked frequency and duration mismatch negativity deficits endophenotypic for schizophrenia? High-density electrical mapping in clinically unaffected first-degree relatives and first-episode and chronic schizophrenia. Biol Psychiatry, 2008;64(5):385–391. 110. Tost H, Meyer-Lindenberg A, Ruf M, et al. [One decade of functional imaging in schizophrenia research. From visu-

21

111.

112.

113.

114.

115.

116.

117.

118.

119.

120.

121.

122.

123.

124.

125. 126.

Functional and Structural Endophenotypes in Schizophrenia alisation of basic information processing steps to molecular-genetic oriented imaging]. Radiologe 2005;45:113–118, 120–113. Perlman WR, Weickert CS, Akil M, Kleinman JE. Postmortem investigations of the pathophysiology of schizophrenia: the role of susceptibility genes. J Psychiatry Neurosci 2004;29:287–293. Strafella AP, Paus T, Fraraccio M, Dagher A. Striatal dopamine release induced by repetitive transcranial magnetic stimulation of the human motor cortex. Brain 2003;126:2609–2615. Zangen A, Hyodo K. Transcranial magnetic stimulation induces increases in extracellular levels of dopamine and glutamate in the nucleus accumbens. Neuroreport 2002;13:2401–2405. Venables NC, Bernat EM, Sponheim SR. Genetic and disorder-specific aspects of resting state EEG abnormalities in schizophrenia. Schizophr Bull, 2008; [Epub ahead of print] Weisbrod M, Hill H, Sauer H, et al. Nongenetic pathologic developments of brain-wave patterns in monozygotic twins discordant and concordant for schizophrenia. Am J Med Genet B Neuropsychiatr Genet 2004;125B:1–9. Hans SL, Auerbach JG, Auerbach AG, Marcus J. Development from birth to adolescence of children at-risk for schizophrenia. J Child Adolesc Psychopharmacol 2005;15:384–394. Erlenmeyer-Kimling L, Rock D, Roberts SA, et al. Attention, memory, and motor skills as childhood predictors of schizophrenia-related psychoses: the New York High-Risk Project. Am J Psychiatry 2000;157:1416–1422. Erlenmeyer-Kimling L. Neurobehavioral deficits in offspring of schizophrenic parents: liability indicators and predictors of illness. Am J Med Genet 2000;97:65–71. Galderisi S, Maj M, Kirkpatrick B, et al. Catechol-Omethyltransferase Val158Met polymorphism in schizophrenia: associations with cognitive and motor impairment. Neuropsychobiology 2005;52:83–89. Westphal KP. The Bereitschaftspotential in schizophrenia and depression. In: Jahanshahi M, Hallett M, eds. The Bereitschaftspotential - movement-related cortical potentials. New York: Kluwer/Plenum; 2003:155–174. Karaman T, Ozkaynak S, Yaltkaya K, Buyukberker C. Bereitschaftpotential in schizophrenia. Br J Psychiatry 1997;171:31–34. Dreher JC, Trapp W, Banquet JP, Keil M, Gunther W, Burnod Y. Planning dysfunction in schizophrenia: impairment of potentials preceding fixed/free and single/sequence of self-initiated finger movements. Exp Brain Res 1999;124:200–214. Kubota F, Miyata H, Shibata N, Yarita H. A study of motor dysfunction associated with schizophrenia based on analyses of movement-related cerebral potentials and motor conduction time. Biol Psychiatry 1999;45:412–416. Northoff G, Pfennig A, Krug M, et al. Delayed onset of late movement-related cortical potentials and abnormal response to lorazepam in catatonia. Schizophr Res 2000;44:193–211. Adler LE, Pecevich M, Nagamoto H. Bereitschaftspotential in tardive dyskinesia. Mov Disord 1989;4:105–112. Tecce JJ. Dopamine and CNV: studies of drugs, disease and nutrition. Electroencephalogr Clin Neurophysiol Suppl 1991;42:153–164.

83

127. Tecce JJ, Gardos G, Cole JO, Bowers PA. A CNV typology of chronic schizophrenics and response to drug treatment. Psychopharmacol Bull 1979;15:39–41. 128. Timsit-Berthier M, Delaunoy J, Koninckx N, Rousseau JC. Slow potential changes in psychiatry. I. Contingent negative variation. Electroencephalogr Clin Neurophysiol 1973;35:355–361. 129. Wagner M, Rendtorff N, Kathmann N, Engel RR. CNV, PINV and probe-evoked potentials in schizophrenics. Electroencephalogr Clin Neurophysiol 1996;98:130–143. 130. Chiarenza GA, Papakostopoulos D, Dini M, Cazzullo CL. Neurophysiological correlates of psychomotor activity in chronic schizophrenics. Electroencephalogr Clin Neurophysiol 1985;61:218–228. 131. Singh J, Knight RT, Rosenlicht N, Kotun JM, Beckley DJ, Woods DL. Abnormal premovement brain potentials in schizophrenia. Schizophr Res 1992;8:31–41. 132. Fuller R, Nathaniel-James D, Jahanshahi M. Movementrelated potentials prior to self-initiated movements are impaired in patients with schizophrenia and negative signs. Exp Brain Res 1999;126:545–555. 133. Fuller R, Jahanshahi M. Impairment of willed actions and use of advance information for movement preparation in schizophrenia. J Neurol Neurosurg Psychiatry 1999;66:502–509. 134. Fuller R, Jahanshahi M. Concurrent performance of motor tasks and processing capacity in patients with schizophrenia. J Neurol Neurosurg Psychiatry 1999;66:668–671. 135. van den Bosch RJ, Rozendaal N. Subjective cognitive dysfunction, eye tracking, and slow brain potentials in schizophrenic and schizoaffective patients. Biol Psychiatry 1988;24:741–746. 136. Verleger R, Wascher E, Arolt V, Daase C, Strohm A, Kompf D. Slow EEG potentials (contingent negative variation and post-imperative negative variation) in schizophrenia: their association to the present state and to Parkinsonian medication effects. Clin Neurophysiol 1999;110:1175–1192. 137. Klein C, Rockstroh B, Cohen R, Berg P. Contingent negative variation (CNV) and determinants of the post-imperative negative variation (PINV) in schizophrenic patients and healthy controls. Schizophr Res 1996;21:97–110. 138. Klein C, Andresen B, Berg P, Kruger H, Rockstroh B. Topography of CNV and PINV in schizotypal personality. Psychophysiology 1998;35:272–282. 139. Kathmann N, Jonitz L, Engel RR. Cognitive determinants of the postimperative negative variation. Psychophysiology 1990;27:256–263. 140. Rockstroh B, Cohen R, Berg P, Klein C. The postimperative negative variation following ambiguous matching of auditory stimuli. Int J Psychophysiol 1997;25:155–167. 141. Rockstroh B, Cohen R, Hauk O, et al. Topography of the post-imperative negative variation in schizophrenic patients and controls obtained from high-resolution ERP maps. Electroencephalogr Clin Neurophysiol Suppl 1999;49: 210–214. 142. Strandburg RJ, Marsh JT, Brown WS, Asarnow RF, Guthrie D. Information-processing deficits across childhood- and adult-onset schizophrenia. Schizophr Bull 1994;20: 685–695. 143. Yee BK, Keist R, von Boehmer L, et al. A schizophreniarelated sensorimotor deficit links alpha 3-containing

84

144.

145.

146.

147.

148.

149.

150.

151.

152.

153.

154.

155.

156.

157.

158.

159. 160.

S. Bender et al. GABAA receptors to a dopamine hyperfunction. Proc Natl Acad Sci U S A 2005;102:17154–17159. Westphal KP, Grozinger B, Becker W, et al. Spectral analysis of EEG during self-paced movements: differences between untreated schizophrenics and normal controls. Biol Psychiatry 1992;31:1020–1037. Mattay VS, Callicott JH, Bertolino A, et al. Abnormal functional lateralization of the sensorimotor cortex in patients with schizophrenia. Neuroreport 1997;8:2977–2984. Schroder J, Wenz F, Schad LR, Baudenstiel K, Knopp MV. Sensorimotor cortex and supplementary motor area changes in schizophrenia. A study with functional magnetic resonance imaging. Br J Psychiatry 1995;167:197–201. Schroder J, Essig M, Baudenstiel K, et al. Motor dysfunction and sensorimotor cortex activation changes in schizophrenia: A study with functional magnetic resonance imaging. Neuroimage 1999;9:81–87. Bertolino A, Blasi G, Caforio G, et al. Functional lateralization of the sensorimotor cortex in patients with schizophrenia: effects of treatment with olanzapine. Biol Psychiatry 2004;56:190–197. Erlenmeyer-Kimling L, Hans S, Ingraham L, et al. Handedness in children of schizophrenic parents: data from three high-risk studies. Behav Genet 2005;35:351–358. Johnstone EC, Ebmeier KP, Miller P, Owens DG, Lawrie SM. Predicting schizophrenia: findings from the Edinburgh High-Risk Study. Br J Psychiatry 2005;186:18–25. Leask SJ, Done DJ, Crow TJ. Adult psychosis, common childhood infections and neurological soft signs in a national birth cohort. Br J Psychiatry 2002;181:387–392. Obiols JE, Serrano F, Caparros B, Subira S, Barrantes N. Neurological soft signs in adolescents with poor performance on the continous performance test: markers of liability for schizophrenia spectrum disorders? Psychiatry Res 1999;86:217–228. Niethammer R, Weisbrod M, Schiesser S, et al. Genetic influence on laterality in schizophrenia? A twin study of neurological soft signs. Am J Psychiatry 2000;157:272–274. Hans SL, Marcus J, Nuechterlein KH, Asarnow RF, Styr B, Auerbach JG. Neurobehavioral deficits at adolescence in children at risk for schizophrenia: The Jerusalem Infant Development Study. Arch Gen Psychiatry 1999;56:741–748. Browne S, Clarke M, Gervin M, et al. Determinants of neurological dysfunction in first episode schizophrenia. Psychol Med 2000;30:1433–1441. Bottmer C, Bachmann S, Pantel J, et al. Reduced cerebellar volume and neurological soft signs in first-episode schizophrenia. Psychiatry Res 2005;140:239–250. Lu BY, Martin KE, Edgar JC, et al. Effect of catechol O-methyltransferase val(158)met polymorphism on the p50 gating endophenotype in schizophrenia. Biol Psychiatry 2007;62:822–825. Freedman R, Olincy A, Ross RG, et al. The genetics of sensory gating deficits in schizophrenia. Curr Psychiatry Rep 2003;5:155–161. Leonard S, Adams C, Breese CR, et al. Nicotinic receptor function in schizophrenia. Schizophr Bull 1996;22:431–445. Cadenhead KS, Light GA, Shafer KM, Braff DL. P50 suppression in individuals at risk for schizophrenia: the convergence of clinical, familial, and vulnerability marker risk assessment. Biol Psychiatry 2005;57:1504–1509.

161. Light GA, Braff DL. The “incredible shrinking” P50 eventrelated potential. Biol Psychiatry 1998;43:918–920. 162. Light GA, Geyer MA, Clementz BA, Cadenhead KS, Braff DL. Normal P50 suppression in schizophrenia patients treated with atypical antipsychotic medications. Am J Psychiatry 2000;157:767–771. 163. Myles-Worsley M. P50 sensory gating in multiplex schizophrenia families from a Pacific island isolate. Am J Psychiatry 2002;159:2007–2012. 164. Myles-Worsley M, Ord L, Blailes F, Ngiralmau H, Freedman R. P50 sensory gating in adolescents from a pacific island isolate with elevated risk for schizophrenia. Biol Psychiatry 2004;55:663–667. 165. Winterer G, Egan MF, Radler T, Coppola R, Weinberger DR. Event-related potentials and genetic risk for schizophrenia. Biol Psychiatry 2001;50:407–417. 166. Schulze KK, Hall MH, McDonald C, et al. P50 auditory evoked potential suppression in bipolar disorder patients with psychotic features and their unaffected relatives. Biol Psychiatry 2007;62:121–128. 167. Weisser R, Weisbrod M, Roehrig M, Rupp A, Schroeder J, Scherg M. Is frontal lobe involved in the generation of auditory evoked P50? Neuroreport 2001;12:3303–3307. 168. Kisley MA, Polk SD, Ross RG, Levisohn PM, Freedman R. Early postnatal development of sensory gating. Neuroreport 2003;14:693–697. 169. Marshall PJ, Bar-Haim Y, Fox NA. The development of P50 suppression in the auditory event-related potential. Int J Psychophysiol 2004;51:135–141. 170. de Wilde OM, Bour LJ, Dingemans PM, Koelman JH, Linszen DH. A meta-analysis of P50 studies in patients with schizophrenia and relatives: differences in methodology between research groups. Schizophr Res 2007;97: 137–151. 171. Anokhin AP, Vedeniapin AB, Heath AC, Korzyukov O, Boutros NN. Genetic and environmental influences on sensory gating of mid-latency auditory evoked responses: a twin study. Schizophr Res 2007;89:312–319. 172. Cadenhead KS, Light GA, Geyer MA, McDowell JE, Braff DL. Neurobiological measures of schizotypal personality disorder: defining an inhibitory endophenotype? Am J Psychiatry 2002;159:869–871. 173. Gottesman, II, Gould TD. The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry 2003;160:636–645. 174. Arnold SE, Talbot K, Hahn CG. Neurodevelopment, neuroplasticity, and new genes for schizophrenia. Prog Brain Res 2005;147:319–345. 175. Hashimoto R, Straub RE, Weickert CS, et al. Expression analysis of neuregulin-1 in the dorsolateral prefrontal cortex in schizophrenia. Mol Psychiatry 2004;9:299–307. 176. Weickert CS, Hyde TM, Lipska BK, et al. Reduced brainderived neurotrophic factor in prefrontal cortex of patients with schizophrenia. Mol Psychiatry 2003;8:592–610. 177. Gogtay N, Sporn A, Clasen LS, et al. Comparison of progressive cortical gray matter loss in childhood-onset schizophrenia with that in childhood-onset atypical psychoses. Arch Gen Psychiatry 2004;61:17–22. 178. Gogtay N, Sporn A, Clasen LS, et al. Structural brain MRI abnormalities in healthy siblings of patients with childhoodonset schizophrenia. Am J Psychiatry 2003;160:569–571.

21

Functional and Structural Endophenotypes in Schizophrenia

179. Gogtay N, Giedd J, Rapoport JL. Brain development in healthy, hyperactive, and psychotic children. Arch Neurol 2002;59:1244–1248. 180. Pantelis C, Velakoulis D, McGorry PD, et al. Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison. Lancet 2003;361:281–288. 181. Pantelis C, Yucel M, Wood SJ, et al. Structural brain imaging evidence for multiple pathological processes at different stages of brain development in schizophrenia. Schizophr Bull 2005;31:672–696. 182. Sporn AL, Greenstein DK, Gogtay N, et al. Progressive brain volume loss during adolescence in childhood-onset schizophrenia. Am J Psychiatry 2003;160:2181–2189. 183. Thompson PM, Vidal C, Giedd JN, et al. Mapping adolescent brain change reveals dynamic wave of accelerated gray matter loss in very early-onset schizophrenia. Proc Natl Acad Sci U S A 2001;98:11650–11655. 184. Gogtay N, Giedd JN, Lusk L, et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci U S A 2004;101:8174–8179. 185. Cannon TD, van Erp TG, Huttunen M, et al. Regional gray matter, white matter, and cerebrospinal fluid distributions in schizophrenic patients, their siblings, and controls. Arch Gen Psychiatry 1998;55:1084–1091. 186. McDonald C, Grech A, Toulopoulou T, et al. Brain volumes in familial and non-familial schizophrenic probands and their unaffected relatives. Am J Med Genet 2002;114:616–625. 187. Cannon TD, van Erp TG, Bearden CE, et al. Early and late neurodevelopmental influences in the prodrome to schizophrenia: contributions of genes, environment, and their interactions. Schizophr Bull 2003;29:653–669. 188. Buchsbaum MS, Friedman J, Buchsbaum BR, et al. Diffusion tensor imaging in schizophrenia. Biol Psychiatry 2006;60:1181–1187. 189. Winterer G, Coppola R, Goldberg TE, et al. Prefrontal broadband noise, working memory, and genetic risk for schizophrenia. Am J Psychiatry 2004;161:490–500. 190. Winterer G, Weinberger DR. Genes, dopamine and cortical signal-to-noise ration in schizophrenia. Trends Neurosci 2004;27:683–690. 191. Weinberger DR, Lipska BK. Cortical maldevelopment, anti-psychotic drugs, and schizophrenia: a search for common ground. Schizophr Res 1995;16:87–110. 192. Yoon JH, Minzenberg MJ, Ursu S, Walters R, Wendelken C, Ragland JD, Carter CS Association of dorsolateral prefrontal cortex dysfunction with disrupted coordinated brain activity in schizophrenia: relationship with impaired cognition, behavioral disorganization, and global function. Am J Psychiatry, 2008;165(8):1006–1014. 193. Spencer KM, Nestor PG, Perlmutter R, et al. Neural synchrony indexes disordered perception and cognition in schizophrenia. Proc Natl Acad Sci U S A 2004;101:17288–17293. 194. Winterer G, Coppola R, Egan MF, Goldberg TE, Weinberger DR. Functional and effective frontotemporal connectivity and genetic risk for schizophrenia. Biol Psychiatry 2003;54:1181–1192.

85

195. Bender S, Weisbrod M, Bornfleth H, Resch F, OelkersAx R. How do children prepare to react? Imaging maturation of motor preparation and stimulus anticipation by late contingent negative variation. Neuroimage 2005;27:737–752. 196. Fish B, Kendler KS. Abnormal infant neurodevelopment predicts schizophrenia spectrum disorders. J Child Adolesc Psychopharmacol 2005;15:348–361. 197. Weaver IC, Cervoni N, Champagne FA, et al. Epigenetic programming by maternal behavior. Nat Neurosci 2004;7:847–854. 198. Kato T, Iwamoto K, Kakiuchi C, Kuratomi G, Okazaki Y. Genetic or epigenetic difference causing discordance between monozygotic twins as a clue to molecular basis of mental disorders. Mol Psychiatry 2005;10:622–630. 199. Caruncho HJ, Dopeso-Reyes IG, Loza MI, Rodriguez MA. A GABA, reelin, and the neurodevelopmental hypothesis of schizophrenia. Crit Rev Neurobiol. 2004;16:25–32. 200. Riley B. Linkage studies of schizophrenia. Neurotox Res 2004;6:17–34. 201. Lewis CM, Levinson DF, Wise LH, et al. Genome scan meta-analysis of schizophrenia and bipolar disorder, part II: Schizophrenia. Am J Hum Genet 2003;73:34–48. 202. Iwata Y, Nakajima M, Yamada K, et al. Linkage disequilibrium analysis of the CHRNA7 gene and its partially duplicated region in schizophrenia. Neurosci Res 2007;57: 194–202. 203. Puri V, McQuillin A, Thirumalai S, et al. Failure to confirm allelic association between markers at the CAPON gene locus and schizophrenia in a British sample. Biol Psychiatry 2006;59:195–197. 204. Fallin MD, Lasseter VK, Wolyniec PS, et al. Genomewide linkage scan for schizophrenia susceptibility loci among Ashkenazi Jewish families shows evidence of linkage on chromosome 10q22. Am J Hum Genet 2003;73: 601–611. 205. Abou Jamra R, Schmael C, Cichon S, Rietschel M, Schumacher J, Nothen MM. The G72/G30 gene locus in psychiatric disorders: a challenge to diagnostic boundaries? Schizophr Bull 2006;32:599–608. 206. Schulze KK, Hall MH, McDonald C, et al. P50 Auditory Evoked Potential Suppression in Bipolar Disorder Patients With Psychotic Features and Their Unaffected Relatives. Biol Psychiatry 2007;62:121–128. 207. Bramon E, Dempster E, Frangou S, et al. Is there an association between the COMT gene and P300 endophenotypes? Eur Psychiatry 2006;21:70–73. 208. Jin Y, Bunney WE, Jr., Sandman CA, et al. Is P50 suppression a measure of sensory gating in schizophrenia? Biol Psychiatry 1998;43:873–878. 209. de Wilde O, Bour L, Dingemans P, Koelman J, Linszen D. Failure to find P50 suppression deficits in young first-episode patients with schizophrenia and clinically unaffected siblings. Schizophr Bull 2007;97:137–151. 210. Arnfred SM, Chen AC, Glenthoj BY, Hemmingsen RP. Normal p50 gating in unmedicated schizophrenia outpatients. Am J Psychiatry 2003;160:2236–2238.

Chapter 22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders Daniel Mamah, Deanna M. Barch, and John G. Csernansky

Abstract Abnormal size of cortical and subcortical brain structures is strongly associated with schizophrenia, and has also been reported in unaffected relatives of patients, and in those with associated diagnoses such as schizotypal personality disorder. Such volumetric measures have been considered as potential candidates for schizophrenia endophenotypes as they are heritable, co-segregate with the broadly defined neurocognitive and behavioral phenotypes within first degree relatives, and are frequently present in unaffected family members. In recent years, shape analyses have become of increasing interest due to their potential to precisely locate surface defects and their increased sensitivity for subtle volume changes. Statistical analysis of shape variables have recently been shown to improve the discrimination of individuals with schizophrenia from healthy controls, when included with overall volume. Computerized methods of shape analysis have been used to detect systematic differences in regional cortical thickness and gyral patterns between individuals. Methods such as largedeformation high-dimensional brain mapping and spherical harmonics have identified localized abnormalities within the hippocampus, thalamus and basal ganglia in schizophrenia, which were similar to those observed in unaffected siblings. The chapter reviews the status of current research involving both shape and volume of brain structures that applies to schizophrenia,

and discusses future research directions required to establish these measures as endophenotypes. Keywords Schizophrenia, Schizophrenia Spectrum Disorders, Schizotypal Personality Disorder, Neuroimaging, MRI, Morphometry, Shape Analysis, Brain, Cortex, Subcortical Structures.

Abbreviations AHC: Amygdala–hippocampal complex; BG: Basal ganglia; CA: Computational anatomy; CC: Corpus callosum; CG: Cingulate gyrus; COS: Childhood-onset schizophrenia; CSF: Cerebrospinal fluid;CSP: Cavum septum pellucidi; CT: Computer tomography; DSM: Diagnostic and Statistical Manual (of Mental Disorders); DZ: Dizygotic; GM: Gray matter; HDBM-LD: Large-deformation high-dimensional brain mapping; IFG: Inferior frontal gyrus; LCMDM: Labeled Cortical Mantle Distance Mapping; MFC: Medial frontal cortex; MFG: Middle frontal gyrus; MR: Magnetic resonance; MRI: Magnetic resonance imaging; MTL: Medial temporal lobe; MZ: Monozygotic; NV:Normal volunteer; PFC: Prefrontal cortex; SANS: Scale for the assessment of negative (psychotic) symptoms; SAPS: Scale for the assessment of positive (psychotic) symptoms; SCID: Structured clinical interview for DSM disorders SPD:Schizotypal personality disorder SPHARM: Spherical harmonics; STG: Superior temporal gyrus; VBM: Voxel based morphometry; WM: White matter

D. Mamah Department of Psychiatry, Washington University Medical School, St. Louis, Missouri. D.M. Barch Department of Psycology, Psychiatry and Radiology, Washington University Medical School, St. Louis, Missouri. J.G. Csernansky Department of Psychiatry and Behavioral Sciences, Northwestern University Medical School, Chicago, Illinois

Introduction The concept of ‘schizophrenia spectrum disorders’ has been used to describe those psychiatric conditions that appear to have genetic, familial and etiologic associations

M.S. Ritsner (ed.), The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, © Springer Science + Business Media B.V. 2009

87

88

to schizophrenia.1–3 Diagnoses generally included as part of the spectrum are those classified as “psychotic disorders” in the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM) such as schizoaffective disorder, as well as the ‘cluster A personality disorders’ i.e. schizotypal, schizoid and paranoid personality disorders. Individuals with these disorders also often have common phenomenological and biological characteristics with patients with schizophrenia. For example, patients with schizotypal personality disorder (SPD) share their persistent asociality and cognitive impairment, albeit to a milder degree.3 Similar, but varying degrees of imaging and physiological findings also have been reported among individuals with schizophrenia spectrum disorders.4,5 Although traditionally not considered a schizophrenia spectrum disorder, individuals with bipolar disorder, especially those with a history of psychotic symptoms, appear to have genetic, familial and symptomatic overlap with schizophrenia.6–8 The unaffected siblings and other first-degree relatives of those with schizophrenia are also at a significantly higher risk for developing the disorder than the general population, owing to the role of genetics in the etiology of schizophrenia. These relatives often demonstrate prodromal symptoms as well as biological abnormalities similar to those with schizophrenia, and thus from a practical sense, could be included in the schizophrenia spectrum. Studies of unaffected relatives and individuals with schizophrenia-spectrum personality disorders have been beneficial to better understand the pathophysiology of schizophrenia. These individuals are freer from the multiple artifacts that potentially confound research in schizophrenia, including the effect of long-term and usually ongoing medication treatment, multiple hospitalizations or institutionalization, and prolonged functional impairment secondary to chronic psychosis and social deterioration. Among the spectrum disorders, the vast majority of imaging studies have focused on schizophrenia. Recent structural studies of psychiatric disorders typically are evaluated from MRI scans of the brain and use some mapping method to estimate the volume of a brain structure of interest. The term “morphometry” originates from the Greek word “morph” meaning shape or form. In the research literature however, ‘morphometric studies’ are most often used to quantify the volumes or cross-sectional areas of brain regions. For example, the neuroimaging analysis method, “Voxel

D. Mamah et al.

Based Morphometry (VBM)” involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects, and thus allows for the investigation of focal differences in brain volume.9 While immensely valuable, these measures do not reflect the rich variety of structure seen in MR images of the brain. More recently, studies have also investigated the shape of deep brain structures of interest, such as the hippocampus and thalamus,10–12 that can give valuable information about regional surface contouring of a given brain structure. Changes in such surface contouring may reflect important changes in the neurodevelopment of these regions, as described in more detail below. In the cortex, measures of sulcal depth, surface smoothness, surface area and cortical thickness can also provide additional quantitative information in addition to volume.13–15 Schizophrenia is thought to be caused by the interaction of genetic effects and environmental insults that disturbs neurodevelopment and leads to changes in the structure and function of a network of connected structures.16 As suggested by the term ‘neurodevelopment’, the structural abnormalities of brain volume observed in schizophrenia could be influenced by intrauterine, perinatal, and extrauterine insults, as well as neurobiological maturational processes. MR studies have demonstrated volume reductions in various brain structures in schizophrenia. However, simple MR volumetric reductions are not enough to be evidence of the neurodevelopmental abnormality because they are also observed in neurodegenerative cortical atrophy. More advanced morphometric tools are needed in order to elucidate structural deformities derived from the abnormal neurodevelopmental processes. There is growing evidence to suggest that shape deformations in brain structure may reflect abnormalities in neurodevelopment,17 as have been suggested in other brain disorders. Temporal lobe epilepsy, another neurodevelopmental disorder, can manifest with shape disturbances in the brain.18 Abnormal brain morphology in schizophrenia has been demonstrated in various deeply lying gray matter structures such as the hippocampus or hippocampus–amygdala complex,10,19,20 thalamus11,12 and basal ganglia,21–23 as well as the corpus callosum.24 In addition, the cortical surface has also an object of the shape analysis in schizophrenia.13,15 Thus, analysis of aspects of surface structure in addition to overall volume can have significant implications for psychiatric neuroscience. Shape analysis

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

methods can (1) preserve information on individual variability across many complex regions in three dimensions, (2) detect fine details in gross morphology, including very local shape differences and asymmetries, (3) provide maps of anatomic differences in a common stereotaxic coordinate system, (4) point to pathophysiological mechanisms that target different functional systems in psychiatric disorders, and that may implicate specific abnormal developmental or degenerative processes, and (5) allow direct comparison of functional and structural data that can be combined across studies.25

The Concept of Endophenotypes and Brain Structure in Schizohprenia Endophenotypes are measurable components unseen by the unaided eye along the pathway between disease and distal genotype.26 Endophenotypes represent simpler clues to genetic underpinnings than the disease syndrome itself. In addition to furthering genetic analysis, endophenotypes can clarify classification and diagnosis as well as foster the development of animal models. However, to be most useful, endophenotypes of disorders must meet certain criteria, including association with a candidate gene or gene region, heritability, cosegregation in families and state-independence (i.e., manifest in an individual whether or not the illness is active). Over the past 3 decades, there has been an impressive body of literature in support of alterations of the size of various brain structures in schizophrenia,27,28 and these have emerged as promising candidates as schizophrenia endophenotypes.29 Volumetric brain changes are robustly associated with schizophrenia, are state-independent and may cut across the diagnostic boundaries of major psychotic illnesses. The size of several brain structures tends to be heritable, co-segregate with the broadly defined neurocognitive and behavioral phenotypes within the first degree relatives of schizophrenia patients, and are present in the unaffected family members more frequently than the general population. Recently shape abnormalities have also been considered as potential endophenotypes in schizophrenia.12,22,30 Shape measures have been reported to show significant difference in patients and controls even in the absence of volumetric differences.12,22 This suggests that since

89

measures of shape account for subtle surface differences in a brain structure, they can potentially more powerfully discriminate groups than volumetric measures alone. Thus, shape measures may be particularly well suited for identifying endophenotypic markers in at risk populations who may have more subtle deficits. The heritability of shape of gray matter structures have been suggested in schizophrenia by studying unaffected siblings of patients.12,22,31 Similarities found in shape alterations of the corpus callosum, the major white matter tract in the brain, between patients with SPD and schizophrenia also support the concept of heritability of shape measures.32 It is also notable, that previous shape studies in schizophrenia do not show correlations between current severity of psychopathology12,22 suggesting state-independence. Rather correlations between structural measures were only significant when lifetime measures of psychopathology were studied. Whether the structural measures observed are specific to schizophrenia, particularly regarding measures of shape, has however not been extensively studied.

Methods of Morphometric Analyses Used in Schizophrenia The first MRI study of schizophrenia was conducted in 1984,33 which was later followed by the development of second-generation MR scanners and more sophisticated image acquisition and post-processing techniques. The accompanying improvement in spatial resolution made MR ideal for the analysis of subtle brain changes between normal controls and schizophrenia patients. The brain volume reductions observed in schizophrenia are relatively small compared to controls and thus, improved measurement techniques became necessary to identify structural abnormalities. Quantitative assessment of the physical integrity of individual brain structures in neuroimaging most often includes segmentation followed by volume measurements. Volume changes are intuitive features as they might result from atrophy or dilation of structures due to illness. On the other hand, structural changes focused at a specific location of a structure or changes like bending/flattening are not sufficiently reflected in global volume measurements. Three-dimensional shape analysis incorporating information about statistical

90

biological variability can thus produce additional information about brain structure. There are two major categories involved in a shape analysis: the quantitative description and the model based representation. The quantitative description includes use of the skeleton or medial axis, to extract shape features to obtain summary measures of all the structures.34–36 The model based representation covers physically-based shape representations (such as thinplate-splines and fiducials), and elastically deformable contour and surface models, to measure regional differences in the shape.20,37–44 One of the critical issues in many shape analysis methods is defining the homologous points between the template brain scan and the target brain scans evaluated. Using highdimensional transformation, the template surface is first represented as a three-dimensional surface, which is transformed into each and every target.39,45 However, this approach assumes no transformation error, and needs manually placed landmarks, which can introduce intra- or inter-rater variability. Shenton et al.20 used a Fourier descriptor to align the entire hippocampus, by translation to the surface centroid and rotation to the three axes of the first-order approximation. As only a coarse alignment is applied, errors could be introduced when comparing the regional changes of the corresponding points. Another important issue in the shape analysis is to normalize for brain size and gender, since structural differences are sensitive to these factors. Estimates of shape have been previously evaluated in schizophrenia using simpler measures of shape. For example, shape has been estimated by geometric measures of brain regions in transverse or sagittal sections.46,47 Levitt et al.48 used a surface area–volume ratio to generate a quantitative index of how much a given shape differs from a sphere. Others have used the ‘fractal dimension’, which estimates the gyral complexity of the cortical surface using a single numerical value.49–51 The following lists some of the computational shape analysis methods previously used in the analysis of the brains of individuals with schizophrenia. While other related tools for computational anatomy are currently in use or in development and may be used in schizophrenia (see for example, Khan et al.52), these are not listed here.

D. Mamah et al.

Large-Deformation High-Dimensional Brain Mapping (HDBM-LD) HDBM-LD is based on Grenanders’ general pattern theory by representing the typical brain structures via templates and their variabilities via probabilistic transformations applied to the templates.53–56 First, a template MR scan is collected from a control subject, who is not used in the analyses. The deep brain structure of interest is manually outlined and a template surface generated by superimposing a triangulated mesh of points onto its external boundary. Next, in the template scan and in the scan of all subjects in the study, landmarks are placed in the external boundary of the brain, the intersection of the anterior and posterior commisures in the midsagittal plane, and at preselected points within and around the structure of interest. Finally, the template surface is mapped onto the left and right sides of each subject in a two-step process that first uses the landmarks to provide “anchor” point for critical scan registration. This initial registration is then followed by high-dimensional transformation56,57 (Fig. 22.1). The transformations are diffeomorphisms constrained by the laws of continuum mechanics while allowing all data points independent freedom to match, so that the geometric properties of neuroanatomical substructures are preserved (e.g. unbroken surfaces) and their details are maintained. When the boundaries of template brain structures are carried along with the transformations, the volume and shape of the same brain structures in the target scans can be quantified. Deformation vectors generated during transformation can be used for statistical analysis of shape. HDBM-LD has been used for evaluating the surface structure of deeply lying gray matter structures conceptually enclosed by a single surface, such as the hippocampus,10,39,58 thalamus,10,12 basal ganglia21,22 and amygdala (Csernansky et al., 2008), with identification of regional abnormalities within the structure of interest. HDBM-LD has also been used to quantify the asymmetries of paired subcortical structures,45 and for quantifying changes in neuroanatomical shapes over time.59

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

91

Fig. 22.1 Large-deformation high-dimensional transformation and surface generation. (a) High-dimensional deformation of template scan into a target. Automatic fluid transformation is depicted in a subvolume (around the hippocampus) of the coarsely transformed three-dimensional template brain scan (1). Fully automatic, intensity-based transformation is computed for the subvolume so that the

transformed template matches a subvolume containing the target subjects’ hippocampus (2). (b) Three-dimensional surface renderings compare the hippocampus of schizophrenia patients (blue) and that of healthy subjects (purple), and a difference image (bottom) with surface vector differences are shown (Reprinted with permission from Haller et al. (1997), Radiology57)

Spherical Harmonic (SPHARM) Based Shape Analysis

tion scheme, low order spherical harmonic functions represent coarse features of the three-dimensional structure, whereas adding higher order functions successfully add details of object surfaces (Fig. 22.2). Each individual SPHARM description is composed of a set of coefficients, weighting the basis spherical harmonic functions for the three surface coordinates separately. All the objects are aligned by translation to the surface centroid, and rotation to the three axes of the first order approximation, which is a 3D ellipsoid (Fig. 22.2). Using an icosahedron subdivision based sampling of the spherical parameterization, the SPHARM-PDM description is computed. Prior to shape analysis, group average objects are computed for each subject group, and an overall average

The SPHARM-PDM (point discrimination model) description is a sampled surface description derived from a hierarchical, parametric SPHARM shape description suitable for single connected brain structures such as the hippocampus, caudate or the lateral ventricles. Volumetric segmentations of the brain structures are first converted into parametric surface nets expanded into shape descriptions using spherical harmonic expansion.60 The spherical parameterization is computed via optimizing an equal area mapping of the 3D voxel mesh onto the sphere and minimizing angular distortions. In this shape descrip-

92

D. Mamah et al.

Fig. 22.2 Hierarchical Fourier surface representation of the amygdala–hippocampal complex. This figure shows reconstructions up to order 1 (top left), 3 (bottom left), 7 (top right) and 12 (bottom right). Of note, more and more details are added with increasing

order (i.e. from 1 to 12). The first order representation is an ellipsoid and is used for a spatial alignment of shapes by translation and rotation (Reprinted from Shenton et al. (2002) Psychiatry Research: Neuroimaging.20 Copyright with permission from Elsevier)

Fig. 22.3 Medial shape analysis (schematically shown in 2D). The differences in the thickness (radius, top graph) and position properties (lower graph) between two m-reps can be studied separately. The

properties express different kinds of underlying processes, i.e. growth versus deformation (Reprinted from Styner et al. (2003), Medical Image Analysis.66 Copyright with permission from Elsevier)

object is computed over all group average objects. Each average structure is computed by averaging the 3D coordinates of corresponding surface points across the group. The overall average object is then employed in the traditional SPHARM-PDM analysis as a template for the computation of a distance map. At every boundary point for each object, the distance map represents the signed local Euclidean surface distance magnitude to the template object. The signed template distance is then analyzed locally for

group differences via standard univariate statistical analysis methods such as ANOVA.19,20,61 In addition to this traditional template based univariate analysis, a newer template-free multi-variate analysis analyzes shape differences locally via the Hotelling T2 group difference.62,63 SPHARM has been used to study the shape of the hippocampus,19 the hippocampus–amygdala complex,20 the basal ganglia64 and the lateral ventricles61 in schizophrenia.

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

Medial Shape Analysis via M-rep An M-rep65 is a linked set of medial primitives called medial atoms. The medial atoms are grouped by intrafigural links into figures that are connected by interfigural links. Via interpolation, a fully connected boundary is implied by the M-rep. The single figure M-rep of a hippocampus object is shown in Fig. 22.3, with its implied boundary. The individual M-rep description is determined by fitting a previously computed M-rep model to the object-boundary. The model is computed such that it adequately represents the underlying anatomy in a given training population.66 A fully automatic optimization procedure computes both the set of medial figures and the set of medial atoms of the medial manifolds. The optimization finds the minimal M-rep model that represents the training population with a predefined maximal approximation error. For the traditional shape analysis, first the overall average object is computed by averaging the position and radius for each medial atom across the group. The overall average object serves as the template. Then, the signed position differences to the template are computed for each M-rep, while the thickness (radius) information is analyzed in its raw form. In contrast to boundary shape analysis (e.g. SPHARM), a medial shape analysis separately studies the two medial shape properties: local position and thickness.66 This separation of shape differences gives the medial shape analysis a higher intuitiveness, as especially local growth or degeneration processes are captured separately from any volume preserving deformation processes. The template-free approach described for the SPHARMPDM shape analysis is applicable here as well. The M-rep medial analysis has been previously used to investigate the shape of the hippocampus,19 the lateral ventricle,61 and the caudate.23

Cortical Pattern Matching Cortical pattern matching67,68 has been to study the surface morphology of the cerebral cortex. It has been used to localize disease effects on cortical anatomy over time and to increase the power to detect systematic group differences and changes. The approach models and controls for gyral pattern variations across

93

subjects. It visualizes average maps of cortical change in a population and encodes its variance and any group differences. From each subject’s cortical models at different time points, a 3D deformation vector field is computed measuring brain surface shape change across the time interval.13,68 This accommodates any brain shape changes when cortical gray matter is compared within a subject across time. The deformation reconfigures the earlier cortex into the shape of the later one, matching the entire gyral patterns and cortical surfaces in the pair of 3D image sets. To create an average 3D cortical model for each group of subjects, all sulcal-gyral landmarks are flattened into a two-dimensional (2D) plane along with the cortical model. A color code (Fig. 22.4) retains the original 3D position of each cortical point as a red, green, and blue color triplet plotted in the two-dimensional parameter space. Once data are in this flat space, sulcal features are aligned across subjects with a warping technique. Local measures of gray matter density are convected along with these warps and plotted on the average cortex before statistical analysis. To allow data to be averaged and compared across corresponding cortical regions, a deformation can also be computed that matches gyral patterns across all the subjects in a study, in addition to the deformation that matches anatomy over time.69 A set of sulcal landmarks per brain is used to constrain the mapping of one cortex onto another. This associates corresponding cortical regions across subjects. Interrater and intrarater reliability of this method has been reported previously.70 Cortical pattern matching has also been used to compare differences in the surface morphology of the two cerebral hemispheres, i.e. asymmetries.14

Labeled Cortical Mantle Distance Mapping (LCMDM) The mantle of the cerebral cortex is a thin laminar structure (i.e., approximately 3 mm in thickness) with a large surface area. In recent years, cortical mantle reconstruction via statistical decision methods has been developed.40,71–74 Automated generation of 2D surface coordinate system on the cortex has improved dramatically as well.71,75,76 LCMDM was specifically designed and validated for the analysis of macroscopic features of

94

D. Mamah et al.

Fig. 22.4 Demonstration of cortical flattening. Cortical flattening and sulcal matching are anatomic modeling steps used to help create 3D average cortical models and gray matter maps for subjects with schizophrenia and their matched controls. Each individual magnetic resonance image (a), after removal of nonbrain tissues, is segmented into gray matter, white matter, and cerebrospinal fluid (b). A 3-D cortical model is created in each subject and medial sulci and reference lines are traced as 3D curves directly on this surface model (c). The surface is made up

of discrete triangular tiles (d), and a geometric flattening process is applied to lay out the cortical regions, and the sulcal curves that delimit them, as features in two dimensions (e). Information on where these cortical points originally came from in three dimensions is preserved in this 2D image format: using a colorcoding system, cortical points’ 3D locations are given unique colors, and these colors are plotted into the flat map (e) (Reprinted with permission from Vidal et al. (2006), Archives of General Psychiatry68)

the neocortical surface, i.e. volume, thickness and surface area.74 Because it was designed to be applied locally within specific cortical regions, it offers improved tissue segmentation72,73 as compared to whole-brain methods that are more affected by image inhomogeneities caused by magnetic resonances field bias.71,77 In LCMDM,78 the 2D manifold surface associated with the gray matter/ white matter (GM/WM) interface is first identified. Then each GM, WM and CSF voxel is labeled by its distance to this interface. Using this approach, the characteristics of specific regions of the cortical mantle can be quantified via maps of frequency occurrence of the labeled voxels as functions of distance to GM/WM

interface (Fig. 22.5). Maps generated by LCMDM are sensitive to variabilities in the GM surface area, volume and thickness that might be characteristic of neuropsychiatric diseases. Thus probabilistic measures of the volume, thickness and gray matter distribution of the selected subregion of the cortical mantle can be characterized using LCMDM. LCMDM has been previously used in assessing the volume, surface area and thickness of the cortical mantle within the anterior and posterior segments of the cingulate gyrus in schizophrenia15 as well as Alzheimer’s disease,78 the prefrontal cortex in schizophrenia,79 and in the evaluation of the planum temporale.80

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

95

Fig. 22.5 Labeled cortical mantle distance mapping. (a) Region of interest (ROI): brain scan region containing the cingulate gyrus. (b) Mixture of Gaussians estimation in Bayesian tissue classification. (c) Delineated surface for the cingulate gyrus (red and teal) from the gray/white matter interface isosurface (blue).

(d) Probabilistic density functions for WM, GM and CSF. After integration to obtain cumulative distribution function (CDF), GM thickness is calculated as the distance at the 90th percentile of the GM CDF (Reprinted from Wang et al. (2007), Schizophrenia Research.15 Copyright with permission from Elsevier)

Other Methods of Computational Shape Analysis in Schizophrenia

Davatzikos et al.41 applied a whole-brain high-dimensional nonlinear patter classification technique81 to compare gray matter, white matter and ventricular structure in schizophrenia and control subjects. After automated segmentation, mass-preserving shape transformations warped individual images into a template, and the total amount of tissue in any region was preserved for subsequent analysis. A classifier was then applied by forming a morphologic signature of the individual brain via the high-dimensional shape transformation.

Several other computation analysis tools have been developed and used in psychiatric disorders. The underlying methods of some of these may be related to the previously described ‘named’ shape analysis methodologies. We list a few examples of some other shape analysis methods that have been used in evaluating brain structure in schizophrenia.

96

Jang et al.82 used a landmark-based structural and surface shape analysis of the insula in schizophrenia and control subjects. Following manual tracing of this cortical region, three points of anatomical landmarks for structural analysis were defined. An active deformable surface model was used for analyzing the outer surface of the cortical region, after subdividing the region into multiple polygons. Points of statistically significant difference within the cortical regions between groups were color coded. Narr et al.24,83 used a surface-based mesh-modeling approach to examine cortical complexity in schizophrenia

Fig. 22.6 Thin-plate spline analysis of the corpus callosum. (a) The corpus callosum (CC) is traced on sagittal MRI section. (b) The CC spline curve is fitted and divided into several segments. (c) Lines are draw connecting each outline division. (d) Several areas are created based on perpendiculars to the midline. (e) Sagittal shape differences can be visualized between subject groups after overlaying average surface points on CC outlines (Reprinted from Downhill et al. (2000), Schizophrenia Research.32 Copyright with permission from Elsevier)

D. Mamah et al.

and control subjects. After extracting cortical surfaces, a spherical mesh surface was continuously deformed to fit tissue intensity values from “scalp-edited” volumes. The resulting cortical surfaces consisted of many thousands of polygons forming high-resolution meshes of discrete triangular elements. Cortical complexity was calculated in distinct neuroanatomic regions by using sulcal outlines as landmarks for delineation. The manually outlined sulcal curves were reparameterized to render digitized points uniformly spaced. Average models of each curve were obtained by matching equally spaced points from corresponding sulci in each subject.

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

97

Downhill et al.32 traced the corpus callosum on midsagittal slices and used thin-plate spline analysis to compare groups of patients. The CC was traced by placing designated points, and a spline curve was fitted to these points (Fig. 22.6a). Twenty-nine evenly spaced points on the spline curve were located between designated landmark points along the top and the bottom edges of the CC (Fig. 22.6b). Each of the 29 points on the top of the ROI was connected with its opposite member on the bottom and the connecting line was bisected. The resultant points, along with the terminal anchoir points, were connected using a computer-generated spline to create an axial line (Fig. 22.6c), and a new set of 29 evenly spaced points were placed on this line. Finally, the CC was divided into 30 areas, based on an average perpendicular line across these points (Fig. 22.6d). To compare the sagittal shape of the CC between groups, average surface points and outlines in probands were overlaid on an average image obtained by warping MRIs of individual normal subjects into the same space (see Fig. 22.6e). Buchsbaum et al.84 used a similar method to trace and subdivide the ventricular system. DeQuardo et al.85,86 also used a landmark-based thin-plate spline shape analysis of multiple brain structures in schizophrenia. Frumin et al.87 used a two-dimensional skeleton extraction method to evaluate the shape of the corpus callosum of first-episode psychotic individuals. A disk was placed inside the segmented CC and expanded until it touched the outline of the CC on opposite points of the outline. The centers of serial expanded disks defined points that when combined become a skeleton, or curve, which in turn defined the shape in two dimensions.

gray and −0.19 for white matter.92 Although whole brain volume is reduced compared to controls when studies are combined, most individual studies fail to find a significant difference between schizophrenia and control subjects.27 The high percentage of studies with negative findings might be due to variable methods used across studies (i.e., the use of different magnetic field strengths, different slice thicknesses [1 mm to 1 cm for a slab], or contiguous slices vs. gaps between slices). Such methodological difference may prevent the detection of very small differences between groups. Very small differences in brain volume between groups however may not be inconsequential. For example, small brain volume has been reported in childhood-onset schizophrenia, suggesting that small differences in overall brain volume may reflect a more severe form of the disorder.93 Finally, there is a need for more careful selection of comparison subjects and of well-defined and well-delineated subgroups of patients; such selection might lead to the detection and clarification of very small volume differences. Progressive changes in cortical gray matter have also been found. findings from several prospective studies in early schizophrenia have consistently documented volume reductions between initial access to care assessment and follow-up MRI’s in: both cerebral hemispheres after 4 years,94 and in global gray matter after 10 months95 and 12 months.96 In the only large, randomized controlled trial with longitudinal MRI in early schizophrenia, Lieberman et al.97 documented global gray matter reductions as early as 12 weeks in haloperidol-treated schizophrenia patients but not in olanzapine-treated subjects. Shape analysis of this cohort of patients is currently ongoing.58

Structural Neuroantomical Findings in the Schizophrenia Spectrum

Unaffected Relatives

Whole Brain Schizophrenia Whole brain volume in schizophrenia has been reviewed in several meta-analyses and systematic reviews,27,88–91 which indicate mostly a 3–4% whole brain volume reduction, more apparent in cortical gray matter. The effect size appears to be around −0.31 for

In recent years, several studies have measured brain volumes in unaffected relatives of schizophrenic patients compared with those of healthy subjects. Most of these relatives showed smaller total brain volumes98–102 in relatives, but other did not.103,104 Boos et al.105 conducted a meta-analysis of brain structure in first-degree relative of schizophrenia patients and found overall gray matter volume was small in relatives compared with healthy control subjects, although these effects were small. Analyses of volumes of the total brain, intracranial space, lateral ventricles, and white matter

98

did not show significant effects. However, the analysis of total brain and white matter volume showed a trend toward significance (total brain: d = 0.28; p = 0.06; white matter: d = 0.04; p = 0.07); both smaller in relatives compared with healthy subjects.

Schizotypal Personality Disorder Brain size has not been well studied in SPD, but significant decreases have not been reported compared to controls. Dickey et al.106 found men with SPD had somewhat smaller cortical gray matter relative to comparison subjects, although this effect was not significant. In a more recent study, Koo et al.107 found decreases in neocortical gray matter in SPD.

Temporal Cortex

D. Mamah et al.

lobe. Studying unaffected relatives, Mcdonald et al.8 found that risk for schizophrenia is associated with reduction in gray matter volume in left lateral temporal regions. They also found associations with white matter volume abnormality in temporal regions usually occupied by the major interhemispheric tracts: the superior longitudinal fasciculus (which connects the frontal lobes to temporal, parietal and occipital cortices), and the inferior longitudinal fasciulus (which connects temporal pole to occipital lobes). Lawrie et al.111 found that only high-risk relatives with psychotic symptoms showed temporal lobe reduction longitudinally, after 2 years.

Schizotypal Personality Disorder Reduced gray matter volume of the temporal lobe has been previously reported in schizotypal personality disorder.112

Whole Temporal Lobe The temporal lobes lie at the sides of the brain beneath the lateral or Sylvian fissure. The temporal cortex contains the auditory cortices and is involved in auditory processing. It is also involved in semantics in speech and vision. In addition, temporal lobe also contains the hippocampal regions and is therefore also involved in declarative memory.

Schizophrenia In the majority of MRI investigations of whole temporal lobe volume, in which all structures are grouped together, findings were positive, with decreased volumes.27 Variable degrees of gray matter reduction are also generally reported for the temporal cortex in schizophrenia.27,89,108 Progressive decrease in temporal gray matter have been noted after 4 years.109

Unaffected Relatives Abnormalities in different temporal lobe regions have been noted in the unaffected relatives of schizophrenia patients.110 Most studies, however, focus on subregions of the temporal lobe, and especially of the medial temporal

Medial Temporal Lobe (Including the Hippocampus) The medial temporal lobe (MTL) includes a system of anatomically related structures that are essential for declarative memory (i.e. conscious memory for facts and events). This system consists of the hippocampal region, and the adjacent perirhinal, entorhinal and parahippocampal cortices. The hippocampus in particular, is thought to play an important role in learning and memory processing, and impairment in memory, attention and decision-making are commonly found in schizophrenia. The amygdala is sometimes included in the MTL due to its close proximity to the hippocampus, and because its delineation from the hippocampus is difficult with modern MRI segmentation tools.

Schizophrenia Compared with findings of MRI studies focusing on the whole temporal lobe, findings of MRI studies of medial temporal lobe structures are usually more striking, with most studies showing reduction in volume.27,113 Most of the MRI studies of the medial temporal lobe found more anterior amygdala-hippocampal volume reduction, although three studies found more prominent

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

posterior amygdala-hippocampal volume reduction.114,115 Moreover, most found more prominent volume reduction on the left, and more frequently in male schizophrenic patients.114,116–119 Progressive reduction in hippocampal volumes after as much as 4 years has also been reported.109 More recently, computational methods of shape analysis have been able to identify regional structural abnormalities in medial temporal lobe structure. For example, Lee et al.120 using a multiple surface deformation algorithm to obtain the parameterized surface of each hippocampus, found bilateral inward deformities in the anterior and posterior regions of the hippocampus and a unilateral outward deformity in the right anterior region of the hippocampus in patients with schizophrenia. Csernansky et al.10,39 previously used HDBM-LD to evaluate hippocampal structure in schizophrenia patients and controls, and also found similar bilateral inward deformities in the anterior region (head) of the hippocampus, with minor inward deformities in the tail. Using only the most significant eigenvectors representing shape variability, almost 60% of schizophrenia and 80% of control subjects could be correctly classified. Using both shape analysis using spherical harmonics and an M-rep shape analysis, Styner et al.19 found that compared to controls, the hippocampus is differently shaped in schizophrenia subjects, more pronounced on the right side. With M-rep shape analysis, a significant abnormality in schizophrenia was only found for hippocampal position but not thickness. Visualizing distance maps of group averages show that local shape differences are mainly located at the hippocampal tail, at a position where it connects to the fimbria. Also, Narr et al.83 reported group displacements in surface meshes in the medial and superior surfaces of the anterior hippocampus, and a smaller bilateral difference between medial/superior surfaces of the posterior hippocampus. However, using spherical harmonics, Shenton et al.20 found no differences between groups in overall volume or shape of the hippocampus–amygdala complex. In addition to group differences, asymmetries of shape have also been studied. For example, Csernansky et al.10,39 found that the hippocampus in both the schizophrenia and control subjects showed a significant hemispheric asymmetry in shape. Specifically, the left hippocampus had a less prominent lateral surface and exaggerated bending along its longitudinal axis compared with the right in both schizophrenia and control

99

subjects. However, comparing schizophrenia and control subjects, there was an exaggeration of these normative (L < R) patterns of hippocampal asymmetry in the subjects with schizophrenia. Wang et al.45 also using HDBM-LD found schizophrenia subjects have exaggerated asymmetries compared to controls, specifically related to the subiculum. Kim et al.,121 detected that the shape asymmetry of the schizophrenia group was smaller in the head and middle of the superior subiculum CA3/CA4 and the tail of the inferior CA1. Narr et al.83 reported that the noted volume asymmetries in the anterior hippocampus were more significant in males. In the study by Shenton et al.,20 left/right amygdala-hippocampal asymmetry was significantly larger in patients than controls for both relative volume and shape. The local brain regions responsible for the left/right asymmetry differences in patients with schizophrenia were in the tail of the hippocampus and in portions of the amygdala body. Interestingly, they noted that a combined analysis of volume and shape asymmetry resulted in improved differentiation between groups. Classification function analyses correctly classified 70% of cases using volume, 73.3% using shape, and 87% using combined volume and shape measures. These findings suggested that shape provides important new information toward characterizing the pathophysiology of schizophrenia, and that combining volume and shape measures provides improved group discrimination in studies investigating brain abnormalities in schizophrenia. Several interesting correlations between medial temporal lobe volume reduction and clinical symptoms have been observed. For example, Bogerts et al.114 reported a correlation between bilateral reduction of medial temporal structures (grouped together) and the psychotic factor, measured by the Brief Psychiatric Rating Scale. Other authors found a correlation between left hippocampal volume reductions and increased positive symptoms, along with disruptions in logical memory, in the affected compared with the unaffected monozygotic twin.122,123 Nestor et al.124 also noted a correlation between parahippocampal gyrus volume reduction, STG volume reduction, and poor scores on tests of verbal memory, abstraction, and categorization. In a more recent analysis by the same group of subjects studied in Harms et al.12 and Mamah et al.,22 a negative correlation was found between lifetime hallucination severity, using scores derived from the SCID (as in Mamah et al.22), and either hippocampal volume

100

(right: r = −0.61, p = 0.0001; left: r = −0.40, p = 0.047) or hippocampal canonical shape score (r = −0.42, p = 0.099). As this group previously did not find a correlation between hippocampal structure and current measures of psychopathology, it suggested that ‘lifetime measures of psychopathology’ which are more stable, may better capture structure–symptom relationships.

Unaffected Relatives Medial temporal lobe structures were reportedly smaller in several studies99,103,104 but this finding has not been universally replicated.98,101,110 Thus, although brain abnormalities have been found in first-degree relatives of schizophrenic patients, the findings are inconsistent. Moreover, effect sizes in the individual studies have not been quantitatively reviewed and integrated. Boos et al.105 conducted a meta-analysis of brain volumes in first-degree relatives of schizophrenia patients. They found that the largest difference between relatives and healthy control subjects was found in hippocampal volumes, with relatives having smaller volumes than controls. They found the largest effect in the left hippocampal volume (Cohen’s d = 0.47, p = 0.04, compared to right hippocampus: d = 0.23; p = 0.04). This finding is consistent with findings from lesion and functional MRI studies in healthy subjects, suggesting more involvement of the left hippocampus in encoding and recognition of verbal as opposed to visual or pictorial material.125 The suggestion of smaller left hippocampal volume as a vulnerability indicator for schizophrenia, put forward by Seidman et al.103 is also consistent with these observations. There was also a significant effect for hippocampal–amygdala complex (i.e. measured together) with a combined effect Cohen d of 0.52 (p = 0.005). Tepest et al.31 evaluated shape and volume of the hippocampus in schizophrenia subjects, their unaffected siblings and controls using HDBM-LD. In addition to decreased hippocampal volumes, they found similar shape abnormalities in both schizophrenia subjects and their siblings, with inward deformations noted in the head of the hippocampus after three-dimensional surface reconstruction. This pattern of hippocampal surface abnormality was similar to that observed in a nonoverlapping group of schizophrenia subjects.10

D. Mamah et al.

The finding of hippocampal volume reduction in relatives of schizophrenic patients also correlates with results of recent meta-analyses regarding cognitive functioning in relatives.126,127 In these studies, lower performance in relatives of patients compared with healthy control subjects was reported on several cognitive domains, including verbal and declarative memory, executive functioning, and attention. Interestingly, Sitskoorn et al.127 found that the largest effect size was obtained for verbal memory being significantly worse in relatives of patients than in healthy subjects. Deficits in verbal memory have generally been associated with smaller (left) hippocampal volume128 as is also the case in patients with schizophrenia129,130 and their relatives.103,131

Schizotypal Personality Disorder Interestingly, SPD subjects have not been reported to show MTL volume abnormalities similarly to schizophrenia subjects. Takahashi et al.4 did not find a difference in the size of the medial temporal lobe between SPD subjects and healthy controls. Evaluating only male SPD subjects, Dickey et al.132 also did not find a difference in the volumes of the hippocampus or parahippocampus from gendermatched controls. In a more recent study however, Dickey et al.133 reported decreased hippocampal volumes in female SPD subjects compared to controls. It was noted that a high co-morbidity of major depression, which have been associated with small hippocampal volumes,134 in the female SPD subjects may have confounded results.

Superior Temporal Gyrus The superior temporal gyrus (STG) is bounded by the lateral sulcus above, and contains several important structures of the brain including the primary auditory cortex, the cortical regions responsible for the sensation of sound; and Wernicke’s area, an important region for the processing and understanding of speech. As auditory hallucinations, a perceptual abnormality of hearing, is common in schizophrenia, abnormalities of the STG may be related functionally to schizophrenia.

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

Schizophrenia It has been reported that STG abnormalities are evident in schizophrenia when gray matter alone is evaluated but when gray and white matter are combined in the analyses, this finding is less robust.27 Nevertheless, STG size has been found to be decreased in size to various degrees in schizophrenia.89,108,113,116 Barta et al.,116 the first to investigate the STG using MRI technology, reported an 11% volume reduction in anterior STG that was associated with auditory hallucinations. Shenton et al.119 found a 15% volume reduction in the posterior STG with formal thought disorder. More specifically, left posterior STG volume reduction was associated with greater severity of formal thought disorder. This finding was intriguing because the posterior STG includes the planum temporale, a brain region long implicated as a neuroanatomical substrate of language. Others have noted progressive volume reductions, with left STG reduction after 1.5 years.135

Unaffected Relatives Few studies have investigated the superior temporal gyrus in healthy relatives of schizophrenia relatives. Goghari et al.110 found decreased surface area in the superior temporal gyrus in first degree relatives of schizophrenia subjects. A bilateral reduction in the thickness of the gray matter of the superior temporal sulcus, which borders the STG and medial temporal gyrus, has also been reported.136 These two findings suggested that both gyrus and sulcus gray matter are globally affected in at risk individuals. Rajarethinam et al.137 studied the offspring of individuals with schizophrenia and found reduced volumes of the STG in the offsprings compared to controls.

101

the heart of Wernicke’s area, one of the most important functional brain areas for language. The planum temporale shows a significant asymmetry. In about twothirds of individuals, the left planum temporale is larger than the right. In some brains, the planum temporale is more than five times larger on the left, making it the most asymmetrical structure in the brain. Some have speculated that impairment in language processing and symptoms of suspiciousness in schizophrenia may be related to planum temporale abnormalities.138

Schizophrenia Planum temporale asymmetry is established during neural development has been shown to be abnormal in schizophrenia, suggesting the importance of neurodevelopmental influences in the etiology of schizophrenia.138–140 MRI investigations of the planum temporal have been somewhat inconsistent, possibly due to variation in the measurements used. For example, some investigators141 used the bank of the Sylvian fissure to estimate planum temporale, whereas others142 used a measure of length. Most positive findings show a reduction in size of the left planum temporale or a reversal of asymmetry.119,143

Unaffected Relatives There has been a lack of structural studies of the planum temporale in relatives. Frangou et al.144 failed to find a significant difference in planum temporale asymmetry from control subjects. However an absence of a difference in asymmetry was also noted for the schizophrenia subjects also evaluated.

Schizotypal Personality Disorder Schizotypal Personality Disorder Although few studies have investigated the STG in schizotypal subjects, reduced gray matter volumes have been reported.112,132

No difference in the size of the planum temporal has been reported in schizotypal personality disorder.145

Planum Temporale

Heschl’s Gyrus

The planum temporal is the cortical area just posterior to the auditory cortex (Heschl’s gyrus) within the Sylvian fissure. It is a triangular regions which forms

The Heschl’s gyrus (sometimes called transverse temporal gyrus) is found in the area of primary auditory cortex in the superior temporal gyrus. It is the first

102

cortical structure to process incoming auditory information. Abnormalities in this region are of interest do to its possible role in the pathophysiology of auditory hallucinations.

Schizophrenia The limited studies investigating the size of this structure have generally reported volume reductions in schizophrenia compared to controls in the left hemisphere.119,143

Unaffected Relatives No published studies of the structure of the Heschl’s gyrus in first-degree relatives of schizophrenia subjects have been conducted.

Schizotypal Personality Disorder The size of the Hesch’s gyrus has been reported to be decreased on the left side in schizotypal personality disorder.145

Frontal Cortex The frontal cortex is involved in some of the most complex processing of information and modulates many aspects of human functioning, including movement and cognition. The prefrontal cortex (PFC), anterior to the premotor cortex, has been widely implicated in the pathophysiology of schizophrenia. The PFC has been implicated in planning complex cognitive behaviors, personality expression and moderating correct social behavior.

Schizophrenia MRI findings regarding this brain region have been inconsistent, however, with findings being positive in some studies89,108,130 but not others.119,143 One contributing factor may be the small number of slices used to estimate this large brain region. There is also some

D. Mamah et al.

suggestion that the conflicting findings may be due to differences in the patient populations studied. For example, some patients with more negative symptoms may present with more prefrontal lobe abnormalities while patients with more positive symptoms with more temporal lobe abnormalities,146 thus highlighting the importance of carefully characterizing patients subgroups. Others have reported regional or shape abnormalities in the frontal cortex in schizophrenia. Kuperberg et al.147 found significant cortical thinning in the medial frontal areas of adult patients, and gray matter reductions have been reported in the medial premotor cortex,148 medial frontal gyri149 and the medial prefrontal cortex.150 Narr et al.25 using a surface-based mesh-modeling approach found a in schizophrenia significant deviations in gyral complexity asymmetry in the superior frontal cortex compared to controls. Cortical complexity, which reflects the number of sulcal bifurcations in a cortical region, was greater in the schizophrenia group in the superior frontal cortex. They however did not find significant differences in gyral complexity in other cortical regions. Using more simplistic measures of shape, Falkai et al.46 reported a significant difference in the prefrontal lobe in schizophrenia subjects compared to controls. On coronal MRI slices and at defined planes at the corpus callosum boundary, measures of lobe width, height and length showed an increased height in the prefrontal lobe in schizophrenia patients, suggesting these patients have a more circular prefrontal lobe compared to controls that are more elliptical. Studies of longitudinal changes in frontal cortex volume have provided unique insights into abnormalities in schizophrenia. Progressive reduction of frontal volumes in schizophrenia has been reported after 2.5 years,130 3 years152 and 4 years.109 In a 5-year longitudinal study using ‘cortical pattern matching’ to evaluate cortical structure, Vidal et al.68 found a profound, progressive gray matter loss in childhood-onset schizophrenia patients in the superior medial frontal regions (peak values, >5% loss per year; Fig. 22.7). They also reported a significant difference between this loss rate between these patients and controls. Individuals with psychotic disorders, that were not diagnosed with schizophrenia, also showed a significantly greater longitudinal gray matter loss in the medial frontal cortex than controls, but lesser profound than that of schizophrenia

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

103

Fig. 22.7 Average rate of cortical gray matter loss in schizophrenia patients and normal adolescents. The maps show the average local rates of loss in gray matter in the medial cortex of subjects with childhood-onset schizophrenia (COS) (top row), normal volunteer (NV) adolescents (middle row), and the difference (Diff) between both groups (bottom

row). The color code shows the percentage of gray matter lost per year. (b) Shows the average gray matter loss rate in the superior medial frontal cortex (MF) and the cingulate gyrus (CG) in both left and right hemispheres (Hem) (Reprinted with permission from Vidal et al. (2006), Archives of General Psychiatry68)

subjects. Some protective effect of atypical medications on progressive cortical loss has been observed in adult patients.152

and MFG volume more related to the schizophrenic disease process itself.

Schizotypal Personality Disorder Unaffected Relatives Frontal gray matter reductions have been found in those at genetic risk for schizophrenia and in the prodromal phase of the illness.153,154 Both Job et al.155 and Diwadkar et al.156 found reduced gray matter (GM) density in PFC in relatives at high risk for schizophrenia. Harms et al.79 reported that the inferior frontal gyrus (IFG) volume of unaffected siblings was intermediate between that of schizophrenia and control subjects; whereas the middle frontal gyrus (MFG) volume was similar to that of controls. Since both MFG and IFG volumes were decreased in schizophrenia, they suggested that IFG volume may be more related to genetic (or shared environmental effects)

Frontal cortical volume, appears to be relatively preserved in studies of SPD,157,158 compared with the reductions in frontal volume found in many studies of patients with schizophrenia. However, relative reductions in frontal volume have been correlated with the deficit-like symptoms of SPD in healthy volunteers, implying that patients with lower frontal volume will be more likely to display traits such as asociality.159,160 It has been suggested that a number of factors extrinsic to the illness itself, including sustained neuroleptic treatment, alcohol abuse (rare in SPD), and chronic psychosis, might contribute to the differences between SPD/normal comparison subjects and schizophrenia/normal comparisons of frontal cortical volumes.

104

Cingulate Cortex The cingulate cortex is situated in the medial aspect of the brain above the corpus callosum, and is part of the limbic cortex. Abnormalities of the neuroanatomy of the gray matter of the cingulate gyrus, especially its anterior segment, have been suggested to be an important characteristic of schizophrenia.161 The anterior cingulate cortex is of special interest as it appears to play a role in both affective and cognitive functions, and in regulating autonomic functions (e.g. blood pressure and heart rate). The anterior cingulate cortex receives its afferent axons primarily from the anterior nucleus of the thalamus. Its major efferents are to the anterior thalamic nuclei and to other limbic areas.

D. Mamah et al.

and thickness, but not surface area. Across both groups, there was a significant L > R asymmetry in thickness of the anterior cingulate, and a significant L > R asymmetry in the surface area of the posterior cingulate. In the individuals with schizophrenia, thinning of anterior cingulate was correlated with a longer duration of illness and a greater severity of psychotic symptoms. In a 5-year longitudinal study using cortical pattern matching (see above), Vidal et al.68 found a subtle gray matter loss in childhood-onset schizophrenia patients in the cingulate gyrus, although not as profound as in frontal regions (Fig. 22.7). This rate was however significantly different than that of controls. It was also notable that individuals with psychotic disorders, that were not diagnosed with schizophrenia, also showed a significantly greater longitudinal gray matter loss in the cingulate than controls, but lesser profound than that of schizophrenia subjects.

Schizophrenia Neuroimaging studies of the cingulate gyrus in schizophrenia have been somewhat inconclusive, with some studies showing evidence of gray matter volume reduction,51,162,163 and others showing no group difference.164,165 Differential effects of anterior and posterior cingulate cortex volume have been previously suggested, with the anterior cingulate cortex showing more significant volume reductions. A recent meta-analysis by Ellison-Wright et al.166 showed decreased volume in the anterior cingulate cortex in both first-episode and chronic schizophrenia. Studies of cingulate gyrus structure in individuals with schizophrenia have also suggested gender-related volume differences,162 and the absence of the normative leftward asymmetry.167 Recently, using surface based morphometric methods, Fornito et al.168 found bilateral reductions in the anterior cingulate cortex, and Zetzsche et al.169 found differential reductions in gray matter volume in various regions within the anterior cingulate cortex. Wang et al.15 applied Labeled Cortical Mantle Distance Mapping (see above) to assess the volume, surface area and thickness of the cortical mantle within the anterior and posterior segments of the cingulate gyrus (excluding the associated paracingulate gyrus). After covarying for total cerebral volume, individuals with schizophrenia showed smaller anterior cingulate gray matter volume, thickness, but not surface area compared to control subjects. Similar group differences were found for posterior cingulate gray matter volume

Unaffected Relatives Goghari et al.110 reported bilateral decreased gray matter thickness of the cingulate gyrus, and decreased volume and surface area in the right cingulate gyrus. A reversal of asymmetry of sulcal gray matter of the cingulate has also been reported.136 Abnormalities and a decreased volume of the anterior cingulate cortex of relatives have also been described.155,170 Others however failed to find volumetric differences in the cingulate171 Schizotypal Personality Disorder There have been few studies on the cingulate cortex in SPD. Haznedar et al.172 did not find significant differences in the volume of the cingulate in SPD and controls, but reported that cingulate volumes in SPD where nevertheless intermediate between that of schizophrenia and control subjects. Takahashi et al.173 reported a lack of normal asymmetry in females from a reduction in gray matter volume on the right side in SPD.

Parietal and Occipital Cortex Few studies have evaluated the parietal and occipital lobes, despite the fact that functions such as language, eye tracking, and attention, which are known to be disrupted in schizophrenia, may involve abnormalities

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

in the parietal lobe146,174 and that visual hallucinations may result from disruption in the occipital lobe containing the visual cortex.

105

tives of individuals with schizotypal personality disorder and schizophrenia associated with a linkage marker reported to be associated schizophrenia spectrum disorders on the short arm of chromosome 5(5p14.1–13.1).178

Schizophrenia Results of the few studies available have been mixed. In studies showing positive findings, volume reductions appear to occur predominantly on the left side.27,89,175 Parietal cortical volume reductions have also been reported prospectively after 4 years in schizophrenia patients.109 Equivocal findings may be due to the fact that the parietal lobe has not been investigated with the same level of detail as that which has characterized MRI studies of the temporal lobe. This difference in level of detail is also true for the small number of occipital lobe studies, which were also inconsistent in terms of findings of abnormalities.

Unaffected Relatives Ho176 reported that first- and second-degree relatives did not have the decreases in parietal gray matter which was evident in schizophrenia subjects; however parietal white matter was increased in both schizophrenia subjects and the relatives. Interestingly, they found that in relatives, white matter deficits correlated significantly with greater severity of prodromal symptoms 1 year later. They did not however find any differences in occipital gray of white matter between groups, although a trend level decrease was noted in the white matter regions in relatives. Goghari et al.110 however did not find a difference in either occipital and parietal gray matter volumes and surface area in relatives. Some studies have suggested that abnormal parietooccipital findings in schizophrenia are environmentally induced, and not illness related.103,177 Gogtay et al.,177 for example, found only minimal parietal gray matter deficits at early ages in siblings, with little overlap with deficits found in probands.

Schizotypal Personality Disorder There has been no data on the size of the parietal or occipital lobe in schizotypal personality disorder. Parietal atrophy, however, have been found in the rela-

Insular Cortex The insular cortex, or insula, is a triangular-shape cortical region, which lies deep to the brain’s lateral surface, within the sylvian fissure which separates the temporal and parietal cortices. These overlying cortical areas are referred to as opercula (meaning “lids”) formed by parts of the frontal, temporal and parietal lobes. Phylogenetically, the insula is an older portion of the telencephalon, and it connects with various cortical areas of the frontal, parietal and temporal lobes, limbic structures, including the amygdaloid body, as well as subcortical areas such as the caudate nucleus, putamen, claustrum and dorsal thalamus.179 Numerous studies have indicated that the insula can be related to a variety of functions, such as memory, drive, higher autonomic control, gestation and olfaction.180

Schizophrenia Volumetric abnormalities in the insula have been reported in some MR studies in schizophrenia. For example, volumetric reduction in the insula was identified in patients with first-episode schizophrenia164 and chronic schizophrenia.181 This structural deficit in the insula was also demonstrated in a voxel-based morphometric study.182 Jang et al.82 used a landmark-based shape analysis of the insula, and found that frontotemporal sides of the right insula were deformed in schizophrenia patients.

Unaffected Relatives Using voxel-based morphometry, Honea et al.183 found that unaffected siblings tended to share gray matter decreases in the left insular cortex, but results failed to reach significance after correcting for multiple comparisons.

106

Schizotypal Personality Disorder Takahashi et al.184 did not find the reductions in insular cortical regions in SPD which were found in schizophrenia subjects, and suggested that insular cortex reductions may be specific to schizophrenia. Of interest, Yoneyama et al.185 found that in those SPD patients with schizophrenia-related code types on the Minnesota Multiphasic Personality Inventory, decreased gray matter volumes of insular regions are present bilaterally. This implied that only a subgroup of SPD may be structurally related to schizophrenia.

D. Mamah et al.

et al.83 reported right amygdala volume increases in schizophrenia patients and controls. Furthermore, group outward displacement in surface meshes in the anterior region of the amygdala.

Unaffected Relatives Decreased volumes of the amygdala have been noted in the relatives of schizophrenia subjects,189–191 although others failed to find such findings.171,192,193

Schizotypal Personality Disorder

Amygdala The amygdalae are almond-shaped nuclei located deep within the medial temporal lobe. They are believed to have a primarily role in processing and memory of emotional reactions, and are considered part of the limbic system. It is of interest to schizophrenia as it receives moderate dopaminergic input from mesolimbic pathways, which have been implicated in schizophrenia pathophysiology. Furthermore, the amygdala connects to several regions that show structural abnormalities in patients including the hippocampus, cingulate and frontal cortex.

Schizophrenia Morphometry of the amygdala on its own has not been widely studied. They are more commonly evaluated together with the hippocampus (as the ‘hippocampus– amygdala complex’) due to difficulty reliably delineated these structures on MRI scans. When included with hippocampal measurements however, the amygdala may contribute to decreased volume of the hippocampus/amygdala complex in schizophrenia.186 Significant decreases in the isolated amygdala have however been reported in schizophrenia118,187 or schizophrenia-spectrum disorders in children.188 Others did not find statistically significant differences of amygdala volume from controls.114 Increased amygdala volumes have also been reported in schizophrenia. Gur et al.113 found increased amygdala volumes in female schizophrenia subjects compared to gender-matched controls. Analyzing three-dimensional surface maps, Narr

No difference in the volume of the amygdala has been reported in schizotypal personality disorder.132

Basal Ganglia The basal ganglia (BG) are a group of gray matter nuclei deep within the brain, and consist of the caudate nucleus, putamen, globus pallidus, nucleus accumbens, substantia nigra and the subthalamic nucleus. Among these, the caudate, putamen and globus pallidus are the ones usually evaluated in structural imaging studies due to their larger size and relative ease in delineating from surrounding structures. The BG are associated with a variety of functions, including the regulation of motor control, cognition and emotions, due to their connections with difference cortical regions.194 The BG are of particular interest to schizophrenia because they receive significant dopaminergic input, and antipsychotic medications (the mainstay of schizophrenia treatment) block dopamine receptors.

Schizophrenia The most frequently reported basal ganglia abnormality in schizophrenia is an increase in volume, although this is believed to be secondary to antipsychotic medication administration by patients195–197 The increase in basal ganglia volume is specifically associated with older generation ‘typical’ antipsychotics (e.g. haloperidol and fluphenazine), which have more specific antagonism at D2-type dopamine receptors. Compensatory increases

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

107

in dopaminergic dendritic structures after long-term neuroleptic administration are believed to account largely for striatal volume increases that have been rather consistently reported in schizophrenia.89,193,198,199 The newer generation ‘atypical’ antipsychotics (e.g. risperidone and olanzapine) are generally devoid of the effects on increasing BG volume,200,201 although among atypical antipsychotics, clozapine have been reported to increase BG volume.201 Neuroleptic-naïve or first-episode schizophrenia subjects usually have reduced or similar BG volumes compared to controls,130,202,203 supporting the role of antipsychotics in structural changes. Using HDBM-LD, Mamah et al.21 found shape abnormalities in the caudate, putamen and globus pallidus in schizophrenia (Fig. 22.8), in addition to volume increases after controlling for brain size. In the caudate inward deformations (i.e. regional volume decreases) were noted in the anterior pole, and an apparent anterior deflection of the tail. As anterior regions of the caudate have reciprocal connections to prefrontal cortical

regions, abnormalities in this region suggested selective abnormality of anterior regions within the caudate. Similar patterns of shape abnormalities were noted in the caudate and putamen in a non-overlapping group of schizophrenia subjects22 Using measures of lifetime psychopathology, these authors reported correlations between lifetime hallucination severity in schizophrenia subjects and the volume of the caudate, putamen and globus pallidus volume, as well as the shape score of the caudate, obtained from canonical analyses of reduced dimensional measures of surface variability. Vetsa et al.23 evaluated shape of the caudate using a template-free M-rep analysis, but did not find global shape differences between schizophrenia and control subjects. There was a trend toward significance when control caudate was compared to that of schizophrenia subjects treated with either atypical or typical neuroleptic medications. There was however, a significant shape difference between schizophrenia subjects treated with atypical medications and those treated with typical

Fig. 22.8 Three-dimensional basal ganglia surface abnormalities in schizophrenia patients and their unaffected siblings. Structures shown are: the caudate (C), the putamen (P), the globus pallidus (G) and the nucleus accumbens (A). Figures represent mean estimated displacement between subject groups, controlling for gender. Surface displacement maps were obtained by first computing for every structure and subject the surface-normal component of the displacement of each surface point of that structure relative to the average surface of all subjects. The least square mean

of these displacements for each group (and surface point) was then computed, controlling for gender. Finally, the difference of these least square means between the two selected subject groups was displayed as a color map (overlaid on the mean surface of the control subjects). Purple-to-blue shading denotes regions of inward deformation (smaller) compared with controls (max: −0.5 mm). Red-to-orange shading denotes regions of outward deformation (larger) compared to CON (max: 0.5 mm) (Unpublished figures adapted from research in Mamah et al. (2008)22)

108

D. Mamah et al.

medications. They also reported that the major shape change was in the head of the caudate, with typical antipsychotic treated patients having larger caudate heads than those treated with atypical antipsychotics. These shape findings suggested that typical antipsychotics may show preferential enlargement in anterior caudate regions.

higher right and left head of the caudate shape scores correlated significantly with poorer neuropsychological performance on tasks of visuospatial memory and auditory/verbal working memory respectively.

Unaffected Relatives

The thalamus is the largest nuclear mass in the entire nervous system, although it can be separated into several smaller nuclei, which are generally not distinguishable at the resolution of current MRI methodology. The thalamus is believed to both process and relay sensory information selectively to various parts of the cerebral cortex, and may be involved in planning response strategies. It has reciprocal connections with many brain regions, including prefrontal cortex and limbic structures, and has been implicated in schizophrenia.204

There have been relatively few studies of the basal ganglia structure in the unaffected relatives of patients with schizophrenia. Relatives do not demonstrate the neuroleptic-induced increased basal ganglia volumes often seen in schizophrenia patients, as they are largely medication naïve. Reduced volumes of the right putamen have also been reported in unaffected siblings.187 Other authors however, did not find significant differences between siblings and controls.140,193 Mamah et al.22 evaluated the shape of the caudate, putamen and globus pallidus in schizophrenia and control subjects and their respective siblings using HDBM-LD. 3D surface maps showed similar abnormalities in the all BG structures in schizophrenia subjects and their unaffected siblings, albeit less in severity. In the caudate and putamen, regions of abnormalities were similar to that found in a prior study of a non-overlapping group of schizophrenia subjects21 Using canonical analysis of reduced measures of surface variability the shape abnormalities of all structures in unaffected siblings were intermediate between that of schizophrenia and control subjects.22

Schizotypal Personality Disorder The size of the caudate has been reported to be either reduced or to be no different from controls.48,198 Reduced volume of the putamen198 has also been reported in schizotypal personality disorder. Levitt et al.48 evaluated the shape of the head of the caudate nucleus in schizotypal personality disorder using the surface area–volume ratio to generate a quantitative index of how much a given shape differs from a sphere. In relation to comparison subjects, neuroleptic never-medicated SPD subjects had significantly higher (more “edgy”) head of the caudate shape index scores, lateralized to the right side. Additionally, for SPD subjects,

Thalamus

Schizophrenia The majority of studies of thalamic structure have reported abnormalities in schizophrenia27 Positive volumetric findings in schizophrenia have generally shown slightly reduced volumes compared to comparison subjects.89,205,206 Portas et al.207 who found no volume differences, did report correlations between thalamic and prefrontal white matter volumes in patients with schizophrenia, a correlation not observed in the control sample. These findings, taken together, suggest that thalamic nuclei should be studied further using higher spatial resolution imaging techniques that optimize the delineation of specific nuclei. Using HDBM-LD, the shape of the thalamus, in particular, the anterior and posterior extremes of the structure, was deformed in schizophrenia subjects11,12 Thalamic asymmetry in both schizophrenia and control groups was smaller than that in the hippocampus, but still measurable. The left thalamus was slightly smaller than the right in the dorsal-medial portion of its surface. However, comparing schizophrenia and control subjects, there was an exaggeration of these normative (L < R) patterns of thalamic asymmetry in the subjects with schizophrenia. Surface maps of the thalamus comparing schizophrenia to control subjects revealed inward deformations in anterior and posterior regions in schizophrenia11,12 This suggested that

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

thalamic regions corresponding to the anterior nuclei, ventral anterior nuclei and the pulvinar (with efferent connections to the cingulate, prefrontal cortex and visual association cortices respectively) may be disrupted in schizophrenia. Harms et al.12 used a canonical weighing function, derived from the contrast between schizophrenia and control subjects, to generate a canonical shape score for all subjects. They did not find a significant correlation of structural measures (volume or shape) with any measure of current psychopathology derived from the SANS or SAPS. Subsequent analysis of correlation of these measures in schizophrenia subjects with ‘lifetime psychopathology’ using scores derived from the SCID (as in Mamah et al.22) found correlations with structural measures (Mamah et al., 2008). Specifically, there was a negative relationship of lifetime hallucination severity and either thalamic volume (right: r = −0.47, p = 0.017; left: r = −0.59, p = 0.002) or thalamic canonical shape score (r = −0.42, p= 0.038). Other regional analyses of the thalamus suggested that schizophrenia subjects had fewer MRI pixels in the left anterior thalamus.208 Postmortem studies of schizophrenia brain suggest that the cellular basis for volume and shape abnormalities may be reductions in neuronal number within specific thalamic nuclei (i.e. anterior nucleus, mediodorsal nucleus and pulvinar).209–212

Unaffected Relatives The literature suggests thalamus reduction exists in first-degree relatives compared to controls, while patient-relative differences are consistently reported as trends, with schizophrenia patients tending to have smaller thalamic volumes.193,213–215 In addition, reductions in the thalamus have been replicated as a measure of genetic liability to psychosis.7,43 Using HDBM-LD, similar but less severe inward deformation in the anterior and posterior extremes were noted in the unaffected siblings of schizophrenia subjects as in the schizophrenia subjects themselves.12 In this study, a canonical weighting function was derived from the contrast between schizophrenia and control subjects and then used to generate a canonical shape score for all subjects. The thalamic canonical scores of the siblings of the schizophrenia probands were intermediate between the probands and healthy control subjects. This suggested that shape abnormalities

109

in the thalamus are likely to be, at least partly, mediated by genetic effects.

Schizotypal Personality Disorder In individuals with SPD, the size of the thalamus has been reported to be similar to that of controls.206 Volumetric differences in individual nuclei, however, have been reported. The pulvinar, which has close connections with temporal lobe structures, has been found to be reduced in subjects with SPD, as it is in patients with schizophrenia. However, the volume of the medial dorsal nucleus, associated with the prefrontal cortex, is not reduced in SPD, in contrast to reductions observed in patients with schizophrenia.206 Thus, reductions in the subcortical nuclei relaying from the thalamus to cortex may parallel reductions in associated cortical regions in schizotypal personality disorder – i.e., temporal but not frontal volume reductions. In a separate study, Hazlett et al.208 however reported fewer MRI pixels in the right mediodorsal nucleus in SPD.

Cerebellum In addition to having a role in coordinating motor function, research suggests that the cerebellum may play a part in higher cognitive functions, including attention and the processing of language, music and other temporal stimuli.216,217 In light of the complex roles of the cerebellum, its role in schizophrenia has been suggested.218

Schizophrenia Findings of MRI studies of the cerebellum in schizophrenia have been largely negative, although some have described regional volumetric abnormalities.193,219–222 Many studies, however, have included only a single midsagittal slice of the vermian area rather than volume measures. Moreover, separation of gray matter and white matter in the cerebellum (as often done for cerebral regions) is generally not performed in studies, however this may be useful to further clarify the role of the cerebellum in the pathophysiology of schizophrenia.

110

Unaffected Relatives Many authors have failed to find a volumetric difference of the relatives of schizophrenia subjects compared to controls193,223 although some have found decreases.171 Goldman et al.192 did not find an increase cerebellar volume in relatives which were found in schizophrenia subjects.

Schizotypal Personality Disorder No data exists for the size of the cerebellum in schizotypal personality disorder.

Corpus Callosum The corpus callosum, the largest white-matter fiber tract in the brain, provides the majority of axonal transmission between the two cerebral hemispheres and subserves interhemispheric information transfer. Since there is substantial evidence that schizophrenic patients have difficulties with cognitive tasks that require interhemispheric transfer of information,224,225 studies of the corpus callosum might reveal the anatomic correlates of these deficits. The regions of the corpus callosum are topographically organized, with anterior regions carrying fibers from the frontal cortex, and more posterior regions carrying fibers from temporal, parietal and occipital cortices.226

Schizophrenia A number of studies have investigated differences in shape and size of the corpus callosum in schizophrenia.219 The most frequently measured parameter has been total area of the sagittal section of the CC, either as an absolute measure or in relation to brain area or volume. In addition, width or thickness and length (in one dimension) have been measured. Although the majority of studies that examined the midsagittal area of the CC showed a decrease in schizophrenia, about a third found no difference between patients and control participants.27,227,228 The meta-analysis by Woodruff et al.229 confirmed smaller callosal area in schizophrenia

D. Mamah et al.

(after controlling for total brain volume) but no significant differences in length of the corpus callosum or ratio of corpus callosum/sagittal brain. Some of the inconsistent results may be due to technical differences. Many studies, for example, have used one midsagittal slice to define the area of the corpus callosum. Such an approach is problematic because there is the possibility of errors due to brain alignment, slice thickness, and choice of slice. These methodological problems also make it difficult to compare findings across studies. Gender also needs to be taken into account, because there is evidence to suggest sexual dismorphism in both the shape and size of the corpus callosum.85 More refined analysis to address the shape of the corpus callosum may enhance our understanding of the frequently studied size variations. Schizophrenia patients have been described as showing an increased curvature of the CC,32,87,230 with midbody,230 genu and splenium32 being most affected in their shape. DeQuardo et al.85 using a thin-plate spline shape analysis, showed a thinner, more arched corpus callosum in schizophrenia, a result similar to the increased curvature measured by Casanova et al.231 and interpreted this as reflecting ventricular enlargement. However, Tibbo et al.232 failed to confirm shape differences between schizophrenia and control subjects. Narr et al.233 found vertical shape displacement (i.e. upward bowing), especially in male schizophrenia patients compared to controls. Several MRI studies of schizophrenia have approached the issue of corpus callosal shape by dividing the structure into three or more anterior/posterior segments. While some did not find regional differences between groups,231,234 others have showed variable regional size decreases in schizophrenia subjects.32,227,235 Woodruff et al.235 found a significant reduction in the mid-corpus callosum which communicates between temporal lobes. Rotarska-Jagiela227 divided the CC into nine segments and found the most significant area decrease in the posterior genu. The four regions posterior to the anterior mid-body and the superior genu also showed deceased areas, but these disappeared after controlling for multiple comparisons. Downhill et al.32 who divided the CC into 30 anteroposterior sections, found significant decreases in the genu and splenium regions in schizophrenia compared to controls. A downward bowing of the CC in schizophrenia was also noted.

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

111

Unaffected Relatives

Schizophrenia

Chua et al.228 found no significant difference either in the mid-sagittal area of the corpus callosum or in that of its four subsections between schizophrenia, their unaffected first degree relatives and controls. They suggested that decrements in size of the CC that have been reported in schizophrenia are more likely to be determined by environmental (e.g. obstetric complications, trauma or infections during the period of development and growth of the CC), rather than genetic factors. Interestingly, similar shape displacements were also found in the unaffected monozygotic twins (but not dizygotic twins) of schizophrenia patients, which supported the role of genes in altered corpus callosum morphology in schizophrenia. These authors found little evidence for size difference among groups however.

Although relatively few MRI studies of the CSP have been conducted in schizophrenia, findings have been largely positive showing increased volumes mostly in men.27,236,238 A correlation between a large CSP and temporal lobe or hippocampus volume reduction has also been reported236,237 in patients with chronic schizophrenia.

Unaffected Relatives Rajarethinam et al.239 did not find a difference in the size of the CSP between first episode schizophrenia patients, their offspring an healthy controls. Similarly, Choi et al.240 failed to find CSP abnormalities in relatives, and suggested abnormal CSP maybe a normal anatomical variant unrelated to schizophrenia.

Schizotypal Personality Disorder The total volume of the corpus callosum in SPD has not been shown to have a significant volume difference from controls. However, Downhill et al.32 reported that compared to controls, SPD subjects had a larger genu as well as a smaller splenium that was intermediate between schizophrenia and control subjects. Thus for the splenium, SPD subjects appeared to be in a continuum between those with schizophrenia and controls. The authors suggested that the lesser cognitive impairment often found in SPD compared to schizophrenia, could reflect the larger size of the genu, which might serve to compensate for the mild size decrease in the splenium in SPD. In addition to volumetric findings, Downhill et al.32 also reported a downward bowing of the CC (Fig. 22.6).

Cavum Septum Pellucidum The CSP is the space between the two leaflets of the septum pellucidum, a midline membrane separating the lateral ventricles. In normal development, 85% of individuals show a fusing of these two leaflets within the first 6 months of life.236–238 Fusion of the leaflets may also be associated with the growth of the hippocampus and corpus callosum, at either end of this membrane, and thus incomplete fusion of the septum pellucidum may reflect possible neurodevelopmental abnormalities of these two brain structures.

Schizotypal Personality Disorder The CSP has been previously found to be slightly increased in schizotypal personality disorder.236

Ventricles The ventricles are the major cerebrospinal fluid filled cavities within the brain. The largest of these, the lateral ventricle, consists of its ‘body’ located largely within the parietal lobe, as well as an anterior (frontal), posterior (occipital) and inferior (temporal) horns. The third and fourth ventricles are much smaller in comparison, and are connected inferiorly with the lateral ventricle. Ventricular enlargement may be indicative of tissue loss or reduction of brain regions surrounding the ventricles (e.g. medial temporal lobe structures surrounding the lateral ventricles). Ventricular enlargement may also suggest a failure of normal brain development. Schizophrenia Abnormalities in ventricular size have been among the most robust and consistent in schizophrenia patients. Prominent lateral ventricular enlargement has been reported in a large majority of both CT and MRI findings.27,241 Davatzikos et al.,41 for example, reported

112

a 20% increase in ventricular CSF volume in schizophrenia; and almost double in women compared with men with schizophrenia. An increase in the temporal horn of the lateral ventricles has also been reported by several investigators.114,116,117,242–245 Of note, this increase tends to be lateralized to the left, consistent with postmortem study findings of tissue loss within the medial temporal lobe, including the amygdala–hippocampal complex and parahippocampal gyrus. Other authors have found increased volume in the left anterior horn, most prominent in temporal and frontal areas.175 Ventricular abnormalities appear to worsen through the course of the illness. For example, Lieberman et al.246 evaluating progressive structural changes in schizophrenia, found ventricular enlargement after 2.5 years in patients with persistent symptoms. Using a spherical harmonic-based shape description, Styner et al.61 examined the size and shape of the lateral ventricles in schizophrenia and healthy twin pairs. While volume of the lateral ventricles did not differ between groups, both affected and unaffected twins showed lateral ventricle shape differences from controls. Average distance maps suggested that these differences were localized mainly to the posterior (atrium and occipital horn) aspects and to a lesser degree the anterior (frontal horn) aspect of the lateral ventricles, relative to the middle. This findings suggested that lateral ventricle shape is more strongly influenced by genetic relatedness (and therefore vulnerability to schizophrenia) than to the presence of disease itself. Furthermore, this study implied that shape differences between groups show higher statistical significance even when the volumetric differences do not. In a related study, and using three-dimensional surface maps, Narr et al.83 found lateral ventricular enlargement in the superior and lateral dimensions in schizophrenia, and pronounced in the left hemisphere. Group average displacement was greatest in the posterior tip of the left posterior horn. Buckley et al.247 found a statistically significant and highly localized shape deformity at the foramen of Monro and at the proximal temporal horn of the lateral ventricle in male (but not female) schizophrenia patients relative to controls. Fewer MRI studies have evaluated the third ventricle, but in the majority of these studies, findings show enlargement in schizophrenia.89,193 With regard to the fourth ventricle, findings have been uniformly negative.27,89

D. Mamah et al.

Unaffected Relatives Few studies have reported significantly enlarged lateral ventricles in relatives as compared with controls; although most of the studies give results in that direction.213 The fewer comparisons of patients and siblings are almost always significantly enlarged compared to controls213 suggesting notable changes as people move from being at risk to ill. A meta-analysis conducted by Boos et al.105 did not find significant volumetric differences in the volume of the lateral ventricles between the unaffected first-degree relatives of schizophrenia patients and healthy controls. The volume of the third ventricle was however larger in the relatives compared with healthy control subjects, although these effects were small.

Schizotypal Personality Disorder In schizotypal personality disorder, the lateral ventricles have not been shown to be significantly larger than that in controls. However, the anterior and left temporal horns have been shown to be increased.84,106 No abnormalities have been reported in the third and fourth ventricles in SPD.84,106

Conclusion and Future Directions Schizophrenia spectrum disorders encompass disorders which appear to have genetic, familial or etiological associations with schizophrenia. Schizophrenia and schizotypal personality disorders have been the most commonly studied of these, although the latter much less so than the former. The unaffected first degree relatives of schizophrenia subjects also share these characteristics but are traditionally not included as spectrum disorders. Since individuals with schizotypal personality disorder and unaffected relatives are less influenced by factors that may confound schizophrenia patients, such as antipsychotic medications, they can provide unique insights into the genetic basis of biological abnormalities of schizophrenia. Endophenotypes of psychiatric disorders are biological markers that are more closely related to an underlying genotype, than the symptomatic presentation of the disorder itself (i.e. the ‘phenotype’). Brain structural abnormalities are

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

promising candidates for schizophrenia endophenotypes as they appear to fit the required criteria, including heritability, co-segregation in families, state-independence, and clear associations with the disorder. There is a significant convergence of evidence to suggest that brain abnormalities are present in schizophrenia, and these abnormalities do not appear to affect the brain regions equally. The majority of structural imaging studies of schizophrenia have evaluated global volumes of structures of interest. While these are valuable, they do not capture the complexity of structures’ surfaces, i.e. their shapes. Evaluating the shapes of brain structures can provide information on regional abnormalities within a structure that may not be apparent by volume measurements alone. Recently, methods of computational anatomy have emerged for evaluating the shape of deep brain structures and the complexities of the cortical surface. Such morphometric methods have been able to improve the discrimination of normal from pathologic states. Among the structural findings in schizophrenia, the most robust are lateral ventricular enlargement and temporal lobe abnormalities, the latter including reduced volume in the medial temporal lobe (especially the hippocampus) and reduced neocortical superior temporal gyrus. Temporal lobe abnormalities are frequently associated with positive symptoms such as delusions, hallucinations, and functional impairments in language including formal thought disorder and impairments of verbal memory and other associative links in memory. Reduced volumes are sometimes noted in other brain regions, such as the thalamus, frontal cortex and cingulate cortex. Corpus callosum, findings are variable, and methodological differences likely account for the inconsistent findings reported; while MRI studies of the cerebellum have been sparse. The basal ganglia is usually noted to be enlarged compared to controls as a result of antipsychotic medication effects. Relative to volume analysis, the newer shape analyses have been very limited. However, regional shape deformations have been observed in the hippocampus, thalamus, basal ganglia, corpus callosum, lateral ventricles, and cortical regions. Unaffected relatives appear to show similar volume deficits as schizophrenia subjects, but lesser in magnitude. Shape analysis of the hippocampus, thalamus and basal ganglia in unaffected siblings has also shown surface shape abnormalities which were intermediate between those of schizophrenia and healthy controls. While these similarities in brain structure suggest

113

genetic effects, they could also have been partly caused by shared environmental factors, such as obstetric complications, social stressors or substance use. Individuals with schizotypal personality disorder tend to show abnormalities in the STL, the temporal horn region of the lateral ventricle, corpus callosum, thalamus, and the CSP. Differences from schizophrenia patients appear to be in medial temporal lobe regions and the lateral ventricles, where schizotypal patients do not show abnormalities. The significance of these findings is unclear due to a paucity of studies of these populations, but they may give clues as to the brain regions that are associated with the frank psychosis in schizophrenia. The future of structural neuroanatomical calls for further studies of unaffected relatives and individuals with other schizophrenia spectrum disorders, to get a clearer understanding of the genetic cause of structural brain abnormalities. Schizophrenia patients have a higher prevalence of psychiatric medication use as well as recreational drug use, which can confound structural findings. For structural measures to become valid endophenotypes, there is also a need to determine the specificity of such findings to schizophrenia. Few studies have compared structural findings between schizophrenia and other psychiatric disorders. Mood-disordered patients have been associated with a smaller hippocampus, which is also seen in schizophrenia, and ventricular enlargement is often observed in Alzheimer’s disease of Huntington’s disease. Comparing surface shape between individuals with different diagnoses may reveal specific abnormalities to schizophrenia, but these have not yet been conducted in schizophrenia research. Progress in neuroimaging and computational anatomy is expected to improve scan resolution and allow for the detection of more subtle structural abnormalities. It may allow, for example, the delineation of individual thalamic nuclei and evaluation of individual structural differences between groups. Future studies are also likely progress at a faster pace as more automated-segmentation tools are developed for identifying individual brain structures. It is yet unclear whether divergence sometimes noted between results from different morphometric mapping methods is related to differences between the mapping methods or between the studied populations. In the future, applying different methods could result in a unique sample set that has the potential to decouple a series of methodological differences from the population differences. Such an advance would also help researchers

114

to create a normative database for different brain regions so that normative standards can be developed and pathological brains compared. Finally, the study of well-defined and well-characterized patient groups will be important in reducing unknown variation due to population differences across studies. Moreover, a careful delineation of patient groups, not necessarily according to current nosology, will hopefully lead to the discovery of important clinic-pathological correlations that might in turn lead to the development of more medications targeted at specific regions of brain dysfunction.

References 1. Adler CM, Strakowski SM. Boundaries of schizophrenia. Psychiat Clin North Am 2003;26:1–23. 2. Cadenhead KS. Vulnerability markers in the schizophrenia spectrum: implications for phenomenology, genetics, and the identification of the schizophrenia prodrome. Psychiat Clin North Am 2002;25:837–853. 3. Siever LJ, Davis KL. The pathophysiology of schizophrenia disorders: perspectives from the spectrum. Am J Psychiat 2004;161:398–413. 4. Takahashi T, Suzuki M, Zhou SY, Hagino H, Niu L, Kawasaki Y, Seto H, Kurachi M. Temporal lobe gray matter in schizophrenia spectrum: a volumetric MRI study of the fusiform gyrus, parahippocampal gyrus, and middle and inferior temporal gyri. Schizophr Res 2006;87:116–126. 5. Hazlett EA, Romero MJ, Haznedar MM, New AS, Goldstein KE, Newmark RE, Siever LJ, Buchsbaum MS. Deficient attentional modulation of startle eyeblink is associated with symptom severity in the schizophrenia spectrum. Schizophr Res 2007;93:288–295. 6. Potash JB, Chiu YF, MacKinnon DF, Miller EB, Simpson SG, McMahon FJ, McInnis MG, DePaulo JR Jr. Familial aggregation of psychotic symptoms in a replication set of 69 bipolar disorder pedigrees. Am J Med Genet B Neuropsychiat Genet 2003;116B(1):90–97. 7. McIntosh AM, Job DE, Moorhead WJ, Harrison LK, Whalley HC, Johnstone EC. Genetic liability to schizophrenia or bipolar disorder and its relationship to brain structure. Am J Med Genet B Neuropsychiat Genet 2006;141:76–83. 8. McDonald C, Bullmore ET, Sham PC, Chitnis X, Wickham H, Bramon E, Murray RM. Association of genetic risks for schizophrenia and bipolar disorder with specific and generic brain structural endophenotypes. Arch Gen Psychiat 2004;61:974–984. 9. Ashburner J, Friston KJ. Voxel-based morphometry – the methods. Neuroimage 2000;11:805–821. 10. Csernansky JG, Wang L, Jones D et al. Hippocampal deformities in schizophrenia characterized by high dimensional brain mapping. Am J Psychiat 2002;159:2000–2006. 11. Csernansky JG, Schlinder MK, Splinter NR et al. Abnormalities of thalamic volume and shape in schizophrenia. Am J Psychiat 2004;161:896–902.

D. Mamah et al. 12. Harms MP, Wang L, Mamah D, Barch DM, Thompson PA, Csernansky JG. Thalamic shape abnormalities in individuals with schizophrenia and their nonpsychotic siblings. J Neurosci 2007;27:13835–13842. 13. Thompson PM, Vidal C, Giedd JN, Gochman P, Blumenthal J, Nicolson R, Toga AW, Rapoport Jl. Mapping adolescent brain change reveals dynamic wave of accelerated gray matter loss in very early-onset schizophrenia. Proc Natl Acad Sci USA 2001;98:11650–11655. 14. Narr KL, Bilder RM, Luders E, Thompson PM, Woods RP, Robinson D, Szeszko PR, Dimtcheva T, Gurbani M, Toga AW. Asymmetries of cortical shape: effects of handedness, sex and schizophrenia. Neuroimage 2007;34:939–948. 15. Wang L, Hosakere M, Trein JCL, Miller A, Ratnanather JT, Barch DM, Thompson PA, Qiu A, Gado MH, Miller MI, Csernansky JG. Abnormalities of cingulate gyrus neuroanatomy in schizophrenia. Schizophr Res 2007;93:66–78. 16. Cannon TD, van Erp TG, Bearden CE et al. Early and late neurodevelopmental influences in the prodrome to schizophrenia: contributions of genes, environment, and their interactions. Schizophr Bull 2003;29:653–669. 17. Weinberger DR. From neuropathology to neurodevelopment. Lancet 1995;346:552–557. 18. Sisodiya SM, Free SL, Duncan JS, Stevens JM. Bilateral periventricular and subcortical heterotopias in a man with refractory epilepsy. Epilepsia 2000;41:352–354. 19. Styner M, Lieberman JA, Pantazis D, Gerig G. Boundary and medial shape analysis of the hippocampus in schizophrenia. Med Image Anal 2004;8:197–203. 20. Shenton ME, Gerig G, McCarley RW, Szekely G, Kikinis R. Amygdala-hippocampal shape differences in schizophrenia: the application of 3D shape models to volumetric MR data. Psychiat Res Neuroimaging 2002;115:15–35. 21. Mamah D, Wang L, Barch D, de Erausquin GA, Gado M, Csernansky JG. Structural analysis of the basal ganglia in schizophrenia. Schizophr Res 2007;89(1–3):59–71. 22. Mamah D, Harms MP, Wang L, Barch D, Thompson P, Kim J, Miller MI, Csernansky JG. Basal ganglia shape abnormalities in the unaffected siblings of schizophrenia patients. Biol Psychiat 2008 Jul 15;64(2):111–120. 23. Vetsa S, Styner M, Pizer S, Lieberman J, Gerig G. Caudate shape discrimination in schizophrenia using template-free non-parametric tests. In: MICCAI 2003; pp. 661–669. 24. Narr KL, Cannon TD, Woods RP, Thompson PM, Kim S, Asunction D, van Erp TGM, Poutanen V-P, Huttunen M, Lonnqvist J, Standerksjold-Nordenstam C-G, Kaprio J, Mazziotta JC, Toga AW. Genetic contributions to altered callosal morphology in schizophrenia. J Neurosci 2002;22:3720–3729. 25. Narr KL, Thompson PM, Shama T, Moussai J, Zoumalan C, Rayman J, Toga AW. Three-dimensional mapping of gyral shape and cortical shape asymmetries in schizophrenia: gender effects. Am J Psychiat 2001;158:244–255. 26. Gottesmann II, Gould TD. The endophenotypes concept in psychiatry: etymology and strategic intentions. Am J Psychiat 2003;160:636–645. 27. Shenton ME, Dickey CC, Frumin M, McCarley RW. A review of MRI findings in schozphrenia. Schizophr Res 2001;49:1–52. 28. Shenton ME, Frumin M, McCarley RW, Maier SE, Westin C, Fischer IA, Dickey C, Kikinis R. Morphometric magnetic resonance imaging studies: findings in schizophrenia.

22

29.

30.

31.

32.

33.

34.

35.

36.

37.

38.

39.

40.

41.

42.

43.

44.

45.

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders In: Dougherty DD, Rauch SL (Eds.), Psychiatric Neuroimaging Research: Contemporary Strategies. Washington, DC: American Psychiatric Publishing, 2001; pp. 1–60. Keshavan MS, Prasad KM, Pearlson G. Are brain structural abnormalities useful as endophenotypes in schizophrenia? Int Rev Psychiat 2007;19:397–406. Vila-Rodriguez F, French L, Barakauskas V, Mead CL, Khorram B. Thalamic shape: a possible endophenotype. J Neurosci 2008;28:3533–3534. Tepest R, Wang L, Miller MI, Falkai P, Csernansky JG. Hippocampal deformities in the unaffected siblings of schizophrenia subjects. Biol Psychiat 2003;54:1234–1240. Downhill JE, Buchsbaum MS, Wei TS, Spiegel-Cohen J, Hazlett EA, Haznedar MM,Silverman J, Siever LJ. Shape and size of the corpus callosum in schizophrenia and schizotypal personality disorder. Schizophr Res 2000;42:193–208. Smith RC, Calderon M, Ravichandran GK, Largen J, Vroulis G, shvartsburd A, Gordon J, Schoolar JC. Nuclear magnetic resonance in schizophrenia: a preliminary study. Psychiat Res 1984;12:137–147. Golland P, Grimson WE, Kikinis R. Statistical shape analysis using fixed topology skeletons: corpus callosum study. In: Kuba A (Ed.), IPMI LNCS. Berlin: Springer, 1991; Vol. 161, pp. 382–387. Kimia B, Tannebaum A, Zucker S. Shapes, shocks and deformations: I: The components and shape and the reaction-diffusion space. Int J Comp Vision 1995;15:189–224. Naf M, Szekely G, Kikinis R, Shenton ME, Kubler O. 3D voronoi skeletons and their usage for the characterization and recognition of 3D organ shape. Comp Vision Image Understanding 1997;66:640–646. Bookstein FL. Landmark methods for forms without landmarks: localizing group differences in outline shape. Med Image Anal 1997;1:225–243. Christensen GE, Joshi SC, Miller MI. Volumetric transformation of brain anatomy. IEEE Trans Med Imag 1997;16:864–877. Csernansky JG, Joshi SC, Wang L, Gado M, Miller JP, Grenander U, Miller MI. Hippocampal morphometry in schizophrenia by high dimensional brain mapping. PNAS 1998;95:11406–11411. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. II: Inflation, flattening and a surface-based coordinate system. Neuroimage 1999;9:195–207. Davatzikos C, Shen D, Gur RC, Wu X, Liu D, Fan Y, Hughett P, Turetsky BI, Gur RE. Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities. Arch Gen Psychiat 2005;62:1218–1227. Kelemen A, Székely G, Gerig G. Elastic model-based segmentation of 3-D neuroradiological data sets. IEEE Trans Med Imag 1999 Oct;18(10):828–839. McDonald C, Bullmore E, Sham P et al. Regional volume deviations of brain structure in schizophrenia and psychotic bipolar disorder: computational morphometry study. Br J Psychiat 2005;286:369–377. Miller MI, Trouve A, Younes L. On the metrics and Euler– Lagrange equations of computational anatomy. Ann Rev Biomed Eng 2002;4:375–405. Wang L, Joshi SC, Miller MI, Csernansky JG. Statistical analysis of hippocampal asymmetry in schizophrenia. Neuroimage 2001;14:531–545.

115

46. Falkai P, Tepest R, Honer WG, Dani I, Ahle G, Pfeiffer U, Vogeley K, Schulze TRG, Rietschel M, Cordes J, Schonell H, Gaebel W, Kuhn KU, Maier W, Traber F, Block W, Schld HH, Schneider-Admann T. Shape changes in prefrontal, but not parieto-occipital regions: brains of schizophrenic patients come closer to a circle in coronal and sagittal view. Psychiat Res 2004;132:261–271. 47. Connor SEJ, Ng V, McDonald C, Schulze K, Morgan K, Dazzan P, Murray RM. A study of hippocampal shape anomaly in schizophrenia and in families multiply affected by schizophrenia or bipolar disorder. Neuroradiology 2004;46:523–534. 48. Levitt JJ, Westin CF, Nestor PG, Estepar RS, Dickey CC, Voglmaier MM, Seidman LJ, Kikinis R, Jolesz FA, McCarley RW, Shenton ME. Shape of the caudate nucleus and its cognitive correlates in neuroleptic-naïve schizotypal personality disorder. Biol Psychiat 2004;55:177–184. 49. Sandu A-L, Rasmussen I-A Jr, Lundervold A, Kreuder F, Neckelmann G, Hugdahl K, Specht K. Fractal dimension analysis of MR images reveals grey matter structure irregularities in schizophrenia. Comp Med Imaging Graphics 2008;32:150–158. 50. Narr KL, Bilder RM, Kim S, Thompson PM, Szeszko P, Robinson D et al. Abnormal gyral complexity in first-episode schizophrenia. Biol Psychiat 2004;55:859–867. 51. Ha TH, Youn T, Ha KS, Rho KS, Lee JM, Kim IY, Kim SI, Kwon JS. Gray matter abnormalities in paranoid schizophrenia and their clinical correlations. Psychiat Res 2004;132:251–260. 52. Khan AR, Wang L, Beg MF. FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using large deformation diffeomorphic metric mapping. Neuroimage 2008;41:735–746. 53. Christensen GE, Rabbitt RD, Miller MI. A deformable neuroanatomy textbook based on viscous fluid mechanics. In: Prince J, Runolfsson T (Eds.), 27th Annual Conference on Information Sceinces and Systems. Baltimore, MD: Johns Hopkins University, 1993; pp. 211–216. 54. Christensen GE, Rabbitt RD, Miller MI, Joshi SC, Grenander U, Coogan TA. Topological properties of smooth anatomical maps. Boston, MA: Kluwer, 1995; pp. 101–112. 55. Joshi S, Miller MI, Christensen GE, Coogen TA, Grenandur U. The generalized dirichlet problem for mapping brain manifolds. Int’l Symp on Optical Science, Engineering and Instrumentation; 1995. 56. Miller MI, Grenander U, Osullivan JA, Snyder DL. Automatic target recognition organized via jump-diffusion algorithms. IEEE Trans Image Process 1997;6(1):157–174. 57. Haller JW, Banerjee A, Christensen GE, Gado M, Joshi S, Miller MI, Sheline YI, Vannier MW, Csernansky JG. 3D hippocampal morphometry by high dimensional transformation of a neuroanatomical atlas. Radiology 1997;202:504–510. 58. Mamah D, Wang L, Harms MP, Barch DM, Styner M, Lieberman J. Olanzapine and haloperidol effects on hippocampal structure in first-episode psychosis. Abstract, Society for Neuroscience Annual Meeting 2008. 59. Wang L, Swank JS, Glick IE, Gado MH, Miller MI, Morris JC, Csernansky JG. Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging. Neuroimage 2003;20:667–682.

116 60. Brechbuhler C, Gerig G, Kubler O. Parameterization of closed surfaces for 3-D shape description. CVGIP: Image Understanding 1995;61:154–170. 61. Styner M, Lieberman JA, McClure RK, Weinberger DR, Jones DW, Gerig G. Morphometric analysis of lateral ventricles in schizophrenia and healthy controls regarding genetic and disease-specific factors. PNAS 2005;102:4872–4877. 62. Styner M, Oguz I, Xu S, Pantazis D, Gerig G. Statistical group differences in anatomical shape analysis using hotelling T 2 metric. Proc SPIE Med Imaging, 2007;6512:65123Z-1–65123Z-11. 63. Pantazis D, Leahy RM, Nichols TM, Styner M. Statistical Surface Based Morphometry Using a Non-parametric Approach, IEEE Symposium on Biomedical Imaging ISBI 2004,1283–1286. 64. Styner M, Xu SC, El-Sayed M, Gerig G. Correspondence Evaluation in Local Shape Analysis and Structural Subdivision, IEEE Symposium on Biomedical Imaging ISBI 2007, 1192–1195. 65. Pizer S, Fritsch D, Yushkevich P, Johnson V, Chaney E. Segmentation, registration, and measurement of shape variation via image object shape. IEEE Trans Med Imaging 1999;18:851–865. 66. Styner M, Gerig G, Lieberman J, Jones D, Weinberger D. Statistical shape analysis of neuroanatomical structures based on medial models. Med Image Anal 2003;7:207–220. 67. Thompson PM, Hayashi KM, de Zubicaray G. Dynamics of gray matter loss in Alzheimer’s disease. J Neurosci 2003;23:994–1005. 68. Vidal CN, Rapoport JL, Hayashi KM, Geaga JA, Sui Y, McLemore LE, Alaghaband Y, Giedd JN, Gochman P, Blumenthal J, Gogtay N, Nicolson R, Toga AW, Thompson PM. Dynamically spreading frontal and cingulate deficits mapped in adolescents with schizophrenia. Arch Gen Psychiat 2006;63:25–34. 69. Thompson PM, Woods RP, Mega MS, Toga AW. Mathematical/computational challenges in creating deformable and probabilistic atlases of human brain. Hum Brain Mapp 2000;9:81–92. 70. Sowell ER, Thompson PM, Tessner KD, Toga AW. Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: inverse relationships during postadolescent brain maturation. J Neurosci 2001;21:8819–8829. 71. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33:341–355. 72. Joshi M, Cui J, Doolittle K, Joshi S, Van Essen D, Wang L, Miller MI. Brain segmentation and the generation of cortical surfaces. Neuroimage 1999;9:461–476. 73. Miller MI, Massie AB, Ratnanather JT, Botteron KN, Csernansky JG. Bayesian construction of geometrically based cortical thickness metrics. Neuroimage 2000;12:676–687. 74. Ratnanather JT, Wang L, Nebel MB, Hosakere M, Han X, Csernansky JG, Miller MI. Validation of semiautomated methods for quantifying cingulate cortical metrics in schizophrenia. Psychiat Res 2004;132:53–68. 75. Van Essen DC, Maunsell JHR. Two-dimensional maps of the cerebral cortex. J Comp Neurol 1980;191:255–281. 76. Van Essen DC, Drury HA, Joshi S, Miller MI. Functional and structural mapping of human cerebral cortex: solutions are in the surfaces. Proc Natl Acas Sci USA 1998;95:788–795.

D. Mamah et al. 77. Van Leemput K, Maes F, Vandermeulen D, Suetens P. A unifying framework for partial volume segmentation of brain MR images. IEEE Trans Med Imag 2003;22:105–119. 78. Miller MI, Hosakere M, Barker AR et al. Labeled cortical mantle distance maps in the cingulate quantify cortical differences distinguishing dementia of the Alzheimer type and healthy aging. Proc Nat Acad Sci 2003;100:15172–15177. 79. Harms MP, Campanella C, Wang L, Aldridge K, Moffitt AJ, Kuelper J, Ratnanather JT, Miller MI, Barch DM, Csernansky JG. Abnormalities of prefrontal cortex neuroanatomy in siblings at risk for schizophrenia. Society for Neuroscience Annual Meeting Abstr 2008, Washington, DC. 80. Ratnanather JT, Barta PE, Honeycutt NA, Lee N, Morris HM, Dziorny AC, Hurdal MK, Pearlson GD, Miller MI. Dynamic programming generation of boundaries of local coordinatized submaniforlds in the neocortex: application to the planum temporale. Neuroimage 2003;209:359–377. 81. Shen D, Davatzikos C. Very high-resolution morphometry using mass-preserving deformations and HAMMER elastic registration. Neuroimage 2003;18:28–41. 82. Jang D-P, Kim J-J, Chung T-S, An SK, Jung YC, Lee J-L, Lee J-M, Kim I-Y, Kim SI. Shape deformation of the insula in schizophrenia. Neuroimage 2006;32:220–227. 83. Narr KL,Thompson PM, Sharma T, Moussai J, Blanton R, Anvar B, Edris A, Krupp R, Rayman J, Khaledy M, Toga AW. Three-dimensional mapping of temporo-limbic regions and the lateral ventricles in schizophrenia: gender effects. Biol Psychiat 2001;50:84–97. 84. Buchsbaum M, Yang S, Hazlett E, Siegel B, Germans M, Haznedar M, O’Flaithbheartaigh S, Wei T, Silverman J, Siever LJ. Ventricular volume and asymmetry in schizotypal personality disorder and schizophrenia assessed with magnetic resonance imaging. Schizophr Res 1997;27:45–53. 85. DeQuardo JR, Tandon R, Brunberg JA, Green WD, Bookstein FL. Spatial relationships of neuroanatomic landmarks in schizophrenia. Psychiat Res Neuroimaging 1996;67:81–95. 86. DeQuardo JR, Keshavan MS, Bookstein FL, Bagwell WW, Green WD, Sweeney JA, Haas GL, Tandon R, Schooler NR, Pettegrew JW. Landmark-based morphometric analysis of first-episode schizophrenia. Biol Psychiat 1999;45:1321–1328. 87. Frumin M, Golland P, Kikinis R, Hirayasu Y, Salisbury DF, Hennen J, Dickey CC, Anderson M, Jolesz FA, Grimson WEL, McCarley RW, Shenton ME. Shape differences in the corpus callosum in first-episode schizophrenia and firstepisode psychotic affective disorder. Am J Psychiat 2002;159:866–868. 88. Steen RG, Mull C, McClure R, Hamer RM, Lieberman JA. Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging study. Br J Psychiat 2006;188:510–518. 89. Wright IC, Rabe-Hesketh S, Woodruff PWR, David AS, Murray RM, Bullmore ET. Meta-analysis of regional brain volumes in schizophrenia. Am J Psychiat 2000;157:16–25. 90. Woodruff PWR, McManus IC, David AS. Meta-analysis of corpus callosum size in schizophrenia. J Neurol Neurosurg Psychiat 1995;58:457–461. 91. Harrison PJ. The neuropathology of schizophrenia: a critical review of the data and their interpretation. Brain 1999;122:593–624.

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

92. McIntosh A, Lawrie S. Structural magnetic resonance imaging. In: Lawrie S, Johnstone E, Weinberger D (Eds.), Schizophrenia from Neuroimaging to Neuroscience. Oxford: Oxford University Press; 2004. 93. Jacobsen LK, Giedd JN, Vaituzis AC et al. Temporal lobe morphology in childhood-onset schizophrenia. Am J Psychiat 1996;153:355–361. 94. DeLisi LE, Sakuma M, Tew W, Kushner M, Hoff AL, Grimson R. Schizophrenia as a chronic active brain process: a study of progressive brain structural change subsequent to the onset of schizophrenia. Psychiat Res 1997;74:129–140. 95. Theberge J, Williamson KE, Aoyama N, Drost DJ, Manchandra R, Malla AK et al. Longitudinal grey-matter and glutamatergic losses in first-episode schizophrenia. Br J Psychiat 2007;191:325–334. 96. Van Haren NEM, Cahn W, Hulshoff Pol HE, Schnack HG, Caspers E, Lemstra A. Brain volumes as predictor of outcome in recent-onset schizophrenia: a multi-central MRI study. Schizophr Res 2003;64:41–52. 97. Lieberman J, Tollefson GD, Charles C, Zipursky R, Sharma T, Kahn RS et al. Antipsychotic drug effects on brain morphology in first-episode psychosis. Arch Gen Psychiat 2005;62:361–370. 98. Wood SJ, Yucel M, Velakoulis D, Phillips LJ, Yung AR, Brewer W, McGorry PD, Pantelis C. Hippocampal and anterior cingulate morphology in subjects at ultra-high-risk for psychosis: the role of family history of psychotic illness. Schizophr Res 2005;75:295–301. 99. Van Erp TG, Saleh PA, Rosso IM, Huttunen M, Lonnqvist J, Pirkola T, Salonen O, Valanne L, Poutanen VP, Standerstskjold-Nordenstam CG, Cannon TD. Contributions of genetic risk and fetal hypoxia to hippocampal volume in patients with schizophrenia or schizoaffective disorder, their unaffected siblings and healthy unrelated volunteers. Am J Psychiat 2002;159:1514–1520. 100. Noga JT, Bartley AJ, Jones DW, Torrey EF, Weinberger DR. Cortical gyral anatomy and gross brain dimensions in monozygotic twins discordant for schizophrenia. Schizophr Res 1996;22:27–40. 101. Van Haren NE, Picchioni MM, McDonald C, Marshall N, Davis N, Ribchester T, Hulshoff Pol HE, Sharma T, Sham P, Kahn RS, Murray R. A controlled study of brain structure in monozygotic twins concordant and discordant for schizophrenia. Biol Psychiat 2004;56:454–461. 102. Baare WF, van Oel CJ, Hulshoff Pol HE et al. Volumes of brain structures in twins discordant for schizophrenia. Arch Gen Psychiat 2001;58:33–40. 103. Seidman LJ, Faraone SV, Goldsein JM, Kremen WS, Horton NJ, Makris N, Toomey R, Kennedy D, Caviness VS, Tsuang MT. Left hippocampal volume as a vulnerability indicator for schizophrenia: a magnetic resonance imaging morphometric study of nonpsychotic first-degree relatives. Arch Gen Psychiat 2002;59:839–849. 104. Lawrie SM, Whalley HC, Abukmeil SS, Kestelman JN, Donnelly L, Miller P et al. Brain structure, genetic liability and psychotic symptoms in subjects at high risk of developing schizophrenia. Biol Psychiat 2001;49:811–823. 105. Boos HBM, Aleman A, Cahn W, Kahn RS. Brain volumes in relatives of patients with schizophrenia: a meta-analysis. Schizophr Res 2006;81:41.

117

106. Dickey CC, Shenton ME, Hirayasu Y, Fischer I, Voglmaier MM, Niznikiewicz MA, Seidman LJ, Fraone S, McCarley RW. Large CSF volume not attributable to ventricular volume in schizotypal personality disorder. Am J Psychiat 2000;157:48–54. 107. Koo MS, Dickey CC, Park HJ, Kubicki M, Ji NY, Bouix S, Pohl KM, Levitt JJ, Nakamura M, Shenton ME, McCarley RW. Smaller neocortical gray matter and larger sulcal cerebrospinal fluid volumes in neuroleptic-naive women with schizotypal personality disorder. Arch Gen Psychiat 2006 Oct;63(10):1090–1100. 108. Davidson LL, Heinrichs RW. Quantification of frontal and temporal lobe brain-imaging findings in schizophrenia: a meta-analysis. Psychiat Res 2003;122:69–87. 109. Rapoport JL, Giedd JN, Blumenthal J, Hamburger S, Jeffries N, Fernandez T et al. Progressive cortical change during adolescence in childhood-onset schizophrenia: a longitudinal magnetic resonance imaging study. Arch Gen Psychiat 1999;56:649–654. 110. Goghari VM, Rehm K, Carter CS, MacDonald AW 3rd. Regionally specific cortical thinning and gray matter abnormalities in the healthy relatives of schizophrenia patients. Cereb Cortex 2007;17:315–324. 111. Lawrie SM, Whalley HC, Abukmeil SS, Kestelman JN, Miller P, Best JJ, Owens DG, Johnstone EC. Temporal lobe volume changes in people at high risk of schizophrenia with psychotic symptoms. Br J Psychiat 2002;181:138–143. 112. Downhill JE, Buchsbaum MS, Hazlett EA, Barth S, LeesRoitman S, Nunn M, Lekarev O, Wei T, Siever LJ. Temporal lobe volume determined by magnetic resonance imaging in schizotypal personality disorder and schizophrenia. Schizophr Res 2001;48:187–199. 113. Gur RE, Turetsky BI, Cowell PE, Finkelman C, Maany V, Grossman RI, Arnold SE, Bilker WB, Gur RC. Temporolimbic volume reductions in schizophrenia. Arch Gen Psychiat 2000;57:769–775. 114. Bogerts B, Ashtari M, Degreef G et al. Reduced temporal limbic structure volumes on magnetic resonance images in first episode schizophrenia. Psychiat Res 1990;35:1–13. 115. Flaum M, O’Leary DS, Swayze VW 2nd, Miller DD, Arndt S, Andreasen NC. Symptom dimensions and brain morphology in schizophrenia and related psychotic disorders. J Psychiat Res 1995;29:261–276. 116. Barta PE, Pearlson GD, Powers RE et al. Auditory hallucinations and smaller superior or temporal gyral volume in schizophrenia. Am J Psychiat 1990;147:1457–1462. 117. Kawasaki Y, Maeda Y, Urata K et al. A quantitative magnetic resonance imaging study of patients with schizophrenia. Eur Arch Psychiat Clin Neurosci 1993;242:268–272. 118. Rossi A, Stratta P, Mancini F et al. Magnetic resonance imaging findings of amygdala-anterior hippocampus shrinkage in male patients with schizophrenia. Psychiat Res 1994;52:43–53. 119. Shenton ME, Kikinis R, Jolesz FA et al. Abnormalities of the left temporal lobe and thought disorder in schizophrenia: a quantitative magnetic resonance imaging study. N Engl J Med 1992;327:604–612. 120. Lee J-M, Kim SH, Jang DP, Ha TH, Kim J-J, Kim IY, Kwon JS, Kim SI. Deformable model with surface registration for hippocampal shape deformity analysis in schizophrenia. Neuroimage 2004;22:831–840.

118 121. Kim SH, Lee JM, Kim HP, Jang DP, Shin YW, Ha TH, Kim JJ, Kim IY, Kwon JS, Kim SI. Asymmetry analysis of deformable hippocampal model using the principal component in schizophrenia. Hum Brain Mapp. 2005 Aug;25(4):361–369. 122. Goldberg TE, Ragland JD, Tortey EF, Gold JM, Bigelow LB, Weinberger DR. Neuropsychological assessment of monozygotic twins discordant for schizophrenia. Arch Gen Psychiat 1990;47:1066–1072. 123. Weinberger DR, Zigun JR, Bartley AJ, Jones DW, Torrey EF. Anatomical abnormalities in the brains of monozygotic twins discordant and concordant for schizophrenia. Clin Neuropsychopharmacol 1992;15(Suppl 1 Pt A):122A–123A. 124. Nestor PG, Shenton ME, McCarley RW, Harrison J, Smith RS, O’Donnell B, Kimble M, Kikinis R, Jolesz FA. Neuropsychological correlates of MRI temporal abnormalities in schizophrenia. Am J Psychiat 1993;150:1849–1855. 125. Powell HW, Koepp MJ, Symms MR, Boulby PA, SalekHaddadi A, Thompson PJ, Duncan JS, Richardson MP. Material-specific lateralization of memory encoding in the medial temporal lobe: blocked versus event-related design. Neuroimage 2005;27:231–239. 126. Toulopoulou T, Morris RG, Rabe-Hesketh S, MurrayRM. Selectivity of verbal memory deficit in schizophrenic patients and their relatives. Am J Med Genet B Neuropsychiat Genet 2003;116:1–7. 127. Sitskoorn MM, Aleman A, Ebisch SJ, Appels MC, Kahn RS. Cognitive deficits in relatives of patients with schizophrenia: a meta-analysis. Schizophr Res 2004;71:285–295. 128. Geuze E, Vermetten E, Bremner JD. MR-based in vivo hippocampal volumetrics: 2. Findings in neuropsychiatric disorders. Mol Psychiat 2005 Feb;10(2):160–184. 129. Goldberg TE, Torrey EF, Berman KF, Weinberger DR. Relations between neuropsychological performance and brain morphological and physiological measures in monozygotic twins discordant for schizophrenia. Psychiat Res 1994;55:51–61. 130. Gur RE, Maany V, Mozley PD, Swanson C, Bilker W, Gur RC. Subcortical MRI volumes in neuroleptic-naïve and treated patients with schizophrenia. Am J Psychiat 1998;155:1711–1717. 131. O’Driscoll GA, Florencio PS, Gagnon D, Wolff A-LV, Benkelfat C, Mikula L et al. Amygdala-hippocampal volume and verbal memory in first-degree relatives of schizophrenic patients. Psychiat Res Neuroimaging 2001;107:75–85. 132. Dickey CC, McCarley RW, Voglmaier MM, Niznikiewicz MA, Seidman LJ, Hirayasu Y, Fischer I, The EK, Van Rhoads R, Jakab M, Kikinis R, Jolesz FA, Shenton ME. Schizotypal personality disorder and MRI abnormalities of temporal lobe gray matter. Biol Psychiat 1999;45: 1393–1402. 133. Dickey CC, McCarley RW, Xu ML, Seidman LJ, Voglmaier MM, Niznikiewicz MA, Connor E, Shenton M. MRI abnormalities of the hippocampus and cavum septum pellucid in females with schizotypal personality disorder. Schizophr Res 2007;89:49–58. 134. Caetano S, Hatch J, Brambilla P, Sassi R, Nicoletti M, Mallinger A, Frank E, Kupfer D, Keshavan M, Soares J. Anatomical MRI study of hippocampus and amygdala in

D. Mamah et al. patients with current and remitted major depression. Psychiat Res Neuroimaging 2004;132:141–147. 135. McCarley RW, Wible CG, Frumin M, Hirayasu Y, Levitt JJ, Fischer IA, Shenton ME. MRI anatomy of schizophrenia. Rev Biol Psychiat 1999;45:1099–1119. 136. Goghari VM, Rehm K, Carter CS, MacDonald AW. Sulcal thickness as a vulnerability indicator of schizophrenia. Br J Psychiat 2007;191:229–233. 137. Rajarethinam R, Sahni S, Rosenberg DR, Keshavan MS. Reduced superior temporal gyrus volume in young offspring of patients with schizophrenia. Am J Psychiat 2004;161:1121–1124. 138. Kwon JS, McCarley RW, Anderson JE, Hirayasu Y, Fischer IA, Kikinis R, Jolesz FA, Shenton ME. Left planum temporal volume reduction in schizophrenia. Arch Gen Psychiat 1999;56:142–148. 139. Barta PE, Pearlson GD, Brill LB, Royall R, McGilchrist, IK, Pulver AE, Powers RE, Casanova MF, Tien AY, Frangou S, Petty PG. Planum temporal asymmetry reversal in schizophrenia: replication and relationship to gray matter abnormalities. Am J Psychiat 1997;154:661–667. 140. Petty RG, Barta PE, Pearlson GD, McGilchrist IK, Lewis RW, Tien AY, Pulver A, Vaughn DD, Casanova MF, Powers RE. Reversal of asymmetry of the planum temporal in schizophrenia. Am J Psychiat 1995;152:715–721. 141. Rossi A, Stratta P, Mattei P, Cupillari M, Bozzao A, Gallucci M, Casacchia M. Planum temporale in schizophrenia: a magnetic resonance study. Schizophr Res 1992;7:19–22. 142. DeLisi LE, Hoff AL, Neale C, Kushner M. Asymmetries in the superior temporal lobe in male and female first-episode schizophrenic patients: measures of the planum temporale and superior temporal gyrus by MRI. Schizophr Res 1994;12:19–28. 143. Hirayasu Y, McCarley RW, Salisbury DF, Tanaka S, Kwon JS, Frumin M, Snyderman D, Yurgelun-Todd D, Kikinis R, Jolesz FA, Shenton ME. Planum temporale and Heschl’s gyrus volume reduction in schizophrenia: a magnetic resonance imaging study of first-episode patients. Arch Gen Psychiat 2000;57:692–699. 144. Frangou S, Sharma T, Sigmudsson T, Barta P, Pearlson G, Murray RM. The Maudsley Family Study. 4. Normal planum temporale asymmetry in familial schizophrenia. A volumetric MRI study. Br J Psychiat 1997 Apr;170:328–333. 145. Dickey CC, McCarley RW, Voglmaier MM, Frumin M, Niznikiewicz MA, Hirayasu Y, Fraone S, Seidman LJ, Shenton ME. Smaller left Heschl’s gyrus volume in patients with schizotypal personality disorder. Am J Psychiat 2002 Sept;159(9):1521–1527. 146. Shenton ME, Wible CG, McCarley RW. A review of magnetic resonance imaging studies of brain abnormalities in schizophrenia. In: Rama Krishnan KR, Doraiswamy PM (Eds.), Brain Imaging in Clinical Psychiatry. New York: Marcel Dekker, 1997; pp. 297–380. 147. Kuperberg GR, Broome MR, McGuire PK, David AS, Eddy M, Ozawa F, Goff D, West WC, Williams SC, van der Kouwe AJ, Salat DH, Dale AM, Fischl B. Regionally localized thinning of the cerebral cortex in schizophrenia. Arch Gen Psychiat 2003 Sep;60(9):878–888. 148. Honey GD, Sharma T, Suckling J, Giampietro V, Soni W, Williams SC, Bullmore ET. The functional neuroanatomy

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

of schizophrenic subsyndromes. Psychol Med 2003 Aug;33(6):1007–1018. 149. Paillère-Martinot M, Caclin A, Artiges E, Poline JB, Joliot M, Mallet L, Recasens C, Attar-Lévy D, Martinot JL. Cerebral gray and white matter reductions and clinical correlates in patients with early onset schizophrenia. Schizophr Res 2001 May 30;50(1–2):19–26. 150. Ananth H, Popescu I, Critchley HD, Good CD, Frackowiak RS, Dolan RJ. Cortical and subcortical gray matter abnormalities in schizophrenia determined through structural magnetic resonance imaging with optimized volumetric voxel-based morphometry. Am J Psychiat 2002 Sept;159(9):1497–1505. 151. Ho B-C, Andreasen NC, Nopoulos P, Arndt S, Magnotta V, Flaum M. Progressive structural brain abnormalities and their relationship to clinical outcome: a longitudinal magnetic resonance imaging study early in schizophrenia. Arch Gen Psychiat 2003;60:585–594. 152. Lieberman JA, Phillips M, Gu H, Stroup S, Zhang P, Kong L, Ji Z, Koch G, Hamer RM. Atypical and conventional antipsychotic drugs in treatment-naïve first-episode schizophrenia: a 52-week randomized trial of clozapine vs. chlorpromazine. Neuropsychopharmacology 2003;28:995–1003. 153. Cannon TD, Thompson PM, van Erp TG, Toga AW, Poutanen VP, Huttunen M, Lonnqvist J, StanderskjoldNordenstam CG, Narr KL, Khaledy M, Zoumalan CI, Dail R, Kaprio J. Cortex mapping reveals regionally specific patterns of genetic and disease-specific gray-matter deficits in twins discordant for schizophrenia. Proc Natl Acad Sci USA 2002;99:3228–3233. 154. Pantelis C, Velakoulis D, McGorry PD. Neuroanatomical abnormalities before and after onset of psychosis: a crosssectional and longitudinal MRI comparison. Lancet 2003;361:281–288. 155. Job DE, Whalley HC, McConnnell S, Glabus MF, Johnstone EC, Lawrie SM. Voxel based morphometry of grey matter densities in subjects at high risk of schizophrenia. Schizophr Res 2003;64:1–13. 156. Diwadkar VA, Montrose DM, Dworakowski D, Sweeney JA, Keshavan MS. Genetically predisposed offspring with schizotypal features: an ultra high-risk group of schizophrenia? Prog Neuropsychopharmacol Biol Psychiat 2006;30:23–238. 157. Siever LJ, Buchsbaum MS, Shihabuddin L, Downhill J, Byne WM, Hazlett EA. Cortical and subcortical volumes in patients with schizotypal personality disorder (abstract). Biol Psychiat 2000;47:120S. 158. Buchsbaum MS, Nenadic I, Hazlett E, Spiegel-Cohen J, Fleischman MB, Akhavan A, Silverman JM, Siever LJ. Differential metabolic rates in prefrontal and temporal Brodmann areas in schizophrenia and schizotypal personality disorder. Schizophr Res 2002;54:141–150. 159. Siever LJ, Kalus O, Keefe R. The boundaries of schizophrenia. Psychiat Clin North Am 1993;16:217–244. 160. Raine A, Sheard C, Reynolds GP, Lencz T. Prefrontal structural and functional deficits associated with individual differences in schizotypal personality. Schizophr Res 1992;7:237–247. 161. Todtenkopf MS, Vincent SL, Benes FM. A cross-study meta-analysis and three-dimensional comparison of cell

119

counting in the anterior cingulate cortex of schizophrenic and bipolar brain. Schziophr Res 2005;73:79–89. 162. Goldstein JM, Seidman LJ, O’Brien LM, Horton N, Kennedy DN, Makris N, Caviness Jr VS, Faraone SV, Tsuang MT. Impact of normal sexual dimorphisms on sex differences in structural brain abnormalities in schizophrenia assessed by magnetic resonance imaging. Arch Gen Psychiat 2002;59:154–164. 163. Mitelman SA, Shihabuddin L, Brickman AM, Hazlett EA, Buchsbaum MS. Volume of the cingulate and outcome in schizophrenia. Schizophr Res 2005;72:91–108. 164. Crespo-Facorro B, Kim J, Andreasen NC, O’Leary DS, Magnotta V. Regional frontal abnormalities in schizophrenia: a quantitative gray matter volume and cortical surface size study. Biol Psychiat 2000;48:110–119. 165. Hirayasu Y, Shenton ME, Salisbury DF, Kwon JS, Wible CG, Fischer IA, Yurgelun-Todd D, Zarate C, Kikinis R, JOlesz FA, McCarley RW. Subgenual cingulate cortex volume in firstepisode psychosis. Am J Psychiat 1999;156:1091–1093. 166. Ellison-Wright I, Glahn DC, Laird AR, Thelen SM, Bullmore E. The anatomy of first-episode and chronic schizophrenia: an anatomical likelihood estimation metaanalysis. Am J Psychiat 2008 Aug;165(8):1015–1023. 167. Yucel M, Pantelis C, Stuart GW, Wood SJ, Maruff P, Velakoulis D, Pipingas A, Crowe SF, Tochon-Danguy HJ, Egan GF. Anterior cingulate activation during Strrop task performance: a PET to MRI coregistration study of individual patients with schizophrenia. Am J Psychiat 2002;159:251–254. 168. Fornito A, Yücel M, Dean B, Wood SJ, Pantelis C. Anatomical abnormalities of the anterior cingulate cortex in schizophrenia: bridging the gap between neuroimaging and neuropathology. Schizophr Bull 2008 Apr 23 [Epub ahead of print]. 169. Zetzsche T, Preuss U, Frodi T, Watz D, Schmitt G, Koutsouleris N, Born C, Reiser M, Moller JH, Meisenzahl EM. In-vivo topography of structural alterations of the anterior cingulate in patients with schizophrenia: new findings and comparison with the literature. Schizophr Res 2007;96:34–45. 170. Yucel M, Wood SJ, Phillips LJ, Stuart GW, Smith DJ, Yung A, Velakoulis D, McGorry PD, Pantelis SC. Morphology of the anterio cingulate cortex in young men at ultra-high risk of developing a psychotic illness. Br J Psychiat 2003;182:518–524. 171. Marcelis M, Suckling J, Woodruff P, Hofman P, Bullmore E, van Os J. Searching for a structural endophenotypes in psychosis using computational morphometry 2003;122:153–167. 172. Haznedar MM, Buchsbaum MS, Hazlett EA, Shihabuddin L, New A, Siever LJ. Cingulate gyrus volume and metabolism in the schizophrenia spectrum. Schizophr Res 2004;71:249–262. 173. Takahashi T, Suzuki M, Kawasaki Y, Kurokawa K, Hagino H, Yamashita I, Zhou SY, Nohara S, Nakamura K, Seto H, Kurachi M. Volumetric magnetic resonance imaging study of the anterior cingulate gyrus in schizotypal disorder. Eur Arch Psychiat Clin Neurosci 2002;252:268–277. 174. Pearlson GD, Petty RG, Ross CA, Tien AY. Schizophrenia: a disease of heteromodal association cortex? Neuropsychopharmacology 1996;14:1–17.

120 175. Lawrie SM, Abukmeil SS. Brain abnormality in schizophrenia: a systematic and quantitative review of volumetric magnetic resonance imaging studies. Br J Psychiat 1998;172: 110–120. 176. Ho BC. MRI brain volume abnormalities in young, nonpsychotic relatives of schizophrenia probands are associated with subsequent prodromal symptoms. Schizophr Res 2007;96:1–13. 177. Gogtay N, Greenstein D, Lenane M, Clasen L, Sharp W, Gochman P, Butler P, Evans A. Cortical brain development in nonpsychotic siblings of patients with childhood-onset schizophrenia. Rapoport J Arch Gen Psychiat 2007;64: 772–780. 178. Shihabuddin L, Silverman JM, Buchsbaum MS, Siever LJ, Luu C, Germans MK, Metzger M, Mohs RC, Smith CJ, Spiegel-Cohen J, Davis KL. Ventricular enlargement associated with linkage marker for schizophrenia-related disorders in one pedigree. Mol Psychiat 1996;1:215–222. 179. Ture U, Yasargil DC, Al-Mefty O, Yasargil MG. Topographic anatomy of the insular region. J Neurosurg 1999;90:720–733. 180. Augustine JR. Circuitry and functional aspects of the insular lobe in primates including humans. Brain Res Brain Res Rev 1996;22:229–244. 181. Kim JJ, Youn T, Lee JM, Kim IY, Kim SI, Kwon JS. Morphometric abnormality of the insula in schizophrenia: a comparison with obsessive-compulsive disorder and normal control using MRI. Schizophr Res 2003;60:191–198. 182. Wright IC, Ellison ZRK, Sharma T, Friston KJ, Murray RM, McGuire PK. Mapping of grey matter changes in schizophrenia. Schizophr Res 1999;35:1–14. 183. Honea RA, Meyer-Lindenberg A, Hobbs KB, Pezawas L, Mattay VS, Egan MF, Verchinski B, Passingham RE, Weinberger DR, Callicott JH. Is gray matter volume an intermediate phenotype for schizophrenia? A voxel-based morphometry study of patients with schizophrenia and their healthy siblings. Biol Psychiat 2008;63:465–474. 184. Takahashi T, Suzuki M, Zhou SY, Hagino H, Tanino R, Kawasaki Y, Nohara S, Yamashita I, Seto H, Kurachi M. Volumetric MRI study of the short and long insular cortices in schizophrenia spectrum disorders. Psychiat Res 2005;138:209–220. 185. Yoneyama E, Matsui M, Kawasaki Y, Nohara S, Takahashi T, Hagino H, Suzuki M, Seto H, Kurachi M. Acta Psychiat Scand 2003;108:333–340. 186. Nelson MD, Saykin AJ, Flashman LA et al. Hippocampal volume reduction in schizophrenia as assessed by magnetic resonance imaging: a meta-analytic study. Arch Gen Psychiat 1998;55:443–440. 187. Breier A, Buchanan RW, Elkashef A et al. Brain morphology and schizophrenia: a magnetic resonance imaging study of limbic, prefrontal cortex and caudate structure. Arch Gen Psychiat 1992;49:921–926. 188. Yeo RA, Hodde-Vargas J, Hendren RL et al. Brain abnormalities in schizophrenia-spectrum children: implications for a neurodevelopmental perspective. Psychiat Res Neuroimaging 1997;76:1–13. 189. Seidman LJ, Faraone SV, Goldstein JM, Goodman JM, Kremen WS, Matsuda G, Hoge EA, Kennedy D, Makris N, Caviness VS, Tsuang MT. Reduced subcortical brain vol-

D. Mamah et al. umes in nonpsychotic siblings of schizophrenic patients: a pilot magnetic resonance imaging study. Am J Med Genet 1997;74:507–514. 190. Keshavan MS, Montrose DM, Pierri JN, Dick EL, Rosenberg D, Talagala L, Sweeney JA. Magnetic resonance imaging and spectroscopy in offspring at risk for schizophrenia: preliminary studies. Prog Neuro-Psychopharmacol Biol Psychiat 1997;21:1285–1295. 191. Faraone SV, Seidman LJ, Kremen WS, Kennedy D, Makris N, Caviness VS, Goldstein J, Tsuang MT. Structural brain abnormalities among relatives of patients with schizophrenia: implications for linkage studies. Schizophr Res 2003;6:125–140. 192. Goldman AL, Pezawas L, Mattay VS, Fischl B, Verchinski BA, Zoltick B, Weinberger DR, MeyerLindenberg A. Heritability of brain morphology related to schizophrenia: a large-scale automated magnetic resonance imaging segmentation study. Biol Psychiat 2008;63:475–483. 193. Staal WG, Hulshoff Pol HE, Schnack HG, Hoogendoorn MLC, Jellema K, Kahn RS. Structural brain abnormalities in patients with schizophrenia and their healthy siblings. Am J Psychiat 2000;157:416–421. 194. Lehéricy S, Ducros M, Van de Moortele PF, Francois C, Thivard L, Poupon C, Swindale N, Ugurbil K, Kim DS. Diffusion tensor fiber tracking shows distinct corticostriatal circuits in humans. Ann Neurol 2004 Apr;55(4):522–529. 195. Chakos MH, Lieberman JA, Bilder RM, Borenstein M, Lerner G, Bogerts B, Wu H, Kinon B, Ashtari M. Increase in caudate nuclei volumes of first-episode schizophrenic patients taking antipsychotic drugs. Am J Psychiat 1994;151:1430–1436. 196. Chakos MH, Lieberman JA, Alvir J, Bilder R, Ashtari M. Caudate nuclei volumes in schizophrenic patients treated with typical antipsychotics or clozapine. Lancet 1995;345:456–457. 197. Keshavan MS, Bagwell WW, Haas GL, Sweeney JA, Schooler NR, Pettegrew JW. Changes in caudate volume with neuroleptic treatment. Lancet 1994;344:1434. 198. Shihabuddin L, Buchsbaum MS, Hazlett EA, Silverman J, New A, Brickman AM, Mitropoulou V, Nunn M, Fleishman MB, Tang C, Siever LJ. Striatal size and glucose metabolic rate in schizotypal personality disorder and schizophrenia. Arch Gen Psychiat 2001;58:877–884. 199. Berman KF. Functional neuroimaging in schizophrenia. In: Davis KL, Charney DC, Coyle JT, and Nemeroff C (eds.), Neuropsychopharmacology: The Fifth Generation of Progress, Lippincott Williams and Wilkins Publishers, Philadelphia, pp 745–756, 2002. 200. Lang DJ, Kopala LC, Vandorpe RA, Rui Q, Smith GN, Goghari VM, Lapointe JS, Honer WG. Reduced basal ganglia volumes after switching to olanzapine in chronically treated patients with schizophrenia. Am J Psychiat 2004;161:1829–1836. 201. Andersson C, Hamer RM, Lawler CP, Mailman RB, Lieberman JA. Striatal volume changes in the rat following long-term administration of typical and atypical antipsychotic drugs. Neuropsychopharmacology 2002;27:143–151. 202. Corson PW, Nopoulos P, Andreasen NC, Heckel D, Arndt S. Caudate size in first-episode neuroleptic-naïve schizo-

22

Neuromorphometric Measures as Endophenotypes of Schizophrenia Spectrum Disorders

phrenic patients measured using an artificial neural network. Biol Psychiat 1999;46:712–720. 203. Keshavan MS, Rosenberg D, Sweeney JA, Pettegrew JW. Decreased caudate volume in neuroleptic-naïve psychotic patients. Am J Psychiat 1998;155:774–778. 204. Andreasen NC. The role of the thalamus in schizophrenia. Can J Psychiat 1997;42:27–33. 205. Konick LC, Friedman L. Meta-analysis of thalamic size in schizophrenia. Biol Psychiat 2001;49:28–38. 206. Byne W, Buchsbaum MS, Kemether E, Hazlett E, Shinwari A, Siever LJ. MRI assessment of medial and dorsal pulvinar nuclei of the thalamus in schizophrenia and schizotypal personality disorder. Arch Gen Psychiat 2001;58:133–140. 207. Portas CM, Goldstein JM, Shenton ME, Hokama HH, Wible CG, Fischer I, Kikinis R, Donnino R, Jolesz FA, McCarley RW. Volumetric evaluation of the thalamus in schizophrenic male patients using magnetic resonance imaging. Biol Psychiat 1998;43:649–659. 208. Hazlett EA, Buchsbaum MS, Byne W, Wei TC, SpiegelCohen J, Geneve C, Kinderlehrer R, Haznedar MM, Shihabuddin L, Sievel LJ. Three-dimensional analysis with MRI and PET of the size, shape and function of the thalamus in the schizophrenia spectrum. Am J Psychiat 1999;156:1190–1199. 209. Byne W, Buchsbaum MS, Mattiace LA et al. Postmortem assessment of thalamic nuclear volumes in subjects with schizophrenia. Am J Psychiat 2002;159:59–65. 210. Manaye K, German D, Liang C-L, Hicks P, Young K. Reduced number of mediodorsalthalamic neurons in schizophrenics. Soc Neurosci Abst 1997;23:2200. 211. Pakkenberg B. Pronounced reduction of total neuron number in mediodorsal thalamic nucleus and nucleus accumbens in schizophrenics. Arch Gen Psychiat 1990;47:1023–1028. 212. Young KA, Manaye KF, Liang C, Hicks PB, German DC. Reduced number of mediodorsal and anterior thalamic neurons in schizophrenia. Biol Psychiat 2000;47:944–953. 213. Lawrie SM. Premorbid structural abnormalities in schizophrenia. In: Keshavan M, Kennedy J, Murray R (Eds.), Neurodevelopment and Schizophrenia. Cambridge: Cambridge University Press, 2004; pp. 347–372. 214. Staal WG, Hulshoff Pol HE, Schnack H, van der Schot AC, Kahn RS. Partial volume decrease of the thalamus in relatives of patients with schizophrenia. Am J Psychiat 1998;155:1784–1786. 215. Steel R, Whalley H, Miller P, Best JJK, Johnstone EC, Lawrie SM. Structural MRI of the brain in presumed carriers of genes for schizophrenia, their affected and unaffected siblings. J Neurol Neurosurg Psychiat 2002;72:455–458. 216. Schmahmann JD, Caplan D. Cognition, emotion and the cerebellum. Brain 2006 Feb;129(Pt 2):290–292. 217. Wassink TH, Andreasen NC, Nopoulos P, Flaum M. Cerebellar morphology as a predictor of symptom and psychosocial outcome in schizophrenia. Biol Psychiat 1999;45:41–48. 218. Andreasen NC, Pierson R. The role of the cerebellum in schizophrenia. Biol Psychiat 2008;64:81–88. 219. Jacobsen LK, Giedd JN, Rajapakse JC, Hamburger SD, Vaituzis AC, Frazier JA, Lenane MC, Rapoport JL. Quantitative magnetic resonance imaging of the corpus callosum in childhood onset schizophrenia. Psychiat Res Neuroimaging 1997;68:77–86.

121

220. James AC, James S, Smith DM, Javaloyes A. Cerebellar, prefrontal cortex, and thalamic volumes over two time points in adolescent-onset schizophrenia. Am J Psychiat 2004;161:1023–1029. 221. Levitt JJ, McCarley RW, Nestor PG, Petrescu C, Donnino R, Hirayasu Y et al. Quantitative volumetric MRI study of the cerebellum and vermis in schizophrenia: clinical and cognitive correlates. Am J Psychiat 1999;156:1105–1107. 222. Loeber RT, Cintron CM, Yurgelun-Todd DA. Morphometry of individual cerebellar lobules in schizophrenia. Am J Psychiat 2001;158:952–954. 223. McDonald C, Grech A, Toulopoulou T, Schulze K, Chapple B, Sham P, Walshe M, Sharma T, Sigmundsson T, Chitnis X, Murray RM. Brain volumes in familial and non-familial schizophrenic probands and their unaffected relatives. Am J Med Genet 2002;114:616–625. 224. David AS. Schizophrenia and the corpus callosum: developmental, structural and functional relationships. Behav Brain Res 1994;64:203–211. 225. Coger RW, Serafetinides EA. Schizophrenia, corpus callosum and interhemispheric communication: a review. Psychiat Res 1990;34:163–184. 226. Pandya D, Seltzer B. The topography of commissural fibers. In: Lepore F, Petito M, Jasper H (Eds.), Two Hemispheres – One Brain: Functions of the Corpus Callosum. New York: Alan R. Lis, 1986. 227. Rotarska-Jagiela A, Schonmeyer R, Oertel V, Haenschel C, Vogeley K, Linden DEJ. The corpus callosum in schizophrenia-volume and connectivity changes affect specific regions. Neuroimage 2008;39:1522–1532. 228. Chua SE, Sharma T, Takei N, Murray RM, Woodruff PW. A magnetic resonance imaging study of corpus callosum size in familial schizophrenic subjects, their relatives, and normal controls. Schizophr Res 2000;41:397–403. 229. Woodruff PW, McManus IC, David AS. Meta-analysis of corpus callosum size in schizophrenia. J Neurol Neurosurg Psychiat 1995 Apr;58(4):457–461. 230. Narr KL, Thompson PM, Sharma T, Moussai J, Cannestra AF, Toga AW. Mapping morphology of the corpus callosum in schizophrenia. Cereb Cortex 2000;10:40–49. 231. Casanova MF, Sanders RD, Goldberg TE, Bigelow LB, Christison G, Torrey EF, Weinberger DR. J Neurol Neurosurg Psychiat 1990;53:416–421. 232. Tibbo P, Nopoulos P, Arndt S, Andreasen NC. Corpus callosum shape and size in male patients with schizophrenia. Biol Psychiat 1998;44:405–412. 233. Narr KL, Cannon TD, Woods RP, Thompson PM, Kim S, Asunction D, van Erp TG, Poutanen VP, Huttunen M, Lönnqvist J, Standerksjöld-Nordenstam CG, Kaprio J, Mazziotta JC, Toga AW. Genetic contributions to altered callosal morphology in schizophrenia. J Neurosci 2002 May 1;22(9):3720–3729. 234. Woodruff PW, Phillips ML, Rushe T, Wright IC, Murray RM, David AS. Corpus callosum size and inter-hemispheric function in schizophrenia. Schizophr Res. 1997 Feb 28;23(3):189–196. 235. Woodruff PW, Pearlson GD, Geer MJ, Barta PE, Childcoat HD. A computerized magnetic resonance imaging study of corpus callosum morphology in schizophrenia. Psychol Med 1993;23:45–56.

122 236. Kwon JS, Shenton ME, Hirayasu Y, Salisbury DF, Fischer IA, Dickey CC, Yurgelun-Todd D, Tohen M, Kikinis R, Jolesz FA, McCarley RW. MRI study of cavum septi pellucid in schizophrenia, affective disorder and schizotypal personality disorder. Am J Psychiat 1998;155:509–515. 237. Nopoulos P, Swayze V, Andreasen NC. Pattern of brain morphology in patients with schizophrenia and large cavum septi pellucid. J Neuropsychiat Clin Neurosci 1996;8:147–152. 238. Nopoulos P, Swayze V, Flaum M, Erhardt JC, Yuh WT, Andreasen NC. Cavum septum pellucid and patients with schizophrenia as detected by magnetic resonance imaging. Biol Psychiat 1997;41:1102–1108. 239. Rajarethinam R, Sohi J, Arfken C, Keshavan MS. No difference in the prevalence of cavum septum pellucidum (CSP) between first-episode schizophrenia patients, offspring of schizophrenia patients and healthy controls. Schizophr Res. 2008 Aug;103(1–3):22–25. 240. Choi JS, Kang DH, Park JY, Jung WH, Choi CH, Chon MW, Jung MH, Lee JM, Kwon JS. Cavum septum pellucidum in subjects at ultra-high risk for psychosis: compared with first-degree relatives of patients with schizophrenia and healthy volunteers. Prog Neuropsychopharmacol Biol Psychiat 2008;32:1326–1330. 241. Shelton RC, Weinberger DR. X-ray computerized tomography studies in schizophrenia: a review and synthesis. In:

D. Mamah et al. Nasrallah HA, Weinberger DR (Eds.), The Handbook of Schizophrneia, Vol I: The Neurology of Schziophrenia. New York: Elsevier, 1986; pp. 207–250. 242. Dauphinais ID, DeLisi LE, Crow TJ et al. Reduction in temporal lobe size in siblings with schizophrenia: a mag-netic resonance imaging study. Psychiat Res 1990;35:137–147. 243. Degreef G, Bogerts B, Ashtari M et al. Ventricular system morphology in first episode schizophrenia: a volumetric study of ventricular subdivisions on MRI (abstract). Schizophr Res 1990;3:18. 244. Johnstone EC, Owens DG, Crow TJ et al. Temporal lobe structure as determined by nuclear magnetic resonance in schizophrenia and bipolar affective disorder. J Neurol Neurosurg Psychiat 1989;52:736–741. 245. Shenton ME, Kikinis R, McCarley RW et al. Application of automated MRI volumetric measurement techniques to the ventricular system in schizophrenics and normal controls. Schizophr Res 1991;5:103–113. 246. Lieberman J, Chakos M, Wu H, Alvir J, Hoffman E, Robinson D et al. Longitudinal study of brain morphology in first episode schizophrenia. Biol Psychiat 2001;49:487–499. 247. Buckley PF, Dean D, Bookstein FL, Friedman L, Kwon D, Lewis JS, Kamath J, Lys C. Three-dimensional magnetic resonance-based morphometrics and ventricular dysmorphology in schizophrenia. Biol Psychiat 1999;45:62–67.

Chapter 23

Magnetic Resonance Imaging Biomarkers in Schizophrenia Research Heike Tost, Shabnam Hakimi, and Andreas Meyer-Lindenberg

Abstract In the preceding decades, neuroimaging techniques have emerged as a pivotal tool for the noninvasive, in vivo examination of subtle brain dysfunctions in psychiatric patient populations. Methods such as magnetic resonance imaging (MRI) and positron emission tomography (PET) have proven successful in bridging the gap between genetic and molecular mechanisms and psychopathological phenomena by characterizing associated structural and functional anomalies on the intermediate neural systems level. This development has been of particular relevance for schizophrenia research, where neuroimaging has helped to identify core deficits of dopaminergic neurotransmission, frontal lobe efficiency, and neuronal plasticity. The current chapter reviews our current knowledge on MRI neuroimaging biomarkers in schizophrenia. Special consideration is given to the neurocognitive domains most critically affected by the disorder, as well as the characterization of effects of schizophrenia susceptibility genes and therapeutic drugs. Keywords Schizophrenia, functional magnetic resonance imaging • voxel-based morphometry • gray matter volume • imaging genetics • dopamine • prefrontal cortex • motor functioning • working memory • attention

H. Tost and S. Hakimi Clinical Brain Disorders Branch, Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of Health, Bethesda, USA A. Meyer-Lindenberg Faculty of Clinical Medicine Mannheim, University of Heidelberg, Germany

Abbreviations ACC: Anterior cingulate cortex; AGI: Automated gyrification index; AI: Adhesio interthalamica; CB: Cerebellum; COMT: Catecholamine-O-methyltransferase; CS: Cross-sectional design; DARPP: Dopamineand cAMP-regulated phosphoprotein; DISC1: Disrupted-in-schizophrenia 1; DLPFC: Dorsolateral prefrontal cortex; DTI: Diffusion tensor imaging; FA: Fractional anisotropy; fMRI: Functional magnetic resonance imaging; FSS: Freesurfer subcortical segmentation; GAD: Glutamic acid decarboxylase; GM: Gray matter; GPCR: G protein-coupled receptor; GRM3: Glutamate receptor-modulating gene; HC: Hippocampus; HV: Healthy volunteers; INS: Insular cortex/operculum; ITG: Inferior temporal/fusiform gyrus; l: Left; LD: Longitudinal design; med +/−: On/ off antipsychotic medication; MedFL: Medial frontal lobe; MedTL: Medial temporal lobe; MRSI: Magnetic resonance spectroscopy; MTG: Middle temporal gyrus; MZ: Monozygotic; NAA: N-acetylaspartate; NPSY: Neuropsychological testing; NRG1: Neuregulin-1; OFC: Orbitofrontal cortex; paraICA: Parallel independent component analysis; PCC: Posterior cingulate cortex; PFC: Prefrontal cortex; PHG: Parahippocampal gyrus; PPC: Posterior parietal cortex; PUT: Putamen; r: Right; rCBF: Regional cerebral blood flow; RGS4: Regulator of G-protein signaling 4; ROI: Region of interest; SBM: Source-based morphometry; SCZ: Schizophrenia; SEG: Semi-automated segmentation; SFG: Superior frontal gyrus; SFO: Sequential finger opposition; SIB: Siblings of SCZ patients; SMA: Supplementary motor area; SMC: Sensorimotor cortex; SNP: Single-nucleotide polymorphism; SPD: Schizotypal personality disorder; STG: Superior temporal gyrus; TRAX: Translinassociated factor X; VBM: Voxel-based morphometry; VLPFC: Ventro-lateral prefrontal cortex; WM: White

M.S. Ritsner (ed.), The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, © Springer Science + Business Media B.V. 2009

123

124

matter; ↓: Significant reduction; ↑: Significant increase ø: No significant differences.

Neuroimaging Biomarkers in Psychiatry: Vast Potentials and Great Challenges Schizophrenia is a highly heritable and devastating mental disorder that imposes significant emotional and financial burdens on the affected individuals, their families, and the social health care system. Assumptions about the presumed neurobiological origin of the disease can be traced back to the first formal description of the disease as “dementia praecox” by Emil Kraepelin1 in 1919. Until recently, however, the elucidation of the pathophysiology underlying these symptoms was limited by the restricted methodological spectrum available for the in vivo examination of the structural and functional properties of the brain. Until the early 1980s, biomarker research in psychiatry was largely restricted to neurobiochemical and post-mortem research as well as invasive radiological techniques, which yielded some limited brain systems level insights (e.g., cerebral ventriculography, an obsolete nuclear imaging method used to demonstrate the ventricular enlargement in affected patients). During the last two decades, the development of more sophisticated neuroimaging methods has revolutionized our understanding of the nature of mental disease states by providing pivotal tools for the delineation of the structural and functional correlates associated with these phenomena. Techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) have enabled the successful integration of psychiatric neuroimaging in modern translational research by facilitating multimodal research strategies that bridge the gap between genetic and molecular mechanisms and psychological and behavioral phenomena. It is because of the groundbreaking insights derived from these non-invasive medical imaging techniques that psychiatry is recognized as a neuroscience discipline today, and that mental health is perceived as a precious good that can be described in terms of the functional, biochemical, and microstructural integrity of the brain. This chapter reviews the current scientific knowledge on MRI biomarkers in schizophrenia. The search

H. Tost et al.

for biomarkers is unique in the psychiatric discipline in several respects, especially as psychiatric biomarkers may provide a scientific account that can be examined empirically while spanning levels of description from genetic susceptibility factors and elementary biological processes to disturbances in behavior and social adaptation. Unfortunately, there is no physical or technical test available that can diagnose a given psychiatric disorder with certainty, a fact that poses a major problem for defining valid and reliable biomarkers. In order to overcome this obstacle, current research on neuroimaging biomarkers in schizophrenia aims to delineate neurobiological processes that satisfy two main criteria. First, the process of interest should be systematically related to schizophrenia and/or relevant clinical parameters such as severity of symptoms, course of the illness, or treatment response. Second, the identified marker should be specific to the underlying pathophysiology, i.e., it should specify a quantitative neurobiological measure that captures a process at the core of the disorder. Although challenging, the outcome of this research is expected to transform the psychiatric field substantially by helping to identify rational treatment targets and stimulate new drug developments necessary for individualized, sciencebased patient care. This development is eventually expected to lead to the identification of valid pathophysiological markers of disease states, and to overcome our current traditional methods of diagnosis and differential diagnosis that are still mainly based on behavioral observation and introspection. In the last 15 years, numerous cognitive, functional, morphological, and metabolic anomalies of the brain have been described in schizophrenia patients, suggesting an overall heterogeneous disease entity rather than a circumscribed pathology. At the same time, major progress has been made with respect to the delineation of a number of schizophrenia core phenomena, especially disturbances of dopaminergic neurotransmission, frontal lobe efficiency, and neural plasticity. This chapter reviews the current scientific knowledge on magnetic resonance imaging (MRI) biomarkers in schizophrenia. In doing so, it attempts to give special consideration to the neurocognitive domains most critically affected by the disorder, while examining advances in the visualization of antipsychotic medication effects and the impact of schizophrenia susceptibility genes on the neural system level. Due to the sheer

23

Magnetic Resonance Imaging Biomarkers in Schizophrenia Research

volume of published results, this overview does not claim to be all-inclusive, but rather focuses on selected areas of this vital and still-expanding research field.

Brain Structural Biomarkers The original pathogenetic theory of Kraepelin postulates that schizophrenia is a progressive neurodegenerative disease state with an atypically early age of onset.1 This historic assumption and the repeated finding of ventricular enlargement in affected patient populations has stimulated a large number of post-mortem studies attempting to shed light on the question of whether or not a primarily degenerative process is involved in the development of the disease.2–4 In the last several decades, these studies evidenced a variety of subtle histopathological changes in the brains of schizophrenia patients, especially aberrantly located neurons in the enthorhinal cortex,5,6 smaller perikarya of cortical pyramidal neurons,7–9 and a reduction in dendritic arborization.10,11 These data were complemented by evidence suggesting alterations in volume, density, or total number of neurons in several subcortical structures, especially basal ganglia, thalamus, hippocampus, and amygdale.12–14 Major neuropathological hallmarks of a neuronal degenerative process were, however, lacking, especially the proliferation of astrocytes and microglia typically seen in the context of a neural degenerative process (e.g., reactive gliosis).15,16 This observation led to the formulation of alternative pathogenetic disease models that have postulated a (non-progressive) prenatal disturbance in neurodevelopment as main pathogenetic mechanism. One influential hypothesis assumes that schizophrenia emerges from intrauterine disturbances of temporolimbic-prefrontal network formation, resulting in the manifestation of overt clinical symptoms in early adulthood.17–19 The concept is supported by data showing that candidate susceptibility genes of schizophrenia20,21 and known epigenetic risk factors22 interfere with neuronal migration processes during central nervous system (CNS) development. Since the mid-1980s, the availability of magnetic resonance imaging has allowed for the non-invasive examination of brain structural biomarkers in schizophrenia patients (see 23,24 for a comprehensive review and Table 23.1 for a summary of recent findings).

125

Earlier studies in the field demonstrated global abnormalities in terms of increased ventricular size25 and smaller mean cerebral volume.26,27 Subsequent regional analyses were performed by manually delineating a priori defined regions of interest ROIs on MRI scans, an approach that yielded one of the most consistent findings in schizophrenia research, a bilateral decrease in hippocampal gray matter volume.2 Available metaanalyses suggest that the mean hippocampal gray matter (GM) volume in schizophrenia patients is decreased to 95% of the volume of normal controls.23 In contrast, other subcortical structures like the basal ganglia have repeatedly been reported to show a volumetric increase in patient populations. This particular observation, however, is most likely explained by effects of longterm exposure to neuroleptic agents, as this finding has not been observed in antipsychotic-naïve patients,28,29 and has proven to reverse when the medication regimen is switched from conventional neuroleptics to atypical antipsychotic substances (e.g., olanzapine).30,31 In the last decade, the implementation of new automated processing techniques has allowed for the largescale analysis of structural MRI datasets. Voxel-based morphometry (VBM) is a fully automated method that allows for the unbiased analysis of structural differences of the whole brain on a voxel-by-voxel basis.32 The method has been shown to be sensitive to subtle regional changes in tissue volume and concentration that are otherwise inaccessible to standard imaging techniques.33–35 This approach may be superior to conventional ROI analyses in terms of regional sensitivity and reliability of structural findings, and is especially helpful in situations where pathology does not reflect traditional neuroanatomical boundaries. Further methodological advances like the development of fullyautomated whole brain segmentation methods have also facilitated the volumetric analysis of subcortical structures in large cohorts.36 One of our own largescale, automated MRI segmentation studies,37 which was conducted in 221 healthy controls, 169 patients with schizophrenia and 183 unaffected siblings, demonstrated a bilateral decrease in hippocampal and cortical gray matter volume in schizophrenia patients compared to the healthy controls. Moreover, evidence for the heritability of cortical and hippocampal volume reductions was derived. Recently, a thorough meta-analysis of VBM studies identified GM reductions in the bilateral superior temporal gyrus (left: 57% of studies, right: 50% of studies),

126

H. Tost et al.

Table 23.1 Structural neuroimaging biomarkers Study

Design

Subjects

Med

Method

Measure

Result

Meisenzahl et al.

CS

High-risk subjects, HV

−

VBM

GM volume

↓(MedTL, PFC)

Xu et al.181

CS

SCZ, HV

+

SBM

GM volume

Goldman et al.37

CS

SCZ, unaffected siblings, HV

+

FSS

GM volume

Honea et al.182

CS

SCZ, unaffected siblings, HV

+

VBM

GM volume

Zinkstok et al.183

CS

SCZ

+

VBM

GM density

Hazlett et al.184

CC

SPD, SCZ, HV

− (SPD)

ROI

GM volume

Bonilha et al.185

CC

SCZ, HV

+

VBM

GM volume

Koutsouleris et al.186

CC

SCZ, HV

+

VBM

GM volume

van Haren et al.187

LD

SCZ, HV

+

SEG

GM volume

Nesvag et al.188 Ettinger et al.189

CC CS

SCZ, HV MZ twins, HC

+ +

ROI ROI

GM Thickness GM volume

Ho et al.190

LD

SCZ

+

SEG

GM volume

Szeszko et al.191

CS

SCZ, HV DISC1 leu607phe

+

ROI

GM volume

Harris et al.192

LD

+

ROI

AGI

Wang et al.193

CS

High-risk subjects, HV SCZ, HV

+

ROI

GM Thickness

Kuroki et al.194

CS

+

ROI

GM volume

Hulshoff Pol et al.195 Davatzikos et al.196 Nierenberg et al.197 Onitsuka et al.198 Narr et al.199

CS CS CS CS CS

+ + + + +

VBM VBM ROI ROI ROI

GM density GM volume GM volume GM volume GM volume

↓GM ↑WM (OFC, SMC) ↓GM (HC, ACC, OFC), ↑CSF ↓GM left angular gyrus ↓GM (lMTG, lSTG, ITG) Left HC ↓GM and shape difference

Sporn et al.200

LD

+

ROI

GM volume

Cahn et al.43

LD

First episode SCZ, HV MZ twins, HC SCZ, HV SCZ, HV SCZ, HV First-episode SCZ, HV Childhood-onset SZ, HV First-episode SCZ, HV

↓GM in five different ICA source areas including ACC, VLPFC, STG, TH, INS ↓GM (HC, cortex), ↑GM (striatum), ↑CSF. Heritable: GM volume (HC, cortex) ↓GM (PFC, temporal and parietal cortex). Heritable: GM (HC volume) PFC ↓GM in met-allele carriers (BDNF val66met) Fronto-temporal ↓GM in SPD and SCZ Fronto-temporal ↓GM (lMFG, SFG, lSTG, rIFG). PFC GM predicts NPSY deficits Fronto-temporal ↓GM. Differential pattern for positive, negative, disorganized symptoms. Excessive brain volume loss (↓GM, ↑CSF) in SCZ before age 45 Fronto-temporal ↓cortical thickness Linear ↓GM (TH): HC> discordant non-SCZ > discordant SCZ> concordant SCZ Progressive brain volume changes in PFC ↓GM in met-allele carriers (BDNF val66met) SCZ and HV: phe carriers ↓GM (ACC, SFG), ↑positive symptoms ↑Gyrification (rPFC) predicts development of SCZ ↓GM volume and thickness (ACC), correlated with illness duration and symptom severity ↓GM (MTG, ITG)

+

SEG

GM volume

Excessive longitudinal GM loss, especially through adolescence Progressive ↓GM early in the course of the illness

180

left medial temporal lobe (69% of studies), and left medial and inferior frontal gyrus (50% of studies) as the most consistent regional findings in schizophrenia.24 Other studies have shown that some of these morpho-

logical abnormalities are already observable at disease onset, and are thus unlikely to be solely attributable to the effects of illness chronicity or medication.29,38–40 Longitudinal studies performed in first-episode,38,41–43

23

Magnetic Resonance Imaging Biomarkers in Schizophrenia Research

chronic,44,45 and childhood-onset46 patients suggest a progressive development of gray matter volumetric reduction over the course of the illness. In view of the post-mortem data reviewed above, the neurobiological significance of the observed MR signal changes remains to be elucidated.47 Though most likely of neurodevelopmental origin, the implications of the evidenced brain volumetric changes are still a matter of controversy, especially regarding the question of whether the evidenced alterations in the GM and CSF compartments reflect an actual loss of neurons or, instead, reductions in neuropil and neuronal size.48

Functional Imaging Markers in Schizophrenia Since the early 1990s, brain functional anomalies in mental disease states have been predominately examined by means of one neuroimaging technique, functional magnetic resonance imaging (fMRI). The popularity of the method can be explained by numerous favorable attributes, especially the broad availability of clinical scanners, the non-invasiveness of the technique, and the broad spectrum of complementary MRI applications that can offer additional insights into the structure and biochemistry of the living brain. Concurrent with the growing popularity of this technique, the development of fMRI experiments has been refined from simple paradigms with blockwise stimulation to rapid, event-related task designs. One the data analysis side, sophisticated methods for connectivity analyses of neural network interactions have been developed.49,50 In the meantime, the success of this method has given rise to an enormous amount of published fMRI studies in schizophrenia research. The following section reviews major findings in the neurocognitive domains most critically affected by the disorder.

Visual Information Processing Impairments in early visual information processing are well-known features of schizophrenia patients in neuropsychological settings.51 Classical examples are the deficits seen during “visual backward masking,” where the identification of a target stimulus is complicated by

127

the subsequent presentation of a visual distractor.52–55 Within the visual system, a preferential involvement of the dorsal visual processing path has been suggested in schizophrenia.56–58 While the ventral (parvocellular) pathway mediates the processing of object specific stimulus properties, the dorsal (magnocellular) processing stream consists of cortical areas that are specialized in the processing of motion and depth cues. This network includes, among others, the motion-sensitive visual area V5 (hMT), the posterior-parietal cortex (PPC), frontal eye fields (FEF), and the dorsolateral prefrontal cortex (DLPFC).59,60 The observation of pronounced deficits during the processing of moving visual stimuli led to the hypothesis that a circumscribed deficit of motion signals in area V5 is responsible for other downstream processing errors, especially deficits in velocity discrimination and smooth pursuit eye movements.61,62 As presumed trait markers of disease vulnerability, these deficits are well-known features of unaffected relatives of schizophrenia patients and thus present promising targets for diagnosis and treatment.63,64 Several fMRI studies have been conducted to delineate the underlying neural correlates of these “dorsal” perceptual deficits (see Table 23.2). One of our own studies65 examined the cortical response to passive visuo-acoustic stimulation in a sample of first-episode, neuroleptic-naive schizophrenia patients. Compared to healthy controls, patients demonstrated significant activation decreases of the thalamus and higher order areas of the dorsal processing stream (PPC, FEF, and DLPFC). With respect to these data, the current literature is conflicting and suggests that a potential deficiency of area V5 is, at the least, a task-related functional deficit. On the one hand, a focal decrease of the V5 hemodynamic response in schizophrenia has been reported in the context of the performance of smooth-pursuit eye movements.66 On the other hand, one of our own follow-up studies evidenced preserved V5 function during the observation and discrimination of moving visual stimuli, while higher order control areas of the dorsal stream (PPC, DLPFC) proved to be hypofunctional (see Fig. 23.1).67 Thus, although fMRI evidence has elucidated some of the dysfunctional properties of the visual system, the focus of the presumed dorsal stream dysfunction is still a matter of debate. Current evidence is in line with several pathophysiological theories, especially a “bottom up” processing deficit in V5, an early “filter dysfunction” of the thalamus, and a “top down” control deficits of the PFC.54,68,69

128

H. Tost et al.

Table 23. 2 Functional neuroimaging biomarkers Motor functioning

Design

Subjects

Med

Task

Result

Kaladjian et al.201

CS

SCZ, HV

+

Go-NoGo

Response inhibition: ↓(VLPFC)

Vink et al.106

CS

+

Go-NoGo

Honey et al.134

CS

SCZ, SIB, HV SCZ, HV

+

Rogowska et al.103 Menon et al.202 Schröder et al.99 Mattay et al.96

CS CS CS CS

SCZ, HV SCZ, HV SCZ, HV SCZ, HV

+ + + +

Continuous Performance Test SFO Pronation-supination Pronation-supination SFO

SCZ and SIB: missing Striatum↑ during movement anticipation Attentional modulation: ↓(ACC, CB), altered connectivity ACC-SFG-CB ↓(SMC), altered hemispheric lateralization ↓(TH), altered connectivity TH-PUT. ↓(SMC) Decreased hemispheric lateralization

Visual system Gur et al.71

CS

SCZ, HV

+

Facial emotion identification

Yoon et al.70

CS

SCZ, HV

+

Surguladze et al.72

CS

SCZ, HV

+

Holt et al.73

CS

SCZ, HV

+

Lencer et al.66

CS

SCZ, HV

+

Braus et al.65

CS

SCZ, HV

−

Face and object presentations Emotional face presentations Emotional face presentations Smooth-pursuit eye movement Passive visuo-acoustic stimulation

Auditory system Weinstein et al.84

CS

SCZ, HV

+

Speech perception

Allen et al.203

CS

SCZ, HV

+

Speech identification

Kiehl et al.204

CS

SCZ, HV

+

Auditory oddball

Lawrie et al.205

CS

SCZ, HV

+

Sentence completion

Wible et al.206 Dierks et al.81

CS −

SCZ, HV Case study

+ +

Auditory mismatch −

SCZ, SIB, HV

+

Selective attention CS Delawalla et al.207

Gur et al.208

CS

SCZ, HV

+

Continuous Performance Test (CPT-AX) Visual oddball task

Weiss et al.209

CS

SCZ, HV

−

Cognitive interference

Eyler et al.132

CS

SCZ, HV

+

Heckers et al.135 Weiss et al.210

CS CS

SCZ, HV SCZ, HV

+ +

Continuous Performance Test (CPT-X) Cognitive interference Cognitive interference

Barch et al.120

CS

SCZ, HV

−

Volz et al.130

CS

SCZ, HV

+

Continuous Performance Test (CPT-AX) Continuous Performance Test (CPT-TT)

Fearful faces: abnormal amygdala activation predicts emotion misidentification, affective flattening ø Fusiform face area (magnitude and extend of activation) Fearful stimuli: ↓(PHG), neutral stimuli: ↑(PHG), predicts severity of reality distortion ↑ (HC, AMY) ↓ Motion processing area V5 predicts reduced smooth-pursuit velocity ↓(rTH,rPFC, PPC lSTG) in neuroleptic-naïve first-episode patients lSTG response is correlated to severity of thought disorder SCZ with auditory hallucinations: ↑misidentification, functional anomalies ACC, lSTG ↓(TH, PFC, PPC, STG, AMY, CB) during target detection and novelty processing ↓DLPFC-STG functional connectivity, predicts with severity of auditory hallucinations Mismatch trials: ↓(STG) Activation of Heschl’s gyrus during auditory hallucinations SIB: ↑(DLPFC) Targets: ↓(STG, PFC, ACC, TH, PUT), ↑(INS, PCC, PPC); Distractors: ↓(occipital cortex), ↑(PFC) Unmedicated SCZ, acute episode: ↓(DLPFC, ACC), ↑(STG, PCC); Simple choice reaction: ↓(rIFG)

Dislocated or absent activation of the dorsal ACG Additional recruitment of DLPFC and ACG resources Neuroleptic-naive patients: deficient DLPFC activation ↓(MedFL, ACC)

(continued)

23

Magnetic Resonance Imaging Biomarkers in Schizophrenia Research

129

Table 23. 2 (continued) Motor functioning

Design

Subjects

Med

Task

Result

CS

SCZ, HV

+

N-back

CS

SCZ, HV

+

N-back

Tan et al.125

CS

SCZ, HV

+

Sternberg

Callicott et al.212 Schlosser et al.213

CS CS

SIB, HV SCZ, HV

− +

N-back N-back

Callicott et al.123

CS

SCZ, HV

+

N-back

Manoach et al.214

CS

SCZ, HV

+

N-back

Callicott et al.113

CS

HV

−

N-back

Callicott et al.215 Volz et al.216

CS CS

SCZ, HV SCZ, HV

+ +

N-back Wisconsin Card Sorting Test

↓(DLPFC), failure to deactivate ACC (“default network”) ↓(DLPFC) loss of functional specialization and integration, compensatory ↑(VLPFC) First episode SCZ: “dysfunctional” ↓(DLPFC), “compensatory” ↑(VLPFC) Unaffected siblings: ↑(PFC) Altered effective connectivity CB - TH (↓), CB-PFC (↓), TH-PFC (↑) Prefrontal inefficiency: high-performers: Areas of PFC (↓) and PFC (↑), low-performers: PFC (↓) lPFC (↑), enhanced spatial heterogeneity of PFC activation patterns Inverted-U shaped PFC response with increasing task load PFC (↓) rPFC (↓)

Working memory PomarolClotet et al.211 Tan et al.126

Fig. 23.1 Higher order visual processing deficits in schizophrenia. Statistical parametric maps of dorsal visual stream activation in healthy controls (a) and chronic schizophrenia patients (b). Left panel: Main functional effect of passive visual stimula-

tion. Right panel: Activation increase of the human motion processing area (hMT) during the processing of moving visual stimuli. All illustrations are thresholded at a significance level of p < 0.001 (uncorrected) for presentation purposes

130

Within the parvocellular “ventral” processing stream, previous neuroimaging work in schizophrenia has suggested preserved function of the fusiform face area during the passive perceptual processing of face presentations.70 In the context of the processing of emotional face presentations, however, several studies have demonstrated functional anomalies in higher order limbic areas71–73 (see Table 23.2). Evidence for an anatomical basis for visual processing deficits in upstream areas of the dorsal and ventral stream is provided by recent VBM and DTI work, suggesting microstructural alterations of the optic radiations,74 the main white matter tract subserving the extrastriate areas of the ventral stream (inferior longitudinal fasciculus),75 and the fusiform gyrus.76

Auditory Perception and Language Processing Abnormalities of the superior temporal gyrus (STG) and associated language areas of the temporal and frontal lobe have emerged as one of the most prominent biomarkers in schizophrenia research. In the last two decades, structural and functional deficits of these regions have been extensively examined in the context of auditory hallucinations, a cardinal “positive” symptom of schizophrenia that has been conceptualized as dysfunctional processing of silent inner articulations.77 Early milestone work in schizophrenia research demonstrated the spontaneous activity of speech areas during hallucinatory experiences, a fact that explains why these internal voices are accepted as “real,” despite arising in the absence of any external sensory stimulation.78–81 Subsequently, several fMRI studies have provided evidence for a functional deficit that affects multiple levels of auditory perception and language processing. On a lower level, a diminished response of primary auditory cortex to external speech has been observed during hallucinatory experiences, a fact that is best explained by the competition of physiological and pathological processes for limited neural processing resources.82,83 It should be noted that the extent of STG dysfunction during speech processing seems to predict the severity of the patient’s thought disorder, a clinical symptom that manifests as irregularities in speech.84 On the neural networks level, an impaired functional coupling of the STG and anterior cingulate

H. Tost et al.

gyrus (ACG) seems to be at the core of the misattribution of own versus alien voices that frequently characterizes patients with auditory hallucinations.85 In terms of brain structure, mounting scientific evidence points to an anatomical correlate of the acoustic hallucinations in schizophrenia. Early morphometric studies repeatedly reported a decrease in the physiological leftward asymmetry of the planum temporale, a higher order auditory processing area that overlaps with Wernicke’s area.86–88 Recent voxel-based morphometry studies have confirmed a significant gray matter decrease in the STG that predicted the severity of experienced auditory hallucinations.89 Moreover, folding abnormalities of the STG and Broca’s area, as well as microstructural changes of the main connecting white matter tract to the frontal lobe (arcuate fasciculus), seem to promote the emergence of hallucinatory symptoms.90–93

Motor Functioning Disturbances of psychomotor functions are wellknown features of schizophrenia, ranging from subtle deficits like neurological soft signs to extensive behavioral abnormalities like stereotypia and catatonia.94,95 In the early years of schizophrenia imaging research, the examination of motor dysfunctions was very popular, usually involving a block-design approach where simple repetitive motor activities alternated with rest conditions (e.g., finger tapping, pronation-supination; see Table 23.2). In addition to other findings, hypoactivations of the primary and higher order supplementary motor and premotor cortices have been reported in schizophrenia patients.96–101 Moreover, within the highly lateralized motor system, a physiologically abnormal symmetry (i.e., reduced laterality) of recruited neural resources has emerged as a cardinal fMRI biomarker of motor system dysfunctions in schizophrenia.96,102,103 On the neural networks level, a functional deficiency of transcallosal glutamergic projections has been suggested96; this assumption is in line with current neurodevelopmental disease models and has been encouraged by corresponding electrohysiological findings.104 In recent years, fMRI studies in the motor domain have been scarce. The pronounced impact of antipsychotic agents on the examined circuitry and the lack of

23

Magnetic Resonance Imaging Biomarkers in Schizophrenia Research

studies in unmedicated patient samples offer two potential explanations for this fact.105 Recent evidence suggests that both schizophrenia patients and their unaffected relatives are characterized by a lack of striatal activation increase during movement anticipation.106 Although this finding is suggestive of an endophenotypic trait marker, it still awaits replication. On the structural level, the functional findings are complemented by reports of gray matter volume decreases and impaired structural integrity of the connecting white matter tracts of the higher order processing areas of the motor system.107–109

Working Memory Convergent evidence from several neuroscience disciplines (genetic, molecular, cellular, physiological, and neuroimaging) suggests that schizophrenia is a genetically complex disorder of brain development that promotes downstream disturbances in dopaminergic neurotransmission, and subsequent impairments in prefrontal cortical efficiency. As a result, schizophrenia patients suffer from a variety of higher order cognitive functions known to be dependent on the integrity of the dorsolateral and medial prefrontal lobe, especially cognitive flexibility, selective attention, response inhibition, and working memory (WM). Previous experimental work in animals and humans has shown that mesocortical dopamine (DA), especially the stimulation of the dopamine D1 receptor subtype, plays a crucial role in the modulation of WM (dys)functions.110–112 According to this evidence, an “inverted U”-shaped relationship exists between the amount of D1 receptor stimulation, WM-related activation of PFC neurons, and prefrontal cognitive efficacy.110,113 While balanced D1 dopaminergic tone seems to augment the robustness of PFC network representations by making them less susceptible to background neural “noise”, states of excessive or lacking dopaminergic drive seem to weaken the system’s robustness to interfering stimuli and, as a result, promote the development of cognitive deficits and of psychotic symptoms.114,115 In the past decade, the neurobiological basis of WM dysfunctions in schizophrenia has been extensively examined with fMRI. On a general level, this cognitive asset can be divided into maintenance and manipulation subprocesses. Classical WM paradigms challenge

131

the active storage and online manipulation of information, often assessed in the form of so-called “n-back” tasks, where patients are asked to monitor an ongoing sequence of stimulus presentations and respond to items that match the one presented n stimuli before. The majority of studies reported disorder-related dysfunctions of the DLPFC (Brodmann areas 46 and 9), as well as anomalies in the functional coupling of this area to the medial temporal lobe.116,117 The precise pathophysiological background of these findings is still, however, a matter of debate, as diverse anomalies in the form of hypofunctions,118–121 increased activations,122 and combined hyper- and hypoactive states123 have been reported. This inconsistency has raised questions about the traditional theory of pure functional “hypofrontality” in schizophrenia and has stimulated the formulation of more complex models of prefrontal cortex dysfunction.122–124 fMRI work in first-episode schizophrenia patients confirmed a preferential impairment of the manipulation component of WM and reported a disproportional engagement of the ventrolateral prefrontal cortex (VLPFC, Brodmann areas 44, 45, 47) during task performance.125 This finding has been interpreted as a deficiency in the functional specialization and hierarchical organization of the prefrontal cortex. The deficit is thought to manifest itself in terms of reduced efficiency in DLPFC functioning, which in turn triggers the compensatory recruitment of hierarchically inferior and less specialized areas in the VLPFC.126

Selective Attention On a general level, selective attention describes the mental capacity to maintain a behavioral or cognitive set in the face of distracting or competing stimuli. The term covers several cognitive subprocesses, especially the top-down sensitivity control, competitive selection, and automatic bottom-up filtering for salient stimuli.127 Since Bleuler’s first clinical descriptions of schizophrenia,128 attentional deficits have been considered core symptoms of the condition that eventually lead to overt psychopathological symptoms like thought disorder, incoherence of speech, and disorganized behavior. Previous neuroimaging research in psychiatry has adopted different experimental techniques to examine the neurobiological correlates of these deficits, the most popular being the so-called “continuous performance

132

test” (CPT). The term describes a heterogenous array of non-standardized paradigms where participants are meant to selectively respond to target presentations, while inhibiting responses to non-targets. Besides these basic features, most CPTs additionally challenge other cognitive subprocesses like visual attention (e.g., “degraded CPTs”), cognitive interference monitoring, or working memory (e.g., “contingent CPTs” like CPT-AX, CPT-IP, and CPT-double-T). The heterogeneity of CPT paradigms must be taken into account while interpreting available fMRI data on attentional dysfunctions in schizophrenia. The vast majority of studies in the field have used variations of a “contingent CPT” to examine the neural correlates of attentional dysfunctions in schizophrenia. Most of these studies reported functional anomalies of the DLPFC129–131 and VLPFC132 in schizophrenia patients, findings that are most likely related to the moderate working memory demands of this particular task type. Similar findings have been observed in firstepisode neuroleptic-naive patients during CPT task performance, suggesting this functional biomarker is at the core of the disorder, and not merely a medication induced phenomenon.120 In line with this assumption, Delawalla et al.207 recently observed task-related hyperactivation of DLPFC in unaffected siblings of schizophrenia patients, suggesting that this deficit resembles an intermediate phenotype of the genetic susceptibility to the illness. In contrast, most of the studies that used paradigms challenging interference monitoring observed dysfunctions of the anterior cingulate gyrus (ACG). Reported anomalies in schizophrenia include regional ACG hypoperfusion,133 disturbed interregional connectivity to the prefrontal cortex,132,134 and the absence or dislocation of activation foci within the ACG.135 Evidence from structural MRI studies suggests that the observed functional and behavioral deficits relate to a morphological and microstructural impairment of the ACG and its main white matter fiber tract, the cingulum bundle.136–140

Imaging of Genetic Susceptibility Factors Schizophrenia is a highly heritable mental disorder with a complex genetic architecture. Current evidence suggests that multiple genetic risk variants, each

H. Tost et al.

accounting for only a small increment in risk for the development of the disorder, interact both with one another and with the environment. On the neural systems level, this interaction interferes with the functional properties of multiple brain circuits that in turn shape a variety of different cognitive, emotional, and behavioral functions and dysfunctions (see Fig. 23.2 for an illustration of these concepts).141 Clearly, there is no one-to-one mapping between risk gene variants and neural system mechanisms, or between neural mechanisms and psychopathology, a fact that renders the identification of valid biomarkers on the basis of genetics alone difficult. Imaging genetics, the characterization of susceptibility gene mechanisms on the neural systems level using multimodal neuroimaging, has proven a successful research strategy for overcoming this obstacle. Many gene variants associated with an enhanced risk for the development of schizophrenia are frequent in healthy individuals. The imaging genetics approach assumes that the penetrance of gene effects is greater at the neurobiological level than at the level of complex behavior, and that these gene effects are traceable at the neural systems level in carriers of risk alleles even when no overt clinical signs of the disease phenotype are expressed. In recent years, the attempt to genetically dissect schizophrenia with neuroimaging methods has led to the characterization of several intermediate phenotypes (i.e., core pathophysiologic characteristics observable at the level of the neural substrate that bridge the gap between genetic variation and psychiatric symptoms). The strongest evidence for the efficacy of this approach arises from intermediate phenotype studies on catechol-O-methyltransferase (COMT), a major enzyme for the degradation of catecholamines in the central nervous system. The COMT gene is located in a chromosomal region that has been implicated in schizophrenia linkage studies,142,143 the 22q11 deletion syndrome (22q11DS),144 which is a hemideletion syndrome with a 30-fold increased risk to develop a schizophrenia-like illness,145 and, more recently, in copy number variation in sporadic schizophrenia.146 Due to the lack of dopamine transporters in the PFC, the regulation of extracellular dopamine levels in the PFC is critically dependent on COMT functioning.147 As has been previously shown, a common val(108/158) met substitution in the COMT gene interferes with the thermostability of the transcribed protein, leading to significantly decreased enzyme efficacy.148 Studies

23

Magnetic Resonance Imaging Biomarkers in Schizophrenia Research

133

DRD2 (11q23)

a GRM3 (7q21.1–q21.2) Schizophrenia

PFC

Caudate Putamen MB

COMT (22q11.2)

b

BA 25

Episodic memory

Working memory

AM

OFC PFC Emotional regulation

HF

Reward

Depression

MAOA VNTR (Xp11.23)

5-HTTLPR/SLC6A4 (17q11.1–q12)

Fig. 23.2 The complex phenotype path from genes to behavioral and disease phenotype.141 Multiple genetic risk variants, through interactions with one another and with the environment, affect multiple neural systems linked to several neuropsychological and behavioral domains that are impaired, in differing proportions, in psychiatric diseases. As examples, the following genetic variants are depicted (chromosomal variation in parentheses): GRM3 single nucleotide polymorphism 4 (7q21.1–q21.2),174 dopamine receptor D2 (DRD2) Taq 1a (11q23),175 catechol-O-methyltransferase (COMT) Val66Met (22q11.2),149,176 serotonin transporter length polymorphism

(5-HTTLPR/SLC6A4) (17q11.1–q12)177,178 and monoamine oxidase A variable number tandem repeat (MAOA VNTR) (Xp11.23).179 These variants are shown to affect a circuit that links the prefrontal cortex (PFC) with the midbrain (MB) and striatum (caudate and putamen); this circuit is (a) relevant for schizophrenia, and a circuit that connects the amygdala (AM) with regulatory cortical and limbic areas, and (b) is implicated in depression and anxiety. These circuits, in turn, are shown to mediate risk for schizophrenia, depression, various neuropsychological functions. BA 25, Brodmann’s area 25; HF, hippocampal formation; OFC, orbitofrontal cortex

from our laboratory have demonstrated that the val(108/158)met coding variant in the COMT gene impacts on PFC functional measures during working memory performance,149 modulates the performance in neuropsychological tests challenging executive functioning,150 and influences the cortical response to amphetamine in healthy subjects.115 These data extend basic evidence for an “inverted-U” functional response curve to increasing dopamine signaling in the PFC, and validate the concept of prefrontal cortical inefficiency as

key endomechanism promoting the risk for schizophrenia. According to this model, the COMT genotype places individuals at predictable points along the inverted U-shaped curve that links prefrontal dopaminergic stimulation, neuronal activity, and PFC efficiency. Homozygotes for the val-encoding allele are thought to be positioned to the left of met allele carriers at a point of decreased PFC efficiency, while the met allele carriers seem to be optimally located near the peak of that curve (val/val: COMT efficacy↑,

134

synaptic DA↓; met/met: COMT efficacy↓, synaptic DA↑). The genetic risk associated with the COMT val(108/158)met coding variant is thought to be mediated by a reduced signal-to-noise ratio in the PFC, an idea that is supported by the finding that WM-related and WM-unrelated PFC activity are inversely related to neuroimaging markers of midbrain DA synthesis, which in turn is directionally dependent on the COMT val(108/158)met genotype.151 Recent work from our laboratory152 indicates that a frequent haplotype of the gene encoding the dopamineand cAMP-regulated phosphoprotein DARPP-32 (PPP1R1B) is associated with the risk for schizophrenia, and impacts on measures of frontostriatal structure, function, and cognition. DARPP-32 is a major target for dopamine-activated adenylyl cyclase and serves as an important functional switch integrating the multiple information streams that converge onto dopaminoceptive neurons in the striatum (i.e., striatal neurotransmitters, neuropeptides, and neurosteroids).153 It has been shown that DARPP-32 is a key node in the final common pathway of psychotomimetic action in the prefrontal cortex and striatum,154 making it an attractive candidate gene for schizophrenia. Our imaging genetics study showed a pronounced and convergent effect of genetic variation in PPP1R1B on the function and volume of the striatum, and related measures of frontalstriatal connectivity. Moreover, a pronounced impact of PPP1R1B variation on a wide range of prefrontal cognitive measures was observed. These findings might suggest that PPP1R1B contributes to risk for schizophrenia by disturbed gating155 of fronto-striatal information flow. Interestingly, AKT1, another key molecule in a non-canonical signal transduction pathway for dopaminergic neurons, showed an impact on frontostriatal circuitry as well,156 establishing a convergent neural signature for postsynaptic dopaminergic neurotransmission. Other important neuroimaging findings related to research on schizophrenia intermediate phenotypes are summarized in Table 23.3.

Characterization of Antipsychotic Drug Effects The standard medical treatment for schizophrenia is antipsychotic medication. First generation antipsychotics like haloperidol or fluphenazine (also referred

H. Tost et al.

to as conventional antipsychotics or major tranquilizers) belong to a class of antipsychotic drugs developed in the 1950s. Due to their primarily D2-receptor blocking properties, this substance class is very efficient in relieving positive symptoms, but also bears the potential for unwanted side effects such as EPS (extrapyramidal side effects) or tardive dyskinesia. In the last 15 years, available treatment options for schizophrenia patients were extended substantially by the development of second-generation, or “atypical,” antipsychotic drugs (e.g., clozapine, olanzapine). The term “atypical antipsychotics” refers to a biochemically heterogeneous group of drugs characterized by the absence of EPS and increased efficacy compared to conventional neuroleptics for the treatment of negative symptoms and cognitive deficits (see Miyamoto et al.157 for a comprehensive review). Previous evidence has suggested that the degree and timing of the clinical response to atypical antipsychotics is modulated by the patient’s genetic profile for dopamine catabolic enzymes. As discussed earlier, the met variant of the val158 met COMT gene polymorphism inactivates prefrontal dopamine at a slower rate,158 a mechanism that is associated with a greater improvement of negative symptoms.159 A recent study160 replicated this finding and provided additional evidence for a faster response to atypical antipsychotics in the met allele carriers. Although the precise mechanism of these effects is not yet fully understood, this finding suggests that met allele carrying patients may have greater benefit from olanzapine treatment because of their relative excess of prefrontal cortical dopamine. On the neural systems level, several fMRI studies have provided evidence suggesting a favorable impact of atypical antipsychotics on previously “disturbed” brain functional patterns. Bertolino et al.102 examined the impact of olanzapine on motor loop functioning, and observed an alleviation of the sensorimotor hypoactivations observed in the unmedicated state. Similarly, Stephan et al.161 reported a restoration of cerebellar functional connectivity during motor task performance under olanzapine. Other relevant performance domains that have been associated with favorable functional effects under atypical antipsychotics include working memory,162 verbal fluency,163 and conflict detection.164 The differential effect of atypical versus typical antipsychotic drugs has been mainly examined in cross-sectional designs. Several studies examining fMRI task performance related to motor functioning,97

23

Magnetic Resonance Imaging Biomarkers in Schizophrenia Research

135

Table 23.3 Intermediate phenotypes Study

Gene

Method

Result

Winterer et al.217

NRG1

DTI, VBM

SNP8NRG221533 predicts ↑risk for SCZ and ↓FA of medial frontal white matter

Straub et al.218

GAD1

fMRI, NPSY

Tan et al.156

AKT1

Takahashi et al.219

DRD3, BDNF

fMRI, VBM, NPSY ROI

Hall et al.220

DAOA (G72)

fMRI

McIntosh et al.221

NRG1

DTI, VBM

Tan et al.222

COMT, GRM3

fMRI

Meyer-Lindenberg et al.152 Buckholtz et al.223

DARPP-32

fMRI, VBM, NPSY fMRI, VBM

Meyer-Lindenberg et al.151 Blasi et al.224

PET

Cannon et al.225

COMT Val108/158 Met COMT Val108/158 Met DISC1/TRAX

Callicott et al.226

DISC1

Tan et al.126

COMT

fMRI, VBM, NPSY fMRI

Egan et al.227 Egan et al.149

GRM3 COMT Val108/158 Met

5′SNP predicts ↑risk for SCZ, PFC inefficiency (fMRI), and executive cognitive deficits A allele (rs1130233) predicts inefficient PFC engagement, ↓GM (lCAU, rCAU, rVLPFC), and cognitive deficits DRD3 Ser9Gly, BDNF Val66Met Interaction of ser and met risk gene variants predicts ↓GM (HC), and length of AI M23 and M24 SNP genotypes predict HC and rVLPFC response during verbal performance in high-risk subjects SNP rs6994992: TT genotype ↓WM density ↓FA in the anterior limb of the internal capsule Epistatic interaction of SCZ risk variants inefficient PFC engagement and coupling PPP1R1B haplotype (CGCACTC) predicts cognitive performance, striatal ↓GM, and decreased striatal activity SCZ risk SNP (rs951436): A allele load predicts PFC functional dyscoupling and ↓GM volume (rSTG, rVLPFC, TH) Met-allele predicts ↓midbrain DA synthesis and lower PFC rCBF during working memory performance Val-allele predicts ↓performance and ACC inefficiency during visual attention task SCZ risk haplotypes predict ↓GM density (PFC) and memory deficits Ser704Cys SNP (rs821616) increases risk for SCZ, ↓GM volume, and ↓HC engagement during working memory Complex interaction of genetic variation in COMT predicts PFC response during WM (rs4680, rs2097603, rs165599) SNP4 A allele: PFC inefficiency (fMRI), PFC ↓NAA (MRSI), Val-allele: predicts ↑risk for SCZ, PFC inefficiency (fMRI), and executive cognitive deficits

RGS4

fMRI, NPSY VBM, NPSY

fMRI, MRSI fMRI, NPSY

working memory,134 and prepulse inhibition165 have suggested a superior effect of second-generation antipsychotics over classical neuroleptics on brain functional measures in schizophrenia. On the brain structural level, evidence derived with different MRI techniques supports this notion. In a multi-center, longitudinal study in 161 patients with first-episode psychosis, Lieberman et al.166 observed a reduction of global brain GM volume over time in patients treated with haloperidol, but not in patients treated with olanzapine or healthy control subjects. Similarly, previous MRI spectroscopy studies focusing on the marker N-acetylaspartate have suggested a favorable impact of atypical antipsychotics on neuronal viability in the ACG and PFC that is superior to the effect of traditional neuroleptics.167–170 The neurobiological basis for this differential drug effect, however, remains unknown. Possible explanations include a greater therapeutic effect of atypical antipsychotics on disease-inherent brain morphological changes, as well as the possibility

of a neurotoxic effect associated with the application of conventional neuroleptics. Only a few neuroimaging studies have examined the impact of antipsychotic drugs on brain functional measures in healthy volunteers. In a double-blind crossover drug challenge, Abler et al.171 examined the influence of olanzapine (5 mg) on reward-related brain activations, and observed a drug-related activation decrease in the ventral striatum, ACG, and inferior frontal cortex. Our own prior work in the visual172 and motor system105 on the effect of a single dose of haloperidol (5 mg/kg) confirmed that antipsychotic drugs have a primarily dampening effect on brain functional measures. During motor task performance, for example, a significant drug-related activation decrease in the dorsal striatum and an increased lateralization of motor cortex activations was observed. In line with previous work in the field, a preferential interference of haloperidol with cognitive measures challenging mental and behavioral flexibility (related to dopamine D2 receptor

136

functioning) was evidenced, while working memory performance (related to dopamine D1 receptor functioning) remained unaffected.105 These results emphasize the need to control for medication effects in fMRI studies examining psychiatric populations.

Conclusions and Future Directions Among research disciplines, neuroscience has introduced the most fundamental changes in the way in which mental disease states are conceptualized and pursued today. In terms of funding, modern day psychiatric research increasingly faces the challenge of bridging the gap between theoretical concepts and practical solutions, while focusing available resources on questions that will likely lead to future therapeutic applications. The practical need for this development is not only explained by the tremendous emotional burden that psychiatric illness causes in the affected individuals and their families, but also in the enormous treatment expenses that mental disorders impose on our society in general, an amount that exceeds an estimated $70 billion per year in the USA alone.173 In the last decade, neuroimaging methods have provided unique insights into the core neuropathophysiological processes associated with the development and treatment of schizophrenia. As a crucial part of most multimodal research approaches, these techniques have helped to characterize the mechanisms that translate disease vulnerability from the genetic level to the molecular, cellular, and neural systems level, as well as to the level of overt behavioral disturbances. Within this translational framework, the development and application of neuroimaging methods is expected to pioneer future improvements in disorder prevention, diagnosis, and treatment. Acknowledgements This work was supported in part by the Intramural Research Program of the National Institute of Mental at the National Institutes of Health.

References 1. Kraepelin E: Dementia praecox and paraphrenia. Edinburgh, Livingstone, 1919 2. Henn FA, Braus DF: Structural neuroimaging in schizophrenia. An integrative view of neuromorphology. Eur Arch Psychiat Clin Neurosci 1999; 249(Suppl 4):48–56

H. Tost et al. 3. Nasrallah HA: Neuropathology of the corpus callosum in schizophrenia. Br J Psychiat 1982; 141:99–100 4. Stevens JR: Neuropathology of schizophrenia. Arch Gen Psychiat 1982; 39(10):1131–1139 5. Arnold SE, Ruscheinsky DD, Han LY: Further evidence of abnormal cytoarchitecture of the entorhinal cortex in schizophrenia using spatial point pattern analyses. Biol Psychiat 1997; 42(8):639–647 6. Jakob H, Beckmann H: Prenatal developmental disturbances in the limbic allocortex in schizophrenics. J Neural Transm 1986; 65(3–4):303–326 7. Benes FM, McSparren J, Bird ED, SanGiovanni JP, Vincent SL: Deficits in small interneurons in prefrontal and cingulate cortices of schizophrenic and schizoaffective patients. Arch Gen Psychiat 1991; 48(11):996–1001 8. Benes FM, Sorensen I, Bird ED: Reduced neuronal size in posterior hippocampus of schizophrenic patients. Schizophr Bull 1991; 17(4):597–608 9. Rajkowska G, Selemon LD, Goldman-Rakic PS: Neuronal and glial somal size in the prefrontal cortex: a postmortem morphometric study of schizophrenia and Huntington disease. Arch Gen Psychiat 1998; 55(3):215–224 10. Glantz LA, Lewis DA: Decreased dendritic spine density on prefrontal cortical pyramidal neurons in schizophrenia. Arch Gen Psychiat 2000; 57(1):65–73 11. Garey LJ, Ong WY, Patel TS, Kanani M, Davis A, Mortimer AM, Barnes TR, Hirsch SR: Reduced dendritic spine density on cerebral cortical pyramidal neurons in schizophrenia. J Neurol Neurosurg Psychiat 1998; 65(4):446–453 12. Kreczmanski P, Heinsen H, Mantua V, Woltersdorf F, Masson T, Ulfig N, Schmidt-Kastner R, Korr H, Steinbusch HW, Hof PR, Schmitz C: Volume, neuron density and total neuron number in five subcortical regions in schizophrenia. Brain 2007; 130(Pt 3):678–692 13. Walker MA, Highley JR, Esiri MM, McDonald B, Roberts HC, Evans SP, Crow TJ: Estimated neuronal populations and volumes of the hippocampus and its subfields in schizophrenia. Am J Psychiat 2002; 159(5):821–828 14. Zaidel DW, Esiri MM, Harrison PJ: Size, shape, and orientation of neurons in the left and right hippocampus: investigation of normal asymmetries and alterations in schizophrenia. Am J Psychiat 1997; 154(6):812–818 15. Longson D, Deakin JF, Benes FM: Increased density of entorhinal glutamate-immunoreactive vertical fibers in schizophrenia. J Neural Transm 1996; 103(4):503–507 16. Benes FM: Evidence for altered trisynaptic circuitry in schizophrenic hippocampus. Biol Psychiat 1999; 46(5):589–599 17. Weinberger DR: Implications of normal brain development for the pathogenesis of schizophrenia. Arch Gen Psychiat 1987; 44(7):660–669 18. Lewis DA, Levitt P: Schizophrenia as a disorder of neurodevelopment. Ann Rev Neurosci 2002; 25:409–432 19. Benes FM: The relationship between structural brain imaging and histopathologic findings in schizophrenia research. Harv Rev Psychiat 1993; 1(2):100–109 20. Sei Y, Ren-Patterson R, Li Z, Tunbridge EM, Egan MF, Kolachana BS, Weinberger DR: Neuregulin1-induced cell migration is impaired in schizophrenia: association with neuregulin1 and catechol-o-methyltransferase gene polymorphisms. Mol Psychiat 2007; 12(10):946–957

23

Magnetic Resonance Imaging Biomarkers in Schizophrenia Research

21. Brandon NJ, Handford EJ, Schurov I, Rain JC, Pelling M, Duran-Jimeniz B, Camargo LM, Oliver KR, Beher D, Shearman MS, Whiting PJ: Disrupted in Schizophrenia 1 and Nudel form a neurodevelopmentally regulated protein complex: implications for schizophrenia and other major neurological disorders. Mol Cell Neurosci 2004; 25(1):42–55 22. Meyer U, Yee BK, Feldon J: The neurodevelopmental impact of prenatal infections at different times of pregnancy: the earlier the worse? Neuroscientist 2007; 13(3):241–256 23. Wright IC, Rabe-Hesketh S, Woodruff PW, David AS, Murray RM, Bullmore ET: Meta-analysis of regional brain volumes in schizophrenia. Am J Psychiat 2000; 157(1):16–25 24. Honea R, Crow TJ, Passingham D, Mackay CE: Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiat 2005; 162(12):2233–2245 25. Johnstone EC, Crow TJ, Frith CD, Husband J, Kreel L: Cerebral ventricular size and cognitive impairment in chronic schizophrenia. Lancet 1976; 2(7992):924–926 26. Ward KE, Friedman L, Wise A, Schulz SC: Meta-analysis of brain and cranial size in schizophrenia. Schizophr Res 1996; 22(3):197–213 27. Gur RE, Turetsky BI, Bilker WB, Gur RC: Reduced gray matter volume in schizophrenia. Arch Gen Psychiat 1999; 56(10):905–911 28. Chua SE, Cheung C, Cheung V, Tsang JT, Chen EY, Wong JC, Cheung JP, Yip L, Tai KS, Suckling J, McAlonan GM: Cerebral grey, white matter and csf in never-medicated, first-episode schizophrenia. Schizophr Res 2007; 89(1–3):12–21 29. Keshavan MS, Rosenberg D, Sweeney JA, Pettegrew JW: Decreased caudate volume in neuroleptic-naive psychotic patients. Am J Psychiat 1998; 155(6):774–778 30. Lang DJ, Kopala LC, Vandorpe RA, Rui Q, Smith GN, Goghari VM, Lapointe JS, Honer WG: Reduced basal ganglia volumes after switching to olanzapine in chronically treated patients with schizophrenia. Am J Psychiat 2004; 161(10):1829–1836 31. Scheepers FE, de Wied CC, Hulshoff Pol HE, van de Flier W, van der Linden JA, Kahn RS: The effect of clozapine on caudate nucleus volume in schizophrenic patients previously treated with typical antipsychotics. Neuropsychopharmacology 2001; 24(1):47–54 32. Ashburner J, Friston KJ: Voxel-based morphometry – the methods. Neuroimage 2000; 11(6 Pt 1):805–821 33. Valfre W, Rainero I, Bergui M, Pinessi L: Voxel-based morphometry reveals gray matter abnormalities in migraine. Headache 2008; 48(1):109–117 34. Maguire EA, Gadian DG, Johnsrude IS, Good CD, Ashburner J, Frackowiak RS, Frith CD: Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Acad Sci USA 2000; 97(8):4398–4403 35. Krams M, Quinton R, Ashburner J, Friston KJ, Frackowiak RS, Bouloux PM, Passingham RE: Kallmann’s syndrome: mirror movements associated with bilateral corticospinal tract hypertrophy. Neurology 1999; 52(4):816–822 36. Meda SA, Giuliani NR, Calhoun VD, Jagannathan K, Schretlen DJ, Pulver A, Cascella N, Keshavan M, Kates W, Buchanan R, Sharma T, Pearlson GD: A large scale (N = 400) investigation of gray matter differences in schizophrenia using optimized voxel-based morphometry. Schizophr Res 2008; 101(1–3):95–105

137

37. Goldman AL, Pezawas L, Mattay VS, Fischl B, Verchinski BA, Zoltick B, Weinberger DR, Meyer-Lindenberg A: Heritability of brain morphology related to schizophrenia: a large-scale automated magnetic resonance imaging segmentation study. Biol Psychiat 2008; 63(5):475–483 38. Gur RE, Maany V, Mozley PD, Swanson C, Bilker W, Gur RC: Subcortical MRI volumes in neuroleptic-naive and treated patients with schizophrenia. Am J Psychiat 1998; 155(12):1711–1717 39. Szeszko PR, Goldberg E, Gunduz-Bruce H, Ashtari M, Robinson D, Malhotra AK, Lencz T, Bates J, Crandall DT, Kane JM, Bilder RM: Smaller anterior hippocampal formation volume in antipsychotic-naive patients with first-episode schizophrenia. Am J Psychiat 2003; 160(12):2190–2197 40. Molina V, Sanz J, Sarramea F, Luque R, Benito C, Palomo T: Dorsolateral prefrontal and superior temporal volume deficits in first-episode psychoses that evolve into schizophrenia. Eur Arch Psychiat Clin Neurosci 2006; 256(2):106–111 41. DeLisi LE, Sakuma M, Tew W, Kushner M, Hoff AL, Grimson R: Schizophrenia as a chronic active brain process: a study of progressive brain structural change subsequent to the onset of schizophrenia. Psychiat Res 1997; 74(3):129–140 42. Lieberman J, Chakos M, Wu H, Alvir J, Hoffman E, Robinson D, Bilder R: Longitudinal study of brain morphology in first episode schizophrenia. Biol Psychiat 2001; 49(6):487–499 43. Cahn W, Hulshoff Pol HE, Lems EB, van Haren NE, Schnack HG, van der Linden JA, Schothorst PF, van Engeland H, Kahn RS: Brain volume changes in first-episode schizophrenia: a 1-year follow-up study. Arch Gen Psychiat 2002; 59(11):1002–1010 44. Davis KL, Buchsbaum MS, Shihabuddin L, Spiegel-Cohen J, Metzger M, Frecska E, Keefe RS, Powchik P: Ventricular enlargement in poor-outcome schizophrenia. Biol Psychiat 1998; 43(11):783–793 45. Mathalon DH, Sullivan EV, Lim KO, Pfefferbaum A: Progressive brain volume changes and the clinical course of schizophrenia in men: a longitudinal magnetic resonance imaging study. Arch Gen Psychiat 2001; 58(2):148–157 46. Rapoport JL, Giedd JN, Blumenthal J, Hamburger S, Jeffries N, Fernandez T, Nicolson R, Bedwell J, Lenane M, Zijdenbos A, Paus T, Evans A: Progressive cortical change during adolescence in childhood-onset schizophrenia. A longitudinal magnetic resonance imaging study. Arch Gen Psychiat 1999; 56(7):649–654 47. Weinberger DR, McClure RK: Neurotoxicity, neuroplasticity, and magnetic resonance imaging morphometry: what is happening in the schizophrenic brain? Arch Gen Psychiat 2002; 59(6):553–558 48. Harrison PJ: The neuropathology of schizophrenia. A critical review of the data and their interpretation. Brain 1999; 122(Pt 4):593–624 49. Meyer-Lindenberg A, Hariri AR, Munoz KE, Mervis CB, Mattay VS, Morris CA, Berman KF: Neural correlates of genetically abnormal social cognition in Williams syndrome. Nat Neurosci 2005; 8(8):991–993 50. Stein JL, Wiedholz LM, Bassett DS, Weinberger DR, Zink CF, Mattay VS, Meyer-Lindenberg A: A validated network of effective amygdala connectivity. Neuroimage 2007; 36(3): 736–745 51. Butler PD, Javitt DC: Early-stage visual processing deficits in schizophrenia. Curr Opin Psychiat 2005; 18(2):151–157

138 52. Braff DL, Saccuzzo DP: Information processing dysfunction in paranoid schizophrenia: a two-factor deficit. Am J Psychiat 1981; 138(8):1051–1056 53. Braff DL, Saccuzzo DP: The time course of informationprocessing deficits in schizophrenia. Am J Psychiat 1985; 142(2):170–174 54. Keri S, Antal A, Szekeres G, Benedek G, Janka Z: Visual information processing in patients with schizophrenia: evidence for the impairment of central mechanisms. Neurosci Lett 2000; 293(1):69–71 55. Moritz S, Ruff C, Wilke U, Andresen B, Krausz M, Naber D: Negative priming in schizophrenia: effects of masking and prime presentation time. Schizophr Res 2001; 48(2–3): 291–299 56. Schwartz BD, Maron BA, Evans WJ, Winstead DK: High velocity transient visual processing deficits diminish ability of patients with schizophrenia to recognize objects. Neuropsychiat Neuropsychol Behav Neurol 1999; 12(3):170–177 57. Cadenhead KS, Serper Y, Braff DL: Transient versus sustained visual channels in the visual backward masking deficits of schizophrenia patients. Biol Psychiat 1998; 43(2):132–138 58. O’Donnell BF, Swearer JM, Smith LT, Nestor PG, Shenton ME, McCarley RW: Selective deficits in visual perception and recognition in schizophrenia. Am J Psychiat 1996; 153(5):687–692 59. Ungerleider LG, Courtney SM, Haxby JV: A neural system for human visual working memory. Proc Natl Acad Sci USA 1998; 95(3):883–890 60. Ungerleider LG, Mishkin M: Two cortical visual systems, in Analysis of visual behavior. Edited by Ingle DJ, Goodale MA, Mansfield RJ. Cambridge, MA, MIT Press, 1982, pp. 549–586 61. Chen Y, Palafox GP, Nakayama K, Levy DL, Matthysse S, Holzman PS: Motion perception in schizophrenia. Arch Gen Psychiat 1999; 56(2):149–154 62. Chen Y, Levy DL, Nakayama K, Matthysse S, Palafox G, Holzman PS: Dependence of impaired eye tracking on deficient velocity discrimination in schizophrenia. Arch Gen Psychiat 1999; 56(2):155–161 63. Haraldsson HM, Ettinger U, Magnusdottir BB, Sigmundsson T, Sigurdsson E, Petursson H: Eye movement deficits in schizophrenia: Investigation of a genetically homogenous Icelandic sample. Eur Arch Psychiat Clin Neurosci 2008; 258(6):373–383 64. Chen Y, Bidwell LC, Norton D: Trait vs. state markers for schizophrenia: identification and characterization through visual processes. Curr Psychiat Rev 2006; 2(4):431–438 65. Braus DF, Weber-Fahr W, Tost H, Ruf M, Henn FA: Sensory information processing in neuroleptic-naive first-episode schizophrenic patients: a functional magnetic resonance imaging study. Arch Gen Psychiat 2002; 59(8):696–701 66. Lencer R, Nagel M, Sprenger A, Heide W, Binkofski F: Reduced neuronal activity in the V5 complex underlies smooth-pursuit deficit in schizophrenia: evidence from an fMRI study. Neuroimage 2005; 24(4):1256–1259 67. Tost H, Ende G, Ruf M, Henn FA, Meyer-Lindenberg A: Functional imaging research in schizophrenia. Int Rev Neurobiol 2005; 67:95–118 68. Levin S: Frontal lobe dysfunctions in schizophrenia – I. Eye movement impairments. J Psychiat Res 1984; 18(1):27–55

H. Tost et al. 69. Levin S: Frontal lobe dysfunctions in schizophrenia – II. Impairments of psychological and brain functions. J Psychiat Res 1984; 18(1):57–72 70. Yoon JH, D’Esposito M, Carter CS: Preserved function of the fusiform face area in schizophrenia as revealed by fMRI. Psychiat Res 2006; 148(2–3):205–216 71. Gur RE, Loughead J, Kohler CG, Elliott MA, Lesko K, Ruparel K, Wolf DH, Bilker WB, Gur RC: Limbic activation associated with misidentification of fearful faces and flat affect in schizophrenia. Arch Gen Psychiat 2007; 64(12): 1356–1366 72. Surguladze S, Russell T, Kucharska-Pietura K, Travis MJ, Giampietro V, David AS, Phillips ML: A reversal of the normal pattern of parahippocampal response to neutral and fearful faces is associated with reality distortion in schizophrenia. Biol Psychiat 2006; 60(5):423–431 73. Holt DJ, Kunkel L, Weiss AP, Goff DC, Wright CI, Shin LM, Rauch SL, Hootnick J, Heckers S: Increased medial temporal lobe activation during the passive viewing of emotional and neutral facial expressions in schizophrenia. Schizophr Res 2006; 82(2–3):153–162 74. Butler PD, Hoptman MJ, Nierenberg J, Foxe JJ, Javitt DC, Lim KO: Visual white matter integrity in schizophrenia. Am J Psychiat 2006; 163(11):2011–2013 75. Ashtari M, Cottone J, Ardekani BA, Cervellione K, Szeszko PR, Wu J, Chen S, Kumra S: Disruption of white matter integrity in the inferior longitudinal fasciculus in adolescents with schizophrenia as revealed by fiber tractography. Arch Gen Psychiat 2007; 64(11):1270–1280 76. Onitsuka T, Niznikiewicz MA, Spencer KM, Frumin M, Kuroki N, Lucia LC, Shenton ME, McCarley RW: Functional and structural deficits in brain regions subserving face perception in schizophrenia. Am J Psychiat 2006; 163(3):455–462 77. David A, Cutting J (eds): The neuropsychological origin of auditory hallucinations. Hove, Sussex, Lawrence Erlbaum Associates, 1994 78. Silbersweig DA, Stern E, Frith C, Cahill C, Holmes A, Grootoonk S, Seaward J, McKenna P, Chua SE, Schnorr L: A functional neuroanatomy of hallucinations in schizophrenia. Nature 1995; 378(6553):176–179 79. Ffytche DH, Howard RJ, Brammer MJ, David A, Woodruff P, Williams S: The anatomy of conscious vision: an fMRI study of visual hallucinations. Nat Neurosci 1998; 1(8): 738–742 80. McGuire PK, Shah GM, Murray RM: Increased blood flow in Broca’s area during auditory hallucinations in schizophrenia. Lancet 1993; 342(8873):703–706 81. Dierks T, Linden DE, Jandl M, Formisano E, Goebel R, Lanfermann H, Singer W: Activation of Heschl’s gyrus during auditory hallucinations. Neuron 1999; 22(3):615–621 82. David AS, Woodruff PW, Howard R, Mellers JD, Brammer M, Bullmore E, Wright I, Andrew C, Williams SC: Auditory hallucinations inhibit exogenous activation of auditory association cortex. Neuroreport 1996; 7(4):932–936 83. Woodruff PW, Wright IC, Bullmore ET, Brammer M, Howard RJ, Williams SC, Shapleske J, Rossell S, David AS, McGuire PK, Murray RM: Auditory hallucinations and the temporal cortical response to speech in schizophrenia: a functional magnetic resonance imaging study. Am J Psychiat 1997; 154(12):1676–1682

23

Magnetic Resonance Imaging Biomarkers in Schizophrenia Research

84. Weinstein S, Werker JF, Vouloumanos A, Woodward TS, Ngan ET: Do you hear what I hear? Neural correlates of thought disorder during listening to speech in schizophrenia. Schizophr Res 2006; 86(1–3):130–137 85. Mechelli A, Allen P, Amaro E, Jr., Fu CH, Williams SC, Brammer MJ, Johns LC, McGuire PK: Misattribution of speech and impaired connectivity in patients with auditory verbal hallucinations. Hum Brain Mapp 2007; 28:1213–1222 86. Sumich A, Chitnis XA, Fannon DG, O’Ceallaigh S, Doku VC, Faldrowicz A, Sharma T: Unreality symptoms and volumetric measures of Heschl’s gyrus and planum temporal in first-episode psychosis. Biol Psychiat 2005; 57(8):947–950 87. Kwon JS, McCarley RW, Hirayasu Y, Anderson JE, Fischer IA, Kikinis R, Jolesz FA, Shenton ME: Left planum temporale volume reduction in schizophrenia. Arch Gen Psychiat 1999; 56(2):142–148 88. Shapleske J, Rossell SL, Woodruff PW, David AS: The planum temporale: a systematic, quantitative review of its structural, functional and clinical significance. Brain Res Brain Res Rev 1999; 29(1):26–49 89. Aguilar EJ, Sanjuan J, Garcia-Marti G, Lull JJ, Robles M: MR and genetics in schizophrenia: Focus on auditory hallucinations. Eur J Radiol 2008; 67(3):434–439 90. Cachia A, Paillere-Martinot ML, Galinowski A, Januel D, de Beaurepaire R, Bellivier F, Artiges E, Andoh J, BartresFaz D, Duchesnay E, Riviere D, Plaze M, Mangin JF, Martinot JL: Cortical folding abnormalities in schizophrenia patients with resistant auditory hallucinations. Neuroimage 2008; 39(3):927–935 91. Neckelmann G, Specht K, Lund A, Ersland L, Smievoll AI, Neckelmann D, Hugdahl K: Mr morphometry analysis of grey matter volume reduction in schizophrenia: association with hallucinations. Int J Neurosci 2006; 116(1):9–23 92. Gaser C, Nenadic I, Volz HP, Buchel C, Sauer H: Neuroanatomy of ‘hearing voices’: a frontotemporal brain structural abnormality associated with auditory hallucinations in schizophrenia. Cereb Cortex 2004; 14(1):91–96 93. Hubl D, Koenig T, Strik W, Federspiel A, Kreis R, Boesch C, Maier SE, Schroth G, Lovblad K, Dierks T: Pathways that make voices: white matter changes in auditory hallucinations. Arch Gen Psychiat 2004; 61(7):658–668 94. Schroeder J, Niethammer R, Geider FJ, Reitz C, Binkert M, Jauss M, Sauer H: Neurological soft signs in schizophrenia. Schizophr Res 1991; 6(1):25–30 95. Vrtunski PB, Simpson DM, Weiss KM, Davis GC: Abnormalities of fine motor control in schizophrenia. Psychiat Res 1986; 18:275–284 96. Mattay VS, Callicott JH, Bertolino A, Santha AK, Tallent KA, Goldberg TE, Frank JA, Weinberger DR: Abnormal functional lateralization of the sensorimotor cortex in patients with schizophrenia. Neuroreport 1997; 8(13):2977–2984 97. Braus DF, Ende G, Weber-Fahr W, Sartorius A, Krier A, Hubrich-Ungureanu P, Ruf M, Stuck S, Henn FA: Antipsychotic drug effects on motor activation measured by functional magnetic resonance imaging in schizophrenic patients. Schizophr Res 1999; 39(1):19–29 98. Buckley PF, Friedman L, Wu D, Lai S, Meltzer HY, Haacke EM, Miller D, Lewin JS: Functional magnetic resonance imaging in schizophrenia: Initial methodology and evaluation of the motor cortex. Psychiat Res Neuroimaging 1997; 74:13–23

139

99. Schröder J, Essig M, Baudendistel K, Jahn T, Gerdsen I, Stockert A, Schad LR, Knopp MV: Motor dysfunction and sensorimotor cortex activation changes in schizophrenia: A study with functional magnetic resonance imaging. Neuroimage 1999; 9(1):81–87 100. Schröder J, Wenz F, Schad LR, Baudendistel K, Knopp MV: Sensorimotor cortex and supplementary motor area changes in schizophrenia. A study with functional magnetic resonance imaging. Br J Psychiat 1995; 167(2): 197–201 101. Wenz F, Schad LR, Knopp MV, Baudendistel KT, Flomer F, Schroder J, van Kaick G: Functional magnetic resonance imaging at 1.5 T: activation pattern in schizophrenic patients receiving neuroleptic medication. Magn Reson Imag 1994; 12(7):975–982 102. Bertolino A, Blasi G, Caforio G, Latorre V, De Candia M, Rubino V, Callicott JH, Mattay VS, Bellomo A, Scarabino T, Weinberger DR, Nardini M: Functional lateralization of the sensorimotor cortex in patients with schizophrenia: effects of treatment with olanzapine. Biol Psychiat 2004; 56(3):190–197 103. Rogowska J, Gruber SA, Yurgelun-Todd DA: Functional magnetic resonance imaging in schizophrenia: cortical response to motor stimulation. Psychiat Res Neuroimaging 2004; 130:227–243 104. Hoppner J, Kunesch E, Grossmann A, Tolzin CJ, Schulz M, Schlafke D, Ernst K: Dysfunction of transcallosally mediated motor inhibition and callosal morphology in patients with schizophrenia. Acta Psychiat Scand 2001; 104(3):227–235 105. Tost H, Meyer-Lindenberg A, Klein S, Schmitt A, Hohn F, Tenckhoff A, Ruf M, Ende G, Rietschel M, Henn FA, Braus DF: D2 antidopaminergic modulation of frontal lobe function in healthy human subjects. Biol Psychiat 2006; 60(11):1196–1205 106. Vink M, Ramsey NF, Raemaekers M, Kahn RS: Striatal dysfunction in schizophrenia and unaffected relatives. Biol Psychiat 2006; 60(1):32–39 107. Exner C, Weniger G, Schmidt-Samoa C, Irle E: Reduced size of the pre-supplementary motor cortex and impaired motor sequence learning in first-episode schizophrenia. Schizophr Res 2006; 84(2–3):386–396 108. Matsui M, Yoneyama E, Sumiyoshi T, Noguchi K, Nohara S, Suzuki M, Kawasaki Y, Seto H, Kurachi M: Lack of selfcontrol as assessed by a personality inventory is related to reduced volume of supplementary motor area. Psychiat Res 2002; 116(1–2):53–61 109. Goghari VM, Lang DJ, Flynn SW, Mackay AL, Honer WG: Smaller corpus callosum subregions containing motor fibers in schizophrenia. Schizophr Res 2005; 73(1):59–68 110. Williams GV, Goldman-Rakic PS: Modulation of memory fields by dopamine D1 receptors in prefrontal cortex. Nature 1995; 376(6541):572–575 111. Goldman-Rakic PS: Cellular basis of working memory. Neuron 1995; 14(3):477–485 112. Fuster JM: Prefrontal cortex and the bridging of temporal gaps in the perception-action cycle. Ann N Y Acad Sci 1990; 608:318–329; discussion 330–336 113. Callicott JH, Mattay VS, Bertolino A, Finn K, Coppola R, Frank JA, Goldberg TE, Weinberger DR: Physiological characteristics of capacity constraints in working memory

140 as revealed by functional MRI. Cereb Cortex 1999; 9(1):20–26 114. Durstewitz D, Seamans JK, Sejnowski TJ: Dopaminemediated stabilization of delay-period activity in a network model of prefrontal cortex. J Neurophysiol 2000; 83(3):1733–1750 115. Mattay VS, Goldberg TE, Fera F, Hariri AR, Tessitore A, Egan MF, Kolachana B, Callicott JH, Weinberger DR: Catechol O-methyltransferase val158-met genotype and individual variation in the brain response to amphetamine. Proc Natl Acad Sci USA 2003; 100(10):6186–6191 116. Meyer-Lindenberg AS, Olsen RK, Kohn PD, Brown T, Egan MF, Weinberger DR, Berman KF: Regionally specific disturbance of dorsolateral prefrontal-hippocampal functional connectivity in schizophrenia. Arch Gen Psychiat 2005; 62(4):379–386 117. Meyer-Lindenberg A, Poline JB, Kohn PD, Holt JL, Egan MF, Weinberger DR, Berman KF: Evidence for abnormal cortical functional connectivity during working memory in schizophrenia. Am J Psychiat 2001; 158(11):1809–1817 118. Andreasen NC, O’Leary DS, Flaum M, Nopoulos P, Watkins GL, Boles Ponto LL, Hichwa RD: Hypofrontality in schizophrenia: distributed dysfunctional circuits in neurolepticnaive patients. Lancet 1997; 349(9067):1730–1734 119. Paulman RG, Devous MD, Sr., Gregory RR, Herman JH, Jennings L, Bonte FJ, Nasrallah HA, Raese JD: Hypofrontality and cognitive impairment in schizophrenia: dynamic single-photon tomography and neuropsychological assessment of schizophrenic brain function. Biol Psychiat 1990; 27(4):377–399 120. Barch DM, Carter CS, Braver TS, Sabb FW, MacDonald A, 3rd, Noll DC, Cohen JD: Selective deficits in prefrontal cortex function in medication-naive patients with schizophrenia. Arch Gen Psychiat 2001; 58(3):280–288 121. Weinberger DR, Berman KF, Zec RF: Physiologic dysfunction of dorsolateral prefrontal cortex in schizophrenia. I. Regional cerebral blood flow evidence. Arch Gen Psychiat 1986; 43(2):114–124 122. Callicott JH, Bertolino A, Mattay VS, Langheim FJ, Duyn J, Coppola R, Goldberg TE, Weinberger DR: Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited. Cereb Cortex 2000; 10(11):1078–1092 123. Callicott J, Mattay V, Verchinski BA, Marenco S, Egan MF, Weinberger DR: Complexity of prefrontal cortical dysfunction in schizophrenia: more than up or down. Am J Psychiat 2003; 160(12):2209–2215 124. Manoach DS: Prefrontal cortex dysfunction during working memory performance in schizophrenia: reconciling discrepant findings. Schizophr Res 2003; 60(2–3):285–298 125. Tan HY, Choo WC, Fones CS, Chee MW: fMRI study of maintenance and manipulation processes within working memory in first-episode schizophrenia. Am J Psychiat 2005; 162(10):1849–1858 126. Tan HY, Sust S, Buckholtz JW, Mattay VS, Meyer-Lindenberg A, Egan MF, Weinberger DR, Callicott JH: Dysfunctional prefrontal regional specialization and compensation in schizophrenia. Am J Psychiat 2006; 163(11):1969–1977 127. Knudsen EI: Fundamental components of attention. Ann Rev Neurosci 2007; 30:57–78 128. Bleuler E: Dementia praecox or the group of schizophrenias (1911). New York, International Universities Press, 1950

H. Tost et al. 129. MacDonald AW, 3rd, Carter CS: Event-related FMRI study of context processing in dorsolateral prefrontal cortex of patients with schizophrenia. J Abnorm Psychol 2003; 112(4):689–697 130. Volz H, Gaser C, Hager F, Rzanny R, Ponisch J, Mentzel H, Kaiser WA, Sauer H: Decreased frontal activation in schizophrenics during stimulation with the continuous performance test – a functional magnetic resonance imaging study. Eur Psychiat 1999; 14(1):17–24 131. Perlstein WM, Dixit NK, Carter CS, Noll DC, Cohen JD: Prefrontal cortex dysfunction mediates deficits in working memory and prepotent responding in schizophrenia. Biol Psychiat 2003; 53(1):25–38 132. Eyler LT, Olsen RK, Jeste DV, Brown GG: Abnormal brain response of chronic schizophrenia patients despite normal performance during a visual vigilance task. Psychiat Res 2004; 130(3):245–257 133. Carter CS, Mintun M, Nichols T, Cohen JD: Anterior cingulate gyrus dysfunction and selective attention deficits in schizophrenia: [15O]H2O PET study during single-trial Stroop task performance. Am J Psychiat 1997; 154(12): 1670–1675 134. Honey GD, Pomarol-Clotet E, Corlett PR, Honey RA, McKenna PJ, Bullmore ET, Fletcher PC: Functional dysconnectivity in schizophrenia associated with attentional modulation of motor function. Brain 2005; 128(Pt 11): 2597–2611 135. Heckers S, Weiss AP, Deckersbach T, Goff DC, Morecraft RJ, Bush G: Anterior cingulate cortex activation during cognitive interference in schizophrenia. Am J Psychiat 2004; 161(4):707–715 136. Salgado-Pineda P, Junque C, Vendrell P, Baeza I, Bargallo N, Falcon C, Bernardo M: Decreased cerebral activation during CPT performance: structural and functional deficits in schizophrenic patients. Neuroimage 2004; 21(3): 840–847 137. Yücel M, Pantelis C, Stuart GW, Wood SJ, Maruff P, Velakoulis D, Pipingas A, Crowe SF, Tochon-Danguy HJ, Egan GF: Anterior cingulate activation during Stroop task performance: a PET to MRI coregistration study of individual patients with schizophrenia. Am J Psychiat 2002; 159(2):251–254 138. Sun Z, Wang F, Cui L, Breeze J, Du X, Wang X, Cong Z, Zhang H, Li B, Hong N, Zhang D: Abnormal anterior cingulum in patients with schizophrenia: a diffusion tensor imaging study. Neuroreport 2003; 14(14):1833–1836 139. Kubicki M, Westin CF, Nestor PG, Wible CG, Frumin M, Maier SE, Kikinis R, Jolesz FA, McCarley RW, Shenton ME: Cingulate fasciculus integrity disruption in schizophrenia: a magnetic resonance diffusion tensor imaging study. Biol Psychiat 2003; 54(11):1171–1180 140. Artiges E, Martelli C, Naccache L, Bartres-Faz D, Leprovost JB, Viard A, Paillere-Martinot ML, Dehaene S, Martinot JL: Paracingulate sulcus morphology and fMRI activation detection in schizophrenia patients. Schizophr Res 2006; 82(2–3):143–151 141. Meyer-Lindenberg A, Weinberger DR: Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nat Rev Neurosci 2006; 7(10):818–827 142. Talkowski ME, Kirov G, Bamne M, Georgieva L, Torres G, Mansour H, Chowdari KV, Milanova V, Wood J, McClain

23

Magnetic Resonance Imaging Biomarkers in Schizophrenia Research

L, Prasad K, Shirts B, Zhang J, O’Donovan MC, Owen MJ, Devlin B, Nimgaonkar VL: A network of dopaminergic gene variations implicated as risk factors for schizophrenia. Hum Mol Genet 2008; 17(5):747–758 143. Owen MJ, Williams NM, O’Donovan MC: The molecular genetics of schizophrenia: new findings promise new insights. Mol Psychiat 2004; 9(1):14–27 144. Karayiorgou M, Gogos JA: The molecular genetics of the 22q11-associated schizophrenia. Brain Res Mol Brain Res 2004; 132(2):95–104 145. Murphy KC: Schizophrenia and velo-cardio-facial syndrome. Lancet 2002; 359(9304):426–430 146. Stark KL, Xu B, Bagchi A, Lai WS, Liu H, Hsu R, Wan X, Pavlidis P, Mills AA, Karayiorgou M, Gogos JA: Altered brain microRNA biogenesis contributes to phenotypic deficits in a 22q11-deletion mouse model. Nat Genet 2008; 40(6):751–760 147. Lewis DA, Melchitzky DS, Sesack SR, Whitehead RE, Auh S, Sampson A: Dopamine transporter immunoreactivity in monkey cerebral cortex: regional, laminar, and ultrastructural localization. J Comp Neurol 2001; 432(1): 119–136 148. Chen J, Lipska BK, Halim N, Ma QD, Matsumoto M, Melhem S, Kolachana BS, Hyde TM, Herman MM, Apud J, Egan MF, Kleinman JE, Weinberger DR: Functional analysis of genetic variation in catechol-O-methyltransferase (COMT): effects on mRNA, protein, and enzyme activity in postmortem human brain. Am J Hum Genet 2004; 75(5):807–821 149. Egan MF, Goldberg TE, Kolachana BS, Callicott JH, Mazzanti CM, Straub RE, Goldman D, Weinberger DR: Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci USA 2001; 98(12):6917–6922 150. Goldberg TE, Egan MF, Gscheidle T, Coppola R, Weickert T, Kolachana BS, Goldman D, Weinberger DR: Executive subprocesses in working memory: relationship to catecholO-methyltransferase Val158Met genotype and schizophrenia. Arch Gen Psychiat 2003; 60(9):889–896 151. Meyer-Lindenberg A, Kohn PD, Kolachana B, Kippenhan S, McInerney-Leo A, Nussbaum R, Weinberger DR, Berman KF: Midbrain dopamine and prefrontal function in humans: interaction and modulation by COMT genotype. Nat Neurosci 2005; 8(5):594–596 152. Meyer-Lindenberg A, Straub RE, Lipska BK, Verchinski BA, Goldberg T, Callicott JH, Egan MF, Huffaker SS, Mattay VS, Kolachana B, Kleinman JE, Weinberger DR: Genetic evidence implicating DARPP-32 in human frontostriatal structure, function, and cognition. J Clin Invest 2007; 117(3):672–682 153. Svenningsson P, Nishi A, Fisone G, Girault JA, Nairn AC, Greengard P: DARPP-32: an integrator of neurotransmission. Ann Rev Pharmacol Toxicol 2004; 44:269–296 154. Svenningsson P, Tzavara ET, Carruthers R, Rachleff I, Wattler S, Nehls M, McKinzie DL, Fienberg AA, Nomikos GG, Greengard P: Diverse psychotomimetics act through a common signaling pathway. Science 2003; 302(5649):1412–1415 155. Swerdlow NR, Geyer MA, Braff DL: Neural circuit regulation of prepulse inhibition of startle in the rat: current knowledge and future challenges. Psychopharmacology (Berl) 2001; 156(2–3):194–215

141

156. Tan HY, Nicodemus KK, Chen Q, Li Z, Brooke JK, Honea R, Kolachana BS, Straub RE, Meyer-Lindenberg A, Sei Y, Mattay VS, Callicott JH, Weinberger DR: Genetic variation in AKT1 is linked to dopamine-associated prefrontal cortical structure and function in humans. J Clin Invest 2008; 118(6):2200–2208 157. Miyamoto S, Duncan GE, Marx CE, Lieberman JA: Treatments for schizophrenia: a critical review of pharmacology and mechanisms of action of antipsychotic drugs. Mol Psychiat 2005; 10(1):79–104 158. Lachman HM, Papolos DF, Saito T, Yu YM, Szumlanski CL, Weinshilboum RM: Human catechol-O-methyltransferase pharmacogenetics: description of a functional polymorphism and its potential application to neuropsychiatric disorders. Pharmacogenetics 1996; 6(3):243–250 159. Bertolino A, Caforio G, Blasi G, De Candia M, Latorre V, Petruzzella V, Altamura M, Nappi G, Papa S, Callicott JH, Mattay VS, Bellomo A, Scarabino T, Weinberger DR, Nardini M: Interaction of COMT (Val(108/158)Met) genotype and olanzapine treatment on prefrontal cortical function in patients with schizophrenia. Am J Psychiat 2004; 161(10):1798–1805 160. Bertolino A, Caforio G, Blasi G, Rampino A, Nardini M, Weinberger DR, Dallapiccola B, Sinibaldi L, Douzgou S: COMT Val158Met polymorphism predicts negative symptoms response to treatment with olanzapine in schizophrenia. Schizophr Res 2007; 95(1–3):253–255 161. Stephan KE, Magnotta VA, White T, Arndt S, Flaum M, O’Leary DS, Andreasen NC: Effects of olanzapine on cerebellar functional connectivity in schizophrenia measured by fMRI during a simple motor task. Psychol Med 2001; 31(6):1065–1078 162. Meisenzahl EM, Scheuerecker J, Zipse M, Ufer S, Wiesmann M, Frodl T, Koutsouleris N, Zetzsche T, Schmitt G, Riedel M, Spellmann I, Dehning S, Linn J, Bruckmann H, Moller HJ: Effects of treatment with the atypical neuroleptic quetiapine on working memory function: a functional MRI follow-up investigation. Eur Arch Psychiat Clin Neurosci 2006; 256(8):522–531 163. Jones HM, Brammer MJ, O’Toole M, Taylor T, Ohlsen RI, Brown RG, Purvis R, Williams S, Pilowsky LS: Cortical effects of quetiapine in first-episode schizophrenia: a preliminary functional magnetic resonance imaging study. Biol Psychiat 2004; 56(12):938–942 164. Snitz BE, MacDonald A, 3rd, Cohen JD, Cho RY, Becker T, Carter CS: Lateral and medial hypofrontality in firstepisode schizophrenia: functional activity in a medicationnaive state and effects of short-term atypical antipsychotic treatment. Am J Psychiat 2005; 162(12):2322–2329 165. Kumari V, Antonova E, Geyer MA, Ffytche D, Williams SC, Sharma T: A fMRI investigation of startle gating deficits in schizophrenia patients treated with typical or atypical antipsychotics. Int J Neuropsychopharmacol 2007; 10(4):463–477 166. Lieberman JA, Tollefson GD, Charles C, Zipursky R, Sharma T, Kahn RS, Keefe RS, Green AI, Gur RE, McEvoy J, Perkins D, Hamer RM, Gu H, Tohen M: Antipsychotic drug effects on brain morphology in first-episode psychosis. Arch Gen Psychiat 2005; 62(4):361–370 167. Bertolino A, Callicott JH, Mattay VS, Weidenhammer KM, Rakow R, Egan MF, Weinberger DR: The effect of treatment

142 with antipsychotic drugs on brain N-acetylaspartate measures in patients with schizophrenia. Biol Psychiat 2001; 49(1):39–46 168. Braus DF, Ende G, Weber-Fahr W, Demirakca T, Henn FA: Favorable effect on neuronal viability in the anterior cingulate gyrus due to long-term treatment with atypical antipsychotics: an MRSI study. Pharmacopsychiatry 2001; 34(6):251–253 169. Ende G, Braus DF, Walter S, Weber-Fahr W, Soher B, Maudsley AA, Henn FA: Effects of age, medication, and illness duration on the N-acetyl aspartate signal of the anterior cingulate region in schizophrenia. Schizophr Res 2000; 41(3):389–395 170. Szulc A, Galinska B, Tarasow E, Dzienis W, Kubas B, Konarzewska B, Walecki J, Alathiaki AS, Czernikiewicz A: The effect of risperidone on metabolite measures in the frontal lobe, temporal lobe, and thalamus in schizophrenic patients. A proton magnetic resonance spectroscopy (1H MRS). Pharmacopsychiatry 2005; 38(5):214–219 171. Abler B, Erk S, Walter H: Human reward system activation is modulated by a single dose of olanzapine in healthy subjects in an event-related, double-blind, placebo-controlled fMRI study. Psychopharmacology (Berl) 2007; 191(3):823–833 172. Brassen S, Tost H, Hoehn F, Weber-Fahr W, Klein S, Braus DF: Haloperidol challenge in healthy male humans: a functional magnetic resonance imaging study. Neurosci Lett 2003; 340(3):193–196 173. Mental Health: A Report of the Surgeon General, US Department of Health and Human Services, 1999 174. Egan MF, Straub RE, Goldberg TE, Yakub I, Callicott JH, Hariri AR, Mattay VS, Bertolino A, Hyde TM, ShannonWeickert C, Akil M, Crook J, Vakkalanka RK, Balkissoon R, Gibbs RA, Kleinman JE, Weinberger DR: Variation in GRM3 affects cognition, prefrontal glutamate, and risk for schizophrenia. Proc Natl Acad Sci USA 2004; 101(34):12604–12609 175. Cohen MX, Young J, Baek JM, Kessler C, Ranganath C: Individual differences in extraversion and dopamine genetics predict neural reward responses. Brain Res Cogn Brain Res 2005; 25(3):851–861 176. Meyer-Lindenberg A, Nichols T, Callicott JH, Ding J, Kolachana B, Buckholtz J, Mattay VS, Egan M, Weinberger DR: Impact of complex genetic variation in COMT on human brain function. Mol Psychiat 2006; 11(9):867–877, 797 177. Pezawas L, Meyer-Lindenberg A, Drabant EM, Verchinski BA, Munoz KE, Kolachana BS, Egan MF, Mattay VS, Hariri AR, Weinberger DR: 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression. Nat Neurosci 2005; 8(6):828–834 178. Hariri AR, Mattay VS, Tessitore A, Kolachana B, Fera F, Goldman D, Egan MF, Weinberger DR: Serotonin transporter genetic variation and the response of the human amygdala. Science 2002; 297(5580):400–403 179. Meyer-Lindenberg A, Buckholtz JW, Kolachana B, A RH, Pezawas L, Blasi G, Wabnitz A, Honea R, Verchinski B, Callicott JH, Egan M, Mattay V, Weinberger DR: Neural mechanisms of genetic risk for impulsivity and violence in humans. Proc Natl Acad Sci USA 2006; 103(16):6269–6274 180. Meisenzahl EM, Koutsouleris N, Gaser C, Bottlender R, Schmitt GJ, McGuire P, Decker P, Burgermeister B, Born

H. Tost et al. C, Reiser M, Moller HJ: Structural brain alterations in subjects at high-risk of psychosis: A voxel-based morphometric study. Schizophr Res 2008; 102(1–3):150–162 181. Xu L, Groth KM, Pearlson G, Schretlen DJ, Calhoun VD: Source-based morphometry: The use of independent component analysis to identify gray matter differences with application to schizophrenia. Hum Brain Mapp 2008 182. Honea RA, Meyer-Lindenberg A, Hobbs KB, Pezawas L, Mattay VS, Egan MF, Verchinski B, Passingham RE, Weinberger DR, Callicott JH: Is gray matter volume an intermediate phenotype for schizophrenia? A voxel-based morphometry study of patients with schizophrenia and their healthy siblings. Biol Psychiat 2008; 63(5):465–474 183. Zinkstok J, Schmitz N, van Amelsvoort T, Moeton M, Baas F, Linszen D: Genetic variation in COMT and PRODH is associated with brain anatomy in patients with schizophrenia. Genes Brain Behav 2008; 7(1):61–69 184. Hazlett EA, Buchsbaum MS, Haznedar MM, Newmark R, Goldstein KE, Zelmanova Y, Glanton CF, Torosjan Y, New AS, Lo JN, Mitropoulou V, Siever LJ: Cortical gray and white matter volume in unmedicated schizotypal and schizophrenia patients. Schizophr Res 2008; 101(1–3): 111–123 185. Bonilha L, Molnar C, Horner MD, Anderson B, Forster L, George MS, Nahas Z: Neurocognitive deficits and prefrontal cortical atrophy in patients with schizophrenia. Schizophr Res 2008; 101(1–3):142–151 186. Koutsouleris N, Gaser C, Jager M, Bottlender R, Frodl T, Holzinger S, Schmitt GJ, Zetzsche T, Burgermeister B, Scheuerecker J, Born C, Reiser M, Moller HJ, Meisenzahl EM: Structural correlates of psychopathological symptom dimensions in schizophrenia: a voxel-based morphometric study. Neuroimage 2008; 39(4):1600–1612 187. van Haren NE, Pol HE, Schnack HG, Cahn W, Brans R, Carati I, Rais M, Kahn RS: Progressive brain volume loss in schizophrenia over the course of the illness: evidence of maturational abnormalities in early adulthood. Biol Psychiat 2008; 63(1):106–113 188. Nesvag R, Lawyer G, Varnas K, Fjell AM, Walhovd KB, Frigessi A, Jonsson EG, Agartz I: Regional thinning of the cerebral cortex in schizophrenia: effects of diagnosis, age and antipsychotic medication. Schizophr Res 2008; 98(1–3):16–28 189. Ettinger U, Picchioni M, Landau S, Matsumoto K, van Haren NE, Marshall N, Hall MH, Schulze K, Toulopoulou T, Davies N, Ribchester T, McGuire PK, Murray RM: Magnetic resonance imaging of the thalamus and adhesio interthalamica in twins with schizophrenia. Arch Gen Psychiat 2007; 64(4):401–409 190. Ho BC, Andreasen NC, Dawson JD, Wassink TH: Association between brain-derived neurotrophic factor Val66Met gene polymorphism and progressive brain volume changes in schizophrenia. Am J Psychiat 2007; 164(12):1890–1899 191. Szeszko PR, Hodgkinson CA, Robinson DG, Derosse P, Bilder RM, Lencz T, Burdick KE, Napolitano B, Betensky JD, Kane JM, Goldman D, Malhotra AK: DISC1 is associated with prefrontal cortical gray matter and positive symptoms in schizophrenia. Biol Psychol 2008; 79(1):103–110 192. Harris JM, Moorhead TW, Miller P, McIntosh AM, Bonnici HM, Owens DG, Johnstone EC, Lawrie SM: Increased

23

Magnetic Resonance Imaging Biomarkers in Schizophrenia Research

prefrontal gyrification in a large high-risk cohort characterizes those who develop schizophrenia and reflects abnormal prefrontal development. Biol Psychiat 2007; 62(7): 722–729 193. Wang L, Hosakere M, Trein JC, Miller A, Ratnanather JT, Barch DM, Thompson PA, Qiu A, Gado MH, Miller MI, Csernansky JG: Abnormalities of cingulate gyrus neuroanatomy in schizophrenia. Schizophr Res 2007; 93(1–3):66–78 194. Kuroki N, Shenton ME, Salisbury DF, Hirayasu Y, Onitsuka T, Ersner-Hershfield H, Yurgelun-Todd D, Kikinis R, Jolesz FA, McCarley RW: Middle and inferior temporal gyrus gray matter volume abnormalities in first-episode schizophrenia: an MRI study. Am J Psychiat 2006; 163(12):2103–2110 195. Hulshoff Pol HE, Schnack HG, Mandl RC, Brans RG, van Haren NE, Baare WF, van Oel CJ, Collins DL, Evans AC, Kahn RS: Gray and white matter density changes in monozygotic and same-sex dizygotic twins discordant for schizophrenia using voxel-based morphometry. Neuroimage 2006; 31(2):482–488 196. Davatzikos C, Shen D, Gur RC, Wu X, Liu D, Fan Y, Hughett P, Turetsky BI, Gur RE: Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities. Arch Gen Psychiat 2005; 62(11):1218–1227 197. Nierenberg J, Salisbury DF, Levitt JJ, David EA, McCarley RW, Shenton ME: Reduced left angular gyrus volume in first-episode schizophrenia. Am J Psychiat 2005; 162(8):1539–1541 198. Onitsuka T, Shenton ME, Salisbury DF, Dickey CC, Kasai K, Toner SK, Frumin M, Kikinis R, Jolesz FA, McCarley RW: Middle and inferior temporal gyrus gray matter volume abnormalities in chronic schizophrenia: an MRI study. Am J Psychiat 2004; 161(9):1603–1611 199. Narr KL, Thompson PM, Szeszko P, Robinson D, Jang S, Woods RP, Kim S, Hayashi KM, Asunction D, Toga AW, Bilder RM: Regional specificity of hippocampal volume reductions in first-episode schizophrenia. Neuroimage 2004; 21(4):1563–1575 200. Sporn AL, Greenstein DK, Gogtay N, Jeffries NO, Lenane M, Gochman P, Clasen LS, Blumenthal J, Giedd JN, Rapoport JL: Progressive brain volume loss during adolescence in childhood-onset schizophrenia. Am J Psychiat 2003; 160(12):2181–2189 201. Kaladjian A, Jeanningros R, Azorin JM, Grimault S, Anton JL, Mazzola-Pomietto P: Blunted activation in right ventrolateral prefrontal cortex during motor response inhibition in schizophrenia. Schizophr Res 2007; 97(1–3):184–193 202. Menon V, Anagnoson RT, Glover GH, Pfefferbaum A: Functional magnetic resonance imaging evidence for disrupted basal ganglia function in schizophrenia. Am J Psychiat 2001; 158(4):646–649 203. Allen P, Amaro E, Fu CH, Williams SC, Brammer MJ, Johns LC, McGuire PK: Neural correlates of the misattribution of speech in schizophrenia. Br J Psychiat 2007; 190:162–169 204. Kiehl KA, Stevens MC, Celone K, Kurtz M, Krystal JH: Abnormal hemodynamics in schizophrenia during an auditory oddball task. Biol Psychiat 2005; 57(9):1029–1040 205. Lawrie SM, Buechel C, Whalley HC, Frith CD, Friston KJ, Johnstone EC: Reduced frontotemporal functional connectivity

143

in schizophrenia associated with auditory hallucinations. Biol Psychiat 2002; 51(12):1008–1011 206. Wible CG, Kubicki M, Yoo SS, Kacher DF, Salisbury DF, Anderson MC, Shenton ME, Hirayasu Y, Kikinis R, Jolesz FA, McCarley RW: A functional magnetic resonance imaging study of auditory mismatch in schizophrenia. Am J Psychiat 2001; 158(6):938–943 207. Delawalla Z, Csernansky JG, Barch DM: Prefrontal cortex function in nonpsychotic siblings of individuals with schizophrenia. Biol Psychiat 2008; 63(5):490–497 208. Gur RE, Turetsky BI, Loughead J, Snyder W, Kohler C, Elliott M, Pratiwadi R, Ragland JD, Bilker WB, Siegel SJ, Kanes SJ, Arnold SE, Gur RC: Visual attention circuitry in schizophrenia investigated with oddball event-related functional magnetic resonance imaging. Am J Psychiat 2007; 164(3):442–449 209. Weiss EM, Siedentopf C, Golaszewski S, Mottaghy FM, Hofer A, Kremser C, Felber S, Fleischhacker WW: Brain activation patterns during a selective attention test – a functional MRI study in healthy volunteers and unmedicated patients during an acute episode of schizophrenia. Psychiat Res 2007; 154(1):31–40 210. Weiss EM, Golaszewski S, Mottaghy FM, Hofer A, Hausmann A, Kemmler G, Kremser C, Brinkhoff C, Felber SR, Wolfgang Fleischhacker W: Brain activation patterns during a selective attention test – a functional MRI study in healthy volunteers and patients with schizophrenia. Psychiat Res 2003; 123(1):1–15 211. Pomarol-Clotet E, Salvador R, Sarro S, Gomar J, Vila F, Martinez A, Guerrero A, Ortiz-Gil J, Sans-Sansa B, Capdevila A, Cebamanos JM, McKenna PJ: Failure to deactivate in the prefrontal cortex in schizophrenia: dysfunction of the default mode network? Psychol Med 2008; 38(8):1185–1193 212. Callicott J, Egan MF, Mattay V, Bertolino A, Bone AD, Verchinski BA, Weinberger DR: Abnormal fMRI response of the dorsolateral prefrontal cortex in cognitively intact siblings of patients with schizophrenia. Am J Psychiat 2004; 160(4):709–719 213. Schlosser R, Gesierich T, Kaufmann B, Vucurevic G, Hunsche S, Gawehn J, Stoeter P: Altered effective connectivity during working memory performance in schizophrenia: a study with fMRI and structural equation modeling. Neuroimage 2003; 19(3):751–763 214. Manoach DS, Gollub RL, Benson ES, Searl MM, Goff DC, Halpern E, Saper CB, Rauch SL: Schizophrenic subjects show aberrant fMRI activation of dorsolateral prefrontal cortex and basal ganglia during working memory performance. Biol Psychiat 2000; 48(2):99–109 215. Callicott JH, Ramsey NF, Tallent K, Bertolino A, Knable MB, Coppola R, Goldberg T, van Gelderen P, Mattay VS, Frank JA, Moonen CT, Weinberger DR: Functional magnetic resonance imaging brain mapping in psychiatry: methodological issues illustrated in a study of working memory in schizophrenia. Neuropsychopharmacology 1998; 18(3):186–196 216. Volz HP, Gaser C, Hager F, Rzanny R, Mentzel HJ, Kreitschmann-Andermahr I, Kaiser WA, Sauer H: Brain activation during cognitive stimulation with the Wisconsin Card Sorting Test – a functional MRI study on healthy volunteers and schizophrenics. Psychiat Res 1997; 75(3):145–157

144 217. Winterer G, Konrad A, Vucurevic G, Musso F, Stoeter P, Dahmen N: Association of 5 end neuregulin-1 (NRG1) gene variation with subcortical medial frontal microstructure in humans. Neuroimage 2008; 40(2):712–718 218. Straub RE, Lipska BK, Egan MF, Goldberg TE, Callicott JH, Mayhew MB, Vakkalanka RK, Kolachana BS, Kleinman JE, Weinberger DR: Allelic variation in GAD1 (GAD67) is associated with schizophrenia and influences cortical function and gene expression. Mol Psychiat 2007; 12(9):854–869 219. Takahashi T, Suzuki M, Tsunoda M, Kawamura Y, Takahashi N, Maeno N, Kawasaki Y, Zhou SY, Hagino H, Niu L, Tsuneki H, Kobayashi S, Sasaoka T, Seto H, Kurachi M, Ozaki N: The association of genotypic combination of the DRD3 and BDNF polymorphisms on the adhesio interthalamica and medial temporal lobe structures. Prog Neuropsychopharmacol Biol Psychiat 2008; 32(5): 1236–1242 220. Hall J, Whalley HC, Moorhead TW, Baig BJ, McIntosh AM, Job DE, Owens DG, Lawrie SM, Johnstone EC: Genetic variation in the DAOA (G72) gene modulates hippocampal function in subjects at high risk of schizophrenia. Biol Psychiat 2008; 64(5):428–433 221. McIntosh AM, Moorhead TW, Job D, Lymer GK, Munoz Maniega S, McKirdy J, Sussmann JE, Baig BJ, Bastin ME, Porteous D, Evans KL, Johnstone EC, Lawrie SM, Hall J: The effects of a neuregulin 1 variant on white matter density and integrity. Mol Psychiat 2008; 13(11):1054–1059 222. Tan HY, Chen Q, Sust S, Buckholtz JW, Meyers JD, Egan MF, Mattay VS, Meyer-Lindenberg A, Weinberger DR, Callicott JH: Epistasis between catechol-O-methyltransferase and type II metabotropic glutamate receptor 3 genes

H. Tost et al. on working memory brain function. Proc Natl Acad Sci USA 2007; 104(30):12536–12541 223. Buckholtz JW, Meyer-Lindenberg A, Honea RA, Straub RE, Pezawas L, Egan MF, Vakkalanka R, Kolachana B, Verchinski BA, Sust S, Mattay VS, Weinberger DR, Callicott JH: Allelic variation in RGS4 impacts functional and structural connectivity in the human brain. J Neurosci 2007; 27(7):1584–1593 224. Blasi G, Mattay VS, Bertolino A, Elvevag B, Callicott JH, Das S, Kolachana BS, Egan MF, Goldberg TE, Weinberger DR: Effect of catechol-O-methyltransferase val158met genotype on attentional control. J Neurosci 2005; 25(20): 5038–5045 225. Cannon TD, Hennah W, van Erp TG, Thompson PM, Lonnqvist J, Huttunen M, Gasperoni T, Tuulio-Henriksson A, Pirkola T, Toga AW, Kaprio J, Mazziotta J, Peltonen L: Association of DISC1/TRAX haplotypes with schizophrenia, reduced prefrontal gray matter, and impaired shortand long-term memory. Arch Gen Psychiat 2005; 62(11): 1205–1213 226. Callicott JH, Straub RE, Pezawas L, Egan MF, Mattay VS, Hariri AR, Verchinski BA, Meyer-Lindenberg A, Balkissoon R, Kolachana B, Goldberg TE, Weinberger DR: Variation in DISC1 affects hippocampal structure and function and increases risk for schizophrenia. Proc Natl Acad Sci USA 2005; 102(24):8627–8632 227. Egan MF, Straub RE, Goldberg TE, Yakub I, Callicott JH, Hariri AR, Mattay VS, Bertolino A, Hyde T, ShannonWeickert C, Akil M, Crook J, Vakkalanka RK, Balkissoon R, Gibbs RA, Kleinmann JE, Weinberger DR: Variation in GRM3 affects cognition, prefrontal glutamate, and risk for schiophrenia. PNAS 2004; 101(34):12604–12609

Chapter 24

Neurostructural Endophenotypes In Autism Spectrum Disorder Armin Raznahan, Jay N. Giedd, and Patrick F. Bolton

Abstract Autism Spectrum Disorder (ASD) is a group of behaviourally defined neurodevelopmental disorders of childhood onset characterised by impairments in communication and social reciprocity as well as a range of distinctive non-social symptoms. Despite including some of the most heritable disorders in psychiatry, it has proved difficult to identify risk genes for ASD, and to build models for the neurobiological mechanisms through which putative risk factors might operate to give rise to the ASD behavioural phenotype. In this chapter we detail why measures of brain anatomy derived from structural magnetic resonance images have been put forward as potential alternative endophenotypes that might increase our ability to identify risk genes and associated brain mechanisms for ASD. We then examine the progress that has been made so far in identifying neurostructural endophenotypes for ASD, and consider some of the challenges and opportunities presented by this new line of research in ASD neurobiology. Keywords Autism spectrum disorder • endophenotype • structural magnetic resonance imaging • MRI • development Abbreviations 5HTTLPR: Serotonin transporter linked polymorphic region; ADHD: Attention deficit hyperactivity disorder; ASD: Autism spectrum disorder; BPAD: Bipolar affective disorder; CT: Cortical

A. Raznahan and P. F. Bolton Department of Child and Adolescent Psychiatry, King’s College London, Institute of Psychiatry, London, UK J. N. Giedd Child Psychiatry Branch, National Institute of Mental Health, Bethesda, USA

thickness; CV: Cortical volume; DRD4: Dopamine transporter 4; EP: Endophenotype; FSIQ: Full scale intelligence quotient; GMV: Grey matter volume; HC: Head circumference; MAOA: Monoamine oxidase A; NSEP: Neurostructural endophenotype; PIQ: Performance intelligence quotient; SA: Surface area; SCZ: Schizophrenia; sMRI: Structural magnetic resonance imaging; SNP: Single nucleotide functional polymorphism; TBV: Total brain volume; TMSCCA: Total mid-sagittal corpus callosal area; VNTR: Variable number tandem repeat; WMV: White matter volume

Introduction Autism Spectrum Disorder (ASD) is a group of behaviourally defined,1 aetiologically complex2 neurodevelopmental syndromes characterised by abnormalities in three domains: social communication, social reciprocity and abnormal patterns of repetitive behaviour.3 ASD is increasingly recognised and the latest prevalence estimates suggest that it may be as common as schizophrenia.4 As an early onset condition that persists across the lifespan and one that is commonly associated with profound functional impairment – it can have a dramatic impact on individuals and their families. These consequences often bring individuals with ASD in contact with child (and increasingly adult) mental health services. Recent prevalence estimates for ASD are between 255 and 404 times greater than often quoted 1970s estimate of 2.5 per 10,000 for “core” Kannerian autism.6 Recent prevalence rates for strictly defined autism are 15 times greater,4 indicating that increased prevalence estimates are not just a function of the ASD construct

M.S. Ritsner (ed.), The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, © Springer Science + Business Media B.V. 2009

145

146

including a wider range of presentations than “core” autism. There is little evidence however that the true incidence of autism is increasing,7 and increases in prevalence estimates are thought to be driven by several factors including (i) increased awareness in parents and clinicians, (ii) altered diagnostic criteria, and (iii) diagnostic substitution. An example of diagnostic substitution this would be that compared to past practice, modern clinicians would be more likely to ascribe a primary diagnosis of autism rather than learning disability to an individual with autistic behaviours and global intellectual disability. Autism has the highest heritability estimates of any multifactorial psychiatric disorder.8 This, along with other indicators of a strong neurobiological basis for ASDs has driven the application of evermore powerful genetic and neuroimaging technologies in ASD research. Despite this however, common genetic risk factors of major effect have yet to be identified for ASD,9 and it has proved difficult to establish unified neuropsychobiological models for the heterogenous conditions that fall within the spectrum. Several factors interact to make findings risk genes and brain mechanisms for ASD a challenge. These factors are equally applicable to other highly heritable yet aetiologically complex neuropsychiatric conditions such as Attention-Deficit/ Hyperactivity Disorder (ADHD), Schizophrenia (SCZ), and Bipolar Affective Disorder (BPAD). These conditions all share the following features (i) they are most commonly polygenic, (ii) they are behavioural defined in a way that does not necessarily “carve nature at its (genetic) joints”, and (iii) they must all be understood in the context of the staggering organisational complexity of the developing brain and its relationship with behaviour. Many strategies have been proposed to tackle these challenges. The current focus on endophenotypes (EPs) represents one of the most exciting and hotly debated10,11 of these strategies. Proponents argue that by examining how genes relate to measures that are “deep” to observed behaviour (e.g. performance in neuropsychological tests, brain structure, brain function, mRNA levels) we reduce measurement error, and use biological outcomes that are causally “closer” to genes than behavioural outcomes, and which may be determined by fewer genes. These advantages of EPs over “surface” behaviour would lead to increased power to find risk genes for disorders such as ASD and SCZ, and provide new ways

A. Raznahan et al.

of examining how these risks operate at level of brain. Other researchers urge caution however. They point to the fact that for many proposed EPs heritability is yet to be established, and that it is yet to be proved that EPs are more genetically homogenous that behaviour. Furthermore, they emphasise the considerable challenges of moving beyond an observed correlation between an EP and a disorder, and clearly establishing that the EP is a causal mediator of genetic risks that sits on a biological pathway between genes and behaviour. This chapter will consider the conceptual validity and practical feasibility of the EP approach whilst focussing on the use of structural magnetic resonance imaging (sMRI) to identify potential EPs in ASD. It is divided into six parts: (I) Autism Spectrum Disorder – We will introduce ASD, and highlight the marked heterogeneity within the spectrum at the level of behaviour. The consequences of this for attempts to build neurobiological models for ASD will be outlined. (II) The Endophenotype Approach – We will critically examine proposed criteria that a measure must fulfil in order to be considered an EP. (III) Neurostructural EPs (NSEPs) in ASD – We will begin by specifying the EP criteria against which we will examine candidate neurostructural markers in ASD. After considering the heritability of neuroanatomy, and measurement issues in sMRI more broadly – we will address selected candidate ASD NSEPs in turn. (IV) Challenges in the Application of NSEPs in ASD Research – We will expand on some of the factors that have impacted negatively on NSEP research in ASD to date. (V) Opportunities in the Application of NSEPs in ASD Research – We will highlight study designs that offer most promise in using sMRI to identify and explore the neurostructural correlates of genetic risk for ASD. (VI) Conclusion and Future Directions.

Autism Spectrum Disorder Terminology Autism Spectrum Disorder (ASD) is a widely used term which refers to a group of related disorders

24

Neurostructural Endophenotypes In Autism Spectrum Disorder

included within the Pervasive Developmental Disorder (PDD) category of the two core classificatory schemes for mental disorders; DSM-IV1 and ICD10.12 These two systems have identical criteria for the core ASD diagnoses, although differ slightly in the way “borderline” cases are treated. In this article we will use ICD-10 terminology, which predominates in the UK. The diagnoses included within the autism spectrum are Autism, Asperger Syndrome and Atypical Autism. All three diagnoses are characterised by the presence of (i) impairments in verbal and non-verbal communication, (ii) impairments in reciprocal social interaction, and (iii) the presence of restricted interests and repetitive behaviours. ASD sub-diagnoses differ from each other in early developmental profile and symptom severity. There is even profound behavioural heterogeneity within each sub-diagnosis. A diagnosis of autism could for example refer to both (i) a nonverbal child with severe learning disabilities who shows no social reciprocity or interest and spends a great deal of time engaged in motor stereotypies such as hand-flapping, or (ii) a highly verbal adolescent in mainstream education who seeks out contact with others yet experiences profound difficulties with social interaction. The inclusion of such diverse presentations within the ASD umbrella can be justified with reference to (i) highly specific qualitative similarities at the level of phenomenology that have been evident since these conditions were first described,13,14 (ii) epidemiological commonalities such as early age of onset, male predominance and patterns of neurodevelopmental co-morbidity, (iii) evidence from twin and family studies that clearly indicates shared genetic risks,8,15 (iv) lines of work that have examined the phenotype dimensionally rather than categorically,2,16 and (v) the convergence of methodologically diverse neuroscientific research indicating shared biological substrates17 for ASDs. We will therefore refer to ASD in this chapter, and only make explicit distinctions between ASD diagnoses when such contrasts are directly examined in the studies we site. Having said this, it is important to note that certain sub-populations within ASD remain relatively under-researched. These include females, those with atypical autism (e.g. later age of onset, or sub-syndromal by virtue of symptom profile), and those with learning disabilities or co-morbid mental illness.

147

The Quest for “A Neurobiology of ASD” Like most neuropsychiatric conditions, ASD remains a behaviourally defined construct. The application of increasingly powerful neurobiological research methods in an attempt to establish solid ASD disease models that strive to link genes to behaviours via the brain has been driven by several observations: (i) the fact that autism (the paradigmatic ASD) has the highest heritability estimate of any multifactorial psychiatric disorder,8 (ii) the invariance of ASD epidemiology and presentation across different cultural settings, (iii) their early onset, (iv) the strong association between ASD and learning disability and epilepsy, and (v) the clear links that exist between ASD and medical disorders such as Tuberous Sclerosis18 and Smith-Lemli-Opitz syndrome.19 Although neuroscientific and epidemiological research methods have led to significant advances in our understanding of ASD in the 55 years since autism and Asperger syndrome were first described – there is still no biological marker which reliably distinguishes people with ASD diagnoses from typically developing individuals. The reasons that the search for risk genes and brain mechanisms in ASD has proved so challenging are common to several other major mental disorders such as SCZ, ADHD and BPAD. The genetic architecture of ASD shows great variety, and it is probable that there are both “monogenic” and “polygenic” forms.20 These variants cannot always be distinguished phenotypically and unrecognised mixing in genetic studies can reduce power to identify specific risk atletes. It is likely however that many ASD cases represent the combined action of multiple risk genes. Under such a scenario, any single genetic variant will in isolation impart a small and thus hard to measure risk for the disorder. Indeed those few common risk atletes which have been consistently identified for other aetiologically complex disorders in psychiatry carry odds ratios for the disorder that cluster around 1.2.21 In addition to this, observation it is increasingly recognised that the risk imparted by a given genetic variant for a disorder is modulated (often in a non-linear manner) by other variants within the same gene, other genes, and the environment(s) with which the gene interacts.22 Still further layers of complexity are added by evidence that in ASD, genetic risks may vary according to gender,23 and ASD sub-trait.2,24 It is clear then that the quest for gene-brain-behaviour models in ASD is

148

A. Raznahan et al.

unlikely to be successful if it is restricted to efforts that strive to link single genetic variants to behaviourally defined disorder categories which will almost certainly fail to “carve nature at its joints”. It is in this context that the potential advantages of adopting and EP approach should be assessed.

The Endophenotype Approach Endophenotype Definitions The term “endophenotype” (EP) was born in the world of evolutionary biology25 in the 1960s, and first brought to psychiatry in the 1970s.26 This of course preceded the advances in molecular genetic and neuroimaging techniques that have since been applied in biological psychiatry to reveal ever more complex networks of correlations, associations and (all too rare) causal links that underlie disorders such as ASD and SCZ. As the scale of these complexities has become clearer, attention has increasingly returned to the possibility of taming them through an EP approach.27 By pulling back from the level of behaviour, and studying “internal [endo] phenotypes discoverable by biochemical tests or microscopic examination”26 it was suggested that one could increase the likelihood of finding genetic associations by virtue of being causally “nearer” the level at which genes exert their effects, and studying entities that are determined by fewer genes than are behaviours. Compared to human behaviours, EPs may be more amenable to further examination through animal models.28 The EP concept has been operationalised slightly differently by different authors.10,11,27–34 Most agree however on certain core properties that a measure must demonstrate before it can be considered an EP (see “core criteria” in Table 24.1).

Table 24.1 Proposed endophenotype criteria “Core” criteria 1. Reliably measurable 2. Heritable 3. Associated with disease 4. Segregate with disease amongst affected pedigrees 5. Be found in higher rates in unaffected relatives of probands compared to general population

According to the “core EP criteria” an EP (1) needs to be reliably measurable, (2) must be heritable, (3) must be associated with having the disease of interest regardless of whether the disease is active or not, (4) in affected families should co-segregate with the disease, and (5) should be found at higher rates in unaffected relatives than in the general population. Criteria (3)–(5) act to ensure that the measure of interest reflects genetic liability for the disorder rather than secondary effects caused by the disease state such as medication induced brain changes in SCZ.35 These “core” criteria have also been varyingly emphasised, refined and supplemented (see “proposed extensions” in Table 24.1). Doyle et al.30 have stressed the importance of measurement issues – especially as they relate to neuropsychological EPs. An EP that cannot be reliably measured or one that is not a valid measure of the neuropsychobiological construct in purports to capture will be of little use. A resultant concern therefore is that for many purported EP measures, reliability and validity have not been rigorously established. Viding and Blakemore33 have underlined the importance of recognising that the endophenotypic manifestations of genetic risk very probably change with age.36 Walters and Owen11 stress that if an EP truly indexes genetic risk for a disorder, twin studies should identify shared genetic sources of variance between the EP and the behavioural phenotype it is associated with. Waldman34 focuses on statistical issues in the application of an EP approach. He underlines the importance of demonstrating shared common genetic influences between the EP and the disorder of interest, showing the EP to be associated or linked with genes or loci underlying the disorder of interest over and above the gene’s association with the disorder, and establishing that the EP both mediates and moderates association/linkage between a particular gene/locus and the disorder.

Proposed extensions 6. Share common genetic influences with disorder 7. Show linkage/association with genes involved in disorder 8. Mediate linkage/association between these genes and disorder 9. Moderate linkage/association between these genes and disorder 10. Concurrent validity 11. Specific to a given disorder 12. May require provocation

24

Neurostructural Endophenotypes In Autism Spectrum Disorder

The extent to which each of these proposed refinements upon the core EP criteria is necessary rather than just desirable varies depending on what an EP is being used to achieve. The stringent additional criteria proposed by Waldman34 will for example be particularly important if the aim is to use an EP to find novel risk genes, but less so if an EP approach is being used to better understand the neurobiological correlates of an already established risk factor. Most of the sMRI literature that we will review in this chapter either strives to define potential NSEPs in ASD, or better understand the sMRI correlates of an already described putative genetic risk factor for ASD. To date there are no published studies that have used a defined NSEP to identify novel risk genes for ASD. “Imaging genomics/genetics”37 is a term coined to describe research which uses structural or functional neuroimaging measures as EPs. This strategy has yet to be applied extensively to ASD. It has however been used to very good effect in the study of polygenic conditions such as dementia, SCZ, and BPAD, as well as neurogenetic syndromes such as William syndrome. The large NSEP literature in disorders other than ASD is addressed in detail by other chapters within this book.

149

disease in family” and “increased in unaffected relatives” will be addressed under the sub-heading “Family studies”. Where available, studies that have related molecular genetic measures to the NSEP in question will be considered under the sub-heading “Imaging genetics to date”. Treatment of each putative NSEP will conclude with a “Summary and outstanding issues” section. For potential NSEPs in ASD that have not yet been studied in ASD relatives – we will refer the reader to recent reviews and meta-analyses38–41 of ASD sMRI studies and restrict ourselves to detailing individual studies that have been published since these reviews. Before moving on to address each NSEP in turn however, we will turn to the first two core EP criteria more generally. This is because they pose primary questions that cut across all of the individual neuroimaging measures we will be examining – (i) how reliable and accurate are measures derived from structural magnetic resonance imaging (sMRI) data? (ii) what evidence is there that brain anatomy is under genetic influence? Moreover – do NSEP offer advantages over ASD behavioural phenotypes in these two areas?

Measurement Issues in sMRI

Neurostructural Endophenotypes in ASD Terms of Reference In this section we will address potential ASD neurostructural EPs (NSEPs) in turn – and consider how well each satisfies the “core” EP criteria shown in Table 24.1. We will present information relating to the suggested extensions of these core criteria where this is available. A full consideration of the “associated with the disorder” EP criterion for each and every neuroanatomical index that has been studied in ASD would amount to a systematic review of all ASD structural neuroimaging literature – which is beyond the scope of this chapter. We will therefore focus on the four main neuroanatomical indices which have been examined in ASD probands as well as their relatives – head-circumference (HC), total brain volume (TBV), amygdala volume and cerebellar measures. For each of these, the EP criteria “segregates with

Relating genes to EPs effectively requires that any variance in the EP measure due to measurement error be minimised. This is of course equally true for behavioural “exophenotypes”. The validity of a measure is the extent to which it “really does measure what it sets out to measure”.42 At an intuitive level NSEPs (e.g. total brain volume) are often more concrete than behavioural phenotypes (e.g. disordered social reciprocity) in that they are entities in the real world that can be directly measured – thus their conceptual validity is easier to demonstrate. The validity of a structural imaging-derived measure also relates accuracy with which it measures the target of interest. Accuracy is usually established with reference to a “gold standard”. For sMRI metrics derived from manual measurements, the accuracy of an operator’s measurement is determined by comparison with an agreed objective standard for that measure as established by someone highly proficient in the method. This inter-measurer reliability is then taken to capture accuracy.

150

For sMRI metrics derived from automated techniques, the gold standard is often taken to be the known dimension of a synthesised “phantom” imaging dataset, or manual measurement of imaged/postmortem brains. An exhaustive treatment of all validation work carried out on the wide variety of methods used for image acquisition, pre-processing and measurement in ASD structural neuroimaging is beyond the scope of this chapter. It is although well established that many methodological variables can impact on the accuracy of sMRI derived measures,43,44 including magnet field strength, image acquisition parameters such as slice thickness, protocols used to define anatomical limits of the structure to be measured, inter-rater reliabilities achieved in manual measurement methods and software used for automated image analysis.45 Not only is it important to measure a candidate NSEP accurately, but measurements should show good reliability. This can come in many forms including intra-rater reliability (e.g. the same operator measuring the same structure of different occasions using a manual method), inter-rater reliability (e.g. different operators working on the same sample using a manual method for volume measurement), and “inter-run” reliability (e.g. the same automated measure applied to the same scan at different times). The longitudinal study designs necessary to examine NSEP that are developmental trajectories rather than those that are static, place particular demands on scan–rescan reliability. Issues of accuracy and reliability are also especially pronounced in the context of multicentre trials46 that strive to combine data acquired from different scanners, often using different scanning sequences. Although it is likely that researchers will need to move to multicentre models in future structural imaging work47 – most ASD structural neuroimaging to date has been done on a single site basis. Although further clarification of accuracy and reliability issues for many of the methods used in ASD sMRI research is required, from a pragmatic perspective – the important question is how these compare to the accuracy and reliability with which ASD-related behavioural phenotypes can be measured. Given that behavioural phenotypes are abstract constructs it is not straightforward to quantitatively compare their validity with that of sMRI measures. The reliability of behavioural measures used in the study of developmental psychopathology in general can be highly vari-

A. Raznahan et al.

able48,49 and achieving reliability across informants and across time is notoriously challenging.

Heritability of Brain Anatomy Twin and family studies generally indicate high heritabilities for brain morphometry although the specific heritabilities vary by tissue type, region, and age (Giedd et al. 2007 – Human Brain Mapping). This ever-growing literature has been the subject to two very recent and very thorough systematic reviews,50,51 and as such will not be reviewed in detail here. Most studies examining the heritability of intracranial, total brain, hemispheric, grey matter and white matter volumes have reported heritability estimates above 70%, regardless of whether children52 or older adults are examine.53 The larger and more methodologically robust of these studies tend to produce even higher estimates (80–90%) for such measures.52,54 Findings regarding the heritability of cerebellar volume have been more mixed and range between 88%55 in adult samples and 49% in paediatric samples52 – which suggests that this structure may come under increasing environmental influence with age. Recent work has moved beyond the examination of global volumes to consider the heritability of brain anatomy in a more fine-grained manner using automated labelling techniques to allow a region of interest approach52,56 or adopting spatially non-biased methods to generate heritability maps for across the brain57 or cortical sheet.58,59 It appears that heritability estimates for cortical volume and thickness are highest within fronto-temporal areas. The heritability of cortical volume and thickness within regions such as the dorsal prefrontal cortex, orbitofrontal cortex, bilateral superior parietal cortex, inferior occipital cortex, and left inferior temporal gyri may increase with age.58 In terms of atypical development, the majority of published twin MRI studies relate to SCZ,50 and to date only one sMRI study has been carried out in twins with ASD. In a peadiatric study Kates et al.60 measured whole brain volume, total cerebral grey and white matter volume, lobar cerebral grey and white matter volumes, and cerebellar grey and white matter volumes from sMRI scans in seven monozygotic (MZ) twin pairs that were fully concordant for autism, nine MZ twin pairs that were discordant for

24

Neurostructural Endophenotypes In Autism Spectrum Disorder

autism, and 18 age and gender matched singleton controls. In all discordant ASD twin pairs – one twin had a diagnosis of autism, and the other had ASD symptoms that did not meet criteria for autism. (called “broad phenotype co-twins”). Both concordant and discordant ASD twin pairs had high twin– twin correlations for cerebral grey and cerebral white matter volume (0.9 and 0.86 respectively). Twin–twin correlations for cerebellar grey and cerebellar white matter volume however were significantly lower in discordant (0.1 and 0.5 respectively) compared to concordant (0.86 and 0.9 respectively) twin pairs. This suggests that cerebellar grey and white matter volume in ASD are determined by factors other than DNA sequence – such as gene expression, or environmental risks. Cerebellar grey and white matter volumes did not vary significantly between the group of discordant twins with autism, their “broad-phenotype” co-twins and a group of healthy matched controls. However, the difference within discordant twin pairs in cerebellar grey and white matter volumes correlated positively with the differences in symptom severity within twin pairs. The only significant anatomical differences common to both autistic and broad-phenotype ASD members of discordant twin pairs when compared to a group of control singletons were reductions in total, frontal, temporal and parietal cerebral white matter volumes. Given the presence of significant ASD symptomatology in the “broad-phenotype” cotwins however, it is not possible to infer that these white matter volume reductions necessarily index genetic risk for ASD. It is clear then that in typical development and ASD many sMRI measures of brain anatomy show high heritability estimates, and as such fulfil one of the “core” EP criteria. Furthermore, multivariate modelling of sMRI measures in typically developing twins has led to several other observations of relevance to the use of sMRI measures as EPs. Firstly the genetic influences on intracranial volume overlap greatly with those for total brain volume, total grey matter volume and total white matter volume. There is also a large overlap between the genes that influence white matter and grey matter volumes.54 Shared genetic influences between major gross neural subdivisions are also indicated by a multivariate analysis that was performed on 127 pairs of monozygotic twins (mean age = 11.6, SD = 3.3; age range = 5.6–

151

18.7; 74 [58%] male, 53 female) and 36 pairs of samesex dizygotic twins (mean age = 11.0, SD = 3.7; age range = 5.5–18.2; 18 [60%] male, 12 female).61 The brain measures examined were the cerebrum, thalamus, lateral ventricles, telencephalic and subcortical nuclei, corpus callosum, and cerebellum volumes. In general, within-structure, cross-twin correlations were substantially higher in the MZ than in the DZ twins, suggesting a strong genetic impact on brain volumes’ variation. Factor analysis indicated that much of the genetic effect was accounted for by two common factors. One strongly influenced variance of cerebrum, thalamus, and basal ganglia, with factor loadings (analogous to standardized partial regression coefficients) of about 0.85. This factor also accounted for a substantial proportion of the genetic variance of the cerebellum, and had a low but statistically significant effect on corpus callosum, but no impact on lateral ventricular volumes. The second genetic factor predominantly comprised the modest genetic effects on ventricular volume, with a statistically significant negative factor loading on the basal ganglia compartment. This finding is concordant with evolutionary genetic models of brain development which hypothesize global, genetically-mediated differences in cell division as the driving force behind interspecies differences in total brain volume as well as with the radial unit hypothesis of neocortical expansion proposed by Rakic.62 Secondly, it is possible to identify distinct cortical networks of regions that share common genetic influences. Regions within these networks are also heavily interconnected with each other structurally and functionally.56,61 Thirdly, shared genetic sources of variance have been also been shown for brain anatomy and behaviour.57,59 Such findings indicate that understanding the spatial and temporal distribution of genetic influences on brain structure is of great relevance for our attempt to understand how genes influence human behaviour.

Head Circumference Although measures of head circumference (HC) provide a relatively crude proxy measure of brain anatomy, they can be gathered and analysed quickly and cheaply. Compared to the use of sMRI therefore, the study of HC facilitates larger sample sizes and makes

152

it easier to build longitudinal datasets with measures at multiple time-points for each individual.

Associated with Disease Many ASD HC studies were included in a recent metaanalysis carried out by Redcay and Courchesne41 This study converted the reports of HC measures, in vivo MRI scan measures of whole brain volume, and post mortem measures of brain weight into the percentage difference in whole brain volume between ASD and controls. The combined sample size across all studies was 531. Regardless of whether age was modelled as a continuous or ordinal variable, brain size in ASD was seen to be increased beyond normal values between ages 2 and 4, no different on controls by middle childhood. Three longitudinal HC studies have been published since this meta-analysis.63–65 In a large longitudinal study, Hazlett et al.65 compared head circumference change between birth and 36 months of age in 113 children with ASD to a mixed group of controls (178 typically developing, 11 developmental delay). Rate of HC change increased in ASD relative to controls at around 12 months of age, and then continued to diverge from the typical trajectory. Dawson et al. also found accelerated HC growth in ASD, but limited to the first year of life, with the subsequent deceleration being contemporaneous with symptom deterioration.63 Webb et al.64 replicated the observation of accelerated early HC growth in ASD with a study that examined the age range 0–36 months. Significant increases in ASD HC relative to population norms first became evident between 7 and 10 months of age. Increased HC in ASD was still present in the third year of life. These increases persisted when height was controlled for. A history of developmental regression in ASD was not associated with a different pattern of HC findings. There are fewer reports examining HC in adults with ASD than there are in children. Results are inconsistent with some reports of increased rates of macrocephaly66 compared to normative data, and some of no difference.67 There is a general consensus however that the most robust evidence is for increased HC in children with ASD – with mounting evidence that this is a reflection of accelerated HC growth during the first 3 years of life.

A. Raznahan et al.

Family Studies There are more published reports on HC in relatives of people with ASD than there are for any sMRI-derived measure of brain anatomy. The first study to examine HC in relatives of probands with ASD was by Stevenson et al.68 Twenty-four percent of an ASD sample of mixed gender spanning the age range 15–44 had macrocephaly. In 62% of families with a macrocephalic ASD proband, one or both parents also had macrocephaly. Fidler et al.69 reported that in both parents and siblings of probands with ASD the proportion of individuals with macrocephaly was larger than that seen in the general population. Lainhart et al.70 reported significantly increased HC in both mothers and fathers of ASD probands relative to population norms. Thirtythree percent of ASD probands had either a mother or father with macrocephaly. The proportion of probands with a macrocephalic parent did not however differ as a function of the presence or absence of macrocephaly in the proband. The affected (but not the unaffected) siblings of probands with autism had elevated rates of macrocephaly relative to controls. In the only longitudinal HC study of ASD relatives71 Eldes et al. used a retrospective follow up design to measure HC change with age in infant siblings of probands with ASD. Siblings had a significantly faster rate of HC change between birth and 12 months than population norms and significantly larger HC at 12 months of age. The rate of HC change was not however significantly different from population norms between 12 and 24 months of age. Those siblings with the most marked deceleration in HC change between 12 and 24 months had more social impairments at 24 months of age.

Imaging Genetics Three studies have examined the relationship between genotype at a putative risk gene for ASD and HC.72–74 Conciatori et al.72 studied the HOXA1 gene due to evidence for its role in early brain development, and previous reports of associations between variants within this gene and the ASD behavioural phenotype. The paper combined independent samples from Italy and North America. All participants were genotyped for the non-synonymous HOXA1 A218G (rs10951154) polymorphism as the G allele had been associated with ASD in some but not all previous

24

Neurostructural Endophenotypes In Autism Spectrum Disorder

reports. The authors first examined if the allele was associated with ASD, and report an over-representation of the A allele in a case/control analysis, as well as in one of two transmission disequilibrium test analyses. Hypothesising that the inconsistency between their report and previous findings of G allele overtransmission may be due to phenotypic differences between the ASD samples used, the authors examined the relationship between the HOXA1 A218G SNP and HC. In four separate ASD samples A homozygotes had smaller and more variable HC than those carrying one or more G alleles. This initial finding has since been extended by the same group74 using a much larger sample which included both ASD cases and typically developing controls (total n = 406). Children homozygons for the HOXA1 A218G A allele had smaller and more variable HC than G allele carriers. This significant difference according to genotype was seen in children with ASD as well as in control children – indicating that if HOXA1 related macrocephaly imparts risk for ASD it must do so in interaction with other factors. The finding of no relationship between HOXA1 A218G genotype and HC in adult controls – suggests that the polymorphism may be associated with early differences in HC growth, but that these do not result in different final HC. Sacco et al.73 examined two SNPs within the TPH2 gene, and one within the GLO1 gene. No association was found between any of the genetic variants studied and HC in a multi-site ASD sample.

Summary and Outstanding Issues Several observations support further investigation of HC as a promising EP in ASD; there is a strong association between ASD and increased HC relative to the general population, and HC increases in relatives of probands with ASD have been described in multiple studies. Also – as already noted – compared to other potential EPs, HC is easier and cheaper to measure. There are however certain complexities that need to be borne in mind. Firstly, the extent to which increased HC is associated with ASD regardless of age is unclear. The most consistent association seems to be in childhood – particularly during the pre-school years. Most longitudinal HC studies in early childhood indicate that ASD is ciased with accelerated HC

153

growth, which may return to normal rates as early as the third year of life. The relevant EP may therefore be how HC changes rather than abnormalities at one time-point. Secondly, there is not a clear relationship between variance in HC and behavioural variance in ASD,75,76 and specific genetic variants which have been linked to HC variance in ASD have the same relationship with HC in typical development. This makes it unlikely that there exists a simple causal path from genetic variants to ASD behavioural phenotype via increased HC. Thirdly, accelerated HC increases during early life in ASD are often assumed to indicate disproportionate brain over-growth. However, some reports suggest that HC in ASD is increased as part of a generalised macrosomia,76 or accompanied by macrosomia but still disproportionately increased.70 Thus growth abnormalities in ASD may not be specific to the CNS. A fourth area of uncertainty relates to the robustness of abnormal HC reports in ASD relatives. Methodological limitations include the often limited assessment of ASD-related behavioural traits in relatives (making it hard to control for affectation status), and the relative lack of normative HC data for adults compared to that available for paediatric populations (meaning that different studies often use different control data). This latter limitation may explain the fact that whilst there is limited evidence for increased HC in adults with ASD, there are several reports of increased HC amongst adult relatives of ASD probands. An alternative and as yet highly speculative explanation is that trajectories of HC growth differ between probands with ASD and relatives who may have only a partial complement of risk factors. Thus, although both probands and relatives may have accelerated HC growth in infancy, relatives may fail to show the deceleration of HC growth that has been reported in ASD and linked to symptom onset. Testing this hypothesis and clarifying the relationship between relative affectation status and HC will require longitudinal designs in genetically informative samples with more detailed behavioural phenotyping of relatives than has been available to date.

Global Brain Volumes In this section we will consider sMRI studies of total brain volume (TBV) in ASD. As these often also report on total cerebral grey matter volume (GMV) and total

154

cerebral white matter volume (WMV) – we will also address these putative NSEP here.

A. Raznahan et al.

brain volumes between ASD and typically developing controls have found evidence of abnormal brain maturation in ASD. Age dependency of NSEPs is further addressed below.

Associated with Disease As is the case for HC, increased TBV in ASD relative to typically developing controls during early childhood is now a well replicated finding. In addition to the Redcay meta-analysis described above,41 increased TBV in ASD was also found by a second meta-analytic study39 although this group did not find evidence that differences in brain volume altered as a function of age. One difference between these meta-analyses was that whilst the latter was restricted to sMRI reports, the former also included HC and post-mortem brain weight findings. The one sMRI study of TBV that has been published since the these two meta-analyses found non-significantly increased total brain volume in ASD (High Functioning Autism) compared to age-matched healthy controls within the age range 8–12 years.77 Several studies of TBV in ASD have also sought to establish if there are selective abnormalities of grey or white matter volume in ASD – with inconsistent results. Total GMV in ASD relative to typically developing controls has been reported to be significantly increased65,78 significantly reduced,79 and not significantly different.80 Total WMV in ASD relative to typically developing controls has been reported to be significantly increased in one report65 and not significantly different in others.78–80 Similar inconsistency can be found in reports of cerebral rather than whole brain GMV and WMV. Total cerebral GMV in ASD relative to typically developing controls has been reported to be significantly increased65,81,82 and not significantly different.84 Total cerebral WMV in ASD relative to typically developing controls has been reported to be significantly increased65,81,84 significantly reduced,81 and not significantly different.82,83 Such contrasting results between different studies may reflect true neurostructural heterogeneity within ASD, and/or be due to differences across studies in factors such as ASD sub-diagnosis, IQ range of cases, whether or not TBV was controlled for and the age range studied. Of all these methodological differences, age range appears to be particularly important. Almost all studies examining the effect of age on group differences in global

Family Studies Four studies have examined sMRI measures of global brain volumes in relatives of people with ASD.60,85–87 Rojas et al.87 did not find significant differences in TBV between adults with ASD, parents of adults with ASD and neurotypical adult controls. Two other studies85,86 also failed to find any significant differences in TBV between parents of probands with ASD and typically developing controls. Neither were significant differences found for cortical GMV or WMV,85 or total GMV.86 The only study to assess a paediatric sample60 also found that TBV did not differ significantly between members of monozygotic twins with autism, their co-twins with broad phenotype ASD and typically developing controls. Cerebral WMV was significantly reduced in both MZ twins with autism and their broad-phenotype co-twins compared to typically developing controls. No differences were found for cerebral GMV however.

Imaging Genetics Two studies have been published to date relating genotype to MRI measures of brain anatomy in ASD. In the first of such reports Wassink et al.88 related a common and extensively studied functional insertion/deletion polymorphism of the serotonin transporter gene promoter region (5HTTLPR l/s) to global and lobar measures of brain anatomy in two independent samples of pre-school aged children with autism. Both of these samples had been reported as having significantly increased total brain volume relative to matched controls in previous studies.65,89 In both samples presence of the 5HTTLPR s allele was associated with increased total cerebral cortex volume as well as cerebral cortical GMV in a linear manner. This association was statistically significant in one sample (n = 29) but not the other (n = 15). In the combined sample a statistically significant linear relationship between increasing s allele dose and increasing volume was found for total cerebral cortex, total corti-

24

Neurostructural Endophenotypes In Autism Spectrum Disorder

cal GMV and frontal lobe GMV. Following on from this report, the same group examined the relationship between a functional polymorphism in another serotonergic gene – monoamine oxidase a (MAOA) – and brain anatomy in not only ASD but also a control group.90 As this gene is on the X chromosome, only males were studied. An initial analysis found no association was found between the MAOA promoter VNTR (Variable Number Tandem Repeat) polymorphism and autism diagnosis. Length of the VNTR is thought to influence transcriptional efficiency, resulting in low functioning (MAOA_L) and high functioning (MAOA_H) alleles.91 Gene-brain analyses were then run in 29 pre-school aged males with autism and 39 healthy male controls aged between 7 and 18 years. In autism, but not in controls individuals, relative to the MAOA_H allele the MAOA_L allele was associated with significantly increased TBV, cerebral cortical GMV, cerebral cortical WMV, and frontal lobe cortical WMV. Given the different age ranges for cases and controls statistical “gene by group” interactions were not quantitatively tested. Due to the fact that brain overgrowth in ASD appears to be restricted to early childhood, our group sought to establish if the previously reported relationship between 5HTTLPR l/s genotype and brain anatomy amongst pre-school aged children with ASD88 also holds for older people with ASD. We therefore related 5HTTLPR long/short polymorphism in 43 adolescents and adults with ASD to brain anatomy using structural magnetic resonance imaging and voxel-based morphometry. We did not identify any significant associations between genotype and brain anatomy (total brain, grey matter, white matter volumes). Neither did we identify any genotype-related differences when using a spatially non-biased voxelbased analysis (Raznahan et al., 2008, in press165). One interpretation of the discrepancy between our findings in older ASD subjects, and those of Wassink et al.88 is that the relationship between 5HTTLPR l/s polymorphism and brain anatomy in ASD changes with age. This is consistent with animal models in which altered serotonergic levels in the developing cortex result in only transient changes in brain anatomy.92 Changing relationships between genotype and brain anatomy with age have been found for ApoE4,36 and DRD4 functional polymorphisms93 – the latter in both controls and ADHD. Twin studies also support the

155

notion that genetic influences on brain anatomy change with age.58

Summary and Outstanding Issues There seems to be relatively good evidence in support of the assertion that TBV is increased in ASD relative to typically developing controls during early childhood, with findings becoming more inconsistent in older samples. Increased TBV during the preschool years in ASD appears to be associated with increases in both GMV and WMV, although possibly to differing degrees.65,81 To date all four reports that have measured TBV in relatives of ASD probands from sMRI failed to find significant differences from age-matched controls. All these studies are cross-sectional in nature, and all but one have examined adult ASD relatives only. Cerebral WMV was reduced in 5–14 year old MZ “broad-phenotype” ASD co-twins compared to typically developing controls. The results of planned longitudinal sMRI studies in infant sibling of probands with ASD will be decisive in establishing if abnormalities of TBV, GMV and/or WMV change with age index genetic risk for ASD in the absence of overt symptoms of the disorder. Head circumference (HC), TBV, and total/cerebral GMV and WMV are very global measures and it is well-established that the relationship between brain and behaviour is regional in nature. None of these global measures has shown an ability to discriminate people with ASD from controls. Abnormalities of each can be found in neurodevelopmental disorders other than ASD. These facts suggest that the search for NSEP that might sit on the syndrome-specific causal pathways between genes and behaviour in ASD should also encompass measures of regional anatomy. The brain structure that has perhaps received more attention than any other in ASD research is the amygdala.

Amygdala The amygdala is known to play a key role in emotional processing and its interaction with memory, attention, and social cognition,94 and as such amygdala dysfunction had long been hypothesised to play a role in ASD neurobiology. Postmortem and functional MRI studies95 in ASD provide supportive evidence for the notion

156

A. Raznahan et al.

that amygdala dysfunction is involved in the social deficits seen in ASD.

predicted socialization and communication behaviours at age 6.101

Associated with Disease

Family Studies

When the results of several sMRI studies in ASD87,89,96–98 were combined in a meta-analysis39 abnormalities of amygdala volume were not found in ASD. However evidence for heterogeneity amongst results was identified whereby studies examining younger age groups tended to report increased amygdala volume in ASD, whereas those examining older age ranges did not. Since this meta-analysis there have been two studies examining amygdala volume in ASD.99,100 Palmen et al. examined amygdala volume in 42 medication naïve participants with ASD, and 42 age, sex and IQ matched controls between 7 and 25 years of age.100 No group differences were found for amygdala volume regardless of whether TBV was co-varied for or not. The authors report that the relationship between age and amygdala volume was not significantly different between ASD and controls. The possibility that group differences in amygdala volume between ASD and controls might vary with age is however supported by Nacewicz et al.99 who measured amygdala volume in 54 individuals with ASD and 26 controls. All analyses co-varied for TBV. This group found significantly reduced amygdala volume in ASD compared to controls within the age range 12.5–25 years, but no significant difference when the same comparison was carried out within the age range 8–12.5 years. There was an interaction between age and group (of borderline statistical significance) in that amygdala volume increased with subject age in typically developing controls, but no such increase was seen in ASD. Smaller amygdala volumes in ASD were associated with, slower performance in tasks of emotional processing from faces, reduced time spent looking at the eyes of static facial stimuli, and increased severity of social impairment during early childhood. The authors propose a model in which amygdala hyperactivity in ASD is associated with amygdala hypertrophy in early life. Persistent hyperactivity however would come to be associated with reduced amygdala volumes relative to typically developing individuals. Some support is lent to this model by the finding that right amygdala volume in pre-school aged children with ASD was positively correlated with contemporaneous symptom severity, and

Two studies have measured amygdala volume in the relatives of probands with ASD diagnoses.87,102 Rojas et al.87 found no significant difference in amygdala volumes in parents of probands compared with controls with no family history of ASD. Parents and controls did not differ in mean age. The FSIQ of controls was 120. The FSIQ of ASD parents was not measured, nor were there any reported measures of autistic symptomatology. All analyses included TBV as a co-variate. Dalton et al.102 found decreased mean amygdala volume in unaffected (i.e. without clinical ASD diagnoses) siblings of probands with ASD aged between 8 and 18 years relative to an age and IQ matched controls without a family history of ASD. This difference was statistically significant in an analysis that controlled for age and brain volume. Imaging Genetics to Date No published studies available to date Summary and Outstanding Issues The results of studies comparing amygdala volume in ASD to typically developing controls are inconsistent. There is some evidence39,98,99 to indicate that one reason for such inconsistent may be that the amygdala in ASD shows a different growth trajectory to that seen in typical development. This hypothesis is yet to be confirmed in adequately sized longitudinal studies. Of the many studies examining amygdala volume in ASD, only one89 reports that correction for TBV removed previously significant group differences in amygdala volume. This makes it unlikely that reports of increased amygdala volume are confounded by generalised brain enlargement. There is insufficient data available on amygdala volume in ASD probands to determine if volumetric abnormalities of the amygdala index genetic risk for ASD. If ASD is associated with abnormal structural maturation of the amygdala, then studies in ASD relatives would also clearly benefit from longitudinal designs.

24

Neurostructural Endophenotypes In Autism Spectrum Disorder

Cerebellum The cerebellum is traditionally known for its role in motor functions. However, a converging body of evidence from lesion, imaging, and histological studies indicate that the cerebellum is also involved in a myriad of higher cognitive and emotional functions. For instance, patients with vascular and degenerative cerebellar disease demonstrate substantial effects on cognition and emotion.103,104 Functional MRI studies show robust cerebellar activation in tasks of language, visuo-spatial analyses, learning and working memory.105 Histological studies reveal that neurons of the cerebellum project not only to the motor areas of the cerebral cortex but, largely via the thalamus, to many brain areas relevant to cognition and behaviour, including the dorsolateral prefrontal cortex, the medial frontal cortex, the parietal and superior temporal areas.106 Also, it has also been shown that the phylogenetic development of the neocerebellum, which has undergone dramatic expansion during primate evolution, parallels the prefrontal cortex.107

157

abnormalities of total brain volume and IQ. The age range covered was 7–26 years. All subjects were male and had PIQ above 65. A macrocephalic ASD group was compared with age and PIQ matched healthy controls with “benign” macrocephaly. Also, a normocephalic ASD group was compared with normocephalic controls that did not differ in mean age or mean PIQ. Cerebellar volume correlated significantly and positively with total brain volume for ASD and controls. In people with ASD and controls, cerebellar volume showed a positive correlation with PIQ although this only reached statistical significance in ASD. No group differences were found according to presence/absence of ASD in either macrocephalic or normocephalic samples for any of the cerebellar measures of interest (total volume, total grey matter volume, total white matter volume, surface areas for lobules I–V, VI–VII, VIII, IX–X). Whilst this study sought to control for the effects of macrocephaly, the age range studied in each group was large, there was a marked difference in PIQ range between the normocephalic ASD (69–125) and control (103–136) groups, and sample sizes were small (e.g. 13 normocepahlic ASD vs. 8 normocepahlic controls).

Associated with Disease Family Studies Since the first sMRI case report of cerebellar abnormality in ASD108 many studies have examined cerebellar anatomy in ASD using a variety of indices such as total cerebellar volume and mid-sagittal surface areas of vermal lobules. Findings for each of these measures have been mixed, although the pooled effect-size estimates in a recent meta-analyses by Stanfield et al.39 found that significant cerebellar abnormalities were associated with a diagnosis of ASD. Total cerebellar volume was found to be increased in ASD, although only one109 of the studies included found that this increase persists after controlling for total brain volume. Conversely, the mid-sagittal surface areas for lobules VI–X were reduced in ASD. However, across studies, the reported effects size for reduction of lobule VI–VII surface area in ASD lessened with increasing age and increasing IQ. The one study of cerebellar anatomy in ASD that has been published since the Stanfield meta-analysis110 sought to directly examine the extent to which the cerebellar abnormalities previously observed in ASD were independent of potentially co-occurring

Palmen85 did not identify any abnormalities of cerebellar volume amongst the parents of people with ASD. Kates et al. reports the Members of monozygotic twin pairs with ASD, their co-twins with “broad phenotype” ASD (but not concordant for diagnosis) and unaffected singleton controls did not differ in cerebellar volume. An intriguing finding in this study was that cerebellar but not cerebral volumes were significantly less correlated in non-concordant vs. concordant monozygotic ASD twin pairs. Furthermore, differences in cerebellar (but not cerebral) volume within twin pairs were found to correlate positively with within-pair differences in a measure of symptom severity. This suggests that compared to cerebral volumes, cerebellar volumes in ASD are more influenced by factors other than DNA sequence variation (which is identical in MZ twins). Such factors might include within twin-pair differences in gene expression, or environmental insults (e.g. perinatal adversity) or environmental adaptation.111 In a recent morphometric twin study examining several volume tric brain indices Wallace et al.52 found

158

that environmental factors played a pronounced role in explaining variance in cerebellar volume (e.g. heritability of total cerebellar volume = 49%, heritability of total cerebral volume = 89%).

Imaging Genetics to Date No relationship was found between cerebellar volumes (total, grey or white) and functional polymorphism within either 5HTTLPR88 or MAOA.90

Summary and Outstanding Issues It remains unclear if disturbances of cerebellar volume or mid-sagittal surface area are associated with ASD. The observation of selectively reduced twin–twin correlations in total, grey matter and white matter volume of the cerebellum in discordant compared to concordant ASD MZ twin pairs60 indicates that non-genetic risk factors for ASD may lead to a dysregulation of cerebellar development. The extent of this dysregulation rather than the resultant direction (e.g. increases or decreases) of cerebellar volumetric abnormality it leads to may be most relevant for the ASD behavioural phenotype.

Other Neuroimaging Indices Basal Ganglia The central role played by the basal ganglia in processes such as inhibitory control,112 reward processing113 and repetitive behaviours114 make them an attractive brain system for further study in ASD. The Stanfield meta-analysis39 quantitatively combined the findings of three studies of caudate volume in ASD and found a pooled effect of significantly increased caudate volume in ASD. Two of these reports had not found significant differences between ASD and controls groups for caudate volume,84,115 and one had found a significantly increased total caudate volume in ASD which did not survive co-varying for total brain volume.116 The authors of this third study interpret their findings as suggesting that increased caudate volume in ASD is proportional to increased total brain volume.

A. Raznahan et al.

Subsequently, three studies have examined caudate volumes in ASD.117–119 Voelbel et al.117 found increased left and right caudate volumes in ASD compared to controls. This study included children falling within the age range 7–13 years, and significant differences were only found once intracranial volume was entered as a co-variate. Based on their observation that the proportion of variance in caudate volume accounted for by ASD status was the same in the regression models with and without ICV as a co-variate, the authors argue that caudate increase in ASD is not disproportionate to brain volume. Langen et al.118 also found significant caudate enlargement in ASD relative to controls in two well-matched medication naïve samples – one aged 7–14 years and the other 15–25 years. These findings held regardless of whether total brain volume was controlled for or not. A third study reporting increased (right) caudate volume in adults with ASD119 relative to typically developing controls (when co-varying for total brain volume) also found that in ASD right caudate volume was positively correlated with a severity of a hypothesised sub-group of repetitive behaviour symptoms. Abnormalities of pallidum116 or putamen115,119 have not been found in ASD. Herbert et al.84 found increased mean volume for the product of pallidum and putamen volumes – although this did not persist after total brain volume was entered as a co-variate. In summary increases in caudate volume are a relatively consistent finding in ASD regardless of the age range studied. It remains unclear how independent these findings are of differences in ICV/TBV in ASD. Given these reports of abnormalities of caudate volume in ASD, and the clear role played by the caudate in repetitive behaviour – larger studies in ASD probands, and initial studies in ASD relatives are indicated.

Corpus Callosum There is mounting evidence of structural and functional brain disconnectivity in ASD,120 and as the principle connection between the two cerebral hemispheres examinations of the corpus callosum (CC) are central to this literature. The Stanfield meta-analysis combined the results of five reports121–125 to conclude that total mid-sagittal corpus callosal area (TMSCCA) is reduced in ASD. The result of the studies included in

24

Neurostructural Endophenotypes In Autism Spectrum Disorder

this meta-analysis did not vary significantly as a function of the age or IQ of study participants.39 Two studies have examined TMSCCA since this meta-analysis was published.126,127 Boger-Megiddo et al.126 compared TMSCCA in preschool-aged children with ASD to that seen in chronological-age matched typically developing controls and chronological and mental-age and IQ matched children with idiopathic developmental delay. The group of children with ASD that were used in this study had been previously reported to have increased TBV relative to typically developing controls.89 Once gender and TBV were included as co-variates in analysis, TMSCCA was significantly less in ASD than in typically developing controls, but not different from mental-age matched children with developmental disabilities. When children with ASD were further sub-divided into autism and PDD-NOS, significant reductions in TMSCCA were only seen in children with autism. Kilian et al.127 compared TMSCCA across five groups; normocepahlic autism, macrocephalic autism, normocephalic non-autism, benign macrocephaly and reading disorder. There were significant differences in IQ across the groups, and although groups did not differ in mean age, the age-ranges included in each group were not comparable (e.g. upper age limit varied between 16 and 31 years). Across all groups (excluding those with reading disorder), in analyses controlling for age and total intracranial volume, the presence or absence of macrocephaly was significantly associated with TMSCCA, but ASD diagnosis was not. Various approaches have been adopted to establish if ASD is associated with regional disturbances of callosal anatomy. By far the most common of these is a method described by Witelson128 which divides the CC into seven anterio-posterior segments. Analyses of mid-sagittal SA within each of these CC sub-segments in ASD has not identified a consistent pattern of surface area abnormality, with varying reports of surface area reductions in anterior segments125 in more posterior involvement,129 in sub-regions throughout the CC,126 and in the anterior and posterior extremes only.130 Despite uncertainties regarding the regional distribution of CC abnormalities in ASD, the majority of studies report reductions of CC area in ASD. As such studies of CC anatomy are warranted in the relatives of ASD probands.

159

Challenges in the Application of NSEPs in ASD Research Inconsistency Across Reports of sMRI Abnormalities in ASD A NSEP must show an association with ASD. Of all the measures reviewed above, only increased HC and brain volume in early childhood have been consistently reported in individuals with ASD diagnoses. Inconsistency amongst other sMRI measures in ASD may be contributed to by differences in the hardware and software use for sMRI image acquisition, pre-processing and analyses. These are all known to influence the accuracy and reliability of measures derived from sMRI as outlined in Section III above. Other sources of inconsistency across reports of sMRI abnormalities in ASD are detailed below.

Confounders Brain anatomy has been shown to vary with factors such as gender, 131 handedness, 132 and genotype. 36 Differences between studies on how these potential confounders are handled can lead to inconsistency in reports of sMRI abnormalities associated with ASD. Gender – Most sMRI studies in ASD remove the potential confounder of gender differences between cases and controls by examining males only. The disadvantage of this of course is that we know very little indeed about the neurostructural correlates of ASD in females,133 and there are no studies to date that have quantitatively examined if the neuroanatomical correlates of ASD vary significantly as a function of gender. Handedness – Handedness of participants is not always reported. For example, of the ten sMRI studies published in ASD during in the last calendar year only four mentioned the handedness of participants, and all of these four matched cases and controls on this variable. Genotype – There have been no published sMRI studies in ASD where cases or controls have been selected or matched according to genotype in an attempt to control for the potentially confounding effects of this on brain anatomy.

160

Medication – Psychotropic medication status has been associated with differences in brain anatomy.35 If a sMRI study aims to compare brain anatomy in ASD to that seen in healthy controls, the only way of controlling for this confounder is to make sure that ASD participants are medication naive. Of the ten sMRI studies published in ASD during the previous calendar year, one explicitly used medication naive participants, four detailed the psychotropic medications taken by ASD participants (in one of these brain anatomy in ASD with and without history of medications was compared, in the other three ASD and controls were not matched for medication status), and five made no reference at all to the medication status of participants. For some of the factors that are known to influence brain anatomy – there are downsides to matching cases and controls as doing so risks “throwing the baby out with the bathwater”. Intelligence quotient (IQ) is the paradigmatic example of this. Unless cases and controls are matched for IQ, group differences seen may be confounded by IQ differences rather than the presence or absence of ASD per-se. However, alterations in IQ are commonly seen in the ASD phenotype.134 Many sMRI studies in ASD address this issue by excluding participants with learning disability (mental retardation). One effect of this practice is that we know relatively little about the neurostructural correlates of ASD in the context of learning disability – which is seen in approximately half of all people with ASD.4 Those studies that do include ASD participants with learning disability either make no attempt to match IQ (often arguing that IQ reductions are frequently seen in the ASD phenotype), or include controls that have “idiopathic” learning disabilities. This latter approach makes it very hard to establish equivalence of control groups across studies. How studies deal with the issue of co-morbid psychopathology carries similar considerations. A recent survey found that over two thirds of children with ASD diagnosis had one or more co-morbid psychiatric diagnoses.135 Excluding ASD participants with co-morbid anxiety or attention deficit hyperactivity presentations lessens the possibility of differences between ASD and healthy controls being confounded by neuroanatomical correlates of these co-morbid conditions. However, the conceptual validity of treating disturbances of mood or attention as additional to, rather than part of the ASD phenotype is far from clear.

A. Raznahan et al.

Neurostructural Heterogeneity in ASD There is good evidence that differences between studies in the age and IQ range of included participants can lead to inconsistency in ASD sMRI findings even if subjects with ASD and controls are adequately matched for these variables within individual studies. There is especially strong evidence for this assertion with respect to age.98,99 Differences between ASD and controls in measures such as HC, TBV,41 amygdala volume98,99 and cortical anatomy136,137 have all been shown to vary as a function of the age range studied. There is also mounting evidence from other conditions such as ADHD, and psychosis that neurodevelopmental trajectories are likely to provide more informative NSEPs than cross-sectional differences in brain anatomy.138 Furthermore, the relationship between age and maturational stage may show systematic differences between ASD and controls as suggested by a recent study reporting later age of menarche in females with ASD than in typically developing controls.139 Neurostructural differences amongst people with ASD have also been reported with respect to symptom severity119,140 – indicating that differences across sMRI reports in the symptom profile of ASD participants studies will generate inconsistent findings regarding the neuroanatomical correlates of ASD.

Familiality of a NSEP Establishing Familiality A NSEP indexing genetic liability for the disorder will be more common in relatives of affected probands (regardless of whether they are “affected” or not) compared to population controls. To date, relatively few sMRI studies have examined brain anatomy in the siblings60,102 or parents86 of probands with ASD. There are more studies however of HC in relatives of probands with ASD.68–71 One of these used a longitudinal design in “high-risk” infant siblings who were followed up between birth and 2 years of age.71 Head circumference in these siblings was not different to population norms at birth, but was increased at 12 and 24 months. Although for obvious reasons status regarding the presence or absence of and ASD diagnosis was not available – this study raises the possibility that as

24

Neurostructural Endophenotypes In Autism Spectrum Disorder

in probands, examining neurodevelopmental trajectories in relatives may provide more informative NSEPs than static group differences.

Interpreting Familiality The presence of a neuroanatomical abnormality in probands as well as their unaffected relatives is taken to provide evidence that the abnormality is associated with risk factors for the disorder and not generated in a secondary manner by having the disease. A major challenge for NSEP research in ASD is how to operationalise the construct “unaffected”. Subtle abnormalities of social interaction and communication falling below ASD diagnostic criteria are commonly reported amongst relatives of ASD probands.141,142 The boundaries16 and substructure143 of the behavioural phenotype associated with risk factors for ASD are not clear. Of the HC and sMRI reports published to date in ASD relatives, only two60,71 applied measures of ASD symptoms in siblings. If “unaffected” is taken to mean “without an ASD diagnosis” – then neurostructural abnormalities in relatives may not only be accounted for by the presence of ASD risk genes, but also be secondary to the presence of subthreshold ASD symptoms.

Establishing Shared Genetic Influences Between NSEP and ASD Well-characterised ASD twin samples would allow bivariate modelling of neurostructural and behavioural data to establish the degree to which genetic influences on a putative ASD NSEP overlap with genetic influences for the ASD behavioural phenotype. Such an analytical approach has recently been used to assess a proposed electrophysiological (P50 suppression) in twins with psychotic bipolar affective disorder.144

161

behavioural phenotype level. As yet, empirical evidence in support of these hypotheses is scant. Furthermore animal models indicate that biological traits as causally proximal to genes as mRNA expression still show marked polygenicity.31 There are also emerging reports of epistatic influences on sMRI volumetric measures in typically developing humans145 – raising the possibility that volumetric differences reported for one genetic polymorphism may be confounded by genotype group differences in another unmeasured genetic variant. Despite such findings several high profile publications have reported statistically significant differences in sMRI measures of brain anatomy in typically and atypically developing samples according to genotype at functional polymorphic sites in genes such as the serotonin transporter, catechol-omethyl transferase and brain derived neurotrophic factor. There remains marked controversy about how such reports should be interpreted. Some have argued that in the absence of well established associations between a genetic variant and a behaviourally defined disease state – associations between this genetic variant and an EP may lead to undue significance being placed on the role of this variant in disease pathogenesis. According this position, gene–EP relationships should only be examined for those genetic variants where association with disease has been well-replicated and shown to not be mediated by other nearby polymorphisms.11 This demand assumes that it will be possible to establish replicated associations between genetic variants and behaviourally defined disorders. The lack of progress in this very endeavour however is what has spurred EP research.

Opportunities in the Application of NSEPs in ASD Research Alternative Neuroimaging Indices Moving Beyond Volume

Identifying Genes Associated with NSEPs It has been proposed that risk genes for EPs will be easier to identify than risk genes for disorders because EPs are causally “closer” to genes, and influences by fewer genes – which predicts that a single genetic variant will exert an larger effect size at the EP than the

The most popular morphometric index used in structural neuroimaging is volume. The brain has many other spatial properties upon which the effect of risk genes for ASD might have an impact. For example, in recent years newer methods for the analysis of sMRI data have been used in ASD research and resulted in

162

reports of abnormalities of cortical thickness,146 local cortical curvature,147 sulcal position,148 shape of the corpus callosum125 and ventricular shape.149 The importance of these alternative measures to the NSEP endeavour in ASD is not just that they offer new windows into neurostructural variation, but that different measures may well map onto distinct neurodevelopmental processes, and therefore potentially implicate different neurodevelopmental genes. A good example of this is the difference between cortical thickness (CT) and cortical surface area (SA) – which are the two sole determinants of cortical volume (CV). There are several lines of evidence that suggest CT and SA variance are influenced by very different sets of developmental processes. Firstly, CT and SA differ in their phylogenetic (evolutionary) history. The dramatic increase in CV between mice and man is accompanied by a 1,000-fold increase in SA, but only a twofold increase in CT150 – indicating dissociable genetic bases for these two traits. Surface area and CT also have different biological bases,151 and different developmental trajectories.152,153 Surface area is influenced by progenitor cell division in the embryological periventricular area, and the remarkable SA of the cortical sheet in humans is achieved within the limited intracranial volume through gyrification. However, CT is thought to reflect dendritic arborization/pruning within the grey matter154 or changing myelination at the grey/white matter interface.153 It is also clear that genetic disorders in humans can produce spatially dissociable abnormalities of CT and gyrification (e.g. Williams syndrome155. Thus, establishing the relative contribution of CT and SA abnormalities to observed volumetric differences of the cerebral cortex between ASD and controls implicates distinct sets of genetic and environmental influences which can then be further examined.

Developmental Trajectories as NSEPs in ASD The most consistent neurostructural finding in ASD relates to abnormal brain growth during a particular phase of development rather than to a static excess or deficit.41 The development of longitudinally imaged cohorts of typically and atypically developing individuals has led to several landmark studies which suggest that neurodevelopmental disorders or the extremes of typical development are better characterised

A. Raznahan et al.

by distinct patterns of brain growth rather than developmentally invariant brain changes.138 Attention Deficit Hyperactivity Disorder is the childhood onset neurodevelopmental condition in which most longitudinal sMRI work has been done. This research has proposed novel NSEP indices such as delayed age at which peak CT is attained within particular cortical regions.156 Such longitudinal approaches have yet to be applied in ASD sMRI research. As a preliminary step in characterising cortical maturation in ASD, we used a cross sectional study design to examine differences in age related change of regional CT, SA and CV measures in males with ASD and typically developing controls aged between 10 and 60 years.137 “Age-by-Group” interactions were found for CV and CT in the temporal lobes, and within these the fusiform and middle temporal gyri. Here both measures showed significant negative correlations with age in controls, but not in ASD. Controls had increases in CV and CT relative to ASD in childhood, but reductions by late adulthood. The same pattern of significant age-related group differences was also seen for CT in regions within superior temporal, inferior frontal, medial frontal and inferior parietal cortices. This suggest that people with ASD have age-related differences in cortical anatomy within brain regions previously reported as being important for social cognition and language, and functionally abnormal in ASD. Although replication using longitudinal designs is required, these findings emphasise the need to recognise that the cortical NSEP in ASD may be age-dependent.

Infant Sibling Study Designs Studying infant siblings of children with ASD provides an excellent model for understanding the early developmental neurobiology of ASD.157 In those siblings who later go on to develop an ASD, rich data can be gathered on the early behavioural markers in a manner that carries less potential information bias than retrospective study designs in children with established ASD.158 Infant sibling designs can also provide information on the behavioural phenotype of unaffected relatives of ASD probands in early life. These advantages carry through to the EP level. Although behavioural and EP abnormalities in ASD siblings may be due to genes and/or environments

24

Neurostructural Endophenotypes In Autism Spectrum Disorder

that they share with the proband – the ability to prospectively gather rich data on environmental variables makes it possible to partially account for these sources of variance.

Neurogenetic Syndromes Early articulations of the EP approach seem to assume that the disorder under study is polygenic in nature,26 and that a person will fall within a disease category once a certain threshold of lability is passed. Early work in ASD showed that the familial distribution of ASD was consistent with such a polygenic threshold liability model159 and polygenic models of ASD are still play an important role in research. However, the proportion of ASD cases recognised as being due to single genetic abnormalities of major effect has increased dramatically in recent years.20,160 This is due to (i) increased recognition of the prevalence of ASD symptoms in known neurogenetic syndromes (e.g. Smith-Lemli-Opitz syndrome19), (ii) the description of families where possession of a discrete chromosomal abnormality is associated with an ASD-related behavioural phenotype (e.g. SHANK 3 gene deletion,161 and (iii) the recent observation that DNA “copy number variants” (which often tend to occur in particular chromosomal regions) are seen at a higher rate in ASD than in the general population.162 The classical example of a mendelian neurogenetic syndrome associated with ASD is Tuberous Sclerosis (TS). Tuberous sclerosis is a multi-system genetic disorder. It has a prevalence of approximately 10 per 100,000.163 It is characterised by hamartomatous growths in multiple organs including the brain, skin, eyes, heart, lungs, and kidneys.164 The Central Nervous System (CNS) lesions in TS are associated with a range of neurodevelopmental problems that can give rise to substantial morbidity. Rates of ASD in TS reach 25% compared with 1% in general population, and presence of ASD phenotype has been linked to presence of tubers and epileptic foci in temporal lobes.18 It may be that the neurobiological mechanisms underpinning ASD in the context of TS (and possibly other neurogenetic syndromes associated with ASD) do not generalise to other “idiopathic” instances of ASD. It is in part due to this that sMRI studies striving to identify the neurostructural

163

correlates of “idiopathic” ASD typically exclude cases where ASD occurs in the context of TS and other neurogenetic syndromes that have special associations with ASD such as Fragile X and SmithLemli-Opitz syndrome (SLOS). In combination, these conditions may account for up to 10% of ASD cases. Although these varied conditions have distinct genetic bases and phenotypes their study can inform the search for NSEP in ASD. Aetiologically distinct disorders may impinge on common brain systems that also mediate the impact of genetic influences in polygenic “idiopathic” forms of ASD. Therefore, if it is possible to identify that a particular sMRI abnormality is associated with the presence of ASD across several distinct neurogenetic syndromes – then this would be a very strong candidate NSEP in ASD.

Conclusions and Future Directions Neurobiological models for ASD are highly unlikely to be advanced by research which simply compares the prevalence of a putative genetic or environmental risk factor in individuals with an ASD diagnosis as defined by DSM-IV or ICD-10 to that which is seen in typically developing controls. One of many alternative strategies is to “pull back” from the level of behaviourally defined disorder categories to alternative endophenotypic outcomes. Measures of neurostructural variance derived from sMRI provide one such EP. The putative ASD NSEPs for which there is most empirical support are increased HC and TBV in early childhood. This illustrates the need to consider that trajectories of neurostructural development may provide more informative NSEPs that static excesses or deficits. Capturing such NSEPs will require the use of large, longitudinal and genetically informative ASD samples as well as continued advances in our rapidly evolving understanding of typical brain development. Although ASD research examining twin and infant sibling samples is developing rapidly, many other methodological challenges will also need to be negotiated before the potential of using sMRI to identify EPs in ASD is optimised. This will need to occur in parallel with research designed to better understand the sub-structure and boundaries of the ASD cognitivebehavioural phenotype.

164

A. Raznahan et al.

References 19. 1. American Psychiatric Organisation. Diagnostic and Statistical Manual of Mental Disorders: Test Revision (DSM-IV-TR), 4th edition. Washington, DC: American Psychiatric Association; 2000. 2. Ronald A, Happe F, Bolton P, Butcher LM, Price TS, Wheelwright S, Baron-Cohen S, Plomin R. Genetic heterogeneity between the three components of the autism spectrum: a twin study. J Am Acad Child Adol Psychiat 2006;45:691–699. 3. Volkmar FR, Lord C, Bailey A, Schultz RT, Klin A. Autism and pervasive developmental disorders. J Child Psychol Psychiat Allied Disciplines 2004;45:135–170. 4. Baird G, Simonoff E, Pickles A, Chandler S, Loucas T, Meldrum D, Charman T. Prevalence of disorders of the autism spectrum in a population cohort of children in South Thames: the Special Needs and Autism Project (SNAP). Lancet 2006;368(9531):210–215. 5. Fombonne E, Zakarian R, Bennett A, Meng L, LeanHeywood D. Pervasive developmental disorders in Montreal, Quebec, Canada: prevalence and links with immunizations. Pediatrics 2006;118(1):e139–e150. 6. Wing L, Yeates SR, Brierley LM, Gould J. The prevalence of early childhood autism: comparison of administrative and epidemiological studies. Psychol Med 1976;6:89–100. 7. Gernsbacher MA, Dawson M, Goldsmith HH. Three reasons not to believe in an autism epidemic. Curr Dir Psychol Sci 2005;14:55–58. 8. Bailey A, Le Couteur A, Gottesman I, Bolton P. Autism as a strongly genetic disorder: evidence from a British twin study. Psychol Med 1995;25:63–77. 9. Veenstra-VanderWeele J, Cook EJ. Molecular genetics of autism spectrum disorder. Mol Psychiat 2004;9:819–832. 10. Tan HY, Callicott JH, Weinberger DR. Intermediate phenotypes in schizophrenia genetics redux: is it a no brainer? Mol Psychiat 2008;13:233–238. 11. Walters JT, Owen MJ. Endophenotypes in psychiatric genetics. Mol Psychiat 2007;12:886–890. 12. World Health Organisation. Mental disorders; a glossary and guide to their classification in accordance with the 10th revision of the international classification of diseases-research diagnostic criteria (ICD-10). Geneva: WHO; 1993. 13. Kanner L. Autistic disturbances of affective contact. Nerv Child 1943;2:217–250. 14. Asperger H. Dies “autistischen Psychopathen”. Arch Psychiat Nervenkr 1944;117:76–136. 15. Bolton P, Macdonald H, Pickles A, Rios P. A case-control family history study of autism. J Child Psychol Psychiat 1994;35:877–900. 16. Constantino JN, Lajonchere C, Lutz M, Gray T, Abbacchi A, McKenna K, Singh D, Todd RD. Autistic social impairment in the siblings of children with pervasive developmental disorders. Am J Psychiat 2006;163: 294–296. 17. DiCicco-Bloom E, Lord C, Zwaigenbaum L, Courchesne E, Dager SR, Schmitz C, Schultz RT, Crawley J, Young LJ. The developmental neurobiology of autism spectrum disorder. J Neurosci 2006;26:6897–6906. 18. Bolton PF, Park RJ, Higgins JN, Griffiths PD, Pickles A. Neuro-epileptic determinants of autism spectrum disorders

20.

21. 22.

23.

24.

25.

26. 27.

28.

29.

30.

31. 32. 33.

34.

35. 36.

in tuberous sclerosis complex. Brain 2002;125(Pt 6): 1247–1255. Bukelis I, Porter FD, Zimmerman AW, Tierney E. SmithLemli-Opitz syndrome and autism spectrum disorder. Am J Psychiat 2007;164:1655–1661. Zhao X, Leotta A, Kustanovich V, Lajonchere C, Geschwind DH, Law K, Law P, Qiu S, Lord C, Sebat J, Ye K, Wigler M. A unified genetic theory for sporadic and inherited autism. Proc Natl Acad Sci USA 2007;104: 12831–12836. Kendler KS. “ A gene for…”: the nature of gene action in psychiatric disorders. Am J Psychiat 2005;162:1243–1252. Rutter M, Moffitt TE, Caspi A. Gene-environment interplay and psychopathology: multiple varieties but real effects. J Child Psychol Psychiat Allied Disciplines 2006;47:226–261. Stone JL, Merriman B, Cantor RM, Yonan AL, Conrad GT, Geschwind DH, Nelson SF. Evidence for sex-specific risk alleles in autism spectrum disorder. Am J Hum Genet 2004;75:1117–1123. Szatmari P, Maziade M, Zwaigenbaum L, Merette C, Roy MA, Joober R, Palmour R. Informative phenotypes for genetic studies of psychiatric disorders. Am J Med Genet B Neuropsychiat Genet 2007;144:581–588. John B, Lewis K. Chromosome variability and geographical distribution in insects: chromosome rather than gene variation provide the key to differences among populations. Science 1966;152:711–721. Gottesman II. Genetic theorizing and schizophrenia. Br J Psychiat 1973;122:15–30. Gottesman II, Gould TD. The Endophenotype Concept in Psychiatry: Etymology and Strategic Intentions. Research advances in genetics and genomics: Implications for psychiatry;2003, pp. 63–84. Gould TD, Gottesman II. Psychiatric endophenotypes and the development of valid animal models. Genes Brain Behav 2006;5:113–119. Doyle AE, Faraone SV, Seidman LJ, Willcutt EG, Nigg JT, Waldman ID, Pennington BF, Peart J, Biederman J. Are endophenotypes based on measures of executive functions useful for molecular genetic studies of ADHD? J Child Psychol Psychiat Allied Disciplines 2005;46(7):774–803. Doyle AE, Willcutt EG, Seidman LJ, Biederman J, Chouinard VA, Silva J, Faraone SV. Attention-deficit/hyperactivity disorder endophenotypes. Biol Psychiat 2005;57:1324–1335. Flint J, Munafo MR. The endophenotype concept in psychiatric genetics. Psychol Med 2007;37(2):163–180. Skuse DH. Endophenotypes and child psychiatry. Br J Psychiat 2001;178:395–396. Viding E, Blakemore SJ. Endophenotype approach to developmental psychopathology: implications for autism research. Behav Genet 2006;37:51–60. Waldman ID. Statistical approaches to complex phenotypes: evaluating neuropsychological endophenotypes for attention-deficit/hyperactivity disorder. Biol Psychiat 2005;57(11):1347–1356. Scherk H, Falkai P. Effects of antipsychotics on brain structure. Curr Opin Psychiat 2006;19:145–150. Shaw P, Lerch JP, Pruessner JC, Taylor KN, Rose AB, Greenstein D, Clasen L, Evans A, Rapoport JL, Giedd JN. Cortical morphology in children and adolescents with different apolipoprotein E gene polymorphisms: an observational study. Lancet Neurol 2007;6:494–500.

24

Neurostructural Endophenotypes In Autism Spectrum Disorder

37. Hariri AR, Weinberger DR. Imaging genomics. Br Med Bull 2003;65:259–270. 38. Palmen SJ, van Engeland H. Review on structural neuroimaging findings in autism. J Neural Trans 2004;111: 903–929. 39. Stanfield AC, McIntosh AM, Spencer MD, Philip R, Gaur S, Lawrie SM. Towards a neuroanatomy of autism: a systematic review and meta-analysis of structural magnetic resonance imaging studies. Eur Psychiat 2008;23(4):289–299. 40. Amaral DG, Schumann CM, Nordahl CW. Neuroanatomy of autism. Trends Neurosci 2008;31:137–145. 41. Redcay E, Courchesne E. When is the brain enlarged in autism? A meta-analysis of all brain size reports. Biol Psychiat 2005;58:1–9. 42. Prince M, Stewart R, Ford T. Practical Psychiatric Epidemiology. Oxford: Oxford University Press; 2003. 43. Brierley B, Shaw P, David AS. The human amygdala: a systematic review and meta-analysis of volumetric magnetic resonance imaging. Brain Res Brain Res Rev 2002;39:84–105. 44. Geuze E, Vermetten E, Bremner JD. MR-based in vivo hippocampal volumetrics: 1. Review of methodologies currently employed. Mol Psychiat 2004;10:147–159. 45. Klauschen F, Goldman A, Barra V, Meyer-Lindenberg A, Lundervold A. Evaluation of automated brain MR image segmentation and volumetry methods. Hum Brain Mapp 2008; [Epub ahead of print]. 46. Ewers M, Teipel SJ, Dietrich O, Schonberg SO, Jessen F, Heun R, Scheltens P, van de PL, Freymann NR, Moeller HJ, Hampel H. Multicenter assessment of reliability of cranial MRI. Neurobiol Aging 2006;27:1051–1059. 47. Belmonte MK, Mazziotta JC, Minshew NJ, Evans AC, Courchesne E, Dager SR, Bookheimer SY, Aylward EH, Amaral DG, Cantor RM, Chugani DC, Dale AM, Davatzikos C, Gerig G, Herbert MR, Lainhart JE, Murphy DG, Piven J, Reiss AL, Schultz RT, Zeffiro TA, Levi-Pearl S, Lajonchere C, Colamarino SA. Offering to share: how to put heads together in autism neuroimaging. J Autism Dev Disord 2007;38:2–13. 48. Myers K, Winters NC. Ten-year review of rating scales. II: Scales for internalizing disorders. J Am Acad Child Adol Psychiat 2002;41:634–659. 49. Collett BR, Ohan JL, Myers KM. Ten-year review of rating scales. V: Scales assessing attention-deficit/hyperactivity disorder. J Am Acad Child Adol Psychiat 2003;42:1015–1037. 50. Schmitt JE, Eyler LT, Giedd JN, Kremen WS, Kendler KS, Neale MC. Review of twin and family studies on neuroanatomic phenotypes and typical neurodevelopment. Twin Res Hum Genet 2007;10:683–694. 51. Peper JS, Brouwer RM, Boomsma DI, Kahn RS, Hulshoff Pol HE. Genetic influences on human brain structure: a review of brain imaging studies in twins. Hum Brain Mapp 2007; 28:464–473. 52. Wallace GL, Eric SJ, Lenroot R, Viding E, Ordaz S, Rosenthal MA, Molloy EA, Clasen LS, Kendler KS, Neale MC, Giedd JN. A pediatric twin study of brain morphometry. J Child Psychol Psychiat Allied Disciplines 2006; 47(10):987–993. 53. Pfefferbaum A, Sullivan EV, Swan GE, Carmelli D. Brain structure in men remains highly heritable in the seventh and eighth decades of life. Neurobiol Aging 2000; 21:63–74. 54. Baare WF, Hulshoff Pol HE, Boomsma DI, Posthuma D, De Geus EJ, Schnack HG, van Haren NE, van Oel CJ, Kahn

55.

56.

57.

58.

59.

60.

61.

62.

63.

64.

65.

66.

67.

68.

69.

165 RS. Quantitative genetic modeling of variation in human brain morphology. Cereb Cortex 2001; 11(9):816–824. Posthuma D, De Geus EJ, Neale MC, Hulshoff Pol HE, Baare WF, Kahn RS, Boomsma DI. Multivariate genetic analysis of brain structure in an extended twin design. Behav Genet 2000; 30:311–319. Wright-I-C, Sham P, Murray RM, Weinberger DR, Ballmore ET. Genetic contributions to regional variability in human brain structure: methods and preliminary results. Neroimage 2002; 17(1):256–271. Hulshoff Pol HE, Schnack HG, Posthuma D, Mandl RC, Baare WF, van OC, van Haren NE, Collins DL, Evans AC, Amunts K, Burgel U, Zilles K, de GE, Boomsma DI, Kahn RS. Genetic contributions to human brain morphology and intelligence. J Neurosci 2006; 26(40):10235–10242. Lenroot RK, Schmitt JE, Ordaz SJ, Wallace GL, Neale MC, Lerch JP, Kendler KS, Evans AC, Giedd JN. Differences in genetic and environmental influences on the human cerebral cortex associated with development during childhood and adolescence. Hum Brain Mapp 2009;30(1): 163–174. Thompson PM, Cannon TD, Narr KL, van ET, Poutanen VP, Huttunen M, Lonnqvist J, Standertskjold-Nordenstam CG, Kaprio J, Khaledy M, Dail R, Zoumalan CI, Toga AW. Genetic influences on brain structure. Nat Neurosci 2001; 4:1253–1258. Kates WR, Burnette CP, Eliez S, Strunge LA, Kaplan D, Landa R, Reiss AL, Pearlson GD. Neuroanatomic variation in monozygotic twin pairs discordant for the narrow phenotype for autism. Am J Psychiat 2004; 161(3):539–546. Schmitt JE, Wallace GL, Rosenthal MA, Molloy EA, Ordaz S, Lenroot R, Clasen LS, Blumenthal JD, Kendler KS, Neale MC, Giedd JN. A multivariate analysis of neuroanatomic relationships in a genetically informative pediatric sample. Neuroimage 2007; 35(1):70–82. Rakic P. A small step for the cell, a giant leap for mankind: a hypothesis of neocortical expansion during evolution. Trends Neurosci 1995; 18:383–388. Dawson G, Munson J, Webb SJ, Nalty T, Abbott R, Toth K. Rate of head growth decelerates and symptoms worsen in the second year of life in autism. Biol Psychiat 2007; 61(4):458–464. Webb SJ, Nalty T, Munson J, Brock C, Abbott R, Dawson G. Rate of head circumference growth as a function of autism diagnosis and history of autistic regression. J Child Neurol 2007; 22:1182–1190. Hazlett HC, Poe M, Gerig G, Smith RG, Provenzale J, Ross A, Gilmore J, Piven J. Magnetic resonance imaging and head circumference study of brain size in autism: birth through age 2 years. Arch Gen Psychiat 2005; 62(12):1366–1376. Lainhart JE, Piven J, Wzorek M, Landa R, Santangelo SL, Coon H, Folstein SE. Macrocephaly in children and adults with autism. J Am Acad Child Adol Psychiat 1997; 36(2):282–290. Aylward EH, Minshew NJ, Field K, Sparks BF, Singh N. Effects of age on brain volume and head circumference in autism. Neurology 2002; 59:175–183. Stevenson RE, Schroer RJ, Skinner C, Fender D, Simensen RJ. Autism and macrocephaly. Lancet 1997; 349(9067): 1744–1745. Fidler DJ, Bailey JN, Smalley SL. Macrocephaly in autism and other pervasive developmental disorders. Dev Med Child Neurol 2000; 42:737–740.

166 70. Lainhart JE, Bigler ED, Bocian M, Coon H, Dinh E, Dawson G, Deutsch CK, Dunn M, Estes A, Tager-Flusberg H, Folstein S, Hepburn S, Hyman S, McMahon W, Minshew N, Munson J, Osann K, Ozonoff S, Rodier P, Rogers S, Sigman M, Spence MA, Stodgell CJ, Volkmar F. Head circumference and height in autism: a study by the collaborative program of excellence in autism. Am J Med Genet A 2006; 140:2257–2274. 71. Elder LM, Dawson G, Toth K, Fein D, Munson J. Head circumference as an early predictor of autism symptoms in younger siblings of children with autism spectrum disorder. J Autism Dev Disord 2008; 38:1104–1111. 72. Conciatori M, Stodgell CJ, Hyman SL, O’Bara M, Militerni R, Bravaccio C, Trillo S, Montecchi F, Schneider C, Melmed R, Elia M, Crawford L, Spence SJ, Muscarella L, Guarnieri V, D’Agruma L, Quattrone A, Zelante L, Rabinowitz D, Pascucci T, Puglisi-Allegra S, Reichelt KL, Rodier PM, Persico AM. Association between the HOXA1 A218G polymorphism and increased head circumference in patients with autism. (References). Biol Psychiat 2004; 55:413–419. 73. Sacco R, Papaleo V, Hager J, Rousseau F, Moessner R, Militerni R, Bravaccio C, Trillo S, Schneider C, Melmed R, Elia M, Curatolo P, Manzi B, Pascucci T, Puglisi-Allegra S, Reichelt KL, Persico AM. Case-control and familybased association studies of candidate genes in autistic disorder and its endophenotypes: TPH2 and GLO1. BMC Med Genet 2007; 8:11. 74. Muscarella LA, Guarnieri V, Sacco R, Militerni R, Bravaccio C, Trillo S, Schneider C, Melmed R, Elia M, Mascia ML, Rucci E, Piemontese MR, D’Agruma L, Persico AM. HOXA1 gene variants influence head growth rates in humans. Am J Med Genet B Neuropsychiat Genet 2007; 144B:388–390. 75. Sacco R, Militerni R, Frolli A, Bravaccio C, Gritti A, Elia M, Curatolo P, Manzi B, Trillo S, Lenti C, Saccani M, Schneider C, Melmed R, Reichelt KL, Pascucci T, PuglisiAllegra S, Persico AM. Clinical, morphological, and biochemical correlates of head circumference in autism. Biol Psychiat 2007; 62:1038–1047. 76. Mraz KD, Green J, Dumont-Mathieu T, Makin S, Fein D. Correlates of head circumference growth in infants later diagnosed with autism spectrum disorders. J Child Neurol 2007; 22:700–713. 77. Hardan AY, Girgis RR, Adams J, Gilbert AR, Keshavan MS, Minshew NJ. Abnormal brain size effect on the thalamus in autism. Psychiat Res 2006; 147(2–3):145–151. 78. Waiter GD, Williams JH, Murray AD, Gilchrist A, Perrett DI, Whiten A. A voxel-based investigation of brain structure in male adolescents with autistic spectrum disorder. Neuroimage 2004; 22(2):619–625. 79. McAlonan GM, Cheung V, Cheung C, Suckling J, Lam GY, Tai KS, Yip L, Murphy DG, Chua SE. Mapping the brain in autism. A voxel-based MRI study of volumetric differences and intercorrelations in autism. Brain 2005; 128(Pt 2):268–276. 80. Hardan AY, Muddasani S, Vemulapalli M, Keshavan MS, Minshew NJ. An MRI study of increased cortical thickness in autism. Am J Psychiat 2006; 163(7):1290–1292. 81. Courchesne E, Karns CM, Davis HR, Ziccardi R, Carper RA, Tigue ZD, Chisum HJ, Moses P, Pierce K, Lord C,

A. Raznahan et al.

82.

83.

84.

85.

86.

87.

88.

89.

90.

91.

92.

93.

94.

Lincoln AJ, Pizzo S, Schreibman L, Haas RH, Akshoomoff NA, Courchesne RY. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology 2001; 57:245–254. Lotspeich LJ, Kwon H, Schumann CM, Fryer SL, GoodlinJones BL, Buonocore MH, Lammers CR, Amaral DG, Reiss AL. Investigation of neuroanatomical differences between autism and Asperger syndrome. Arch Gen Psychiat 2004; 61:291–298. Palmen SJ, Hulshoff Pol HE, Kemner C, Schnack HG, Durston S, Lahuis BE, Kahn RS, van EH. Increased graymatter volume in medication-naive high-functioning children with autism spectrum disorder. Psychol Med 2005; 35(4):561–570. Herbert MR, Ziegler DA, Deutsch CK, O’Brien LM, Lange N, Bakardjiev A, Hodgson J, Adrien KT, Steele S, Makris N, Kennedy D, Harris GJ, Caviness VS, Jr. Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys. Brain 2003; 126 (Pt 5):1182–1192. Palmen SJMC. Brain anatomy in non-affected parents of autistic probands: a MRI study. Psychol Med 2005; 35:1411–1420. Peterson E, Schmidt GL, Tregellas JR, Winterrowd E, Kopelioff L, Hepburn S, Reite M, Rojas DC. A voxel-based morphometry study of gray matter in parents of children with autism. Neuroreport 2006; 17(12):1289–1292. Rojas DC, Smith JA, Benkers TL, Camou SL, Reite ML, Rogers SJ. Hippocampus and amygdala volumes in parents of children with autistic disorder. Am J Psychiat 2004; 161(11):2038–2044. Wassink TH, Hazlett HC, Epping EA, Arndt S, Dager SR, Schellenberg GD, Dawson G, Piven J. Cerebral cortical gray matter overgrowth and functional variation of the serotonin transporter gene in autism. Arch Gen Psychiat 2007; 64:709–717. Sparks BF, Friedman SD, Shaw DW, Aylward EH, Echelard D, Artru AA, Maravilla KR, Giedd JN, Munson J, Dawson G, Dager SR. Brain structural abnormalities in young children with autism spectrum disorder. Neurology 2002; 59(2):184–192. Davis LK, Hazlett HC, Librant AL, Nopoulos P, Sheffield VC, Piven J, Wassink TH. Cortical enlargement in autism is associated with a functional VNTR in the monoamine oxidase A gene. Am J Med Genet B Neuropsychiat Genet 2008; 147B:1145–1151. Sabol SZ, Hu S, Hamer D. A functional polymorphism in the monoamine oxidase A gene promoter. Hum Genet 1998; 103:273–279. Boylan CB, Blue ME, Hohmann CF. Modeling early cortical serotonergic deficits in autism. Behav Brain Res 2007; 176:94–108. Shaw P, Gornick M, Lerch J, Addington A, Seal J, Greenstein D, Sharp W, Evans A, Giedd JN, Castellanos FX, Rapoport JL. Polymorphisms of the dopamine d4 receptor, clinical outcome, and cortical structure in attention-deficit/hyperactivity disorder. Arch Gen Psychiat 2007; 64:921–931. Phelps EA, LeDoux JE. Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron 2005; 48:175–187.

24

Neurostructural Endophenotypes In Autism Spectrum Disorder

95. Schultz RT. Developmental deficits in social perception in autism: the role of the amygdala and fusiform face area. Int J Dev Neurosci 2005 May; 23(2–3):125–141. 96. Haznedar MM, Buchsbaum MS, Wei TC, Hof PR, Cartwright C, Bienstock CA, Hollander E. Limbic circuitry in patients with autism spectrum disorders studied with positron emission tomography and magnetic resonance imaging. Am J Psychiat 2001; 157:1994–2001. 97. Howard MA, Cowell PE, Boucher J, Broks P, Mayes A, Farrant A, Roberts N. Convergent neuroanatomical and behavioural evidence of an amygdala hypothesis of autism. Neuroreport 2000; 11(13):2931–2935. 98. Schumann CM, Hamstra J, Goodlin-Jones BL, Lotspeich LJ, Kwon H, Buonocore MH, Lammers CR, Reiss AL, Amaral DG. The amygdala is enlarged in children but not adolescents with autism; the hippocampus is enlarged at all ages. J Neurosci 2004; 24(28):6392–6401. 99. Nacewicz BM, Dalton KM, Johnstone T, Long MT, McAuliff EM, Oakes TR, Alexander AL, Davidson RJ. Amygdala volume and nonverbal social impairment in adolescent and adult males with autism. Arch Gen Psychiat 2006; 63:1417–1428. 100. Palmen SJMC, Durston S, Nederveen H, Van England H. No evidence for preferential involvement of medial temporal lobe structures in high-functioning autism. Psychol Med 2006; 36:827–834. 101. Munson J, Dawson G, Abbott R, Faja S, Webb SJ, Friedman SD, Shaw D, Artru A, Dager SR. Amygdalar volume and behavioral development in autism. Arch Gen Psychiat 2006; 63(6):686–693. 102. Dalton KM, Nacewicz BM, Alexander AL, Davidson RJ. Gaze-fixation, brain activation, and amygdala volume in unaffected siblings of individuals with autism. Biol Psychiat 2007; 61:512–520. 103. Schmahmann JD, Weilburg JB, Sherman JC. The neuropsychiatry of the cerebellum – insights from the clinic. Cerebellum 2007; 6:254–267. 104. Riva D, Giorgi C. The cerebellum contributes to higher functions during development: evidence from a series of children surgically treated for posterior fossa tumours. Brain 2000; 123 (Pt 5):1051–1061. 105. Desmond JE, Chen SH, DeRosa E, Pryor MR, Pfefferbaum A, Sullivan EV. Increased frontocerebellar activation in alcoholics during verbal working memory: an fMRI study. Neuroimage 2003; 19:1510–1520. 106. Middleton FA, Strick PL. Cerebellar projections to the prefrontal cortex of the primate. J Neurosci 2001; 21: 700–712. 107. MacLeod CE, Zilles K, Schleicher A, Rilling JK, Gibson KR. Expansion of the neocerebellum in Hominoidea. J Hum Evol 2003; 44:401–429. 108. Courchesne E, Hesselink JR, Jernigan TL, YeungCourchesne R. Abnormal neuroanatomy in a nonretarded person with autism. Unusual findings with magnetic resonance imaging. Arch Neurol 1987; 44(3):335–341. 109. Hardan AY, Minshew NJ, Harenski K, Keshavan MS. Posterior fossa magnetic resonance imaging in autism. J Am Acad Child Adol Psychiat 2001; 40(6):666–672. 110. Cleavinger HB, Bigler ED, Johnson JL, Lu J, McMahon W, Lainhart JE. Quantitative magnetic resonance image analysis of the cerebellum in macrocephalic

111.

112.

113.

114. 115.

116.

117.

118.

119.

120.

121. 122.

123.

124. 125.

126.

127.

167 and normocephalic children and adults with autism. J Int Neuropsychol Soc 2008; 14:401–413. Kato T. Genetic and epigenic differences causing discordance between monozygotic twins as clue to molecular basis of mental disorders. Mol Psychiat 2005; 10:622–630. Aron AR, Durston S, Eagle DM, Logan GD, Stinear CM, Stuphorn V. Converging evidence for a fronto-basal-ganglia network for inhibitory control of action and cognition. J Neurosci 2007; 27:11860–11864. Balleine BW, Delgado MR, Hikosaka O. The role of the dorsal striatum in reward and decision-making. J Neurosci 2007; 27:8161–8165. Graybiel AM. Habits, rituals, and the evaluative brain. Ann Rev Neurosci 2008; 31:359–387. Hardan AY, Kilpatrick M, Keshavan MS, Minshew NJ. Motor performance and anatomic magnetic resonance imaging (MRI) of the basal ganglia in autism. J Child Neurol 2003; 18(5):317–324. Sears LL, Vest C, Mohamed S, Bailey J, Ranson BJ, Piven J. An MRI study of the basal ganglia in autism. Prog NeuroPsychopharmacol Biol Psychiat 1999; 23(4):613–624. Voelbel GT, Bates ME, Buckman JF, Pandina G, Hendren RL. Caudate nucleus volume and cognitive performance: are they related in childhood psychopathology? Biol Psychiat 2006; 60:942–950. Langen M, Durston S, Staal WG, Palmen SJ, van EH. Caudate nucleus is enlarged in high-functioning medication-naive subjects with autism. Biol Psychiat 2007; 62:262–266. Hollander E, Anagnostou E, Chaplin W, Esposito K, Haznedar MM, Licalzi E, Wasserman S, Soorya L, Buchsbaum M. Striatal volume on magnetic resonance imaging and repetitive behaviors in autism. Biol Psychiat 2005; 58:226–232. Geschwind DH, Levitt P. Autism spectrum disorders: developmental disconnection syndromes. Curr Opin Neurobiol 2007; 17:103–111. Egaas B, Courchesne E, Saitoh O. Reduced size of corpus callosum in autism. Arch Neurol 1995; 52(8):794–801. Elia M, Ferri R, Musumeci SA, Panerai S, Bottitta M, Scuderi C. Clinical correlates of brain morphometric features of subjects with low-functioning autistic disorder. J Child Neurol 2000; 15(8):504–508. Gaffney GR, Kuperman S, Tsai LY, Minchin S, Hassanein KM. Midsagittal magnetic resonance imaging of autism. Br J Psychiat 1987; 151:831–833. Hardan AY, Minshew NJ, Keshavan MS. Corpus callosum size in autism. Neurology 2000; 55:1033–1036. Vidal CN, Nicolson R, DeVito TJ, Hayashi KM, Geaga JA, Drost DJ, Williamson PC, Rajakumar N, Sui Y, Dutton RA, Toga AW, Thompson PM. Mapping corpus callosum deficits in autism: an index of aberrant cortical connectivity. Biol Psychiat 2006; 60:218–225. Boger-Megiddo I, Shaw DW, Friedman SD, Sparks BF, Artru AA, Giedd JN, Dawson G, Dager SR. Corpus callosum morphometrics in young children with autism spectrum disorder. J Autism Dev Disord 2006; 36(6):733–739. Kilian S, Brown WS, Hallam BJ, McMahon W, Lu J, Johnson M, Bigler ED, Lainhart J. Regional callosal morphology in autism and macrocephaly. Dev Neuropsychol 2008; 33:74–99.

168 128. Witelson SF. Hand and sex differences in the isthmus and genu of the human corpus callosum. A postmortem morphological study. Brain 1989; 112(Pt 3):799–835. 129. Piven J, Bailey J, Ranson BJ, Arndt S. An MRI study of the corpus callosum in autism. Am J Psychiat 1997; 154(8):1051–1056. 130. Just MA, Cherkassky VL, Keller TA, Kana RK, Minshew NJ. Functional and anatomical cortical underconnectivity in autism: evidence from an FMRI study of an executive function task and corpus callosum morphometry. Cereb Cortex 2007; 17:951–961. 131. Sowell ER, Peterson BS, Kan E, Woods RP, Yoshii J, Bansal R, Xu D, Zhu H, Thompson PM, Toga AW. Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. Cereb Cortex 2007; 17:1550–1560. 132. Herve PY, Crivello F, Perchey G, Mazoyer B, TzourioMazoyer N. Handedness and cerebral anatomical asymmetries in young adult males. Neuroimage 2006; 29: 1066–1079. 133. Craig MC, Zaman SH, Daly EM, Cutter WJ, Robertson DMW, Hallahan B, Toal F, Reed S, Ambikapathy A, Brammer M, Murphy CM, Murphy DGM. Women with autistic-spectrum disorder: magnetic resonance imaging study of brain anatomy. Br J Psychiat 2007; 191:224–228. 134. Szatmari P, Zwaigenbaum L, Bryson S. Conducting genetic epidemiology studies of autism spectrum disorders: issues in matching. J Autism Dev Disord 2004; 34:49–57. 135. Simonoff E, Pickles A, Charman T, Chandler S, Loucas T, Baird G. Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample. J Am Acad Child Adol Psychiat 2008; 47:921–929. 136. Carper RA, Moses P, Tigue ZD, Courchesne E. Cerebral lobes in autism: early hyperplasia and abnormal age effects. Neuroimage 2002; 16(4):1038–1051. 137. Raznahan A, Toro R, Dale E, Paus T, Bolton PF, Murphy D. Cortical Dysmaturation in Autism Spectrum Disorder. Oral presentation at 7th annual meeting for autism research london. 2008. Ref Type: Internet Communication. 138. Giedd JN, Lenroot RK, Shaw P, Lalonde F, Celano M, White S, Tossell J, Addington A, Gogtay N. Trajectories of anatomic brain development as a phenotype. Novartis Found Symp 2008; 289:101–112. 139. Knickmeyer RC, Wheelwright S, Hoekstra R, Baron-Cohen S. Age of menarche in females with autism spectrum conditions. Dev Med Child Neurol 2006; 48:1007–1008. 140. Rojas DC, Peterson E, Winterrowd E, Reite ML, Rogers SJ, Tregellas JR. Regional gray matter volumetric changes in autism associated with social and repetitive behavior symptoms. BMC Psychiat 2006; 6:56. 141. Murphy M, Bolton PF, Pickles A, Fombonne E, Piven J, Rutter M. Personality traits of the relatives of autistic probands. Psychol Med 2000; 30(6):1411–1424. 142. Pickles A, Starr E, Kazak S, Bolton P, Papanikolaou K, Bailey A, Goodman R, Rutter M. Variable expression of the autism broader phenotype: findings from extended pedigrees. J Child Psychol Psychiat Allied Disciplines 2000; 41(4):491–502.

A. Raznahan et al. 143. Losh M, Childress D, Lam K, Piven J. Defining key features of the broad autism phenotype: a comparison across parents of multiple- and single-incidence autism families. Am J Med Genet B Neuropsychiat Genet 2008; 147B:424–433. 144. Hall MH, Schulze K, Sham P, Kalidindi S, McDonald C, Bramon E, Levy DL, Murray RM, Rijsdijk F. Further evidence for shared genetic effects between psychotic bipolar disorder and P50 suppression: a combined twin and family study. Am J Med Genet B Neuropsychiat Genet 2008;147B(5):619–627. 145. Pezawas L, Meyer-Lindenberg A, Goldman AL, Verchinski BA, Chen G, Kolachana BS, Egan MF, Mattay VS, Hariri AR, Weinberger DR. MET BDNF protects against morphological S allele effects of 5-HTTLPR. Mol Psychiat 2008; 13:654. 146. Hadjikhani N, Joseph R, Snyder J, Tager-Flusberg H. Anatomical differences in the mirror neuron system and social cognition network in autism. Cereb Cortex 2006; 16(9):1276–1282. Epub 2005. 147. Nordahl CW, Dierker D, Mostafavi I, Schumann CM, Rivera SM, Amaral DG, van Essen DC. Cortical folding abnormalities in autism revealed by surface-based morphometry. J Neurosci 2007; 27:11725–11735. 148. Levitt JG, Blanton RE, Smalley S, Thompson PM, Guthrie D, McCracken JT, Sadoun T, Heinichen L, Toga AW. Cortical sulcal maps in autism. Cereb Cortex 2003; 13:728–735. 149. Vidal CN, Nicolson R, Boire JY, Barra V, DeVito TJ, Hayashi KM, Geaga JA, Drost DJ, Williamson PC, Rajakumar N, Toga AW, Thompson PM. Three-dimensional mapping of the lateral ventricles in autism. Psychiat Res 2008; 163:106–115. 150. Rubenstein JL, Rakic P. Genetic control of cortical development. (Review) (40 refs). Cereb Cortex 1999; 9(6):521–523. 151. Chenn A, Walsh CA. Regulation of cerebral cortical size by control of cell cycle exit in neural precursors. Science 2002; 297:365–369. 152. Armstrong E, Schleicher A, Omran H, Curtis M, Zilles K. The ontogeny of human gyrification. Cereb Cortex 1995; 5:56–63. 153. Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW. Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci 24(38):8223–8231, 2004. 154. Huttenlocher PR. Morphometric study of human cerebral cortex development. Neuropsychol 1990; 28(6):517–527. 155. Thompson PM, Lee AD, Dutton RA, Geaga JA, Hayashi KM, Eckert MA, Bellugi U, Galaburda AM, Korenberg JR, Mills DL, Toga AW, Reiss AL. Abnormal cortical complexity and thickness profiles mapped in Williams syndrome. J Neurosci 2005; 25:4146–4158. 156. Shaw P, Eckstrand K, Sharp W, Blumenthal J, Lerch JP, Greenstein D, Clasen L, Evans A, Giedd J, Rapoport JL. Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proc Natl Acad Sci USA 2007; 104:19649–19654. 157. Zwaigenbaum L, Thurm A, Stone W, Baranek G, Bryson S, Iverson J, Kau A, Klin A, Lord C, Landa R, Rogers S,

24

158.

159.

160.

161.

Neurostructural Endophenotypes In Autism Spectrum Disorder Sigman M. Studying the emergence of autism spectrum disorders in high-risk infants: methodological and practical issues. J Autism Dev Disord 2007; 37(3):466–480. Osterling J, Dawson G. Early recognition of children with autism: a study of first birthday home videotapes. J Autism Dev Disord 1994; 24:247–257. Pickles A. Latent-class analysis of recurrence risk for complex phenotypes with selection and measurement error: a twin and family history study of autism. Am J Hum Genet 1995; 57:717–726. Abrahams BS, Geschwind DH. Advances in autism genetics: on the threshold of a new neurobiology. Nat Rev Genet 2008; 9:341–355. Moessner R, Marshall CR, Sutcliffe JS, Skaug J, Pinto D, Vincent J, Zwaigenbaum L, Fernandez B, Roberts W, Szatmari P, Scherer SW. Contribution of SHANK3 mutations to autism spectrum disorder. Am J Hum Genet 2007; 81:1289–1297.

169

162. Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, Walsh T, Yamrom B, Yoon S, Krasnitz A, Kendall J, Leotta A, Pai D, Zhang R, Lee YH, Hicks J, Spence SJ, Lee AT, Puura K, Lehtimaki T, Ledbetter D, Gregersen PK, Bregman J, Sutcliffe JS, Jobanputra V, Chung W et al. Strong association of de novo copy number mutations with autism. Science 2007;316:445–449. 163. O’Callaghan FJ, Shiell AW, Osborne JP, Martyn CN. Prevalence of tuberous sclerosis estimated by capturerecapture analysis. Lancet 1998;351:1490. 164. Roach ES, Gomez MR, Northrup H. Tuberous sclerosis complex consensus conference: revised clinical diagnostic criteria. J Child Neurol 1998;13(12):624–628. 165. Raznahan A, Pugliese L, Barker G, Daly E, Powell J, Bolton P, Murphy DGM. Serotonin transporter genotype and Neuroanatomy in autism spectrum disorders. Psychiat gen, 2008 (in press).

Chapter 25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan Nick C. Patel, Michael A. Cerullo, David E. Fleck, Jayasree J. Nandagopal, Caleb M. Adler, Stephen M. Strakowski, and Melissa P. DelBello

Abstract Bipolar disorder, characterized by recurrent episodes of mania and commonly depression, is a debilitating mental illness that affects millions of children, adolescents, and adults worldwide. Individuals with bipolar disorder often exhibit symptomatology found in other psychiatric disorders, which may lead to misdiagnosis. Approximately half of bipolar patients respond to monotherapy of any single agent and often combinations of medications are necessary in order to achieve optimal mood stabilization. Therefore, strategies to improve the accuracy of diagnosis and selection of appropriate treatment modalities are critical to improve the outcome for individuals with bipolar disorder. One such strategy is the identification of biomarkers through the use of neuroimaging techniques, including structural neuroimaging, diffusion tension imaging, functional magnetic resonance imaging, and magnetic resonance spectroscopy. Neuroimaging studies in bipolar disorder have furthered our understanding of the neuropathophysiology of the illness across the lifespan as well as the neurochemical effects of medications commonly used for mood stabilization. In this chapter, we review the existing neuroimaging literature, focusing on anatomical, functional, and biochemical abnormalities observed in individuals with bipolar disorder that may serve as

N. C. Patel Lifesynch; Department of Psychiatry & Health Behavior, Medical College of Georgia M. A. Cerullo, D. E. Fleck, J. J. Nandagopal, C. M. Adler, S. M. Strakowski, and M. P. DelBello Division of Bipolar Disorders Research, Department of Psychiatry, University of Cincinnati.

biomarkers for the illness and treatment response. Future neuroimaging research in bipolar disorder should aim to address current methodological limitations and identify reliable biomarkers that may lead to improved diagnostic accuracy and early, targeted treatment interventions in order to improve patient outcome. Keywords Bipolar disorder • structural neuroimaging • diffusion tension imaging • functional magnetic resonance imaging • magnetic resonance spectroscopy • mood stabilizers • antipsychotics • children • adolescents • adults Abbreviations 1H: Proton; 7Li: Lithium; 13C: Carbon; 19 F: Fluorine; 23Na: Sodium; 31P: Phosphorus; ADHD: Attention-deficit hyperactivity disorder; ATP: α-, β-, and γ-adenine triphosphate; BOLD: Blood-oxygen-level dependent; CDRS: Children’s Depression Rating Scale; Cho: Choline; Cr: Creatine/phosphocreatine; DTI: Diffusion tension imaging; FA: Fractional anisotropy; fMRI: Functional magnetic resonance imaging; GABA: γ-aminobutyric acid; Glx: Glutamate/glutamine/ γ-aminobutyric acid; IMPase: Inositol monophosphatase; MDD: Major depressive disorder; mI: Myoinositol; MRI: Magnetic resonance imaging; MRS: Magnetic resonance spectroscopy; NAA: N-acetyl aspartate; PCr: Phosphocreatine; PDE: Phosphodiester compounds; PET: Positron emission tomography; Pi: Inorganic phosphate; PME: Phosphomonoester compounds; ROI: Region of interest; sMRI: Structural magnetic resonance imaging; SPECT: Single photon emission computed tomography; TADC: Trace apparent diffusion coefficient; VBM: Voxel-based morphometry; WMH: White matter hyperintensities

M.S. Ritsner (ed.), The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, © Springer Science + Business Media B.V. 2009

171

172

Introduction Bipolar disorder is a chronic, debilitating psychiatric disorder characterized by mood dysregulation as well as alterations in sleep, energy level, thought content and processes, and impulse control.1 Bipolar disorder is associated with significant morbidity and mortality, including high rates of substance abuse or dependence2,3 and increased risk of suicide.4 The lifetime prevalence of bipolar I disorder is estimated at 1.5%,5 and bipolar disorder is among the leading causes of disability worldwide.6 In the US, lifetime costs of bipolar disorder, including direct and indirect costs, are estimated at 24–45 billion US dollars,7 which represents a substantial economic burden. Accurate diagnosis and adequate treatment are among the unmet clinical needs for individuals with bipolar disorder. Surveys of National Depressive and Manic-Depressive Association members with bipolar disorder suggest that a large majority of these patients were misdiagnosed.8,9 Thirty-five percent received treatment for as long as 10 years before they were diagnosed with bipolar disorder.8 Misdiagnosis may lead to the use of ineffective treatment modalities, which subsequently may increase the morbidity and mortality associated with the illness. Without proper mood stabilization, patients with bipolar disorder are at greater risk of recurrence, poor psychosocial functioning, and suicide.10 Moreover, when antidepressants are used for bipolar patients who have been misdiagnosed with major depressive disorder (MDD), potential consequences include the induction of mania and cycle acceleration.11 Although there has been growth in the number of medications available for the treatment of bipolar disorder, achieving optimal mood stabilization remains challenging. Monotherapy is desired to achieve and maintain remission, but approximately half of patients with bipolar disorder fail to respond to a single agent. In fact, patients often have a large number of treatment trials before an effective medication is found. Often times, multiple medications are necessary to achieve adequate mood stabilization.12–14 The use of combination treatment, however, may not be sufficient as almost half the patients with bipolar disorder who experience recovery will subsequently relapse despite guideline-based treatment, including polypharmacy.15 Bipolar disorder in children and adolescents may be difficult to diagnose due to the high rates of co-occurring

N.C. Patel et al.

psychiatric disorders and varying clinical presentation. Specifically, youth with bipolar disorder commonly present with mixed episodes, rapid cycling, irritability, and impulsivity1,16 Furthermore, psychiatric disorders, such as attention-deficit hyperactivity (ADHD) and anxiety disorders, co-occur at high rates in children and adolescents with bipolar disorder, which complicates the diagnosis due to overlap in symptomatology.16 Similar to that seen in adults, misdiagnosis may lead to inappropriate treatment, which may worsen the course of illness in a child or adolescent.17,18 Treatment of bipolar disorder in children and adolescents often requires administration of multiple psychotropic medications.14 The use of polypharmacy in children and adolescents with bipolar disorder may be attributed to their phenotypic resemblance to severely ill adults, as well as the high rates of co-occurring psychiatric disorders. For example, stimulants are often prescribed in bipolar youths due to co-occurring ADHD.14 These medications, independent of ADHD, may lead to potential worsening of the course of illness.17,19 Strategies to improve the accuracy of diagnosis and treatment outcomes in bipolar disorder across the age spectrum are clearly needed. One such strategy is the identification of biomarkers through magnetic resonance imaging (MRI) techniques. Structural magnetic resonance imaging (sMRI) evaluates structural brain alternation in patients with bipolar disorder, while functional magnetic resonance imaging (fMRI) compares the relative activation of brain regions during cognitive and affective tasks compared with rest. Magnetic resonance spectroscopy (MRS) provides in vivo neurochemical information in localized regions of the brain in patients with bipolar disorder. Although these applications have advanced our understanding of the pathophysiology and treatment of bipolar disorder, neuroimaging currently is not used for diagnostic and treatment purposes. However, the applications do hold promise to identify reliable and valid biomarkers. Certain structural, functional, or neurochemical abnormalities in the brains of individuals with bipolar disorder may potentially assist clinicians to diagnose bipolar disorder more accurately and efficiently, allowing the administration of appropriate mood-stabilizing treatment. Neuroimaging biomarkers may also facilitate the sensitive and specific prediction of which genetically predisposed individuals will develop symptoms of bipolar disorder, allowing the possibility of early intervention. Finally, neuroimaging biomarkers may determine which patients will

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

173

Structural MRI involves a variety of non-invasive methods that do not utilize ionizing radiation. Morphometric analysis of the brain are typically performed either by mapping specific regions of interest (ROIs) and calculating the volume enclosed or by using voxel-based morphometry (VBM), an automated technique in which the entire brain is examined and volume is compared across brains at every voxel (Fig. 25.1). The ROI technique can determine differences in small structures

between groups, but is time-consuming and restricts assessments to prespecified regions. Advances in MRI technology have made VBM techniques more common. VBM can identify potential large-scale differences, but may miss subtle differences in smaller structures.20 While ROI analysis and VBM are the state-of-the-art image analysis techniques to examine morphometrics, newer approaches such as cortical thickness analysis and deformation based morphometry are being developed. Diffusion tensor imaging (DTI) is a MRI technique that enables the measurement of the restricted diffusion of water in brain tissue (Fig. 25.1). Its principal application is in the imaging of white matter where the location, orientation, and anisotropy (measure of directionality) of the tracts can be measured. Commonly used DTI measures are fractional anisotropy (FA) and trace apparent diffusion coefficient (TADC). FA represents the degree of anisotropic diffusion of water. Water diffusion in white matter is much higher parallel to the fiber axis than perpendicular to it. Anisotropy within a given white matter voxel is determined mostly by structural features of the tissue, such as fiber diameter and density, degree of myelination and intravoxel fiber-tract coherence. Changes in these features due to disease states can be detected as changes in water diffusion. Lower FA may indicate a loss of axonal integrity or a partial loss of axonal organization, as FA is sensitive to any directional changes in diffusion. TADC represents the mean intravoxel diffusivity of water molecules. Increases in TADC have been associated with axonal demyelination, axonal damage, and localized edema.21

Fig. 25.1 (a) Voxel-based morphometry and (b) diffusion tensor imaging. (a) Voxel-by-voxel comparison map of bipolar (N = 32) versus control (N = 27) subjects, overlaid on a T1-weighted anatomic image. Statistically significant differences in gray mat-

ter volume were defined as p < 0.001, with a cluster size of 200. (b) Representative axial section of a diffusion map obtained using diffusion-weighted spin-echo echo-planar magnetic resonance

respond best to specific medication regimens, allowing an increased likelihood of response and remission and positive impact on course of illness. Such biomarkers may also be useful to identify those patients in which medications may worsen the course of illness, such as antidepressants and stimulants, and should be avoided. In this chapter, we review the existing neuroimaging literature in bipolar disorder, focusing on structural, functional, and biochemical abnormalities observed in youths and adults that may possibly serve as biomarkers for the illness and for treatment response. The studies reviewed were designed to identify differences between groups of patients with bipolar disorder and healthy controls. Results from these investigations provide clues as to potential biomarkers, but are not definitive. Additional research is necessary to test the respective reliability and validity of a biomarker.

Structural Magnetic Resonance Imaging (sMRI)

174

Several factors need to be considered when attempting to differentiate patients with mood disorders on the basis of sMRI. Adult samples are commonly confounded by the effects of illness duration, number of affective episodes, prior psychotropic medication use, long-term substance use, and presence of neuromedical comorbidities with advancing age. Structural MRI and other neuroimaging studies of pediatric and adolescent samples are very useful as these studies are comparatively free of these confounding variables. An understanding of these and other confounding factors, such as illness severity and mood state, that may affect anatomical neuroimaging techniques in bipolar disorder is essential to be able to determine whether anatomical findings may be useful as biomarkers. The neural substrates of bipolar disorder are still incompletely defined. However, building upon computerized tomography, sMRI provides a detailed in vivo examination of neuroanatomy that is beginning to differentiate patients with bipolar disorder from other psychiatric patient groups and from healthy subjects. Structural MRI studies have been useful not only in defining the neural substrates of bipolar disorder, but also in guiding functional and neurochemical studies.

Adult Studies One of the most replicated anatomic abnormalities in the bipolar literature is a greater incidence of T2-signal hyperintensities in deep white matter tracts relative to healthy and psychiatric comparison samples.22 These small lesions may occur more commonly in the frontal lobes, frontal/parietal junction, or periventricular regions and less commonly in subcortical gray matter,23–25 but such findings are not reported consistently.22,26 T2 hyperintensities are likely only secondarily related to bipolar disorder, with primary relations to cardiovascular risk factors, which are more common in patients with bipolar disorder, advancing age, and treatment. The etiology of T2 hyperintensities and their specificity to bipolar disorder has yet to be resolved. However, it has been speculated that these lesions adversely affect signaling between frontosubcortical and limbic structures, thereby influencing mood and cognition.25,26 This possibility is consistent with recent DTI studies implicating abnormalities in white matter tract composition (i.e., increased FA) in bipolar disorder.27

N.C. Patel et al.

In addition to qualitative lesion assessments, the segmentation of gray matter, white matter, and cerebral spinal fluid (defining the ventricular system) has been a common approach in MRI morphometric analyses. Based on this general approach, and post-mortem studies, one of the earliest and most replicated findings in the psychiatric literature is ventricular enlargement in schizophrenia.28 However, enlargement of the lateral and third ventricles are less consistently observed in bipolar disorder.25 Nonetheless, enlargement of the ventricles in bipolar disorder, when present, may represent underdevelopment or degeneration of periventricular structures.29 Consistent with findings in MDD,30–33 sMRI studies suggest that brain abnormalities in bipolar disorder are primarily regional, as global measures of total cerebral and gray and white matter volumes are frequently unaffected in adults.34 This contention is supported by meta-analyses indicating no total brain volume differences in bipolar disorder.35,36 Although abnormalities in cerebral brain regions are reported inconsistently in bipolar disorder, when present, they typically consist of decreased frontal or prefrontal cortical volumes.37 Such findings are consistent with histological studies demonstrating glial and neuronal cell loss in the prefrontal and anterior cingulate cortices.38 In particular, decreased volumes have been observed in cortical regions encompassing the ventrolateral and dorsolateral prefrontal cortices,29,39 which help regulate emotion and cognition. For instance, Sax et al.40 noted that decreased prefrontal volume correlated with decrements in performance on a continuous performance task. Drevets et al.41 specifically examined the subgenual prefrontal cortex and noted that not only was the volume decreased in bipolar patients, but the decrease was associated with decreased metabolic activity. LopezLarson et al.42 reported decreased left and right prefrontal gray matter volumes, suggesting that these abnormalities may be associated with mood instability and inattentiveness. However, McDonald et al.36 who examined the prefrontal and subgenual prefrontal cortices in a meta-analysis, found no volume differences in these brain regions, highlighting the inconsistency of findings in frontal brain regions. Cortical and medial temporal lobe structures may also be abnormal in patients with bipolar disorder.43,44 Specifically, Pearlson et al.43 noted enlargement of the anterior superior temporal gyrus, and other studies observed increased volume of medial temporal structures in patients with bipolar disorder, including the

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

amygdala.45,46 In fact, much of the prior research identifies volumetric changes in the amygdala.43,46,47 Strakowski et al.46 found bilaterally increased amygdala volume in bipolar subjects relative to healthy subjects, and Altshuler et al.47 found increased amygdala volumes in bipolar subjects relative to both schizophrenic and healthy subjects. Pearlson et al.43 reported that bipolar subjects had decreased volume in the left amygdala compared to healthy subjects and no volume differences compared to schizophrenic subjects. In contrast to the amygdala findings, and findings in MDD, there appears to be little change in hippocampal volume in bipolar disorder. Videbech and Ravnkilde48 combined data from six studies reporting hippocampal volumes in bipolar subjects relative to healthy subjects, only one of which showed changes in patients with bipolar disorder. No differences in hippocampal volume were identified in the resulting meta-analysis, consistent with a meta-analysis by McDonald et al.36 as well. Based on such findings, Strakowski et al.37 have suggested that affective illness may involve underdeveloped or atrophied prefrontal regions that lead to a loss of cortical modulation of emotional circuits within limbic networks. Several studies also note morphological differences between bipolar patients and healthy controls in the basal ganglia or portions of the basal ganglia,23,46 as well as the thalamus.46,49 Volumetric studies of the basal ganglia and thalamus have been mixed in bipolar disorder, with most showing no differences compared to healthy subjects.29,39,40,50,51 However, Strakowski et al.46 found bilateral increased striatal volume in bipolar subjects and Aylward et al.23 also found increased basal ganglia volume, but only in male subjects with bipolar disorder. Similarly, although certain investigators report no abnormalities in thalamic volume in bipolar disorder,29,40,50,52 Strakowski et al.46 and Dupont et al.53 found increased thalamic volumes. Finally, decreased cerebellar volume has also been reported.54–57 DelBello et al.55 compared patients with multiple prior manic episodes, a first manic episode, and healthy subjects. Those with prior manic episodes had decreased volume in vermal subregion V3 compared to the other two groups, suggesting a neurodegenerative effect. In a replication study, Mills et al.56 reported similar findings. Another study failed to identify cerebellar volumetric abnormalities. However, this study did not separate bipolar subjects by the number of prior affective episodes.54 The influence of medications used to treat patients with bipolar disorder can have a significant influence

175

on brain structure, complicating the interpretation of sMRI results. The effect of lithium on brain volume has received considerable attention in mood disorders research, relative to other mood stabilizers and antidepressants. An early study demonstrated that increases in T1-weighted signal values in frontal white matter normalized with lithium treatment.58 More recently, Brambilla et al.54 found no effect of lithium on total brain volume in subjects with bipolar disorder, however most extant studies suggest a positive effect. Moore et al.59 found increased total brain volume in subjects with bipolar disorder after 4 weeks of lithium treatment. Figueroa et al.60 found that lithium may prevent volume loss in the dorsolateral prefrontal cortex in subjects with bipolar disorder and Drevets61 found that subjects with bipolar disorder taking either lithium or valproic acid did not exhibit the reductions in subgenual prefrontal cortex volume seen in those not taking these medications. Therefore, sMRI appears to be sensitive to the treatment effects of lithium and possibly, other psychotropic agents and may be useful to predict treatment outcome and assign treatment in the future.62

Child and Adolescent Studies Results from studies of children and adolescents with bipolar disorder investigating white matter hyperintensities (WMH) have been somewhat inconsistent. Botteron et al.63 reported subcortical WMH in an adolescent girl during her first manic episode. The same group found subcortical WMH in 25% of manic children and adolescents and 0% of healthy controls. Although the difference did not reach statistical significance, the rate of WMH reported in this study is similar to that seen in bipolar adults (30%).64 Other studies, comparing adolescents with bipolar disorder, schizophrenia, unipolar depression and disruptive behavior disorders with healthy subjects or adolescents with less severe psychopathologies have observed statistically significant rates of WMH, mainly in the prefrontal regions of bipolar adolescents.65,66 Chang et al.67 did not demonstrate elevated numbers of mild or moderate WMH in their sample of bipolar youth who had a bipolar parent, but noted a statistical trend towards increased rates of severe WMH in these patients. An increase in prefrontal WMH may potentially be used as an intrinsic biomarker for bipolar disorder.

176

Using DTI, changes in FA may represent a loss of bundle coherence, which may be developmental in origin, as white matter formation and development begins during the prenatal period and extends through early adulthood. Indeed, Adler et al.68 reported significantly decreased FA in superior-frontal white matter tracts with no differences in TADC in any regions examined in medication-naïve, first episode manic adolescents. Therefore, prefrontal white matter abnormalities may represent axonal disorganization rather than axonal loss, which may serve as an early biomarker of bipolar disorder. In contrast to adults with bipolar disorder, ventricular enlargement has not been observed in children and adolescents with bipolar disorder.64,67,69 Ventricular enlargement may progress during the course of bipolar disorder,54,70 and may be a biomarker for repeated affective episodes. Global cerebral development abnormalities may be specific to pediatric bipolar disorder, as decreased total cerebral volume71–73 and decreased intracranial volume69 have been reported in children and adolescents with bipolar disorder. Chang et al.67 did not find significant differences in total cerebral volume between bipolar adolescents and healthy subjects in their study of familial pediatric bipolar disorder, but a statistical trend towards decreased total cerebral gray matter volume in the bipolar group was noted. VBM studies have shown decreased gray matter volumes in the anterior cingulate, orbitofrontal74 and left dorsolateral prefrontal75 cortices in children and adolescents with bipolar disorder. However, ROI studies, in general, have not found prefrontal gray matter67 or subgenual prefrontal abnormalities76 in bipolar youth. No prefrontal gray matter abnormalities were found in healthy child offspring or adult bipolar offspring of bipolar parents using either VBM or ROItechniques.67,77,78 Interestingly, Blumberg et al.79 found that ventral prefrontal cortex volumes declined with age and that ventral prefrontal cortex gray and white matter volumes were significantly smaller in young adults with bipolar disorder, relative to healthy subjects. Within the bipolar group, ventral prefrontal cortex volumes were significantly smaller in rapid-cycling patients than patients without rapid-cycling. Current medication use was associated with larger ventral prefrontal cortex volume, independent of age. Therefore, neurodevelopmental changes, bipolar disorder subtype, and medication

N.C. Patel et al.

effects may contribute to the differential expression of neural phenotypes of bipolar disorder from adolescence through adulthood.79 Two pediatric bipolar studies reported no abnormalities in temporal gray matter volume in bipolar youth.67,80, In contrast, others have reported increased temporal sulcal size,69 greater left temporal lobe gray matter,74 and smaller left superior temporal gyral volume, especially in white matter.81 Abnormalities in the superior temporal gyrus may explain the dysfunctional social interactions and peer relationships that are present in children and adolescents with bipolar disorder.82 Decreased amygdala volume has been the most consistent neuroanatomical finding in pediatric bipolar disorder.71–75,80,83 Chen et al.80 reported that amygdala size positively correlated with age in bipolar adolescents and negatively correlated with age in healthy adolescents, suggesting abnormal development of the amygdala in bipolar adolescents. Rosso et al.84 recently reported a decrease in amygdala volume in first-episode bipolar patients suggesting that amygdala volume deficits are present early in the course of illness and may even predate illness onset. However, studies comparing at-risk children of bipolar parents with children of healthy parents did not find significant differences in amygdala volumes between the two groups.77,78 This finding seems to be specific to bipolar disorder, as studies of children and adolescents with other psychopathologies, such as ADHD and autism, have not found reduced amygdala volumes.85,86 Preliminary studies have reported that amygdala size is negatively associated with antidepressant exposure and duration of illness72 and positively associated with lithium or divalproex exposure.83 Studies of hippocampal size in bipolar youth have provided mixed results, as some have reported no abnormalities80,83 and others have reported a reduction.73,87 A recent VBM study by Ladouceur et al.77 comparing healthy bipolar offspring with age-matched healthy low-risk controls found increased gray matter volume in the left parahippocampus/hippocampus that positively correlated with pubertal maturation scores, but not age. The increased parahippocampal/hippocampal gray matter volume in healthy bipolar offspring was speculated to play a protective role in preventing or delaying subsequent development of bipolar disorder, rather than acting as a biomarker for the illness.77 Dasari et al.88 examined thalamic size in adolescents with bipolar disorder and reported no significant dif-

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

ferences in the right or left thalamic area compared with schizophrenic adolescents. However, statistically significant reductions in bilateral thalami were detected when the bipolar and schizophrenic adolescents were combined and compared with a healthy control group.88 Subsequent studies have consistently reported no changes in thalamic size in bipolar youth compared with healthy controls.72,73,83 Singh et al.78 compared children of parents with bipolar I disorder and of parents without any psychiatric disorders and found no significant differences in thalamic volume. Putamen enlargement may be an early trait marker of bipolar disorder, suggesting a possible predisposition for the illness. Some studies have reported putamen enlargement in bipolar adolescents,72,74 whereas, others have reported no differences in putamen or caudate volumes between bipolar and healthy adolescents.83,89 Interestingly, Sanches et al.89 found a significant inverse relationship between age and volumes of the left and right caudate and left putamen in bipolar patients, but not in healthy controls, indicating that abnormalities in striatal development may be involved in the pathophysiology of bipolar disorder. Another study reported reduced left accumbens gray matter in bipolar adolescents using VBM.75 Studies examining children of bipolar probands have been conducted to help identify trait abnormalities associated with bipolar disorder.70 However, a study comparing at-risk children of bipolar parents with children of healthy parents did not find any differences in striatal volumes.78 The failure to observe significant striatal changes in the at-risk group may have been due to the presence of co-occurring ADHD and non-bipolar mood disorders in the at-risk sample.78 In summary, sMRI studies of children and adolescents with bipolar disorder show abnormalities in the prefrontal white matter tracts, anterior cingulate cortex, ventral prefrontal cortex, superior temporal gyrus, amygdala, hippocampus, and putamen. Some of these findings, particularly decreased amygdala volume, appear to be consistent across studies and specific to early-onset bipolar disorder, indicating that this may represent a developmentally specific biomarker of the illness. However, longitudinal studies designed to better understand the developmental neuropathophysiology of bipolar disorder and to identify the developmental epochs during which specific regional abnormalities emerge are necessary.

177

Functional Magnetic Resonance Imaging (fMRI) Functional MRI is a more recent devolvement of MRI technology. Functional MRI is one of several functional imaging modalities along with positron emission tomography (PET) and single photon emission computed tomography (SPECT). Functional imaging techniques create an image of brain activity. To achieve this image, fMRI utilizes the blood-oxygen-level dependent (BOLD) response. The BOLD response depends on the magnetic properties of hemoglobin, which differ depending on whether the molecule is oxygenated or deoxygenated. The MRI signal therefore differs between oxygenated versus deoxygenated blood, and this difference can be detected with fMRI.90 Regions of the brain that are more active have increased blood flow and thus, a higher ratio of oxygenated blood (Fig. 25.2). Various tasks have been used in the MRI scanner to examine the various cognitive and emotional domains in which bipolar patients are thought to have dysfunction, as well as to target specific brain structures thought to be affected in the disorder. An advantage of fMRI over PET or SPECT is that it does not use ionizing radiation and may safely be used several times in the same individual. Another advantage is that no special equipment other than a standard MRI machine is needed for fMRI. However, a disadvantage of fMRI is that the temporal resolution is relatively slow, on the order of seconds. Another limitation of fMRI is that the signal is decreased near air/tissue junctions and thus provides inferior resolution in the orbitofrontal and medial temporal lobes, which are regions that are thought to be involved in the pathophysiology of bipolar disorder. Several sample-specific limitations of the fMRI studies reviewed also need to be considered when interpreting the research. Most of the studies used small sample size (ten patients or less), which considerably limits their statistical power. In addition, most studies did not control for medication use. Finally, many studies combined patients in different mood states. This is a significant confound given that prior studies have shown that mood state can affect brain activation patterns.91–93 These factors may partially explain the inconsistency of brain activation differences observed across studies, even when the same task was used. Despite these limitations, the fMRI

178

N.C. Patel et al.

Fig. 25.2 Functional magnetic resonance imaging. The images demonstrate accurately localized activation in left motor cortex, left putamen and right cerebellum during an event-related right finger tapping task

research provides useful insights into the functional anatomy of the bipolar brain. Future fMRI studies with more subjects and the ability to control for medication use and comorbid psychiatric illnesses are needed to advance our understanding of the functional neuroanatomy of bipolar disorder.

Adult Studies Since the introduction of fMRI technology in the 1990s, there has been an increasing number of studies using fMRI to examine adults with bipolar disorder. These studies have examined the underlying functional neurobiology of the disorder and most often contrasted bipolar subjects with healthy

subjects. A few studies have directly compared different patient groups. Eight prior studies found increased activation in various regions of the prefrontal cortex and two studies found increased activation in the anterior cingulate cortex. Strakowski et al.94 reported that bipolar subjects had increased ventrolateral prefrontal cortex activation bilaterally during an attentional task. During a working memory task, Adler et al.95 found that bipolar subjects had increased activation in the dorsolateral prefrontal and anterior cingulate cortices. A second working memory task96 found increased ventrolateral prefrontal cortex activation in euthymic bipolar patients. Using language processing tasks, Curtis et al. showed increased activation in the left inferior frontal gyrus97 and increased activation in the left prefrontal cortex98 in bipolar subjects compared to healthy subjects.

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

Two studies using facial affect recognition tasks also found increased activation in the prefrontal and anterior cingulate cortices. Lawrence et al.99 compared patients with bipolar disorder and MDD during a depressive period. Compared to the MDD patients, bipolar patients exhibited greater activation in the left medial prefrontal cortex when viewing fearful faces; greater activation in the ventrolateral and ventromedial prefrontal cortices when viewing happy faces; and greater activation in the ventrolateral prefrontal, dorsolateral prefrontal, and right anterior cingulate cortices when viewing sad faces. Chen et al.93 found increased activation in the left superior frontal gyrus and right anterior cingulate cortex in bipolar subjects compared to healthy subjects while viewing happy faces. In contrast, several fMRI studies have found decreased activation in the prefrontal and anterior cingulate cortices using similar tasks. Blumberg et al.91 found decreased activation in the left ventral prefrontal cortex during a color-word Stroop task in bipolar patients in a variety of mood states. In a second study using a color-word Stroop task, bipolar patients showed decreased activation in the dorsolateral prefrontal and ventrolateral prefrontal cortices.100 In a facial affect recognition task, Yurgelun-Todd et al.101 found decreased activation in the dorsolateral prefrontal cortex in bipolar patients when viewing fearful faces. A second study using a facial affect recognition task102 found decreased activation in the subgenual anterior cingulate cortex during the viewing of sad faces in bipolar subjects compared to healthy subjects. Malhi et al.103 found decreased right middle and inferior frontal gyrus activation in bipolar patients during the viewing of negative valence emotional scenes compared to neutral scenes. In a latter study inducing affect by the use of emotional words, Malhi et al.104 found decreased activation in the middle and medial frontal gyrus bilaterally and anterior cingulate cortex during negative affect and decreased activation of the superior frontal gyrus and anterior cingulate cortex during positive affect in bipolar patients. Discrepant results regarding abnormal activation patterns in the prefrontal and anterior cingulate cortices of bipolar adults may be partially attributed to differences in study samples with regard to mood state and psychotropic medication treatment. In addition to increased frontal activation, greater brain activation also has been found in the amygdala, basal ganglia, and thalamus. The direction of activation in these regions has been more consistent than in frontal

179

regions, with most of the studies reporting increased activation. Five prior studies found increased activation in the amygdala, most during the viewing of emotional faces. Altshuler et al.105 found greater activation of the left amygdala in bipolar patients during the viewing of emotional faces. Yurgelun-Todd et al.101 found increased activation in the amygdala in bipolar patients during the viewing of fearful faces. Lennox et al.102 found decreased activation bilaterally in the amygdala in bipolar patients during the viewing of sad faces. Compared to patients with MDD, bipolar patients had greater right amygdala activation when viewing mildly fearful faces and greater left amygdala activation when viewing intensely fearful faces.99 During a continuous performance task, Strakowski et al.94 also found increased activation in the amygdala in bipolar patients. Five prior studies found increased activation in the basal ganglia and thalamus. Caligiuri et al.92 used a motor task, to examine bipolar patients in various mood states. Bipolar patients scanned during a manic episode showed increased activation in the left globus pallidus. Bipolar patients scanned during a depressive episode had greater activation in the right thalamus and right caudate. In another motor task, Marchand et al.106 found that bipolar patients had increased bilateral activation in the striatum compared to healthy subjects. During working memory tasks, bipolar patients showed increased right caudate and thalamus activation in one study96 and increased activation in the basal ganglia and thalamus in a second study.95 Lawrence et al.99 found that bipolar patients had greater activation in the right globus pallidus and anterior thalamus when viewing mildly fearful faces and greater activation in the caudate, thalamus, globus pallidus, and putamen when viewing happy faces. Finally, bipolar patients showed increased activation in the left head of the caudate and in the pulvinar of the left thalamus when viewing negatively valenced emotional scenes103 and increased activation in the thalamus and putamen when viewing negative words.104 The fMRI research discussed provides evidence for overactivation of the anterior limbic system. Excess activation in this network could lead to dysregulation of emotion that may contribute to the emotional symptoms seen in bipolar disorder. Overactivation in the basal ganglia, thalamus, and amygdala was found most consistently. Activation patterns were less consistent in prefrontal regions, with many studies showing activation in the opposite direction using similar tasks. The prefrontal cortex is a much larger brain

180

N.C. Patel et al.

area consisting of many heterogeneous regions, which may partly explain the more varied activation patterns reported. Among the subregions of the prefrontal cortex, ventral regions were most consistently overactive.

Child and Adolescent Studies To date, there have been fewer fMRI studies in children and adolescents than in adults with bipolar disorder (Table 25.1). In the first published fMRI study, Blumberg et al.107 reported that bipolar adolescents did not exhibit the age-related increases in prefrontal cortical activation observed in healthy adolescents during performance of a color-naming Stroop task, suggesting the possibility of the emergence of prefrontal dysfunction occurring during adolescence. In contrast to Blumberg et al.,107 studies thereafter have reported increased activation in various regions of the prefrontal cortex in bipolar youth. Chang et al.108 reported greater activation in the left dorsolateral prefrontal cortex and right inferior

frontal gyrus, and decreased activation in the cerebellar vermis in bipolar subjects compared to healthy subjects during a visuospatial working memory task. During the affective task involving positively and negatively valenced pictures, bipolar patients had increased activation in the bilateral dorsolateral prefrontal cortex, inferior frontal gyrus, and right insula compared to healthy subjects when viewing negatively valenced pictures. When viewing positively valenced pictures, bipolar subjects had increased activation in the left middle/superior frontal gyrus and bilateral caudate compared to healthy subjects. In a study by Nelson et al.,109 bipolar subjects had increased activation in the dorsolateral prefrontal cortex compared to healthy subjects during successful motor inhibition trials. Both medicated and unmedicated bipolar subjects showed increased activation in the left dorsolateral prefrontal cortex compared to controls. Increased activation of the anterior cingulate cortex in bipolar youth has also been reported across studies. Chang et al.108 reported that bipolar subjects had greater activation in the bilateral anterior cingulate cortex,

Table 25.1 Pediatric fMRI studies in bipolar disorder Ref

Populations (N)

Mood state of BP (N)

Mean age Medications Comorbid (year) (On/Off) ADHD (N)

107 108

BP I (10) vs. NC (10) BP I (11) vs. NC (10)

Not stated BPE (11)

14 15

On On

2 11

109

BPE (20), BPHM (5)

15

13 On, 12 Off

15

110

BP I (23), BP II (2) vs. NC (17) BP I (10) vs. NC (10)

BPE (10)

15

Off

111

BP I (10) vs. NC (10)

BPE (10)

15

Off

Not stated but comorbid ADHD allowed 6

112

BP I (23) vs. NC (22)

14

18 On, 5 Off

13

113

BP I (24), BP II (2) vs. NC (17) BP I (20), BP unknown (2) vs. NC (21) BP I (30), BP unknown (4) vs. NC (24) BP I (26)

BPE (11), BPHM (7), BPD (5) BPE (21), BPHM (5)

15

13 On, 13 Off

15

BPE (12), BPD (4), BPHM (6) BPE (18), BPHM (11), BPD (3), BPM (1) BPM (26)

14

18 On, 4 Off

Not stated

14

27 On, 7 Off

16

15

Off

11

BPD (8), in remission during second scan

16

On

6

114 115 116 117

BP I (3), BP II (3), BP NOS (2)

Task Stroop Visuospatial and affective task Motor response inhibition Facial affect recognition

Emotional word matching Facial affect recognition Motor response inhibition Facial affect recognition Facial affect recognition Continuous performance Emotional scenes

ADHD = attention-deficit hyperactivity disorder, BP = bipolar disorder, BP I = bipolar I disorder, BP II = bipolar II disorder, BPD = bipolar disorder, depressed, BPE = bipolar disorder, euthymic, BPHM = bipolar disorder, hypomanic, BPM = bipolar disorder, manic/mixed, BP NOS = bipolar disorder not otherwise specified, NC = normal control, Ref = reference, y = year.

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

during a visuospatial working memory task, and greater activation in the left anterior cingulate cortex when viewing positively valenced pictures during an affective task. Pavuluri et al.110,111 reported that bipolar subjects showed increased activation in the pregenual anterior cingulate cortex across tasks of viewing angry and happy faces and matching negative words. Similarly, Dickstein et al.112 reported increased activation in the anterior cingulate and orbitofrontal cortices of bipolar subjects compared with healthy subjects during successful encoding of happy faces. Decreased activation in the ventral prefrontal cortex has been reported in a number of fMRI studies utilizing various tasks. In the studies by Pavuluri et al.,110,111 bipolar subjects exhibited decreased activation in the right ventrolateral prefrontal cortices when viewing angry and happy faces, and decreased activation in the right rostral ventrolateral prefrontal and dorsolateral prefrontal cortices when matching negative words. Leibenluft et al.113 also reported decreased right ventral prefrontal cortex activation in pediatric bipolar patients compared to healthy controls during a motor response inhibition task. Medicated bipolar subjects had decreased activation in the ventral prefrontal cortex compared to healthy subjects. In contrast, Rich et al.114 reported that during an affective face processing task, bipolar subjects had increased activation in the ventral prefrontal cortex when viewing faces rated as hostile. Several fMRI studies involving affective tasks have reported increased activation of the amygdala, putamen, and thalamus in children and adolescents with bipolar disorder. Pavuluri et al.110,111 reported increased amygdala activation while bipolar subjects viewed angry and happy faces, and increased left amygdala activation while matching negative words. Dickstein et al.112 also reported increased striatal activation in bipolar subjects during successful encoding of happy faces. Rich et al.114 reported increased activation in the left amygdala, accumbens, and putamen of bipolar subjects when viewing hostile faces and greater activation in the left amygdala and bilateral accumbens when viewing faces rated as fearful. In a subsequent study, Rich et al.115 reported that bipolar subjects showed decreased connectivity between the amygdala and right cingulate/precuneus and right fusiform gyrus/parahippocampal gyrus compared to healthy subjects. Additional studies have also showed increased activation in the putamen and thalamus of bipolar subjects during a color-naming Stroop task,107 a visuospatial working memory task,108 and an affective task involving

181

the viewing of valenced pictures.108 Leibenluft et al.113 reported contrasting results, as bipolar subjects had decreased activation in the bilateral caudate and putamen compared to healthy subjects during unsuccessful motor inhibition. The observed decreased striatal activation was independent of ADHD comorbidity and medication status. Comorbid psychiatric illness and mood-stabilizing medications have been shown to affect brain activation patterns in youth with bipolar disorder. In a study by Adler et al.,116 bipolar subjects with comorbid ADHD had increased activation in the posterior parietal cortex and middle temporal gyrus and decreased activation in the ventrolateral prefrontal and anterior cingulate cortices compared to bipolar subjects without ADHD during a continuous performance task alternated with a control task in a block-design paradigm. This is consistent with the results of Leibenluft et al.,113 that showed bipolar subjects with comorbid ADHD had decreased activation in the ventral prefrontal cortex. Comorbid ADHD has also been associated with the abnormal activation of other brain regions, such as the left anterior cingulate cortex and left insula.109 Chang et al.117 scanned eight pediatric bipolar patients at baseline and after 8 weeks of open-label lamotrigine treatment. All the bipolar patients were depressed at baseline and considered responders after 8 weeks of treatment. Decreases in Children’s Depression Rating Scale (CDRS) scores, which indicate severity of depressive symptoms, positively correlated with decreases in right amygdala activation, but not with the change in dorsolateral prefrontal cortex activation. CDRS scores also positively correlated with bilateral amygdala activation at week 8, but not at baseline. The mean ages of the bipolar subjects in all the studies reviewed were between 14 and 16 years and all of the studies were cross-sectional. Therefore, the existing fMRI literature covers early to middle adolescents, but cannot address questions regarding brain activation in younger patients or brain development over time in bipolar patients. The fMRI results reviewed provide evidence for altered brain activation in adolescent bipolar patients. The most consistent findings were overactivation in the putamen, anterior cingulate cortex, dorsolateral prefrontal cortex, amygdala, and thalamus and decreased activation in the ventral prefrontal cortex. These brain regions are all part of the anterior limbic network responsible for emotional regulation. Therefore, altered activation in these brain regions may be contrib-

182

uting to the fluctuations in mood state and impulsivity that are hallmark characteristics of bipolar disorder.

Magnetic Resonance Spectroscopy (MRS) MRS has been used in studies of individuals with bipolar disorder across the age spectrum. MRS provides information about the in vivo concentrations of neurochemicals in localized brain regions, thereby allowing for the evaluation of neurochemical abnormalities representative of bipolar pathophysiology, neurochemical effects of psychotropic medications, and potential neurochemical markers of treatment response. MRS studies in patients with bipolar disorder have primarily used proton (1H), lithium (7Li), or phosphorus (31P) spectroscopy. Other isotopes, including carbon (13C), fluorine (19F), and sodium (23Na), can also be evaluated using MRS.26,118–120 1 H MRS is the most commonly used of MRS techniques in studies of bipolar disorder. In 1H MRS, signals from small neurochemical concentrations (measured in parts per million [ppm]) are detected in a large concentration of water in a localized region of the brain, known as a voxel, over a narrow frequency range (Fig. 25.3). Neurochemicals typically evaluated using 1H MRS include N-acetyl aspartate (NAA), myo-inositol (mI),

N.C. Patel et al.

choline-containing compounds (Cho), creatine/phosphocreatine (Cr), and glutamate/glutamine/γ -aminobutyric acid (Glx) (Fig. 25.3). NAA, a neuronal amino acid that is considered a putative marker of neuronal integrity,121 increases during brain development in childhood (most rapid changes during first 3 years of life) and decreases with age.122,123 Decreases in NAA may reflect neuronal loss, or decreased viability or impaired functioning of neurons. Furthermore, a decrease in NAA may reflect impaired mitochondrial energy production, as NAA is produced in the mitochondria and reduced by mitochondrial respiratory chain inhibitors.124 Myo-inositol is a sugar involved in a number of biological processes, including cellular second messenger signaling pathways. One of these signaling pathways is the phosphoinositide cycle, where lithium is purported to exert its mood stabilizing effects through inhibition of inositol monophosphastase (IMPase). This pharmacologic activity is believed to result in decreased mI concentrations, and in turn, a reduction in overactive neuronal signaling.125,126 Cho consists primarily of phosphorylcholine and glycerophosphorylcholine, and is considered a biomarker for membrane phospholipid metabolism. Increases in Cho may indicate membrane catabolism, which may be suggestive of neurodegeneration.127 Cr is comprised of creatine and creatine phosphate, and is reflective of cellular energy metabolism. A number of 1H MRS studies of patients with bipolar disorder have used the Cr peak as

Fig. 25.3 Proton magnetic resonance spectroscopy. This spectrum was acquired from the medial prefrontal cortex of an adolescent male with bipolar disorder; Cho = choline, Cr = creatine/creatine phosphate, Glx = glutamate/glutamine/γ-aminobutyric acid, mI = myo-inositol, NAA = N-acetyl aspartate

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

an internal reference and reported concentrations of other neurochemicals as ratios because Cr is assumed to be stable. However, the temporal stability of Cr in patients with bipolar disorder has not been determined.128 Glx is comprised of glutamate, glutamine, and γ-aminobutyric acid (GABA), and may reflect glutamatergic neurotransmission. Sustained increases in Glx may represent neurotoxicity.124 31 P MRS detects signals from concentrations of α-, β-, and γ-adenine triphosphate (ATP), phosphocreatine (PCr), phosphodiester compounds (PDE), inorganic phosphate (Pi), and phosphomonoester compounds (PME). The PME and PDE peaks are from membrane phospholipids. Despite the usefulness of 31P MRS to study phospholipid metabolism, this MRS technique is limited in sensitivity and spatial resolution. 31P MRS studies in bipolar disorder are limited in number.7Li MRS has been used to measure brain lithium concentrations in vivo, although without localization to specific regions of the brain.129 A limited number of 7Li MRS studies have been conducted in bipolar disorder.

Adult Studies 1

H MRS

1

H MRS studies of adults with bipolar disorder are summarized in Table 25.2. In most of these studies, subjects were either receiving or had recent exposure to psychotropic medication(s). It is possible that pharmacologic treatment may have affected neurometabolite levels, thereby confounding the results.

N-Acetyl Aspartate (NAA) 1

H MRS studies have demonstrated decreased NAA levels across several brain regions in adult patients with bipolar disorder as compared to normal controls. Reduced NAA levels have been reported in the hippocampus of euthymic adults,130 and reduced NAA/Cr ratios have been reported in the dorsolateral prefrontal cortex,131,132 hippocampus,133,134 and occipital cortex135 of euthymic patients. In manic adults with bipolar disorder, lower NAA concentrations have been found in the basal ganglia,136 hippocampus,137 and medial prefrontal gray matter.138 Basal ganglia NAA/Cr was inversely correlated with number of prior hospitaliza-

183

tions for mania.136 Two studies139,140 have reported decreased hippocampal NAA/Cr in samples of bipolar patients with various mood states. However, reduced NAA levels have not consistently been reported in patients with bipolar disorder. Unaltered NAA levels and NAA/Cr has been observed in the anterior cingulate cortex,141,142 dorsolateral prefrontal cortex,142,143 and basal ganglia144–146 of euthymic patients and, the anterior cingulate cortex,136 dorsolateral prefrontal cortex,147,148 and occipito-parietal white matter136 of manic patients. Similar findings have been reported in adults with bipolar depression across a variety of brain regions, including prefrontal, frontal, temporal, parietal, and occipital cortices, and the basal ganglia.144,149–151 Dorsolateral prefrontal cortex NAA levels were similar between medication-free bipolar adults with various mood states and normal controls.152 In contrast, Deicken et al.153 reported increased thalamic NAA concentrations in euthymic adults with bipolar disorder. Despite conflicting data, alterations in NAA levels in patients with bipolar disorder may suggest impaired neuronal functioning or loss, or mitochondrial dysfunction within localized regions of the brain. Lithium may possess neurotrophic effects,154 as increases in localized brain NAA/Cr (basal ganglia, dorsolateral prefrontal cortex, temporal lobe) secondary to lithium treatment have been reported in cross-sectional studies.143,155,156 In a mixed sample of bipolar depressed adults and normal controls, Moore et al.151 observed significant increases in total brain NAA concentrations following 4 weeks of lithium treatment. In contrast, Friedman et al.157 did not find similar lithium-induced increases in brain NAA in depressed patients, and Kato et al.145 reported no increase in NAA/Cr with lithium in euthymic patients. These discrepant results of lithium’s effects on NAA may be due to differences in sample characteristics, regions under study, and lithium treatment regimens (i.e., dose and duration). Valproate has not been associated with increases in brain NAA,138,156 and in fact, duration of valproate exposure has been shown to be negatively correlated with NAA levels.138 However, a recent 1H MRS investigation in a mixed sample of adults with bipolar disorder suggested possible neuroprotective effects with valproate, as evidenced by increased hippocampal NAA/Cr.133 Other pharmacologic treatments that have shown possible neuroprotective effects in bipolar disorder include lamotrigine150 and ethyl-eicosapentanoic acid.158 There are conflicting data regarding the effect of quetiapine on NAA levels in bipolar disorder,133,159

BPD (12) vs. NC (9)

BPD (11) vs. NC (20)

151

BPE (10) vs. NC (32) BPE (16) vs. NC (20)

143 144

BPD (23) vs. NC (12)

BPE (33) vs. NC (29)

142

150

BP (17) vs. NC (17) BPE (13) vs. NC (15)

140 141

BPE (10) vs. NC (10) BPM (10) vs. NC (10) BPM (8) vs. NC (8) BPD (32) vs. NC (26)

BP (17) vs. NC (17)

139

146 147 148 149

BPM (12) vs. NC (12) BPM (17) vs. NC (21)

137 138

BPE (19) vs. NC (19)

BG

BPM (16) vs. NC (17)

136

145

ACC DLPFC DLPFC BG

BPE (13) vs. NC (13) BPE (16) vs. NC (18)

134 135

FRON, PAR, OCC, TEMP

BG DLPFC DLPFC Regional gm Regional wm ACC

BG

HPC ACC

BG ACC OCC-PAR wm HPC MPFC gm MPFC wm HPC

HPC OCC

DLPFC DLPFC HPC

BPE (13) vs. NC (10) BPE (20) vs. NC (20) BP (30) vs. NC (10)

131 132 133

HPC

BPE (15) vs. NC (20)

130

=, ↑ Li

=, = Li or w/o Li = = = = = =

=

= = =, ↑ Li =

↓ =

= = =

=

= =

= =

= = = = = =

= = = = ↑

=

↑ ↑, = Li, ↑ w/o Li

= =

= =

= = =

↓

Cr

= = = ↑

= =

= = = = = = =

↓ = = ↓ ↓ = ↓ = = =

= = = med-free or med =

↓ ↓ = ↓ med-free, = med ↓ = ↓

Cho Cho/Cr =

NAA/ mI mI/Cr

↓

Table 25.2 Adult 1H MRS studies in bipolar disorder Ref Populations (N) Region(s) NAA Cr

↑ ↑, ↑ Lac = ↑, ↑ Glu = Gln

= ↑

↑ Glu/Gln, ↓ GABA = = =

Glx Glx/Cr

Med-free (18), med (5 Li) Remitters: ↓ Gln with LTG Med-free (12) ≥ 2 weeks

Med-free (3), med (7 Li) Med (8 AP, 9 MS) Med-free (6), med (1 Li) Med-free (32) ≥ 8 weeks

Med-free (4), med (6 Li) BPE: Med-free (2), med (9 Li, 5 AP, 3 AC, 4 AD) BPD: Med-free (2), med (3 Li, 2 AP, 1 AC, 6 AD) Med (10 Li, 9 w/o Li, ± other)

Med (2 Li, 17 AP) Med (13 Li, 4 VPA, 2 CBZ, 4 AD, 1 AP) Med (no details)

Med (6 Li, 6 AC, 4 AP, 3 AD)

Med-free (12) Med (2 Li, 17 DVP, 13 AP, 3 AD)

Med (5 Li, 7 DVP, 5 AP)

Med-free (3), med (7 DVP, 4 Li, 4 AD, 3 OLZ) Med (10 Li, 6 AC, 2 AP) Med-free (20) ≥ 2 weeks Med-free (10), med (10 VPA, 10 VPA + QUET) Med (12 Li or AC, 5 AD, 6 AP) Med-free (16) ≥ 3 mos

Comments

184 N.C. Patel et al.

BP (4) vs. NC (9)

BPE (9) vs. NC (11)

BPE (25) vs. NC (18) BPD (21) vs. NC (12)

BPD (14) BPM (42)

BPE (8) vs. NC (8) BPD (9) vs. NC (14)

BPE (17) vs. NC (22) BPD (11) vs. NC (22) BPE (23) vs. NC (20) BPD (8) vs. NC (20) BPE (7) vs. NC (6) BPE (9) vs. NC (11)

BPE (25) vs. NC (18)

155

156

156 157

158 159

161 162

169

172

BG BG FRON FRON PAR FRON TEMP TEMP

PAR ACC

CC (above) Midfrontal gm

BG OCC FRON TEMP TEMP Regional gm Regional wm

DLPFC THAL

=

↑ =

↑ = = = ↑ Li, = VPA = Li or VPA = Li or VPA ↑ EPA

= =, = Li, VPA, or w/o AD

=

↑ Li, =VPA = Li or VPA

=

= ↑

= ↑, ↑ Li, VPA, or w/o AD = ↑ = = =

=

= Li or VPA = Li or VPA

↑ =

↓ =

= = ↓ Li or VPA

= ↓ vs. BPE

=

= Li or VPA = Li or VPA

↓ ↑

=, ↓ Lac QUET

↓ Li, = VPA = Li or VPA

= Glu

Med (14 Li, 11 VPA, ± other)

BPE: Med-free (3), med (13 Li, 11 AP) BPD: Med-free (2), med (2 Li, 4 AP) Med (7 Li) Med (9 VPA, ± other)

Med (no details)

Med (7 EPA, 7 PCBO) Med (42 QUET) Responders: greater ↓ Lac with QUET Med (8 Li) Med (5 Li, 4 VPA, 5 AD)

Med (14 Li, 11 VPA, ± other) Med (12 Li, 9 VPA)

Med (9 VPA, ± other)

Li ↑ NAA in BPD+NC Med-free (32) ≥ 2 weeks Med-free (2), med (5 Li, 8 DVP, 2 AP, 5 AD) Med (4 Li, ± other)

AC = anticonvulsant, ACC = anterior cingulate cortex, AD = antidepressant, AP = antipsychotic, BG = basal ganglia, BP = bipolar disorder, BPD = bipolar disorder, depressed, BPE = bipolar disorder, euthymic, BPM = bipolar disorder, manic/mixed, CBZ = carbamazepine, CC = corpus callosum, Cho = choline, Cr = creatine/phosphocreatine, DLPFC = dorsolateral prefrontal cortex, DVP = divalproex, EPA = ethyl-eicosapentanoic acid, FRON = frontal lobe, GABA = γ-aminobutyric acid, Gln = glutamine, Glu = glutamate, Glx = glutamate/glutamine/γ-aminobutyric acid, gm = gray matter, HPC = hippocampus, Lac = lactate, Li = lithium, LTG = lamotrigine, med = medicated, med-free = medicationfree, mI = myo-inositol, MPFC = medial prefrontal cortex, MS = mood stabilizer, NAA = N-acetyl aspartate, NC = normal control, OCC = occipital lobe, OLZ = olanzapine, PAR = parietal lobe, PCBO = placebo, QUET = quetiapine, Ref = reference, TEMP = temporal lobe, THAL = thalamus, VPA = valproate, wm = white matter, w/o = without. ↑ = increased, = = no difference, ↓ = decreased.

171 172

170

BP (32) vs. NC (32) BPE (15) vs. NC (15)

152 153

25 Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan 185

186

N.C. Patel et al.

which may be attributed to differences in mood state and rapid cycling, and brain region.

depression and quetiapine in mania also did not alter anterior cingulate cortex mI concentrations.150,159

Myo-inositol (mI)

Choline (Cho)

1

Conflicting evidence exists regarding brain Cho concentrations in adults with bipolar disorder. In euthymic adults with bipolar disorder, elevated Cho/Cr in the basal ganglia has been found,144,145,155,168 albeit not consistently.146,169 Abnormalities in Cho during euthymia may be localized to the basal ganglia, as Cho levels or Cho/Cr have not been shown to be abnormal in other brain regions.130–132,134,141–143,153,161,170–172 In mania, no alterations in Cho levels or Cho/Cr have been reported in the anterior cingulate cortex,136 basal ganglia,136 dorsolateral prefrontal cortex,147,148 hippocampus,137 medial prefrontal gray or white matter,138 or occipito-parietal white matter.136 Adults with bipolar depression have been found to have increased Cho/Cr in the basal ganglia,144,169 but not in frontal gray and white matter.149 Investigations of anterior cingulate cortex Cho in bipolar depression have produced discrepant results.150,162 In the study by Moore et al.,162 severity of depressive symptoms correlated positively with anterior cingulate cortex Cho/ Cr. Medication-free adults with bipolar disorder had significantly lower Cho levels in the left dorsolateral prefrontal cortex compared with normal controls.152 In accordance with lithium’s purported inhibition of choline transport, cross-sectional 1H MRS studies have shown similar or decreased Cho levels or Cho/Cr in various brain regions (anterior cingulate cortex141; basal ganglia145; parietal lobe161,171; temporal lobe172) of euthymic adults with bipolar disorder receiving lithium versus normal controls. Furthermore, Moore et al.163 reported decreased frontal cortex Cho levels in bipolar depression, supporting a normalizing or decreasing effect of lithium on Cho. In contrast, increased Cho/ Cr155,162 or no temporal change in gray or white matter Cho levels have been reported in lithium-treated adults with bipolar disorder. Valproate may have comparable effects on Cho levels as lithium, as localized and regional Cho concentrations in bipolar adults receiving valproate have not been shown to be different than normal controls.133,157,172 Antidepressant treatment has been shown to be associated with lower anterior cingulate cortex Cho/Cr,162 but treatment with lamotrigine in adult bipolar depression150 or quetiapine in adult mania159 may not have any significant effects on Cho levels.

H MRS studies of adults with bipolar disorder across the mood spectrum have not supported the hypothesis of elevated mI concentrations contributing to the neuropathophysiology of the illness. Localized mI levels or mI/Cr in euthymic,132,134,142,160,161 manic,136,138,147 and depressed149,150,162 adults with bipolar disorder have been found to be similar when compared with normal controls. Previous or current psychotropic medication use, including lithium, was common in these studies, thereby making it difficult to determine if abnormal mI concentrations were normalized with mood-stabilizing medications. However, a study of medication-free adults with bipolar disorder reported unaltered mI levels in the left dorsolateral prefrontal cortex.152 Left dorsolateral prefrontal cortex mI concentrations did positively correlate with length of illness, suggesting mI levels in this region may be a marker of chronicity.152 Based on lithium’s activity on the phosphoinositide cycle, investigations of lithium’s in vivo effects on mI have been of interest. Sharma et al.155 reported increased mI/Cr in the basal ganglia of adults with bipolar disorder who were stable on lithium compared to normal controls. Two 1H MRS studies have examined the temporal effects of lithium on mI in adults with bipolar depression. In a study by Moore et al.,163 acute (5–7 days) and chronic (3–4 weeks) lithium treatment resulted in decreased frontal lobe mI concentrations compared to baseline. When corrections for multiple comparisons is applied, the observed decreases do not remain significant. Friedman et al.157 reported increases in regional gray matter mI concentrations with chronic lithium administration, supporting the hypothesis that extended lithium exposure may increase IMPase activity.164 Several spectroscopy studies of lithium in adult normal volunteers have reported no effect on brain mI concentrations,165–167 indicating that lithium-induced changes in mI may be specific to individuals with bipolar disorder. Although it has been suggested from cross-sectional studies that valproate may normalize mI levels or mI/ Cr in adults with bipolar disorder,138,160,162 longitudinal data in adult bipolar depression showed that chronic valproate treatment did not significantly affect regional gray matter mI levels.157 Similarly, lamotrigine in bipolar

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

Creatine/Phosphocreatine (Cr) Although Cr is often used as a reference standard, data regarding Cr levels in adults suggest that there indeed may be considered variability across mood states and brain regions. In euthymic adults, decreased Cr levels in the hippocampus,130 increased Cr levels in the thalamus,153 and unaltered Cr levels in the basal ganglia,144 dorsolateral prefrontal cortex,143 and frontal lobe have been reported.170 Cr concentrations in the prefrontal region,136,138,147,148 basal ganglia,136 and occipito-parietal white matter136 of manic adults have been similar to those in normal controls. In bipolar depressed adults, increased Cr levels in the anterior cingulate cortex150 and unaltered Cr levels in the basal ganglia,144 frontal cortex,170 and regional gray and white matter149 have been observed. Hamakawa et al.170 reported that frontal cortex Cr concentrations in adults with bipolar depression were significantly lower compared with euthymic adults with bipolar disorder. In a recent study of medication-free adults with bipolar disorder, left dorsolateral prefrontal cortex Cr concentrations were decreased, suggesting reduced cellular energy.152 In adults with bipolar depression, lithium and valproate did not alter regional gray or white matter Cr levels157 and lamotrigine did not significantly affect anterior cingulate cortex Cr levels.150 Antipsychotic treatment in bipolar disorder has been associated with higher Cr concentrations in the basal ganglia,144 but a recent study of quetiapine in rapid cycling manic adults showed no temporal effects on midfrontal cortex Cr levels.159

Glutamate/Glutamine/GABA (Glx) Increased levels of Glx have been observed in the dorsolateral prefrontal cortex148 and prefrontal white matter138 of manic adults, the occipital cortex of a mixed sample of adults with bipolar disorder,135 and the anterior cingulate cortex150 and regional gray matter149 of bipolar depressed adults. In contrast, Frye et al.136 reported no differences in Glx levels in the anterior cingulate cortex, basal ganglia, and occipito-parietal white matter of manic adults compared with normal controls. Frey et al.152 also reported similar left dorsolateral prefrontal cortex glutamate concentrations in medication-free bipolar adults and normal controls. Lithium has been found to induce decreases in regional gray matter Glx concentrations in adults

187

with bipolar depression,157 whereas valproate has not.157 Although Frye et al.150 reported no effect on anterior cingulate cortex Glx levels with lamotrigine in bipolar depressed adults, lamotrigine-associated remission corresponded with a decrease in anterior cingulate cortex glutamine. In rapid cycling manic adults, quetiapine did not significantly affect midfrontal cortex Glx concentrations, but was associated with decreases in lactate.159 Reductions in midfrontal cortex lactate levels were significantly greater in responders versus non-responders.159

31

P MRS

Several 31P MRS studies have reported that adults with bipolar disorder who are experiencing mood symptoms, either manic or depressive, display elevated frontal lobe PME compared with patients who are euthymic.173–175 Euthymic adults with bipolar disorder have decreased frontal and temporal lobe PME concentrations in comparison to normal controls.174–178 Collectively, these 31P MRS data in bipolar disorder suggest state-dependent abnormalities in phospholipid metabolism. Because lithium inhibits IMPase, increases in the PME peak are expected with lithium treatment. In accordance, increased PME have been observed in lithium-treated bipolar patients in manic and depressive states.173,175,179,180 In a 31P MRS study, frontal cortex PME concentrations in lithium-treated manic patients were higher than those in lithium-treated euthymic patients.173 This finding further supports the hypothesis of state-dependent phospholipid metabolism abnormalities in bipolar disorder. Lithium’s effects on phospholipid metabolism may be limited to patients with bipolar disorder, as one study in normal subjects reported no change in PME/PCr following lithium treatment.166 Duration of lithium treatment may affect change in PME, as concentrations may normalize with continued administration.181 In contrast, brain lithium concentrations may not influence PME concentrations or intracellular pH.175 While lower intracellular pH is believed to be a positive predictor of lithium response, it may be more a marker of the neuropathophysiology of bipolar disorder, namely WMH, rather than neurochemical effects of lithium.118,182

188 7

N.C. Patel et al.

Li MRS

than serum concentrations, although further investigation is needed.

Available 7Li MRS studies have examined lithium pharmacokinetics in human brain, and brain lithium concentrations in relation to serum lithium concentrations, clinical response, and side effects in patients with bipolar disorder. Although brain and serum lithium concentrations have consistently been shown to be positively correlated, brain concentrations have been found to be lower than serum concentrations.183–187 Patients with bipolar disorder who have a therapeutic serum lithium level (0.8–1.2 mEq/L) may have subtherapeutic brain lithium concentrations.187 7Li MRS studies have also demonstrated that brain-to-serum lithium concentration ratios may be affected by age,188 but there are conflicting data with regard to dosing schedule.189,190 Brain concentrations of lithium in bipolar disorder may be stronger predictors of response184 and toxicity191

Child and Adolescent Studies 1

H MRS

1

H MRS studies of children and adolescents with bipolar disorder are summarized in Table 25.3.

N-acetyl Aspartate (NAA) Results from 1H MRS investigations of NAA levels in children and adolescents with bipolar disorder have been discrepant, paralleling that seen in adult bipolar

Table 25.3 Pediatric 1H MRS studies in bipolar disorder Ref

Populations (N)

NAA mI Region(s) NAA/Cr mI/Cr

192

BPE (15) vs. NC (11)

DLPFC

↓

=

=

193

BP (35) vs. NC (36)

DLPFC

↓

=

=

=

194

BP (14) vs. NC (18)

DLPFC

↓

=

=

195

BP (32) vs. BP-Pro (28) vs. NC (26)

DLPFC

=

=

=

196

BPE (9) vs. NC (9)

CV MPFC FRON wm BP (10) vs. NC (10) FRON BG BPM (11) vs. NC (11) ACC

= = =

= ↑ =

= = =

= = =

= = ↓ Li

= = =

199

BPM (10) vs. IED (10) ACC vs. NC (13)

=

↑

=

=

=

200

BP (22) vs. NC (10)

ACC

=

=

=

=

= Glu, ↓ Gln med-free

201

BP (28) vs. NC (10)

ACC

↑

=

=

=

=

197 198

Cho Cho/Cr Cr

Glx Glx/Cr

= Glu

= = = ↑ ↑ =

Comments Med-free (1), med (14; no details) Med-free (11), med (20 Li, VPA, or CBZ/OXC) Med-free (2) ≥ 4 weeks, med (8 Li, 8 VPA, 4 AD) BP: Med (16% Li, 25% VPA, 41% AP, 31% SSRIs, 25% STIM) BP-Pro: Med (11% Li, 14% VPA, 21% AP, 11% SSRIs, 25% STIM) Med-free (8), med (1 AP)

Med-free (10) ≥ 1 week Med (1 DVP, 5 AP, 4 STIM) Responders: ↓ mI/Cr with Li BPM: Med-free (5), med (2 DVP, 2 AP) IED: Med-free (3), med (2 DVP) Med-free (6), med (2 Li, 1 VPA, 5 OXC, 12 AP, 3 AD) Med-free (28) (continued)

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

189

Table 25.3 (continued) Ref

Populations (N)

202

BPD (28)

203

BPM (19)

204

NAA mI Region(s) NAA/Cr mI/Cr

Cho Cho/Cr Cr

Glx Glx/Cr

↑

=

VLPFC MPFC VLPFC MPFC VLPFC

↑ ↓ Li = = =

↑

↑

= =

↑ OLZ = = =

= =

BPM (10) vs. BP w/RIS (8)

ACC

=

=

=

↓ RIS

205

BPD (11)

DLPFC

↑ LTG

↑ LTG

206

BPD (28)

MPFC VLPFC

= =

207 208

BP (8) BP+ADHD (8) vs. ADHD (15) vs. NC (7)

ACC ACC

= DVP =

Comments Med-free (28)

=

=, ↓ Glx/Ino BP+ADHD

Med-free (19) Remitters: ↑ ACC NAA with OLZ; ↑ baseline ACC Cho BPM: Med-free (5), med (5; no details) BP w/RIS: Med (8 RIS) Med-free (8), med (1 VPA, 1 AP, 1 STIM) Med-free (28) ↑ mI in ACC, VLPFC from day 7–42 Remitters: ↓ baseline ACC mI No details BP+ADHD: Med-free (6), med (2; no details)

ACC = anterior cingulate cortex, AD = antidepressant, ADHD = attention-deficit hyperactivity disorder, AP = antipsychotic, BG = basal ganglia, BP = bipolar disorder, BPD = bipolar disorder, depressed, BPE = bipolar disorder, euthymic, BPM = bipolar disorder, manic/mixed, BP-Pro = prodromal bipolar disorder, CBZ = carbamazepine, Cho = choline, Cr = creatine/phosphocreatine, CV = cerebellar vermis, DLPFC = dorsolateral prefrontal cortex, DVP = divalproex, FRON = frontal lobe, Gln = glutamine, Glu = glutamate, Glx = glutamate/glutamine/γ-aminobutyric acid, Ino = inositol, Li = lithium, LTG = lamotrigine, med = medicated, med-free = medication-free, mI = myo-inositol, MPFC = medial prefrontal cortex, NAA = N-acetyl aspartate, NC = normal control, OLZ = olanzapine, OXC = oxcarbazepine, Ref = reference, RIS = risperidone, SSRIs = selective serotonin reuptake inhibitors, STIM = stimulants, TEMP = temporal lobe, THAL = thalamus, VLPFC = ventrolateral prefrontal cortex, VPA = valproate, wm = white matter, w/ = with. ↑: increased, =: no difference, ↓: decreased.

disorder. Decreased NAA or NAA/Cr in the dorsolateral prefrontal cortex192–194 has been reported, although this decrease has not been reported in at-risk youth.195 Several spectroscopy studies have not found abnormal NAA/Cr in the cerebellar vermis,196 or frontal and temporal cortices of euthymic youth197 or in the anterior cingulate cortex of manic or mixed youth.198–200 Adolescents with bipolar depression were found to have increased prefrontal cortex NAA levels compared with normal controls, possibly associated with decreased prefrontal metabolism.201 Data evaluating the effects of pharmacologic treatment on NAA levels in children and adolescents with bipolar disorder are largely limited to lithium. In manic and mixed youth with bipolar disorder, 7 days of lithium treatment did not increase in anterior cingulate cortex NAA/Cr.198 A recent study in adolescent bipolar depres-

sion also reported no increases in prefrontal cortex NAA concentrations following 6 weeks of lithium treatment.202 In fact, medial prefrontal cortex NAA levels significantly decreased; this reduction may be attributed to increased prefrontal metabolism with lithium, which subsequently resulted in limited energy sources for NAA synthesis and a corresponding decrease. In the same study, subjects achieving remission experienced a decrease in right lateral prefrontal cortex NAA concentrations with chronic lithium treatment (from day 7 to day 42), while subjects not achieving remission experienced an increase.202 First-hospitalization manic adolescents who achieved remission with olanzapine showed increased frontal gray matter NAA levels, suggesting that olanzapine be neuroprotective or may be associated with normalization of mitochondrial dysfunction.203 In contrast, risperidone-

190

treated youth with bipolar disorder did not have altered anterior cingulate cortex NAA/Cr compared with youths not receiving risperidone.204 Some subjects receiving risperidone were also treated with other psychotropic medications concomitantly, which may have confounded the results. As seen in adults, lamotrigine may also be neuroprotective.205

Myo-inositol (mI) Contrary to adults, mI concentrations in children and adolescents have been shown to be elevated, specifically in the medial prefrontal cortex of euthymic196 and manic children.198,199 Several studies, however, have not reported such abnormalities in dorsolateral and medial prefrontal cortex mI levels or mI/Cr in bipolar children,192,193,195,200 or children at risk for bipolar disorder.195 The effects of lithium on mI concentrations have been studied children with mania and adolescents with bipolar depression. Davanzo et al.198 reported a significant reduction in anterior cingulate cortex mI/Cr following 7 days of lithium treatment; this occurred specifically in subjects who achieved symptom response. Patel et al.206 investigated the acute (1 week) and chronic (6 weeks) effects of lithium on prefrontal cortex mI in adolescents with bipolar depression. While mI concentrations in these regions at weeks 1 and 6 were not significantly different from those at baseline, mI concentrations at week 6 were elevated as compared to those at week 1 in the medial and right lateral ventral prefrontal cortices.206 This finding is consistent with the study by Friedman et al.157 in adult bipolar depression and suggests that chronic treatment may increase IMPase activity and subsequently mI concentrations. An unpublished study of children with bipolar disorder showed no temporal effects of valproate on anterior cingulate cortex mI levels.207 In children and adolescents with mania, treatment with olanzapine203 or risperidone204 also did not affect prefrontal cortex mI. Lamotrigine, on the other hand, has been shown to increase dorsolateral prefrontal cortex mI/Cr in adolescent bipolar depression.205

Choline (Cho) In euthymic and manic youth with bipolar disorder, unaltered Cho concentrations or Cho/Cr in the anterior

N.C. Patel et al.

cingulate cortex,198–200 cerebellar vermis,196 dorsolateral prefrontal cortex,192,193,195 frontal lobe,197 and temporal lobe197 have been reported. In contrast, significant elevations in prefrontal cortex Cho concentrations in adolescents with bipolar depression have been observed,201 suggesting impaired phospholipid metabolism secondary to mitochondrial dysfunction, or decreased prefrontal cortex metabolism. Olanzapine has been shown to increase prefrontal cortex Cho in manic or mixed adolescents and higher baseline medial prefrontal cortex Cho concentrations predicted symptom remission, identifying a potential biomarker for successful treatment with olanzapine.203 Treatment with lithium and risperidone in youth with bipolar disorder has not been shown to affect Cho/Cr in the prefrontal cortex.198,204

Creatine/Phosphocreatine (Cr) No significant alterations in Cr concentrations have been observed in the anterior cingulate cortex,199,200 medial prefrontal cortex,196 cerebellar vermis,196 or dorsolateral prefrontal cortex193,194 in euthymic, manic, and mixed samples of youth with bipolar disorder. In adolescents with bipolar depression, increased prefrontal cortex Cr levels were found, possibly reflecting abnormal energy metabolism.201 Olanzapine did not significantly affect prefrontal cortex Cr levels in manic adolescents.203

Glutamate/Glutamine/GABA (Glx) Increased Glx/Cr have been reported in the frontal and temporal lobes of euthymic youth,197 and in the medial prefrontal cortex of adolescents with bipolar depression.201 In contrast, manic children with bipolar disorder have exhibited similar anterior cingulate cortex Glx/Cr as normal controls.198,199 Glutamate levels in the left dorsolateral prefrontal cortex193 were found to be no different in bipolar youth compared with normal controls. In a mixed sample, unmedicated children with bipolar disorder had reduced in comparison with normal controls and medicated children with bipolar disorder.200 Children with bipolar disorder and comorbid ADHD were observed to have significantly lower Glx/mI in the anterior cingulate cortex compared to children with ADHD alone.208 Collectively, these data suggest localized glutamate-associated neurotoxicity or abnormal

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

cellular metabolism secondary to mitochondrial dysfunction may occur early in bipolar disorder.149,209 Lithium and olanzapine have been shown to have no temporal effects on prefrontal cortex Glx levels in manic children and adolescents with bipolar disorder.198,203 In a recent, cross-sectional study of pediatric bipolar disorder, anterior cingulate cortex Glx/Cr in stable bipolar children receiving risperidone were found to be significantly higher than those in children with mania.204

191

from ongoing neurodevelopmental processes during adolescence may be possible confounding factors. In contrast to adults, children and adolescents with bipolar disorder have increased prefrontal mI levels. This may represent an early, specific biomarker of bipolar disorder in children and adolescents. Abnormal Cho levels in adults with bipolar disorder suggest that membrane phospholipid metabolism may be dysfunctional. In youth, however, normal Cho levels have been found, suggesting that alterations in Cho levels may occur with increased illness duration.

Developmental Differences Improvements in MRI Research Methods Structural MRI studies across the lifespan have shown decreased prefrontal and superior temporal volumes, and increased putamen volumes in both adults and youths with bipolar disorder. However, smaller amygdala volumes have only been reported in children and adolescents with bipolar disorder, whereas in adults the amygdala tends to be enlarged. Furthermore, ventricular and thalamic abnormalities have been reported in adults with bipolar disorder, but not in bipolar youth. Although these discrepant findings provide evidence for age-specific sMRI abnormalities, it is unclear whether these structural differences result from underlying differences in neural substrates based on age of onset, neurodevelopmental effects of bipolar disorder occurring in children and adolescents, or confounds associated with illness course in adults. Functional MRI studies in bipolar disorder point to functional abnormalities in subcortical brain regions across the age spectrum. In bipolar adults, functional abnormalities in ventral prefrontal brain regions have been consistently reported. In contrast, reports of abnormal prefrontal activation have been inconsistent across fMRI studies of children and adolescents with bipolar disorder. Discrepant results may be attributed to differences in mood states, cognitive tasks, and rates of co-occurring ADHD between study samples. It is also important to recognize that neurodevelopmental processes, such as pruning, are ongoing during adolescence and rapidly changing this brain region, which may confound the results. In both adults and youth with bipolar disorder, decreased NAA levels have been reported. While this may indicate alterations in neuronal integrity or mitochondrial function across the age spectrum, contributions

Future MRI studies need to address current methodological limitations to facilitate the identification of accurate and reliable neuroimaging biomarkers of bipolar disorder. Variability in study samples has contributed to difficulties in interpretation and consolidation of results. Some MRI studies have combined patients with differing diagnoses, mood states, or medication regimens. While future research should aim to improve the homogeneity of patient samples, there must be a balance with regard to generalizability and the subsequent clinical translation of MRI research in bipolar disorder. In addition to subject variability, MRI studies have differed with regard to brain ROIs. An alternate approach would be to examine neural networks implicated in bipolar disorder, such as the anterior limbic network. In MRS, larger regions of interest encompassing a specific neural network may be investigated through whole brain or multi-slice chemical shift imaging methods. The ideal study design for the evaluation of treatment effects and predictors of treatment response is one that incorporates MRI scans before and after treatment with a single medication.202,203,206 If feasible, it is recommended that subjects be scanned on at least three timepoints to be able to determine acute and chronic effects of medication administrations.202,203,206 These longitudinal designs also require a concurrent normal control group that is demographically matched, allowing for the evaluation of normal variability. For the identification of biomarkers of treatment response, the use of validated rating scales is required.210 Furthermore, the use of empirically-based criteria to define response and remission would enhance the consolidation of

192

neuroimaging datasets from multiple studies, which may be necessary for the identification of reliable predictors of successful treatment. While lithium has been the agent most extensively studied using MRI techniques, there are emerging data examining the effects of other psychotropic medications, such as valproate, lamotrigine, and atypical antipsychotics. MRI studies of a single agent will allow for the determination and/or clarification of in vivo mechanisms of action. However, most patients with bipolar disorder require polypharmacy for adequate mood stabilization. Thus, MRI investigations of combination pharmacological treatment are warranted. Regardless of the treatment regimen under study, adequate washout of previous psychotropic medications is required.

Conclusions and Future Directions MRI techniques have revolutionized our ability to study bipolar disorder and will continue to be useful as a research tool to further our understanding of the neuropathophysiology of the illness and the in vivo effects of mood-stabilizing medications, and to identify biological markers of disease and treatment response. The current body of evidence from MRI studies in patients with bipolar disorder suggests alterations in total and localized brain volumes and ventricle size, functional abnormalities in subcortical and prefrontal regions, and biochemical dysregulation in the basal ganglia, hippocampus, and frontal lobe. In spite of recognized current limitations, the use of MRI techniques have and will continue to generate valuable knowledge that will ultimately help guide clinicians to better identify bipolar disorder and tailor pharmacological treatment regimens in order to achieve favorable outcomes. Acknowledgements The authors thank James Eliassen, Ph.D. at the University of Cincinnati Center for Imaging Research and Department of Psychiatry for his assistance with MRI figures.

Disclosures Dr. Patel has received research support from Takeda and the Thrasher Foundation, and is a consultant for

N.C. Patel et al.

Eli Lilly and Shire. Drs. Cerullo, Fleck, and Nandagopal have no financial disclosures. Dr. Adler has received research support from Abbott, AstraZeneca, Bristol Myers Squibb, Eli Lilly, Janssen, Johnson & Johnson, Martek, Pfizer, Repligen, Shire, and Somerset, and is a consultant and on the lecture bureau for AstraZeneca. Dr. Strakowski has received research support from AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Forest Laboratories, Janssen, Martek, Nutrition 21, Pfizer, and Repligen, has consulted with Eli Lilly, Pfizer, and Tikvah, and is on the lecture bureau for DiMedix and the France Foundation. Dr. DelBello has received research support from AstraZeneca, Bristol Myers Squibb, Eli Lilly, Janssen, Johnson & Johnson, Martek, NARSAD, NIAAA, NIDA, NIMH, Pfizer, Repligen, Shire, Somerset, and the Thrasher Foundation, is a consultant for AstraZeneca, Eli Lilly, the France Foundation, GlaxoSmithKline, Kappa Clinical, NIDA, and Pfizer, and is on the lecture bureau for AstraZeneca, Bristol Myers Squibb, and the France Foundation.

References 1. APA, Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision. Washington, DC: American Psychiatric Press; 2000. 2. Baethge C, Baldessarini RJ, Khalsa HM, Hennen J, Salvatore P, Tohen M. Substance abuse in first-episode bipolar I disorder: indications for early intervention. Am J Psychiat 2005;162:1008–1010. 3. McElroy SL, Altshuler LL, Suppes T, Keck PE, Jr., Frye MA, Denicoff KD, et al. Axis I psychiatric comorbidity and its relationship to historical illness variables in 288 patients with bipolar disorder. Am J Psychiat 2001;158:420–426. 4. Valtonen H, Suominen K, Mantere O, Leppamaki S, Arvilommi P, Isometsa ET. Suicidal ideation and attempts in bipolar I and II disorders. J Clin Psychiat 2005;66: 1456–1462. 5. Weissman MM, Bland RC, Canino GJ, Faravelli C, Greenwald S, Hwu HG, et al. Cross-national epidemiology of major depression and bipolar disorder. JAMA 1996; 276:293–299. 6. Woods SW. The economic burden of bipolar disease. J Clin Psychiat 2000;61:38–41. 7. Stimmel GL. Economic grand rounds: the economic burden of bipolar disorder. Psychiat Serv 2004;55:117–118. 8. Hirschfeld RM, Lewis L, Vornik LA. Perceptions and impact of bipolar disorder: how far have we really come? Results of the national depressive and manic-depressive association 2000 survey of individuals with bipolar disorder. J Clin Psychiat 2003;64:161–174.

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

9. Lish JD, Dime-Meenan S, Whybrow PC, Price RA, Hirschfeld RM. The National Depressive and Manicdepressive Association (DMDA) survey of bipolar members. J Affect Disord 1994;31:281–294. 10. Goodwin FK. Rationale for long-term treatment of bipolar disorder and evidence for long-term lithium treatment. J Clin Psychiat 2002;63(Suppl 10):5–12. 11. Wehr TA, Goodwin FK. Can antidepressants cause mania and worsen the course of affective illness? Am J Psychiat 1987;144:1403–1411. 12. Levine J, Chengappa KN, Brar JS, Gershon S, Yablonsky E, Stapf D, et al. Psychotropic drug prescription patterns among patients with bipolar I disorder. Bipolar Disord 2000;2:120–130. 13. Ghaemi SN, Hsu DJ, Thase ME, Wisniewski SR, Nierenberg AA, Miyahara S, et al. Pharmacological treatment patterns at study entry for the first 500 STEP-BD participants. Psychiat Serv 2006;57:660–665. 14. Bhangoo RK, Lowe CH, Myers FS, Treland J, Curran J, Towbin KE, et al. Medication use in children and adolescents treated in the community for bipolar disorder. J Child Adol Psychopharmacol 2003;13:515–522. 15. Perlis RH, Ostacher MJ, Patel JK, Marangell LB, Zhang H, Wisniewski SR, et al. Predictors of recurrence in bipolar disorder: primary outcomes from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Am J Psychiat 2006;163:217–224. 16. Pavuluri MN, Birmaher B, Naylor MW. Pediatric bipolar disorder: a review of the past 10 years. J Am Acad Child Adol Psychiat 2005;44:846–871. 17. DelBello MP, Soutullo CA, Hendricks W, Niemeier RT, McElroy SL, Strakowski SM. Prior stimulant treatment in adolescents with bipolar disorder: association with age at onset. Bipolar Disord 2001;3:53–57. 18. Biederman J, Mick E, Spencer TJ, Wilens TE, Faraone SV. Therapeutic dilemmas in the pharmacotherapy of bipolar depression in the young. J Child Adol Psychopharmacol 2000;10:185–192. 19. Soutullo CA, DelBello MP, Ochsner JE, McElroy SL, Taylor SA, Strakowski SM, et al. Severity of bipolarity in hospitalized manic adolescents with history of stimulant or antidepressant treatment. J Affect Disord 2002;70:323–327. 20. Chang K, Adleman N, Wagner C, Barnea-Goraly N, Garrett A. Will neuroimaging ever be used to diagnose pediatric bipolar disorder? Dev Psychopathol 2006;18:1133–1146. 21. Beaulieu C. The basis of anisotropic water diffusion in the nervous system – a technical review. NMR Biomed 2002; 15:435–455. 22. Altshuler LL, Curran JG, Hauser P, Mintz J, Denicoff K, Post R. T2 hyperintensities in bipolar disorder: magnetic resonance imaging comparison and literature meta-analysis. Am J Psychiat 1995;152:1139–1144. 23. Aylward EH, Roberts-Twillie JV, Barta PE, Kumar AJ, Harris GJ, Geer M, et al. Basal ganglia volumes and white matter hyperintensities in patients with bipolar disorder. Am J Psychiat 1994;151:687–693. 24. Bearden CE, Hoffman KM, Cannon TD. The neuropsychology and neuroanatomy of bipolar affective disorder: a critical review. Bipolar Disord 2001;3:106–150. 25. Norris SD, Krishnan KR, Ahearn E. Structural changes in the brain of patients with bipolar affective disorder by MRI:

193

a review of the literature. Prog Neuropsychopharmacol Biol Psychiat 1997;21:1323–1337. 26. Strakowski SM, DelBello MP, Adler C, Cecil KM, Sax KW. Neuroimaging in bipolar disorder. Bipolar Disord 2000;2:148–164. 27. Adler CM, Holland SK, Schmithorst V, Wilke M, Weiss KL, Pan H, et al. Abnormal frontal white matter tracts in bipolar disorder: a diffusion tensor imaging study. Bipolar Disord 2004;6:197–203. 28. Shenton ME, Dickey CC, Frumin M, McCarley RW. A review of MRI findings in schizophrenia. Schizophr Res 2001;49:1–52. 29. Strakowski SM, Wilson DR, Tohen M, Woods BT, Douglass AW, Stoll AL. Structural brain abnormalities in first-episode mania. Biol Psychiat 1993;33:602–609. 30. Coffey CE, Wilkinson WE, Weiner RD, Parashos IA, Djang WT, Webb MC, et al. Quantitative cerebral anatomy in depression. A controlled magnetic resonance imaging study. Arch Gen Psychiat 1993;50:7–16. 31. Husain MM, McDonald WM, Doraiswamy PM, Figiel GS, Na C, Escalona PR, et al. A magnetic resonance imaging study of putamen nuclei in major depression. Psychiat Res 1991;40:95–99. 32. Krishnan KR, McDonald WM, Escalona PR, Doraiswamy PM, Na C, Husain MM, et al. Magnetic resonance imaging of the caudate nuclei in depression. Preliminary observations. Arch Gen Psychiat 1992;49:553–557. 33. Kumar A, Bilker W, Jin Z, Udupa J. Atrophy and high intensity lesions: complementary neurobiological mechanisms in late-life major depression. Neuropsychopharmacology 2000;22:264–274. 34. Haldane M, Frangou S. New insights help define the pathophysiology of bipolar affective disorder: neuroimaging and neuropathology findings. Prog Neuropsychopharmacol Biol Psychiat 2004;28:943–960. 35. Hoge EA, Friedman L, Schulz SC. Meta-analysis of brain size in bipolar disorder. Schizophr Res 1999;37:177–181. 36. McDonald C, Zanelli J, Rabe-Hesketh S, Ellison-Wright I, Sham P, Kalidindi S, et al. Meta-analysis of magnetic resonance imaging brain morphometry studies in bipolar disorder. Biol Psychiat 2004;56:411–417. 37. Strakowski S, Adler C, DelBello M. Volumetric MRI studies of mood disorders: do they distinguish unipolar and bipolar disorder? Bipolar Disord 2002;4:80–88. 38. Burzaco J, Stereotactic surgery in the treatment of obsessivecompulsive neurosis. In: C. Perris, G. Struwe, and B. Jansson, eds. Biological Psychiatry. Amsterdam: Elsevier/ North Holland; 1981. 39. Harvey I, Persaud R, Ron MA, Baker G, Murray RM. Volumetric MRI measurements in bipolars compared with schizophrenics and healthy controls. Psychol Med 1994;24:689–699. 40. Sax KW, Strakowski SM, Zimmerman ME, DelBello MP, Keck PE, Jr., Hawkins JM. Frontosubcortical neuroanatomy and the continuous performance test in mania. Am J Psychiat 1999;156:139–141. 41. Drevets W, Price J, Simpson Jr J, Todd R, Reich T, Vannier M, et al. Subgenual prefrontal cortex abnormalities in mood disorders. Nature 1997;24:824–827. 42. Lopez-Larson MP, DelBello MP, Zimmerman ME, Schwiers ML, Strakowski SM. Regional prefrontal gray

194 and white matter abnormalities in bipolar disorder. Biol Psychiat 2002;52:93–100. 43. Pearlson GD, Barta PE, Powers RE, Menon RR, Richards SS, Aylward EH, et al. Ziskind-Somerfeld Research Award 1996. Medial and superior temporal gyral volumes and cerebral asymmetry in schizophrenia versus bipolar disorder. Biol Psychiat 1997;41:1–14. 44. Schlaepfer TE, Harris GJ, Tien AY, Peng LW, Lee S, Federman EB, et al. Decreased regional cortical gray matter volume in schizophrenia. Am J Psychiat 1994; 151:842–848. 45. Altshuler LL, Bartzokis G, Grieder T, Curran J, Mintz J. Amygdala enlargement in bipolar disorder and hippocampal reduction in schizophrenia: an MRI study demonstrating neuroanatomic specificity. Arch Gen Psychiat 1998; 55:663–664. 46. Strakowski S, DelBello M, Sax K, Zimmerman M, Shear P, Hawkins J, et al. Brain magnetic resonance imaging of structural abnormalities in bipolar disorder. Arch Gen Psychiat 1999;56:254–260. 47. Altshuler LL, Bartzokis G, Grieder T, Curran J, Jimenez T, Leight K, et al. An MRI study of temporal lobe structures in men with bipolar disorder or schizophrenia. Biol Psychiat 2000;48:147–162. 48. Videbech P, Ravnkilde B. Hippocampal volume and depression: a meta-analysis of MRI studies. Am J Psychiat 2004;161:1957–1966. 49. McIntosh AM, Job DE, Moorhead TW, Harrison LK, Forrester K, Lawrie SM, et al. Voxel-based morphometry of patients with schizophrenia or bipolar disorder and their unaffected relatives. Biol Psychiat 2004;56:544–552. 50. Dupont RM, Jernigan TL, Heindel W, Butters N, Shafer K, Wilson T, et al. Magnetic resonance imaging and mood disorders. Localization of white matter and other subcortical abnormalities. Arch Gen Psychiat 1995;52:747–755. 51. Swayze VW, 2nd, Andreasen NC, Alliger RJ, Yuh WT, Ehrhardt JC. Subcortical and temporal structures in affective disorder and schizophrenia: a magnetic resonance imaging study. Biol Psychiat 1992;31:221–240. 52. Caetano SC, Sassi R, Brambilla P, Harenski K, Nicoletti M, Mallinger AG, et al. MRI study of thalamic volumes in bipolar and unipolar patients and healthy individuals. Psychiat Res 2001;108:161–168. 53. Dupont RM, Butters N, Schafer K, Wilson T, Hesselink J, Gillin JC. Diagnostic specificity of focal white matter abnormalities in bipolar and unipolar mood disorder. Biol Psychiat 1995;38:482–486. 54. Brambilla P, Harenski K, Nicoletti M, Mallinger AG, Frank E, Kupfer DJ, et al. MRI study of posterior fossa structures and brain ventricles in bipolar patients. J Psychiat Res 2001;35:313–322. 55. DelBello M, Strakowski S, Zimmerman M, Hawkins J, Sax K. MRI analysis of the cerebellum in bipolar disorder: a pilot study. Neuropsychopharmacology 1999;21:63–68. 56. Mills N, DelBello M, Adler C, Strakowski S. MRI analysis of cerebellar vermal abnormalities in bipolar disorder. Am J Psychiat 2005;162:1530–1532. 57. Stoll AL, Renshaw PF, Yurgelun-Todd DA, Cohen BM. Neuroimaging in bipolar disorder: what have we learned? Biol Psychiat 2000;48:505–517.

N.C. Patel et al. 58. Rangel-Guerra RA, Perez-Payan H, Minkoff L, Todd LE. Nuclear magnetic resonance in bipolar affective disorders. AJNR Am J Neuroradiol 1983;4:229–231. 59. Moore GJ, Bebchuk JM, Wilds IB, Chen G, Manji HK. Lithium-induced increase in human brain grey matter. Lancet 2000;356:1241–1242. 60. Figueroa R, Harenski K, Nicoletti M, Brambilla P, Mallinger AG, Frank E, et al. Dorsolateral prefrontal cortex abnormalities in bipolar disorder: possible effects of lithium treatment? Biol Psychiat 2000;47:S103–S104. 61. Drevets WC. Neuroimaging studies of mood disorders. Biol Psychiat 2000;48:813–829. 62. Chen CH, Ridler K, Suckling J, Williams S, Fu CH, MerloPich E, et al. Brain imaging correlates of depressive symptom severity and predictors of symptom improvement after antidepressant treatment. Biol Psychiat 2007;62:407–414. 63. Botteron KN, Figiel GS, Wetzel MW, Hudziak J, VanEerdewegh M. MRI abnormalities in adolescent bipolar affective disorder. J Am Acad Child Adol Psychiat 1992;31:258–261. 64. Botteron KN, Vannier MW, Geller B, Todd RD, Lee BC. Preliminary study of magnetic resonance imaging characteristics in 8- to 16-year-olds with mania. J Am Acad Child Adol Psychiat 1995;34:742–749. 65. Lyoo IK, Lee HK, Jung JH, Noam GG, Renshaw PF. White matter hyperintensities on magnetic resonance imaging of the brain in children with psychiatric disorders. Compr Psychiat 2002;43:361–368. 66. Pillai JJ, Friedman L, Stuve TA, Trinidad S, Jesberger JA, Lewin JS, et al. Increased presence of white matter hyperintensities in adolescent patients with bipolar disorder. Psychiat Res 2002;114:51–56. 67. Chang K, Barnea-Goraly N, Karchemskiy A, Simeonova DI, Barnes P, Ketter T, et al. Cortical magnetic resonance imaging findings in familial pediatric bipolar disorder. Biol Psychiat 2005;58:197–203. 68. Adler CM, Adams J, DelBello MP, Holland SK, Schmithorst V, Levine A, et al. Evidence of white matter pathology in bipolar disorder adolescents experiencing their first episode of mania: a diffusion tensor imaging study. Am J Psychiat 2006;163:322–324. 69. Friedman L, Findling RL, Kenny JT, Swales TP, Stuve TA, Jesberger JA, et al. An MRI study of adolescent patients with either schizophrenia or bipolar disorder as compared to healthy control subjects. Biol Psychiat 1999;46:78–88. 70. Strakowski SM, DelBello MP, Zimmerman ME, Getz GE, Mills NP, Ret J, et al. Ventricular and periventricular structural volumes in first- versus multiple-episode bipolar disorder. Am J Psychiat 2002;159:1841–1847. 71. Blumberg HP, Kaufman J, Martin A, Whiteman R, Zhang JH, Gore JC, et al. Amygdala and hippocampal volumes in adolescents and adults with bipolar disorder. Arch Gen Psychiat 2003;60:1201–1208. 72. DelBello MP, Zimmerman ME, Mills NP, Getz GE, Strakowski SM. Magnetic resonance imaging analysis of amygdala and other subcortical brain regions in adolescents with bipolar disorder. Bipolar Disord 2004;6:43–52. 73. Frazier JA, Chiu S, Breeze JL, Makris N, Lange N, Kennedy DN, et al. Structural brain magnetic resonance

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

imaging of limbic and thalamic volumes in pediatric bipolar disorder. Am J Psychiat 2005;162:1256–1265. 74. Wilke M, Kowatch RA, DelBello MP, Mills NP, Holland SK. Voxel-based morphometry in adolescents with bipolar disorder: first results. Psychiat Res 2004;131:57–69. 75. Dickstein DP, Milham MP, Nugent AC, Drevets WC, Charney DS, Pine DS, et al. Frontotemporal alterations in pediatric bipolar disorder: results of a voxel-based morphometry study. Arch Gen Psychiat 2005;62:734–741. 76. Sanches M, Sassi RB, Axelson D, Nicoletti M, Brambilla P, Hatch JP, et al. Subgenual prefrontal cortex of child and adolescent bipolar patients: a morphometric magnetic resonance imaging study. Psychiat Res 2005;138:43–49. 77. Ladouceur CD, Almeida JR, Birmaher B, Axelson DA, Nau S, Kalas C, et al. Subcortical gray matter volume abnormalities in healthy bipolar offspring: potential neuroanatomical risk marker for bipolar disorder? J Am Acad Child Adol Psychiat 2008;47:532–539. 78. Singh MK, Delbello MP, Adler CM, Stanford KE, Strakowski SM. Neuroanatomical characterization of child offspring of bipolar parents. J Am Acad Child Adol Psychiat 2008;47:526–531. 79. Blumberg HP, Krystal JH, Bansal R, Martin A, Dziura J, Durkin K, et al. Age, rapid-cycling, and pharmacotherapy effects on ventral prefrontal cortex in bipolar disorder: a cross-sectional study. Biol Psychiat 2006;59:611–618. 80. Chen BK, Sassi R, Axelson D, Hatch JP, Sanches M, Nicoletti M, et al. Cross-sectional study of abnormal amygdala development in adolescents and young adults with bipolar disorder. Biol Psychiat 2004;56:399–405. 81. Chen HH, Nicoletti MA, Hatch JP, Sassi RB, Axelson D, Brambilla P, et al. Abnormal left superior temporal gyrus volumes in children and adolescents with bipolar disorder: a magnetic resonance imaging study. Neurosci Lett 2004;363:65–68. 82. DelBello MP, Adler CM, Strakowski SM. The neurophysiology of childhood and adolescent bipolar disorder. CNS Spectr 2006;11:298–311. 83. Chang K, Karchemskiy A, Barnea-Goraly N, Garrett A, Simeonova DI, Reiss A. Reduced amygdalar gray matter volume in familial pediatric bipolar disorder. J Am Acad Child Adol Psychiat 2005;44:565–573. 84. Rosso IM, Killgore WD, Cintron CM, Gruber SA, Tohen M, Yurgelun-Todd DA. Reduced amygdala volumes in firstepisode bipolar disorder and correlation with cerebral white matter. Biol Psychiat 2007;61:743–749. 85. Filipek PA, Semrud-Clikeman M, Steingard RJ, Renshaw PF, Kennedy DN, Biederman J. Volumetric MRI analysis comparing subjects having attention-deficit hyperactivity disorder with normal controls. Neurology 1997;48:589–601. 86. Schumann CM, Hamstra J, Goodlin-Jones BL, Lotspeich LJ, Kwon H, Buonocore MH, et al. The amygdala is enlarged in children but not adolescents with autism; the hippocampus is enlarged at all ages. J Neurosci 2004;24:6392–6401. 87. Bearden CE, Soares JC, Klunder AD, Nicoletti M, Dierschke N, Hayashi KM, et al. Three-dimensional mapping of hippocampal anatomy in adolescents with bipolar disorder. J Am Acad Child Adol Psychiat 2008;47:515–525. 88. Dasari M, Friedman L, Jesberger J, Stuve TA, Findling RL, Swales TP, et al. A magnetic resonance imaging study of

195

thalamic area in adolescent patients with either schizophrenia or bipolar disorder as compared to healthy controls. Psychiat Res 1999;91:155–162. 89. Sanches M, Roberts RL, Sassi RB, Axelson D, Nicoletti M, Brambilla P, et al. Developmental abnormalities in striatum in young bipolar patients: a preliminary study. Bipolar Disord 2005;7:153–158. 90. Adleman NE, Barnea-Goraly N, Chang KD. Review of magnetic resonance imaging and spectroscopy studies in children with bipolar disorder. Expert Rev Neurother 2004;4:69–77. 91. Blumberg HP, Leung HC, Skudlarski P, Lacadie CM, Fredericks CA, Harris BC, et al. A functional magnetic resonance imaging study of bipolar disorder: state- and trait-related dysfunction in ventral prefrontal cortices. Arch Gen Psychiat 2003;60:601–609. 92. Caligiuri MP, Brown GG, Meloy MJ, Eberson SC, Kindermann SS, Frank LR, et al. An fMRI study of affective state and medication on cortical and subcortical brain regions during motor performance in bipolar disorder. Psychiat Res 2003;123:171–182. 93. Chen CH, Lennox B, Jacob R, Calder A, Lupson V, Bisbrown-Chippendale R, et al. Explicit and implicit facial affect recognition in manic and depressed states of bipolar disorder: a functional magnetic resonance imaging study. Biol Psychiat 2006;59:31–39. 94. Strakowski SM, Adler CM, Holland SK, Mills N, DelBello MP. A preliminary fMRI study of sustained attention in euthymic, unmedicated bipolar disorder. Neuropsychopharmacology 2004;29:1734–1740. 95. Adler CM, Holland SK, Schmithorst V, Tuchfarber MJ, Strakowski SM. Changes in neuronal activation in patients with bipolar disorder during performance of a working memory task. Bipolar Disord 2004;6:540–549. 96. Lagopoulos J, Ivanovski B, Malhi GS. An event-related functional MRI study of working memory in euthymic bipolar disorder. J Psychiat Neurosci 2007;32:174–184. 97. Curtis VA, Dixon TA, Morris RG, Bullmore ET, Brammer MJ, Williams SC, et al. Differential frontal activation in schizophrenia and bipolar illness during verbal fluency. J Affect Disord 2001;66:111–121. 98. Curtis VA, Thompson JM, Seal ML, Monks PJ, Lloyd AJ, Harrison L, et al. The nature of abnormal language processing in euthymic bipolar I disorder: evidence for a relationship between task demand and prefrontal function. Bipolar Disord 2007;9:358–369. 99. Lawrence NS, Williams AM, Surguladze S, Giampietro V, Brammer MJ, Andrew C, et al. Subcortical and ventral prefrontal cortical neural responses to facial expressions distinguish patients with bipolar disorder and major depression. Biol Psychiat 2004;55:578–587. 100. Kronhaus DM, Lawrence NS, Williams AM, Frangou S, Brammer MJ, Williams SC, et al. Stroop performance in bipolar disorder: further evidence for abnormalities in the ventral prefrontal cortex. Bipolar Disord 2006;8:28–39. 101. Yurgelun-Todd DA, Gruber SA, Kanayama G, Killgore WD, Baird AA, Young AD. fMRI during affect discrimination in bipolar affective disorder. Bipolar Disord 2000; 2:237–248. 102. Lennox BR, Jacob R, Calder AJ, Lupson V, Bullmore ET. Behavioural and neurocognitive responses to sad facial

196 affect are attenuated in patients with mania. Psychol Med 2004;34:795–802. 103. Malhi GS, Lagopoulos J, Sachdev P, Mitchell PB, Ivanovski B, Parker GB. Cognitive generation of affect in hypomania: an fMRI study. Bipolar Disord 2004;6:271–285. 104. Malhi GS, Lagopoulos J, Owen AM, Ivanovski B, Shnier R, Sachdev P. Reduced activation to implicit affect induction in euthymic bipolar patients: an fMRI study. J Affect Disord 2007;97:109–122. 105. Altshuler L, Bookheimer S, Proenza MA, Townsend J, Sabb F, Firestine A, et al. Increased amygdala activation during mania: a functional magnetic resonance imaging study. Am J Psychiat 2005;162:1211–1213. 106. Marchand WR, Lee JN, Thatcher GW, Jensen C, Stewart D, Dilda V, et al. A functional MRI study of a paced motor activation task to evaluate frontal-subcortical circuit function in bipolar depression. Psychiat Res 2007;155:221–230. 107. Blumberg HP, Martin A, Kaufman J, Leung HC, Skudlarski P, Lacadie C, et al. Frontostriatal abnormalities in adolescents with bipolar disorder: preliminary observations from functional MRI. Am J Psychiat 2003;160:1345–1347. 108. Chang K, Adleman NE, Dienes K, Simeonova DI, Menon V, Reiss A. Anomalous prefrontal-subcortical activation in familial pediatric bipolar disorder: a functional magnetic resonance imaging investigation. Arch Gen Psychiat 2004;61:781–792. 109. Nelson EE, Vinton DT, Berghorst L, Towbin KE, Hommer RE, Dickstein DP, et al. Brain systems underlying response flexibility in healthy and bipolar adolescents: an event-related fMRI study. Bipolar Disord 2007;9:810–819. 110. Pavuluri MN, O’Connor MM, Harral E, Sweeney JA. Affective neural circuitry during facial emotion processing in pediatric bipolar disorder. Biol Psychiat 2007;62:158–167. 111. Pavuluri MN, O’Connor MM, Harral EM, Sweeney JA. An fMRI study of the interface between affective and cognitive neural circuitry in pediatric bipolar disorder. Psychiat Res 2008;162:244–255. 112. Dickstein DP, Rich BA, Roberson-Nay R, Berghorst L, Vinton D, Pine DS, et al. Neural activation during encoding of emotional faces in pediatric bipolar disorder. Bipolar Disord 2007;9:679–692. 113. Leibenluft E, Rich BA, Vinton DT, Nelson EE, Fromm SJ, Berghorst LH, et al. Neural circuitry engaged during unsuccessful motor inhibition in pediatric bipolar disorder. Am J Psychiat 2007;164:52–60. 114. Rich BA, Vinton DT, Roberson-Nay R, Hommer RE, Berghorst LH, McClure EB, et al. Limbic hyperactivation during processing of neutral facial expressions in children with bipolar disorder. Proc Natl Acad Sci USA 2006;103:8900–8905. 115. Rich BA, Fromm SJ, Berghorst LH, Dickstein DP, Brotman MA, Pine DS, et al. Neural connectivity in children with bipolar disorder: impairment in the face emotion processing circuit. J Child Psychol Psychiat 2008;49:88–96. 116. Adler CM, Delbello MP, Mills NP, Schmithorst V, Holland S, Strakowski SM. Comorbid ADHD is associated with altered patterns of neuronal activation in adolescents with bipolar disorder performing a simple attention task. Bipolar Disord 2005;7:577–588. 117. Chang KD, Wagner C, Garrett A, Howe M, Reiss A. A preliminary functional magnetic resonance imaging study of

N.C. Patel et al. prefrontal-amygdalar activation changes in adolescents with bipolar depression treated with lamotrigine. Bipolar Disord 2008;10:426–431. 118. Kato T, Inubushi T, Kato N. Magnetic resonance spectroscopy in affective disorders. J Neuropsychiat Clin Neurosci 1998;10:133–147. 119. Post RM, Speer AM, Hough CJ, Xing G. Neurobiology of bipolar illness: implications for future study and therapeutics. Ann Clin Psychiat 2003;15:85–94. 120. Soares JC, Krishnan KR, Keshavan MS. Nuclear magnetic resonance spectroscopy: new insights into the pathophysiology of mood disorders. Depression 1996;4:14–30. 121. Tsai G, Coyle JT. N-acetylaspartate in neuropsychiatric disorders. Prog Neurobiol 1995;46:531–540. 122. Charles HC, Lazeyras F, Krishnan KR, Boyko OB, Patterson LJ, Doraiswamy PM, et al. Proton spectroscopy of human brain: effects of age and sex. Prog Neuropsychopharmacol Biol Psychiat 1994;18:995–1004. 123. van der Knaap MS, van der Grond J, van Rijen PC, Faber JA, Valk J, Willemse K. Age-dependent changes in localized proton and phosphorus MR spectroscopy of the brain. Radiology 1990;176:509–515. 124. Delbello MP, Strakowski SM. Neurochemical predictors of response to pharmacologic treatments for bipolar disorder. Curr Psychiat Rep 2004;6:466–472. 125. Allison JH, Stewart MA. Reduced brain inositol in lithiumtreated rats. Nat New Biol 1971;233:267–268. 126. Berridge MJ. The Albert Lasker Medical Awards. Inositol trisphosphate, calcium, lithium, and cell signaling. JAMA 1989;262:1834–1841. 127. Moore GJ, Galloway MP. Magnetic resonance spectroscopy: neurochemistry and treatment effects in affective disorders. Psychopharmacol Bull 2002;36:5–23. 128. Glitz DA, Manji HK, Moore GJ. Mood disorders: treatment-induced changes in brain neurochemistry and structure. Semin Clin Neuropsychiat 2002;7:269–280. 129. Soares JC, Boada F, Keshavan MS. Brain lithium measurements with (7)Li magnetic resonance spectroscopy (MRS): a literature review. Eur Neuropsychopharmacol 2000;10:151–158. 130. Deicken RF, Pegues MP, Anzalone S, Feiwell R, Soher B. Lower concentration of hippocampal N-acetylaspartate in familial bipolar I disorder. Am J Psychiat 2003;160:873–882. 131. Molina V, Sanchez J, Sanz J, Reig S, Benito C, Leal I, et al. Dorsolateral prefrontal N-acetyl-aspartate concentration in male patients with chronic schizophrenia and with chronic bipolar disorder. Eur Psychiat 2007;22:505–512. 132. Winsberg ME, Sachs N, Tate DL, Adalsteinsson E, Spielman D, Ketter TA. Decreased dorsolateral prefrontal N-acetyl aspartate in bipolar disorder. Biol Psychiat 2000;47:475–481. 133. Atmaca M, Yildirim H, Ozdemir H, Ogur E, Tezcan E. Hippocampal 1H MRS in patients with bipolar disorder taking valproate versus valproate plus quetiapine. Psychol Med 2007;37:121–129. 134. Scherk H, Backens M, Schneider-Axmann T, Kemmer C, Usher J, Reith W, et al. Neurochemical pathology in hippocampus in euthymic patients with bipolar I disorder. Acta Psychiat Scand 2008;117:283–288. 135. Bhagwagar Z, Wylezinska M, Jezzard P, Evans J, Ashworth F, Sule A, et al. Reduction in occipital cortex gamma-amin-

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

obutyric acid concentrations in medication-free recovered unipolar depressed and bipolar subjects. Biol Psychiat 2007;61:806–812. 136. Frye MA, Thomas MA, Yue K, Binesh N, Davanzo P, Ventura J, et al. Reduced concentrations of N-acetylaspartate (NAA) and the NAA-creatine ratio in the basal ganglia in bipolar disorder: a study using 3-Tesla proton magnetic resonance spectroscopy. Psychiat Res 2007;154:259–265. 137. Atmaca M, Yildirim H, Ozdemir H, Poyraz AK, Tezcan E, Ogur E. Hippocampal 1H MRS in first-episode bipolar I patients. Prog Neuropsychopharmacol Biol Psychiat 2006;30:1235–1239. 138. Cecil KM, DelBello MP, Morey R, Strakowski SM. Frontal lobe differences in bipolar disorder as determined by proton MR spectroscopy. Bipolar Disord 2002;4:357–365. 139. Bertolino A, Frye M, Callicott JH, Mattay VS, Rakow R, Shelton-Repella J, et al. Neuronal pathology in the hippocampal area of patients with bipolar disorder: a study with proton magnetic resonance spectroscopic imaging. Biol Psychiat 2003;53:906–913. 140. Blasi G, Bertolino A, Brudaglio F, Sciota D, Altamura M, Antonucci N, et al. Hippocampal neurochemical pathology in patients at first episode of affective psychosis: a proton magnetic resonance spectroscopic imaging study. Psychiat Res 2004;131:95–105. 141. Amaral JA, Tamada RS, Issler CK, Caetano SC, Cerri GG, de Castro CC, et al. A 1HMRS study of the anterior cingulate gyrus in euthymic bipolar patients. Hum Psychopharmacol 2006;21:215–220. 142. Scherk H, Backens M, Schneider-Axmann T, Usher J, Kemmer C, Reith W, et al. Cortical neurochemistry in euthymic patients with bipolar I disorder. World J Biol Psychiat 2007:1–10. 143. Brambilla P, Stanley JA, Nicoletti MA, Sassi RB, Mallinger AG, Frank E, et al. 1H magnetic resonance spectroscopy investigation of the dorsolateral prefrontal cortex in bipolar disorder patients. J Affect Disord 2005;86:61–67. 144. Hamakawa H, Kato T, Murashita J, Kato N. Quantitative proton magnetic resonance spectroscopy of the basal ganglia in patients with affective disorders. Eur Arch Psychiat Clin Neurosci 1998;248:53–58. 145. Kato T, Hamakawa H, Shioiri T, Murashita J, Takahashi Y, Takahashi S, et al. Choline-containing compounds detected by proton magnetic resonance spectroscopy in the basal ganglia in bipolar disorder. J Psychiat Neurosci 1996;21:248–254. 146. Ohara K, Isoda H, Suzuki Y, Takehara Y, Ochiai M, Takeda H, et al. Proton magnetic resonance spectroscopy of the lenticular nuclei in bipolar I affective disorder. Psychiat Res 1998;84:55–60. 147. Frey BN, Folgierini M, Nicoletti M, Machado-Vieira R, Stanley JA, Soares JC, et al. A proton magnetic resonance spectroscopy investigation of the dorsolateral prefrontal cortex in acute mania. Hum Psychopharmacol 2005;20:133–139. 148. Michael N, Erfurth A, Ohrmann P, Gossling M, Arolt V, Heindel W, et al. Acute mania is accompanied by elevated glutamate/glutamine levels within the left dorsolateral prefrontal cortex. Psychopharmacology (Berl) 2003;168:344–346. 149. Dager SR, Friedman SD, Parow A, Demopulos C, Stoll AL, Lyoo IK, et al. Brain metabolic alterations in medica-

197

tion-free patients with bipolar disorder. Arch Gen Psychiat 2004;61:450–458. 150. Frye MA, Watzl J, Banakar S, O’Neill J, Mintz J, Davanzo P, et al. Increased anterior cingulate/medial prefrontal cortical glutamate and creatine in bipolar depression. Neuropsychopharmacology 2007;32:2490–2499. 151. Moore GJ, Bebchuk JM, Hasanat K, Chen G, SerajiBozorgzad N, Wilds IB, et al. Lithium increases N-acetylaspartate in the human brain: in vivo evidence in support of bcl-2’s neurotrophic effects? Biol Psychiat 2000;48:1–8. 152. Frey BN, Stanley JA, Nery FG, Monkul ES, Nicoletti MA, Chen HH, et al. Abnormal cellular energy and phospholipid metabolism in the left dorsolateral prefrontal cortex of medication-free individuals with bipolar disorder: an in vivo 1H MRS study. Bipolar Disord 2007;9(Suppl 1):119–127. 153. Deicken RF, Eliaz Y, Feiwell R, Schuff N. Increased thalamic N-acetylaspartate in male patients with familial bipolar I disorder. Psychiat Res 2001;106:35–45. 154. Manji HK, Moore GJ, Chen G. Clinical and preclinical evidence for the neurotrophic effects of mood stabilizers: implications for the pathophysiology and treatment of manic-depressive illness. Biol Psychiat 2000;48:740–754. 155. Sharma R, Venkatasubramanian PN, Barany M, Davis JM. Proton magnetic resonance spectroscopy of the brain in schizophrenic and affective patients. Schizophr Res 1992;8:43–49. 156. Silverstone PH, Wu RH, O’Donnell T, Ulrich M, Asghar SJ, Hanstock CC. Chronic treatment with lithium, but not sodium valproate, increases cortical N-acetyl-aspartate concentrations in euthymic bipolar patients. Int Clin Psychopharmacol 2003;18:73–79. 157. Friedman SD, Dager SR, Parow A, Hirashima F, Demopulos C, Stoll AL, et al. Lithium and valproic acid treatment effects on brain chemistry in bipolar disorder. Biol Psychiat 2004;56:340–348. 158. Frangou S, Lewis M, Wollard J, Simmons A. Preliminary in vivo evidence of increased N-acetyl-aspartate following eicosapentanoic acid treatment in patients with bipolar disorder. J Psychopharmacol 2007;21:435–439. 159. Kim DJ, Lyoo IK, Yoon SJ, Choi T, Lee B, Kim JE, et al. Clinical response of quetiapine in rapid cycling manic bipolar patients and lactate level changes in proton magnetic resonance spectroscopy. Prog Neuropsychopharmacol Biol Psychiat 2007;31:1182–1188. 160. Silverstone PH, Wu RH, O’Donnell T, Ulrich M, Asghar SJ, Hanstock CC. Chronic treatment with both lithium and sodium valproate may normalize phosphoinositol cycle activity in bipolar patients. Hum Psychopharmacol 2002;17:321–327. 161. Bruhn H, Stoppe G, Staedt J, Merboldt KD, Hanicke W, Frahm J. Quantitative proton MRS in vivo shows cerebral myo-inositol and cholines to be unchanged in manicdepressive patients treated with lithium. Proc Soc Magn Reson Med 1993:1543. 162. Moore CM, Breeze JL, Gruber SA, Babb SM, Frederick BB, Villafuerte RA, et al. Choline, myo-inositol and mood in bipolar disorder: a proton magnetic resonance spectroscopic imaging study of the anterior cingulate cortex. Bipolar Disord 2000;2:207–216. 163. Moore GJ, Bebchuk JM, Parrish JK, Faulk MW, Arfken CL, Strahl-Bevacqua J, et al. Temporal dissociation between lithium-induced changes in frontal lobe myo-inositol and

198 clinical response in manic-depressive illness. Am J Psychiat 1999;156:1902–1908. 164. Kaya N, Resmi H, Ozerdem A, Guner G, Tunca Z. Increased inositol-monophosphatase activity by lithium treatment in bipolar patients. Prog Neuropsychopharmacol Biol Psychiat 2004;28:521–527. 165. Brambilla P, Stanley JA, Sassi RB, Nicoletti MA, Mallinger AG, Keshavan MS, et al. 1H MRS study of dorsolateral prefrontal cortex in healthy individuals before and after lithium administration. Neuropsychopharmacology 2004;29:1918–1924. 166. Silverstone PH, Hanstock CC, Fabian J, Staab R, Allen PS. Chronic lithium does not alter human myo-inositol or phosphomonoester concentrations as measured by 1H and 31P MRS. Biol Psychiat 1996;40:235–246. 167. Silverstone PH, Hanstock CC, Rotzinger S. Lithium does not alter the choline/creatine ratio in the temporal lobe of human volunteers as measured by proton magnetic resonance spectroscopy. J Psychiat Neurosci 1999;24:222–226. 168. Lafer B, Renshaw PF, Sachs G, Christensen JD, YurgelunTodd DA, Stoll AL. Proton MRS of the basal ganglia in bipolar disorder. Biol Psychiat 1994;35:685. 169. Kato T, Hamakawa H, Shioiri T, Murashita J, Inubushi T, Takahashi S. Proton MRS of the basal ganglia in patients with bipolar disorders. Proc Soc Magn Reson Med 1994:605. 170. Hamakawa H, Kato T, Shioiri T, Inubushi T, Kato N. Quantitative proton magnetic resonance spectroscopy of the bilateral frontal lobes in patients with bipolar disorder. Psychol Med 1999;29:639–644. 171. Stoll AL, Renshaw PF, Sachs GS, Guimaraes AR, Miller C, Cohen BM, et al. The human brain resonance of cholinecontaining compounds is similar in patients receiving lithium treatment and controls: an in vivo proton magnetic resonance spectroscopy study. Biol Psychiat 1992;32:944–949. 172. Wu RH, O’Donnell T, Ulrich M, Asghar SJ, Hanstock CC, Silverstone PH. Brain choline concentrations may not be altered in euthymic bipolar disorder patients chronically treated with either lithium or sodium valproate. Ann Gen Hosp Psychiat 2004;3:13. 173. Kato T, Shioiri T, Takahashi S, Inubushi T. Measurement of brain phosphoinositide metabolism in bipolar patients using in vivo 31P-MRS. J Affect Disord 1991;22:185–190. 174. Kato T, Takahashi S, Shioiri T, Inubushi T. Brain phosphorous metabolism in depressive disorders detected by phosphorus-31 magnetic resonance spectroscopy. J Affect Disord 1992;26:223–230. 175. Kato T, Takahashi S, Shioiri T, Inubushi T. Alterations in brain phosphorous metabolism in bipolar disorder detected by in vivo 31P and 7Li magnetic resonance spectroscopy. J Affect Disord 1993;27:53–59. 176. Deicken RF, Weiner MW, Fein G. Decreased temporal lobe phosphomonoesters in bipolar disorder. J Affect Disord 1995;33:195–199. 177. Deicken RF, Fein G, Weiner MW. Abnormal frontal lobe phosphorous metabolism in bipolar disorder. Am J Psychiat 1995;152:915–918. 178. Kato T, Shioiri T, Murashita J, Hamakawa H, Inubushi T, Takahashi S. Phosphorus-31 magnetic resonance spectroscopy and ventricular enlargement in bipolar disorder. Psychiat Res 1994;55:41–50.

N.C. Patel et al. 179. Kato T, Takahashi S, Shioiri T, Murashita J, Hamakawa H, Inubushi T. Reduction of brain phosphocreatine in bipolar II disorder detected by phosphorus-31 magnetic resonance spectroscopy. J Affect Disord 1994;31:125–133. 180. Kato T, Shioiri T, Murashita J, Hamakawa H, Takahashi Y, Inubushi T, et al. Lateralized abnormality of high energy phosphate metabolism in the frontal lobes of patients with bipolar disorder detected by phase-encoded 31P-MRS. Psychol Med 1995;25:557–566. 181. Renshaw PF, Summers JJ, Renshaw CE, Hines KG, Leigh JS, Jr. Changes in the 31P-NMR spectra of cats receiving lithium chloride systemically. Biol Psychiat 1986; 21:694–698. 182. Kato T, Inubushi T, Kato N. Prediction of lithium response by 31P-MRS in bipolar disorder. Int J Neuropsychopharmacol 2000;3:83–85. 183. Gyulai L, Wicklund SW, Greenstein R, Bauer MS, Ciccione P, Whybrow PC, et al. Measurement of tissue lithium concentration by lithium magnetic resonance spectroscopy in patients with bipolar disorder. Biol Psychiat 1991;29:1161–1170. 184. Kato T, Inubushi T, Takahashi S. Relationship of lithium concentrations in the brain measured by lithium-7 magnetic resonance spectroscopy to treatment response in mania. J Clin Psychopharmacol 1994;14:330–335. 185. Kato T, Shioiri T, Inubushi T, Takahashi S. Brain lithium concentrations measured with lithium-7 magnetic resonance spectroscopy in patients with affective disorders: relationship to erythrocyte and serum concentrations. Biol Psychiat 1993;33:147–152. 186. Kato T, Takahashi S, Inubushi T. Brain lithium concentration by 7Li- and 1H-magnetic resonance spectroscopy in bipolar disorder. Psychiat Res 1992;45:53–63. 187. Sachs GS, Renshaw PF, Lafer B, Stoll AL, Guimaraes AR, Rosenbaum JF, et al. Variability of brain lithium levels during maintenance treatment: a magnetic resonance spectroscopy study. Biol Psychiat 1995;38:422–428. 188. Moore CM, Demopulos CM, Henry ME, Steingard RJ, Zamvil L, Katic A, et al. Brain-to-serum lithium ratio and age: an in vivo magnetic resonance spectroscopy study. Am J Psychiat 2002;159:1240–1242. 189. Jensen HV, Plenge P, Stensgaard A, Mellerup ET, Thomsen C, Aggernaes H, et al. Twelve-hour brain lithium concentration in lithium maintenance treatment of manic-depressive disorder: daily versus alternate-day dosing schedule. Psychopharmacology (Berl) 1996;124:275–278. 190. Soares JC, Boada F, Spencer S, Mallinger AG, Dippold CS, Wells KF, et al. Brain lithium concentrations in bipolar disorder patients: preliminary (7)Li magnetic resonance studies at 3 T. Biol Psychiat 2001;49:437–443. 191. Kato T, Fujii K, Shioiri T, Inubushi T, Takahashi S. Lithium side effects in relation to brain lithium concentration measured by lithium-7 magnetic resonance spectroscopy. Prog Neuropsychopharmacol Biol Psychiat 1996;20:87–97. 192. Chang K, Adleman N, Dienes K, Barnea-Goraly N, Reiss A, Ketter T. Decreased N-acetylaspartate in children with familial bipolar disorder. Biol Psychiat 2003;53:1059–1065. 193. Olvera RL, Caetano SC, Fonseca M, Nicoletti M, Stanley JA, Chen HH, et al. Low levels of N-acetyl aspartate in the left dorsolateral prefrontal cortex of pediatric bipolar patients. J Child Adol Psychopharmacol 2007;17:461–473.

25

Neuroimaging Biomarkers for Bipolar Disorder Across the Lifespan

194. Sassi RB, Stanley JA, Axelson D, Brambilla P, Nicoletti MA, Keshavan MS, et al. Reduced NAA levels in the dorsolateral prefrontal cortex of young bipolar patients. Am J Psychiat 2005;162:2109–2115. 195. Gallelli KA, Wagner CM, Karchemskiy A, Howe M, Spielman D, Reiss A, et al. N-acetylaspartate levels in bipolar offspring with and at high-risk for bipolar disorder. Bipolar Disord 2005;7:589–597. 196. Cecil KM, DelBello MP, Sellars MC, Strakowski SM. Proton magnetic resonance spectroscopy of the frontal lobe and cerebellar vermis in children with a mood disorder and a familial risk for bipolar disorders. J Child Adol Psychopharmacol 2003;13:545–555. 197. Castillo M, Kwock L, Courvoisie H, Hooper SR. Proton MR spectroscopy in children with bipolar affective disorder: preliminary observations. AJNR Am J Neuroradiol 2000;21:832–838. 198. Davanzo P, Thomas MA, Yue K, Oshiro T, Belin T, Strober M, et al. Decreased anterior cingulate myo-inositol/creatine spectroscopy resonance with lithium treatment in children with bipolar disorder. Neuropsychopharmacology 2001; 24:359–369. 199. Davanzo P, Yue K, Thomas MA, Belin T, Mintz J, Venkatraman TN, et al. Proton magnetic resonance spectroscopy of bipolar disorder versus intermittent explosive disorder in children and adolescents. Am J Psychiat 2003;160:1442–1452. 200. Moore CM, Frazier JA, Glod CA, Breeze JL, Dieterich M, Finn CT, et al. Glutamine and glutamate levels in children and adolescents with bipolar disorder: a 4.0-T proton magnetic resonance spectroscopy study of the anterior cingulate cortex. J Am Acad Child Adol Psychiat 2007;46:524–534. 201. Patel NC, Cecil KM, Strakowski SM, Adler CM, DelBello MP. Neurochemical alterations in adolescent bipolar depression: a proton magnetic resonance spectroscopy pilot study of the prefrontal cortex. J Child Adol Psychopharmacol 2008;18:in press. 202. Patel NC, DelBello MP, Cecil KM, Stanford KE, Adler CM, Strakowski SM. Temporal changes in N-acetyl-

199

aspartate concentrations in adolescents with bipolar depression treated with lithium. J Child Adol Psychopharmacol 2008;18:132–139. 203. DelBello MP, Cecil KM, Adler CM, Daniels JP, Strakowski SM. Neurochemical effects of olanzapine in first-hospitalization manic adolescents: a proton magnetic resonance spectroscopy study. Neuropsychopharmacology 2006;31:1264–1273. 204. Moore CM, Biederman J, Wozniak J, Mick E, Aleardi M, Wardrop M, et al. Mania, glutamate/glutamine and risperidone in pediatric bipolar disorder: a proton magnetic resonance spectroscopy study of the anterior cingulate cortex. J Affect Disord 2007;99:19–25. 205. Chang K, Gallelli KA, Howe M, Saxena K, Wagner CM, Spielman D, et al. Prefrontal neurometabolite changes following lamotrigine treatment in adolescents with bipolar depression. Neuropsychopharmacology 2005; 30:S102–S103. 206. Patel NC, DelBello MP, Cecil KM, Adler CM, Bryan HS, Stanford KE, et al. Lithium treatment effects on myo-inositol in adolescents with bipolar depression. Biol Psychiat 2006;60:998–1004. 207. Davanzo P, Thomas M, Barnett S, Yue K, Venkatraman T, Cunanan C, et al. Magnetic resonance spectroscopy in bipolar children before and after valproate treatment. Annual Meeting of the American Academy of Child & Adolescent Psychiatry; San Francisco, CA, 2002. 208. Moore CM, Biederman J, Wozniak J, Mick E, Aleardi M, Wardrop M, et al. Differences in brain chemistry in children and adolescents with attention deficit hyperactivity disorder with and without comorbid bipolar disorder: a proton magnetic resonance spectroscopy study. Am J Psychiat 2006;163:316–318. 209. Stork C, Renshaw PF. Mitochondrial dysfunction in bipolar disorder: evidence from magnetic resonance spectroscopy research. Mol Psychiat 2005;10:900–919. 210. Patel NC, Patrick DM, Youngstrom EA, Strakowski SM, Delbello MP. Response and remission in adolescent mania: signal detection analyses of the young mania rating scale. J Am Acad Child Adol Psychiat 2007;46:628–635.

Chapter 26

Neuroimaging Studies of Pediatric Obsessive-Compulsive Disorder: Special Emphasis on Genetics and Biomarkers Frank P. MacMaster and David R. Rosenberg

Abstract Obsessive-Compulsive Disorder (or OCD) is a severe and chronically debilitating disorder that affects over 3 million people in the United States. People with OCD have distressing obsessions and compulsions that cripple their functioning in every day life. Selective serotonin reuptake inhibitors (SSRIs) are the only FDA approved medications for OCD. However, SSRIs are typically only effective in 40–60% of patients, leaving a substantial number still ill. Indeed, as treatment response is defined by a 20–40% reduction in symptoms, many “responders” are still markedly symptomatic. Approximately 2 million people are not sufficiently served by current medication remedies. Hence, advancement in our understanding of the neurobiology of OCD is sorely needed. The emergence of newer, non-invasive neuroimaging approaches offers great promise in enhancing our understanding of normative brain development and the developmental neurobiologic underpinnings of childhood onset neuropsychiatric disorders. In this chapter, we describe an approach combining comprehensive assessment and treatment with sophisticated neuroimaging studies to elucidate a mechanistic understanding of the pathogenesis and treatment response of pediatric OCD. Our neuroimaging studies have implicated the cortical–striatal–thalamic–cortical (CSTC) loop in the pathophysiology of pediatric OCD. This has led to a focus on the neurotransmitter glutamate. Indeed, our work using proton magnetic resonance spectroscopy (1H-MRS) has shown regionally specific alterations of glutamate/glutamine (or F. P. MacMaster and D. R. Rosenberg Psychiatry & Behavioral Neurosciences, Wayne State University School of Medicine, Child Psychiatry and Psychology, Children’s Hospital of Michigan, USA

Glx) that resolve with effective treatment. This finding has been supported by other neuroimaging and cerebral spinal fluid (CSF) studies. Genetic studies have noted increased susceptibility to OCD in those expressing alterations in the neuronal glutamate transporter gene (SLC1A1) and certain glutamate receptor genes (GRIK2 and GRIN2B). Additionally, a transgenic animal model of OCD has also noted a high level of glutamatergic excitation as well. Hence, 1 H-MRS, CSF, genetic and animal studies have all implicated glutamate in OCD. This has led to the application of the glutamate modulating agents, such as riluzole, to treat OCD symptoms. The approach of bringing neuroimaging and genetic methods to bear on the study of the disorder serves as a model for increasing our understanding of other neuropsychiatric illness. Keywords Anterior cingulate • glutamate • prefrontal cortex • obsessive-compulsive disorder • striatum • thalamus Abbreviations ALS: Amyotrophic lateral sclerosis; CBT: Cognitive behavioral therapy; Cho: Choline compounds; Cr: Creatine/phosphocreatine; CSF: Cerebral spinal fluid; CSTC: Cortical–striatal– thalamic–cortical; DLPFC: Dorsolateral prefrontal cortex; EAAC1: Excitatory amino-acid carrier 1; EAAT2: Excitatory amino acid transporter 2; EAAT3: Excitatory amino acid transporter 3; FDA: Food and Drug Administration; fMRI: Functional magnetic resonance imaging; GABA: Gamma-aminobutyric acid; Glx: Glutamate + glutamine; GRIK2: Glutamate receptor – ionotropic – kainate 2; GRIN2B: Glutamate receptor, ionotropic, N-methyl D-aspartate 2B; H-MRS: Proton magnetic resonance spectroscopy;

M.S. Ritsner (ed.), The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, © Springer Science + Business Media B.V. 2009

201

202

H-MRSI: Proton magnetic resonance spectroscopic imaging; LHPA: Limbic-hypothalamic-pituitaryadrenal; MDD: Major depression; MRI: Magnetic resonance imaging; MRS: Magnetic resonance spectroscopy; NAA: N-acetyl-aspartate; NIMH: National Institute of Mental Health; NMDA: N-methyl D-aspartate; OCD: Obsessive-compulsive disorder; PANDAS: Pediatric autoimmune neuropsychiatric disorders associated with streptococcus; SLC1A1: Solute carrier family 1; SSRI: Selective serotonin reuptake inhibitor; VBM: Voxel based morphometry;

Biomarkers in Child and Adolescent Psychiatry There are three main ways biomarkers may be useful in studies of psychiatric disorders. First, biomarkers may be useful in differentiating those with the disorder from healthy peers. Second, biomarkers can help differentiate the disorder from other commonly co-occurring disorders. Third, biomarkers may be especially useful in examining the effect of treatment. In childhood and adolescent psychiatric disorders, brain-imaging techniques are the most logical source of biomarkers as post-mortem work is minimal. These non-invasive brain imaging methods offer promise in enhancing understanding not only of brain development but also of the neuro-

Fig. 26.1 Cortical–striatal– thalamic–cortical loop

F.P. MacMaster and D.R. Rosenberg

biologic underpinnings of childhood-onset neuropsychiatric disorders. Brain imaging permits unprecedented in vivo ‘biopsies’ of brain structure, chemistry, and function. In this chapter, we present a series of studies aimed at generating a mechanistic understanding of pediatric obsessive-compulsive disorder (OCD), including pathogenesis and treatment response. Pediatric studies are especially critical in advancing our understanding of the disorder as approximately 80% of all cases begin during childhood and adolescence.1 We will also discuss the use of biomarkers to study the neurobiology of pediatric OCD, and their impact on recent genetic findings and the application of glutamate-modulating medications. We will focus special attention on the glutamate hypothesis of OCD, first proposed by Rosenberg and Keshavan.2

Obsessive-Compulsive Disorder Patients as Compared to Healthy Children Basic Neurobiological Model of Obsessive-Compulsive Disorder Studies have most consistently implicated the cortical– striatal–thalamic circuit (Fig. 26.1) in OCD.3 The core excitatory neurotransmitter of this circuit is glutamate.

26

Neuroimaging Studies of Pediatric Obsessive-Compulsive Disorder

Eighty percent of all synapses in the striatum are cortical inputs.4 The cortical regions projecting to the striatum can be divided into ‘motor’ and ‘limbic associative’. Motor projections include somatosensory, motor, and premotor cortex. More pertinent to OCD, the ‘limbic associative’ projections are derived from the amygdala, hippocampus, orbital, frontal, cingulate, parietal, temporal, entorhinal and association cortex.5 The anatomy and organization of the cortical–striatal circuits have been reviewed in depth elsewhere.6–11 Briefly, there are sensorimotor, oculomotor, dorsal cognitive, ventral cognitive, and affective/motivational loops that extend from the cortex to the striatum to the thalamus and back to the cortex.12 In the following section we briefly discuss neurobiological studies of this circuit in pediatric OCD.

Frontal Cortex Neurocognitive testing studies of frontal cortical functions has been limited in pediatric OCD. Spatialperceptual deficits akin to those of patients with frontal lobe lesions have been reported in adolescents with OCD.13 However, this finding has not been replicated.14,15 Recently, Chang et al.15 noted deficits in executive functioning and visual attention in children with OCD as compared to healthy controls. There is also evidence for prefrontal oculomotor abnormalities in pediatric OCD.13,16 These include differences in their ability to suppress responses to stimuli, volitional execution of delayed responses, and anticipation of predictable events. As compared to controls, patients with OCD had more response-suppression failures.13,16 The severity of OCD symptoms correlated with response-suppression deficits.16 Interestingly, no significant differences were observed between patients with OCD and controls on other prefrontal cortical functions such as the delayedresponse task. Using functional magnetic resonance imaging (fMRI), Woolley et al.,17 noted that patients with OCD showed reduced activation in right orbitofrontal cortex, thalamus and basal ganglia compared to controls during the ‘stop’ task. These problems with inhibition may underlie the repetitive behavior that characterizes OCD; indicating abnormalities in orbital prefrontal ventral striatal circuits.13,16 Pediatric OCD patients demonstrated greater gray matter density in the orbital frontal cortex as compared to healthy controls.18 This was confirmed by manual region of-interest (ROI) measurements. In addition, gray matter

203

density in the right lateral orbital frontal cortex correlated with OCD symptom severity, but not with symptoms of anxiety or depression.18 Greater gray matter volume of the anterior cingulate has been noted in pediatric OCD patients compared to age- and sex-matched controls.2,19 The volume of the anterior cingulate correlated positively with age in controls but not in patients with OCD.2 This was replicated in an independent sample; with greater anterior cingulate gray matter volume being noted in patients than in controls using volumetric MRI but not with VBM.18 No difference was noted between groups in anterior cingulate white matter volume.19 Contrary to these reports,2,18,19 a VBM analysis of children with OCD by Carmona et al.20 found lower gray matter density in patients compared to healthy controls in the bilateral anterior cingulate. This indicates possible sensitivity issues with both techniques and fundamental differences in what property of the brain is being measured.21 Proton magnetic resonance spectroscopy (1H-MRS) of the anterior cingulate found lesser glutamatergic concentrations (Glx; see Fig. 26.2) in pediatric patients with OCD as compared to healthy controls22 (Fig. 26.3). In adult females with OCD, lower anterior cingulate Glx was also noted.23 In these patients, anterior cingulate glutamate correlated with OCD symptom severity. No volumetric effects in dorsolateral prefrontal cortex (DLPFC) have been noted to date.2 However, proton magnetic resonance spectroscopic imaging (1H-MRSI) did reveal greater N-AcetylAspartate (NAA) concentration in left but not right DLPFC in pediatric OCD patients.24 Greater NAA in left DLPFC may be indicative of atypical cortical pruning in OCD.

Basal Ganglia Using structural MRI, Rosenberg et al.25 found that pediatric OCD patients have smaller striatal volumes than case-control (age and sex) matched healthy controls. In patients with OCD, striatal volumes correlated inversely with symptom severity but not illness duration.25 Szeszko et al.19 found a smaller globus pallidus in a second sample of pediatric OCD patients. Seemingly converse to these findings, a VBM study showed greater gray matter density in the bilateral putamen in pediatric OCD patients as compared to controls.18 However, VBM

204

Fig. 26.2 Representative fit of glutamate and glutamine using LCModel. Cho = choline compounds, Cr = creatine/ phosphocreatine, Gln = glutamine, Glu = glutamate, mI = myo-inositol, MM = macromolecule, NAA = N-acetyl-aspartate

Fig. 26.3 (a) Caudate glutamatergic concentrations in pediatric obsessive-compulsive disorder (OCD) before and after 12 weeks of selective serotonin reuptake inhibitor (SSRI) treatment. (b) Caudate glutamatergic concentrations in pediatric OCD patients before and after 12 weeks of cognitive behavioral therapy (CBT) treatment. (c) Anterior cingulate glutamatergic concentrations in pediatric OCD patients as compared to controls. Post-TX = Posttreatment

F.P. MacMaster and D.R. Rosenberg

26

Neuroimaging Studies of Pediatric Obsessive-Compulsive Disorder

and manual tracing methods for evaluating regional brain volume have not been validated against each other21 and may not reflect the evaluation of identical aspects of morphology. Greater striatal Glx concentrations were noted in pediatric OCD as compared to matched healthy controls26,27 (Fig. 26.3). Lastly, hyperintensities in subcortical regions appear to occur more frequently in pediatric OCD than in controls.28

Thalamus The volume of the thalamus is larger in pediatric OCD patients than in age- and sex-matched healthy controls.29 However, lower ratios of bilateral medial-thalamic NAA/Creatine (Cr) and NAA/Choline (Cho) were noted in pediatric OCD as compared to healthy controls.30 This seems inconsistent with the aforementioned larger thalamic volume in OCD,29 as lower NAA would suggest a smaller thalamic volume. Using a more advanced quantification technique, a validated phantom-replacement methodology, allowed for absolute quantification and indicated greater medial-thalamic Cho31,32 and Cr33 but not lower NAA31–33 in OCD patients than in healthy controls. This incongruity highlights the risk inbuilt in using metabolite ratios in MRS datasets. The finding of greater Cho concentrations in pediatric OCD is region-specific to the medialthalamus as no difference was observed in the lateral thalamus.31

Corpus Callosum and Pituitary Gland Apart from the widely studied frontal–striatal–thalamic circuit, certain other brain regions have been implicated in pediatric OCD. All subregions except for the isthmus of the corpus callosum were found to be larger in pediatric OCD patients than in controls.34 Corpus callosum area correlated significantly with OCD symptom severity but not illness duration. Additionally, the age-related increase in callosal size observed in normal subjects was not observed in OCD patients. MRI signal intensity, which is thought to be related to myelination, was lower in the genu of the corpus callosum in OCD patients than in healthy controls,35 indicating greater myelination of

205

the genu. This may have lead to the increased volume of the genu noted in the earlier study.34 The genu connects ventral prefrontal cortex and the striatum, two regions critically involved in pediatric OCD. In adult patients with OCD, a dysfunction in the limbic-hypothalamicpituitary-adrenal (LHPA) axis has been reported.36–39 Indeed, pituitary gland volume is smaller in treatmentnaïve pediatric OCD patients than in healthy controls, with the most prominent difference between groups being noted in males.40 This may indicate dysregulation of the LHPA in pediatric OCD. See Fig. 26.4 for a summary of effect sizes of OCD patients as compared to healthy controls.

Obsessive-Compulsive Disorder AS Compared to Major Depression There is considerable overlap between OCD and depression.41,42 This fact makes the need for biomarkers that distinguish the two disorders critical. In the anterior cingulate, pediatric OCD patients demonstrated lower Glx concentrations than controls.22 Indeed, Glx concentrations were also lower in a similar magnitude in MDD patients as compared to healthy children (18.7% in MDD and 15.1% in OCD).22 These findings suggest that reduced anterior cingulate glutamate does not differentiate pediatric patients with OCD from pediatric patients with MDD, indicating a point of neurobiological convergence in the disorders. In the medial thalamus however, OCD patients differed not only from controls with regard to Cho and Cr (both greater in OCD), but also from pediatric MDD patients.32,33 These results suggest that localized functional neurochemical marker alterations in medial thalamic Cho and Cr may differentiate patients with OCD from healthy control subjects and patients with MDD. Furthermore, in the pituitary gland, the smaller gland volume found in pediatric OCD patients contrasts with the larger pituitary volume noted in MDD43,44 and bipolar disorder.45 These results indicate that single measure biomarkers for differentiating common occurring disorders may be of limited utility. However, neurobiological profiles incorporating multiple measures may better distinguish disorders. Further study of the diagnostic specificity of these potential biomarkers is clearly warranted. See Fig. 26.5 for a summary of effect sizes of OCD patients as compared to major depression.

206

Fig. 26.4 Effect sizes of measures in studies of obsessive-compulsive disorder as compare to healthy controls. Cr = creatine/phosphocreatine, DLPFC = dorsolateral prefrontal cortex, Glx = glutamine + glutamate, NAA = N-acetyl-aspartate

Fig. 26.5 Effect sizes of measures in studies of pediatric obsessive-compulsive disorder as compared to pediatric major depression

F.P. MacMaster and D.R. Rosenberg

26

Neuroimaging Studies of Pediatric Obsessive-Compulsive Disorder

The Effect of Treatment for Obsessive-Compulsive Disorder Biomarkers are also useful for monitoring the effect of treatment. In pediatric OCD, increased striatal Glx concentrations were noted in patients that normalized after successful treatment with an SSRI.26,27 A case report indicated that after SSRI discontinuation, this reduction in striatal Glx may persist.46 Interestingly, cognitive behavioral therapy (CBT) did not change caudate Glx concentrations in pediatric OCD patients despite a reduction in symptoms akin to those noted with SSRI treatment.47 These findings indicate that striatal Glx may be a useful biomarker for monitoring psychotropic interventions for pediatric OCD. A biomarker to monitor the biological impact of CBT remains elusive. Neurochemical measures are not the only potential biomarkers available with imaging. Regional brain volume can be affected by medications as well.48–50 Indeed, the volume of the thalamus is larger in pediatric OCD patients at baseline than controls and after 12 weeks of treatment with the SSRI, paroxetine, volume normalized concurrent with a reduction in OCD symptoms.29 Similar to striatal Glx, this reduction in thalamic volume in OCD may be specific to medication as no changes in volume occurred with CBT.51 It should be noted that not all structures normalize after drug treatment, including those previously shown to be

Fig. 26.6 Effect sizes of measures in treatment studies of obsessive-compulsive disorder. CBT = cognitive behavioral therapy, Glx = glutamine + glutamate, SSRI = selective serotonin reuptake inhibitor

207

reactive to psychotropic medications.50 The pituitary gland, shown previously to be significantly smaller in treatment-naïve pediatric OCD patients than in healthy controls,40 does not appear to change with treatment.52 See Fig. 26.6 for a summary of effect sizes of treatment studies of OCD patients.

Genetics of Obsessive-Compulsive Disorder Studies have shown that the estimates of the heritability of obsessive-compulsive symptoms in children and adolescents range from 45% to 65%.53 This is indicative of a strong genetic component to OCD. In this section we will discuss two glutamate-related genes (transporter and receptor) that have shown promise in both explaining the above-described neurobiology of the illness and acting as a biomarker for diagnostic and treatment studies of OCD.

Glutamate Transporter Genes: SLC1A1 In the most compelling piece of evidence, three independent groups have found that the 3′ region of SCL1A1 may contain a susceptibility allele for OCD, primarily in male offspring.54–56 The high-affinity neu-

208

ronal and epithelial transporter (EAAT3, EAAC1) for L-glutamate, L- and D-aspartate, and cysteine is the protein product of SCL1A1.57,58 EAAT3/EAAC1 is expressed in the cortex, basal ganglia, and hippocampus, and has been detected in all parts of the neuron.59 Additionally, EAAT3 may also play a role in regulating GABA synthesis as it is localized to some GABAergic neurons.60 In adults, effective glutamate transport helps to keep extracellular glutamate below neurotoxic concentrations.61 The expression of EAAT3/ EAAC1 is low and is thought to make a minor contribution to synaptic glutamate removal as compared to EAAT1 and EAAT2.62 During early brain development, SLC1A1 is expressed even before astrocytes are functional. This is suggestive of a developmental role for EAAT3/ EAAC1.62 A role in brain development is consistent with studies indicating SLC1A1 as a primary candidate gene in pediatric OCD.54–56 Testosterone and prolactin regulate expression of EAAT3/EAAC1.58 The increase in expression of EAAT3/EAAC1 by testosterone is consistent with association of SLC1A1 being strongest in OCD males.54,55 Mice deficient in EAAC1 develop impaired self-grooming.57 These findings suggest that pediatric OCD may be associated with increased EAAT3 expression.

Glutamate Receptor Genes: GRIN2B GRIN2B is expressed in the striatum and the prefrontal cortex.63 As noted earlier, these regions are consistent with the glutamatergic abnormalities noted in pediatric OCD patients.22,27 In pediatric subjects, Arnold et al.64 observed that the 5072T/G variant of N-methyl-D-aspartate (NMDA) subunit 2B gene (GRIN2B) is significantly associated with OCD.64 Furthermore, the 5072G–5988T haplotype was also linked with OCD. The GRIN2B on chromosome 12p encodes for the NR2B subunit of the ionotropic NMDA glutamate receptor. Genetic studies have also linked GRIN2B to schizophrenia,65 bipolar disorder,66 and attention deficit hyperactivity disorder.67 During development, GRIN2B is thought to play a role in plasticity in the cortex.68 Furthermore, when glutamate reaches neurotoxic levels during the neonatal period, the expression of the NMDA NR2B receptor in the striatum and cortex increases.69 Pediatric OCD may be

F.P. MacMaster and D.R. Rosenberg

associated with increased GRIN2B expression in the striatum as suggested by increases in expression of GRIN2B in response to excess glutamate.70 More recently, our group noted a significant association between the G/G genotype of the functional GRIN2Brs1019385 polymorphism and decreased anterior cingulate Glx but not with occipital Glx (Arnold et al., in submission).

Impact of Research: Pharmacotherapy for Obsessive-Compulsive Disorder Only SSRI medications are FDA-approved for OCD. Unfortunately, SSRI’s are typically only effective in 40–60% of patients.71 This leaves a substantial number of patients still ill. Furthermore, as treatment response is typically defined by a 20–40% reduction in symptoms, a great number of patients classed as “responders” are still markedly symptomatic.71 As symptoms may persist despite treatment and levels of treatment response to SSRI are limited, the serotonin paradigm of understanding OCD does not account fully for the neurobiology of the disorder. As discussed earlier, there is growing evidence of glutamate abnormalities in OCD.22,23,27,54,55,64,72–75 Indeed, very large effect sizes (d > 1.00) were noted for all of the 1H-MRS and CSF measures of glutamate concentration in OCD. This neurobiological evidence has led to the application of medications that modulate glutamate.76 The glutamate-modulating agent riluzole (1-amino-6-trifluoromethoxybenzothiazole) has shown particular promise in treating psychiatric disorders.77–82 Riluzole is FDA approved for treatment of amyotrophic lateral sclerosis (ALS) and is well tolerated by patients.83–85 Riluzole acts primarily as an inhibitor of glutamate release, inactivates voltage-dependant sodium channels in cortical neurons and blocks GABA reuptake.86–88 In a case report and an open-label trial in adults,77,78 riluzole demonstrated an ability to reduce the symptoms of OCD. More recently, an open-label trial in pediatric OCD patients (8–16 years) found that riluzole was both beneficial and well tolerated.89 This success provided the impetus for a larger placebo-controlled trial at NIMH in pediatric OCD patients that is currently underway. This is a rare example of the search

26

Neuroimaging Studies of Pediatric Obsessive-Compulsive Disorder

for a biomarker in a psychiatric illness leading to a novel application of treatment.

209

and animal models, the potential of brain imaging biomarkers to have meaningful clinical impact becomes profound.

Conclusion Issues That Need Further Study The Glutamate Hypothesis of Obsessive-Compulsive Disorder Rosenberg and Keshavan2 first proposed a role of glutamate in pediatric OCD in 1998. Since the initial hypothesis, studies in OCD patients have observed greater striatal Glx,26,27 lower anterior cingulate Glx22,23 and greater Glx/Cr ratios in orbital frontal white matter.72 These brain-imaging reports found further support in genetic studies noting an increased susceptibility to OCD in people expressing alterations in certain glutamate transporter54,55 and receptor genes.64,74 In addition, both peripheral markers73 and animal models75 have provided additional evidence for glutamate dysfunction in OCD. Clinically, medications that modulate glutamate activity have shown promise for treating OCD.77,78,89 In conclusion, 1H-MRS, CSF, genetic, animal and clinical studies have all provided evidence of glutamate involvement in OCD.

Research Strategies for Obsessive-Compulsive Disorder The standard approach of moving from pharmacology to pathophysiology has not generated substantive progress in our ability to understand mental illness.90 By combining strategies (genetic, neuroimaging, pharmacological, animal models, etc.), investigators may achieve the most advancement.90,91 For example, research into diabetes and oncology is focused on curing the disease and prevention. In psychiatry, the bar is set considerably lower with incremental advances being more typical of research.90 The progress described in this chapter regarding biomarkers in pediatric OCD is a rare occurrence in psychiatry, as it is an example of where neurobiological studies of a disorder have directly informed its treatment. When coupled with advances in assessment, genetics, pharmacology

Separation of Glutamate–Glutamine (Glx) Glutamate and glutamine play differing roles in the brain. However, 1H-MRS studies of OCD have reported the combined Glx measure (glutamate and glutamine).22,23,26,27,72 Hence, techniques that allow for the separation of the two similar resonances need to be applied in OCD. These include improved spectral editing92–94 or the use of higher field MRI scanners (3.0 T and above).95

Relation of Glutamate Concentration and Activity of Glutamate Related Genes Combining genetics and imaging methods has remarkable potential for advancing our understanding of psychiatric disorders.96 First-order studies that seek to link previous genetic and imaging findings in pediatric OCD are needed. Next, second-order studies that examine what cellular mechanisms linked to gene polymorphisms may be responsible for the changes noted in the imaging studies are required. These two candidate genes (SLC1A1 and GRIN2B) discussed in this chapter are only the start of tying genetic studies into glutamate-related findings with pediatric OCD, as there are many glutamate related genes yet to be examined. Indeed, for glutamate, there are at least 25 genes for receptors and five genes for neuronal and glial transporters.97

VBM It is not known why some VBM studies conflict with volumetric MRI studies using manual tracing methods.21 For example, in the anterior cingulate in OCD patients Carmona et al.20 noted lower gray matter density bilaterally while Rosenberg and Keshavan2 found greater gray matter volume. Interestingly,

210

Szeszko et al.18 found greater anterior cingulate gray matter volume in patients as compared to healthy volunteers using volumetric MRI, but not with VBM. As the operational definition for gray matter density has not been resolved, it may be that the methods measure two very different things (i.e. volume vs. the statistical probability of a voxel being gray matter). Further work is needed to validate VBM and to resolve the conflict noted with traditional manual tracing techniques.

Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcus (PANDAS) Differentiating the neurobiology of OCD from pediatric autoimmune neuropsychiatric disorders associated with streptococcus (PANDAS) is another critical field of study. Imaging studies of PANDAS have found enlarged basal ganglia volumes in PANDAS patients.98 This differs from published reports of smaller basal ganglia structures in pediatric OCD.19,25 Interestingly, the enlarged basal ganglia volume resolved with plasmapheresis treatment for PANDAS.99 Giedd et al.98 hypothesized that the enlargement of the basal ganglia was a consequence of an autoimmune response to the streptococcal infection.

Limitations Despite the progress demonstrated in this chapter, there are considerable limitations to the published work on the neurobiology of pediatric OCD. These include: (1) the fact that most studies have small samples, (2) the need for more research into the neurobiology of possible subtypes of OCD,100 (3) the need for replication, as many of the studies come from one group. Even with these caveats, the evidence supporting glutamate dysfunction in OCD is robust as it comes from a number of methodological approaches (imaging, genetics, CSF, animal studies). The studies discussed above represent the ‘first pass’ of in vivo research into pediatric OCD. As such, they have laid the groundwork for the exciting next step in the research.

F.P. MacMaster and D.R. Rosenberg

Future Directions The clinical phenomenology and nosology of OCD is well established, making it a viable candidate for studies of potential biomarkers. By applying techniques developed in the emerging field of imaging genetics, researchers can further explain the underlying developmental neurobiology of OCD in children. With advances in high field imaging, direct delineation of regional glutamate concentration is possible. Advances in psychiatric genetics will also lead to better demarcation of control and clinical groups as well. These studies may provide further evidence for the glutamate hypothesis of OCD. Such approaches may also lead to new diagnostic and treatment approaches. Acknowledgments This work was supported in part by the State of Michigan Joe F. Young Sr. Psychiatric Research and Training Program, the Miriam L. Hamburger Endowed Chair at Children’s Hospital of Michigan and Wayne State University, Detroit, MI and grants from the National Institute of Mental Health (MH02037, MH65122, and MH59299).

References 1. Pauls DL, Alsobrook JP, 2nd, Goodman W, et al. A family study of obsessive-compulsive disorder. Am J Psychiat 1995;152(1):76–84 2. Rosenberg DR, Keshavan MS. A.E. Bennett Research Award. Toward a neurodevelopmental model of obsessive– compulsive disorder. Biol Psychiat 1998;43(9):623–640 3. Rosenberg DR, Mac Master FP, Mirza Y, Easter PC, Buhagiar CJ. Neurobiology, neuropsychology and neuroimaging of child and adolescent obsessive-compulsive disorder. In: Storch E, Geffken G, Murphy T, eds. A comprehensive handbook of child and adolescent obsessive-compulsive disorder. Lawrence Erlbaum Associates; Mahwah, New Jersey 2007, pp. 131–161 4. Pasik P, Pasik T, DiFiglia M. Quantitative aspects in the neurostriatum of the macaque monkey. In: Yahr MD, ed. The basal ganglia. New York: Raven Press; 1976, pp. 57–90 5. Mello LEAM, Villares J. Neuroanatomy of the basal ganglia. In: Miguel EC, Rauch SL, Leckman JF, eds. Neuropsychiatry of the basal ganglia. Philadelphia: W.B. Saunders; 1997, Vol. 20, pp. 691–704 6. Alexander GE, Crutcher MD. Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends Neurosci 1990;13(7):266–271 7. Alexander GE. Basal ganglia-thalamocortical circuits: their role in control of movements. J Clin Neurophysiol 1994;11(4):420–431 8. Alexander GE, DeLong MR, Strick PL. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Ann Rev Neurosci 1986;9:357–381

26

Neuroimaging Studies of Pediatric Obsessive-Compulsive Disorder

9. Gerfen CR. The neostriatal mosaic: multiple levels of compartmental organization. Trends Neurosci 1992;15(4):133–139 10. Gerfen CR. The neostriatal mosaic: multiple levels of compartmental organization in the basal ganglia. Ann Rev Neurosci 1992;15:285–320 11. Graybiel AM. Neurotransmitters and neuromodulators in the basal ganglia. Trends Neurosci 1990;13(7):244–254 12. Rauch SL, Savage CR. Neuroimaging and neuropsychology of the striatum. In: Miguel EC, Rauch SL, Leckman JF, eds. Neuropsychiatry of the basal ganglia. Philadelphia: W.B. Saunders; 1997, Vol. 20, pp. 741–768 13. Rosenberg DR, Dick EL, O’Hearn KM, Sweeney JA. Response-inhibition deficits in obsessive-compulsive disorder: an indicator of dysfunction in frontostriatal circuits. J Psychiat Neurosci 1997;22(1):29–38 14. Beers SR, Rosenberg DR, Dick EL, et al. Neuropsychological study of frontal lobe function in psychotropic-naive children with obsessive-compulsive disorder. Am J Psychiat 1999;156(5):777–779 15. Chang SW, McCracken JT, Piacentini JC. Neurocognitive correlates of child obsessive compulsive disorder and Tourette syndrome. J Clin Exp Neuropsychol 2007;29(7):724–733 16. Rosenberg DR, Averbach DH, O’Hearn KM, Seymour AB, Birmaher B, Sweeney JA. Oculomotor response inhibition abnormalities in pediatric obsessive-compulsive disorder. Arch Gen Psychiat 1997;54(9):831–838 17. Woolley J, Heyman I, Brammer M, Frampton I, McGuire PK, Rubia K. Brain activation in paediatric obsessive compulsive disorder during tasks of inhibitory control. Br J Psychiat 2008;192(1):25–31 18. Szeszko P, Christian C, Mac Master FP, et al. Gray matter structural alterations in psychotropic drug-naïve pediatric obsessive-compulsive disorder: an optimized voxel based morphometry study. Am J Psychiat 2008; 165(10): 1299–1307 19. Szeszko PR, MacMillan S, McMeniman M, et al. Brain structural abnormalities in psychotropic drug-naive pediatric patients with obsessive-compulsive disorder. Am J Psychiat 2004;161(6):1049–1056 20. Carmona S, Bassas N, Rovira M, et al. Pediatric OCD structural brain deficits in conflict monitoring circuits: a voxel-based morphometry study. Neurosci Lett 2007;421(3):218–223 21. Thacker NA. Tutorial: a critical analysis of voxel based morphometry (VBM). Manchester: University of Manchester; 2005 22. Rosenberg DR, Mirza Y, Russell A, et al. Reduced anterior cingulate glutamatergic concentrations in childhood OCD and major depression versus healthy controls. J Am Acad Child Adol Psychiat 2004;43(9):1146–1153 23. Yucel M, Wood SJ, Wellard RM, et al. Anterior cingulate glutamate–glutamine levels predict symptom severity in women with obsessive-compulsive disorder. Aust NZJ Psychiatry 2008; 42(6):467– 477 24. Russell A, Cortese B, Lorch E, et al. Localized functional neurochemical marker abnormalities in dorsolateral prefrontal cortex in pediatric obsessive-compulsive disorder. J Child Adol Psychopharmacol 2003;13(Suppl 1):S31–S38 25. Rosenberg DR, Keshavan MS, O’Hearn KM, et al. Frontostriatal measurement in treatment-naive children with obsessive-compulsive disorder. Arch Gen Psychiat 1997;54(9):824–830

211

26. Moore GJ, MacMaster FP, Stewart C, Rosenberg DR. Case study: caudate glutamatergic changes with paroxetine therapy for pediatric obsessive-compulsive disorder. J Am Acad Child Adol Psychiat 1998;37(6):663–667 27. Rosenberg DR, MacMaster FP, Keshavan MS, Fitzgerald KD, Stewart CM, Moore GJ. Decrease in caudate glutamatergic concentrations in pediatric obsessive-compulsive disorder patients taking paroxetine. J Am Acad Child Adol Psychiat 2000;39(9):1096–1103 28. Amat JA, Bronen RA, Saluja S, et al. Increased number of subcortical hyperintensities on MRI in children and adolescents with Tourette’s syndrome, obsessive-compulsive disorder, and attention deficit hyperactivity disorder. Am J Psychiat 2006;163(6):1106–1108 29. Gilbert AR, Moore GJ, Keshavan MS, et al. Decrease in thalamic volumes of pediatric patients with obsessive-compulsive disorder who are taking paroxetine. Arch Gen Psychiat 2000;57(5):449–456 30. Fitzgerald KD, Moore GJ, Paulson LA, Stewart CM, Rosenberg DR. Proton spectroscopic imaging of the thalamus in treatment-naive pediatric obsessive-compulsive disorder. Biol Psychiat 2000;47(3):174–182 31. Rosenberg DR, Amponsah A, Sullivan A, MacMillan S, Moore GJ. Increased medial thalamic choline in pediatric obsessivecompulsive disorder as detected by quantitative in vivo spectroscopic imaging. J Child Neurol 2001;16(9):636–641 32. Smith EA, Russell A, Lorch E, et al. Increased medial thalamic choline found in pediatric patients with obsessivecompulsive disorder versus major depression or healthy control subjects: a magnetic resonance spectroscopy study. Biol Psychiat 2003;54(12):1399–1405 33. Mirza Y, O’Neill J, Smith EA, et al. Increased medial thalamic creatine-phosphocreatine found by proton magnetic resonance spectroscopy in children with obsessive-compulsive disorder versus major depression and healthy controls. J Child Neurol 2006;21(2):106–111 34. Rosenberg DR, Keshavan MS, Dick EL, Bagwell WW, MacMaster FP, Birmaher B. Corpus callosal morphology in treatment-naive pediatric obsessive compulsive disorder. Prog Neuropsychopharmacol Biol Psychiat 1997;21(8):1269–1283 35. MacMaster FP, Keshavan MS, Dick EL, Rosenberg DR. Corpus callosal signal intensity in treatment-naive pediatric obsessive compulsive disorders. Prog Neuropsychopharmacol Biol Psychiat 1999;23(4):601–612 36. Altemus M, Cizza G, Gold PW. Chronic fluoxetine treatment reduces hypothalamic vasopressin secretion in vitro. Brain Res 1992;593(2):311–313 37. Monteleone P, Catapano F, Tortorella A, Maj M. Cortisol response to d-fenfluramine in patients with obsessive-compulsive disorder and in healthy subjects: evidence for a gender-related effect. Neuropsychobiology 1997;36(1):8–12 38. Monteleone P, Catapano F, Tortorella A, Di Martino S, Maj M. Plasma melatonin and cortisol circadian patterns in patients with obsessive-compulsive disorder before and after fluoxetine treatment. Psychoneuroendocrinology 1995;20(7):763–770 39. Catapano F, Monteleone P, Fuschino A, Maj M, Kemali D. Melatonin and cortisol secretion in patients with primary obsessive-compulsive disorder. Psychiat Res 1992;44(3):217–225 40. MacMaster FP, Russell A, Mirza Y, et al. Pituitary volume in pediatric obsessive-compulsive disorder. Biol Psychiat 2006;59(3):252–257

212 41. Carter AS, Pollock RA, Suvak MK, Pauls DL. Anxiety and major depression comorbidity in a family study of obsessivecompulsive disorder. Depress Anxiety 2004;20(4):165–174 42. Fava M, Rankin MA, Wright EC, et al. Anxiety disorders in major depression. Compr Psychiat 2000;41(2):97–102 43. MacMaster FP, Kusumakar V. Pituitary gland volume in adolescent depression. J Psychiat Res 2004;38(3):231–236 44. MacMaster FP, Russell A, Mirza Y, et al. Pituitary volume in treatment-naive pediatric major depressive disorder. Biol Psychiat 2006;60(8):862–866 45. MacMaster FP, Leslie R, Rosenberg DR, Kusumakar V. Pituitary gland volume in adolescent and young adult bipolar and unipolar depression. Bipolar Disord 2008;10(1):101–104 46. Bolton J, Moore GJ, MacMillan S, Stewart CM, Rosenberg DR. Case study: caudate glutamatergic changes with paroxetine persist after medication discontinuation in pediatric OCD. J Am Acad Child Adol Psychiat 2001;40(8):903–906 47. Benazon NR, Moore GJ, Rosenberg DR. Neurochemical analyses in pediatric obsessive-compulsive disorder in patients treated with cognitive-behavioral therapy. J Am Acad Child Adol Psychiat 2003;42(11):1279–1285 48. Chakos MH, Lieberman JA, Bilder RM, et al. Increase in caudate nuclei volumes of first-episode schizophrenic patients taking antipsychotic drugs. Am J Psychiat 1994;151(10):1430–1436 49. Keshavan MS, Bagwell WW, Haas GL, Sweeney JA, Schooler NR, Pettegrew JW. Changes in caudate volume with neuroleptic treatment. Lancet 1994;344(8934):1434 50. MacMaster FP, El-Sheikh R, Upadhyaya AR, Nutche J, Rosenberg DR, Keshavan M. Effect of antipsychotics on pituitary gland volume in treatment-naive first-episode schizophrenia: a pilot study. Schizophr Res 2007;92(1–3):207–210 51. Rosenberg DR, Benazon NR, Gilbert A, Sullivan A, Moore GJ. Thalamic volume in pediatric obsessive-compulsive disorder patients before and after cognitive behavioral therapy. Biol Psychiat 2000;48(4):294–300 52. MacMaster FP. Brain imaging studies of the pituitary gland in pediatric mental illness. Halifax: Anatomy and Neurobiology, Dalhousie University; 2007 53. van Grootheest DS, Cath DC, Beekman AT, Boomsma DI. Twin studies on obsessive-compulsive disorder: a review. Twin Res Hum Genet 2005;8(5):450–458 54. Arnold PD, Sicard T, Burroughs E, Richter MA, Kennedy JL. Glutamate transporter gene SLC1A1 associated with obsessive-compulsive disorder. Arch Gen Psychiat 2006;63(7):769–776 55. Dickel DE, Veenstra-VanderWeele J, Cox NJ, et al. Association testing of the positional and functional candidate gene SLC1A1/EAAC1 in early-onset obsessive-compulsive disorder. Arch Gen Psychiat 2006;63(7):778–785 56. Stewart SE, Fagerness JA, Platko J, et al. Association of the SLC1A1 glutamate transporter gene and obsessive-compulsive disorder. Am J Med Genet B Neuropsychiat Genet 2007;144(8):1027–1033 57. Aoyama K, Suh SW, Hamby AM, et al. Neuronal glutathione deficiency and age-dependent neurodegeneration in the EAAC1 deficient mouse. Nat Neurosci 2006;9(1):119–126 58. Franklin RB, Zou J, Yu Z, Costello LC. EAAC1 is expressed in rat and human prostate epithelial cells; functions as a high-affinity L-aspartate transporter; and is regulated by prolactin and testosterone. BMC Biochem 2006;7:10

F.P. MacMaster and D.R. Rosenberg 59. Guillet BA, Velly LJ, Canolle B, Masmejean FM, Nieoullon AL, Pisano P. Differential regulation by protein kinases of activity and cell surface expression of glutamate transporters in neuron-enriched cultures. Neurochem Int 2005; 46(4):337–346 60. Rothstein JD, Martin L, Levey AI, et al. Localization of neuronal and glial glutamate transporters. Neuron 1994;13(3):713–725 61. Kanai Y, Smith CP, Hediger MA. A new family of neurotransmitter transporters: the high-affinity glutamate transporters. FASEB J 1993;7(15):1450–1459 62. Nieoullon A, Canolle B, Masmejean F, Guillet B, Pisano P, Lortet S. The neuronal excitatory amino acid transporter EAAC1/EAAT3: does it represent a major actor at the brain excitatory synapse? J Neurochem 2006;98(4):1007–1018 63. Loftis JM, Janowsky A. The N-methyl-D-aspartate receptor subunit NR2B: localization, functional properties, regulation, and clinical implications. Pharmacol Ther 2003;97(1):55–85 64. Arnold PD, Rosenberg DR, Mundo E, Tharmalingam S, Kennedy JL, Richter MA. Association of a glutamate (NMDA) subunit receptor gene (GRIN2B) with obsessivecompulsive disorder: a preliminary study. Psychopharmacology (Berlin) 2004;174(4):530–538 65. Li D, He L. Association study between the NMDA receptor 2B subunit gene (GRIN2B) and schizophrenia: a HuGE review and meta-analysis. Genet Med 2007;9(1):4–8 66. Martucci L, Wong AH, De Luca V, et al. N-methyl-Daspartate receptor NR2B subunit gene GRIN2B in schizophrenia and bipolar disorder: polymorphisms and mRNA levels. Schizophr Res 2006;84(2–3):214–221 67. Dorval KM, Wigg KG, Crosbie J, et al. Association of the glutamate receptor subunit gene GRIN2B with attentiondeficit/hyperactivity disorder. Genes Brain Behav 2007;6(5):444–452 68. Sheng M, Cummings J, Roldan LA, Jan YN, Jan LY. Changing subunit composition of heteromeric NMDA receptors during development of rat cortex. Nature 1994;368(6467):144–147 69. Beas-Zarate C, Rivera-Huizar SV, Martinez-Contreras A, Feria-Velasco A, Armendariz-Borunda J. Changes in NMDA-receptor gene expression are associated with neurotoxicity induced neonatally by glutamate in the rat brain. Neurochem Int 2001;39(1):1–10 70. Ueda Y, Doi T, Tsuru N, Tokumaru J, Mitsuyama Y. Expression of glutamate transporters and ionotropic glutamate receptors in GLAST knockout mice. Brain Res Mol Brain Res 2002;104(2):120–126 71. Jenike MA. Clinical practice. Obsessive-compulsive disorder. N Engl J Med 2004;350(3):259–265 72. Whiteside SP, Port JD, Deacon BJ, Abramowitz JS. A magnetic resonance spectroscopy investigation of obsessive-compulsive disorder and anxiety. Psychiat Res 2006;146(2):137–147 73. Chakrabarty K, Bhattacharyya S, Christopher R, Khanna S. GlutamatergicdysfunctioninOCD.Neuropsychopharmacology 2005;30(9):1735–1740 74. Delorme R, Krebs MO, Chabane N, et al. Frequency and transmission of glutamate receptors GRIK2 and GRIK3 polymorphisms in patients with obsessive compulsive disorder. Neuroreport 2004;15(4):699–702 75. Nordstrom EJ, Burton FH. A transgenic model of comorbid Tourette’s syndrome and obsessive-compulsive disorder circuitry. Mol Psychiat 2002;7(6):617–625, 524

26

Neuroimaging Studies of Pediatric Obsessive-Compulsive Disorder

76. Pittenger C, Krystal JH, Coric V. Glutamate-modulating drugs as novel pharmacotherapeutic agents in the treatment of obsessive-compulsive disorder. NeuroRx 2006;3(1):69–81 77. Coric V, Milanovic S, Wasylink S, Patel P, Malison R, Krystal JH. Beneficial effects of the antiglutamatergic agent riluzole in a patient diagnosed with obsessive-compulsive disorder and major depressive disorder. Psychopharmacology (Berlin) 2003;167(2):219–220 78. Coric V, Taskiran S, Pittenger C, et al. Riluzole augmentation in treatment-resistant obsessive-compulsive disorder: an open-label trial. Biol Psychiat 2005;58(5):424–428 79. Zarate CA Jr., Payne JL, Quiroz J, et al. An open-label trial of riluzole in patients with treatment-resistant major depression. Am J Psychiat 2004;161(1):171–174 80. Zarate CA Jr., Quiroz JA, Singh JB, et al. An open-label trial of the glutamate-modulating agent riluzole in combination with lithium for the treatment of bipolar depression. Biol Psychiat 2005;57(4):430–432 81. Sanacora G, Kendell SF, Fenton L, Coric V, Krystal JH. Riluzole augmentation for treatment-resistant depression. Am J Psychiat 2004;161(11):2132 82. Sanacora G, Kendell SF, Levin Y, et al. Preliminary evidence of riluzole efficacy in antidepressant-treated patients with residual depressive symptoms. Biol Psychiat 2007;61(6):822–825 83. Bensimon G, Lacomblez L, Meininger V. A controlled trial of riluzole in amyotrophic lateral sclerosis. ALS/Riluzole Study Group. N Engl J Med 1994;330(9):585–591 84. Lacomblez L, Bensimon G, Leigh PN, et al. A confirmatory dose-ranging study of riluzole in ALS. ALS/Riluzole Study Group-II. Neurology 1996;47(6 Suppl 4):S242–S250 85. Lacomblez L, Bensimon G, Leigh PN, Guillet P, Meininger V. Dose-ranging study of riluzole in amyotrophic lateral sclerosis. Amyotrophic Lateral Sclerosis/Riluzole Study Group II. Lancet 1996;347(9013):1425–1431 86. Stefani A, Spadoni F, Bernardi G. Differential inhibition by riluzole, lamotrigine, and phenytoin of sodium and calcium currents in cortical neurons: implications for neuroprotective strategies. Exp Neurol 1997;147(1):115–122 87. Jehle T, Bauer J, Blauth E, et al. Effects of riluzole on electrically evoked neurotransmitter release. Br J Pharmacol 2000;130(6):1227–1234

213

88. Urbani A, Belluzzi O. Riluzole inhibits the persistent sodium current in mammalian CNS neurons. Eur J Neurosci 2000;12(10):3567–3574 89. Grant P, Lougee L, Hirschtritt M, Swedo SE. An open-label trial of riluzole, a glutamate antagonist, in children with treatment-resistant obsessive-compulsive disorder. J Child Adol Psychopharmacol 2007;17(6):761–767 90. Insel TR, Scolnick EM. Cure therapeutics and strategic prevention: raising the bar for mental health research. Mol Psychiat 2006;11(1):11–17 91. Insel TR, Quirion R. Psychiatry as a clinical neuroscience discipline. JAMA 2005;294(17):2221–2224 92. Rosenberg DR, MacMaster FP, Mirza Y, et al. Reduced anterior cingulate glutamate in pediatric major depression: a magnetic resonance spectroscopy study. Biol Psychiat 2005;58(9):700–704 93. Provencher SW. Automatic quantitation of localized in vivo 1 H spectra with LCModel. NMR Biomed 2001;14(4):260–264 94. Kanowski M, Kaufmann J, Braun J, Bernarding J, Tempelmann C. Quantitation of simulated short echo time 1 H human brain spectra by LCModel and AMARES. Magn Reson Med 2004;51(5):904–912 95. Barker PB, Hearshen DO, Boska MD. Single-voxel proton MRS of the human brain at 1.5 T and 3.0 T. Magn Reson Med 2001;45(5):765–769 96. Hariri AR, Weinberger DR. Imaging genomics. Br Med Bull 2003;65:259–270 97. Schiffer HH. Glutamate receptor genes: susceptibility factors in schizophrenia and depressive disorders? Mol Neurobiol 2002;25(2):191–212 98. Giedd JN, Rapoport JL, Garvey MA, Perlmutter S, Swedo SE. MRI assessment of children with obsessive-compulsive disorder or tics associated with streptococcal infection. Am J Psychiat 2000;157(2):281–283 99. Giedd JN, Rapoport JL, Leonard HL, Richter D, Swedo SE. Case study: acute basal ganglia enlargement and obsessive-compulsive symptoms in an adolescent boy. J Am Acad Child Adol Psychiat 1996;35(7):913–915 100. Eichstedt JA, Arnold SL. Childhood-onset obsessive-compulsive disorder: a tic-related subtype of OCD? Clin Psychol Rev 2001;21(1):137–157

Chapter 27

Structural Brain Alterations in Cannabis Users: Association with Cognitive Deficits and Psychiatric Symptoms Nadia Solowij, Murat Yücel, Valentina Lorenzetti, and Dan I. Lubman

Abstract This chapter will review the evidence for structural brain alterations in cannabis users and consider this in the context of the pathophysiology of schizophrenia. While previous research failed to identify structural brain abnormalities in human cannabis users, more recent studies using high resolution imaging techniques combined with more robust delineations of specific brain regions in very heavy cannabis users have revealed evidence of dose-related alterations in regions implicated in schizophrenia. Moreover, these regional brain volumetric reductions are of similar magnitude to those seen in schizophrenia. We discuss the association between cannabis use and the development of cognitive deficits and psychiatric symptoms in relation to structural brain alterations. We propose that long term heavy cannabis use leads to structural brain changes and associated deleterious functional (cognitive and mental health) sequelae that resemble schizophrenia. These changes may occur not only in individuals who are vulnerable to the development of such disorders, but also in nonvulnerable individuals if cannabis is used heavily for prolonged periods. Keywords Marijuana • brain structure • schizophrenia • psychotic symptoms • neuropsychology • cognition • mental health Abbreviations BDNF Brain-derived neurotrophic factor BSI Brief Symptom Inventory CANTAB Cambridge Neuropsychological Test Automated Battery COMT Catechol-O-methyl transferase CT Computed tomography DSM-IV Diagnostic and

N. Solowij School of Psychology and Illawarra Institute for Mental Health, University of Wollongong, Wollongong, and Affiliated Scientist, Schizophrenia Research Institute, Sydney, Australia

Statistical Manual of Mental Disorders, fourth edition DTI Diffusion tensor imaging fMRI Functional magnetic resonance imaging HDRS Hamilton Depression Rating Scale MRI Magnetic resonance imaging PET Positron emission tomography RAVLT Rey Auditory Verbal Learning Test rCBF Regional cerebral blood flow SANS Scale for the Assessment of Negative Symptoms SAPS Scale for the Assessment of Positive Symptoms THC ∆9-tetrahydrocannabinol VBM Voxel based morphometry

Introduction Statistics on the prevalence of cannabis use across the developed world,1 and particularly among youth,1–3 together with growing evidence for an association between cannabis use and the development of schizophrenia or other psychoses,4 underscore an urgent need to clarify the potentially harmful effects of cannabis use on brain development and function. Within animal models, chronic administration of cannabinoids, and in particular ∆9-tetrahydrocannabinol (THC), the main psychoactive component of cannabis,5 results in dose-dependent neurotoxic changes in brain regions that are rich in cannabinoid receptors. THC-induced neurotoxic effects are particularly prominent within the hippocampus,6–10 amygdala,6 septum11,12 and cerebral cortex.11,13 The nature of these neurotoxic effects includes shrinkage of neural cell nuclei and bodies,6,7 and reductions in pyramidal cell density,10 dendritic length,8 and number of synapses.6 Similar evidence of neurotoxicity from human studies has been unavailable, but a growing literature provides indirect evidence of the deleterious effects of cannabis on human brain structure and function.

M.S. Ritsner (ed.), The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, © Springer Science + Business Media B.V. 2009

215

216

Human studies of chronic cannabis users (during periods of abstinence) show that long-term cannabis use leads to cognitive disturbances that extend beyond the period of acute intoxication. Such effects have been demonstrated for measures of attention,14,15 learning and memory,16–21 executive functions16,22 and intelligence.23,24 In addition, a variety of psychopathological symptoms have been reported in cannabis users, including anhedonia and anxiety,14,25 mood and depressive symptoms,14,26–30 and psychosis4,27,29–34 or psychoticlike experiences.35–37 These findings support the notion that long-term cannabis use may result in persistent alterations in brain function and/or morphology, particularly in brain regions that subserve memory, executive and affective processing, such as the prefrontal and temporal cortices.38 Such regions have also been implicated in the pathophysiology of schizophrenia and other psychoses. A growing number of neuroimaging studies also provide evidence that chronic cannabis use affects brain function. Positron emission tomography (PET) and regional cerebral blood flow (rCBF) studies of cannabis users have demonstrated altered blood flow and metabolism indicative of hypo- or hyper-activity in different brain regions.39–41 Similarly, studies utilising functional magnetic resonance imaging (fMRI), have shown evidence of both hypo- and hyper-activation in brain prefrontal cortical (e.g., anterior cingulate, orbitofrontal, dorsal lateral), cerebellar and temporal (e.g., hippocampus) brain areas42,43 in samples of abstinent chronic cannabis users. Hyperactivation has been interpreted as a compensatory mechanism, such that brain regions involved in performing the task are made to work harder, and additional brain regions may also be recruited. Discrepant findings in similar brain regions (i.e., hypo- vs. hyper-activation) are likely to be a result of differences in test characteristics (i.e., specific cognitive domains and/or demand required by the functional tasks used), as well as the potential influence of state-related factors, such as inter-individual differences in personal characteristics (e.g., temperament, level of anxiety or arousal), and cannabis use related factors (e.g., recency of use, withdrawal symptoms, etc.).44 In contrast, structural anatomical investigations provide a relatively stable brain measure that minimizes state related processes, and may also complement functional findings from fMRI studies. To date, findings from structural neuroimaging studies of long-term

N. Solowij et al.

cannabis users have been contradictory,34 with evidence for the presence37,45 and absence46,47 of morphological brain alterations. However, a number of variables are likely to mediate the relationship between cannabis use and brain structural alterations. We have previously critically reviewed the literature on cannabis and cognitive dysfunction toward an examination of parallels with cognitive endophenotypes of schizophrenia.44 We argued that neurocognitive sequelae of long term heavy cannabis use may serve as a model that can offer insights into the pathophysiology associated with schizophrenia, just as others have argued with regard to the acute effects of cannabis.48,49 In this chapter, we consider potential structural brain alterations in cannabis users in the context of the pathophysiology associated with schizophrenia. We first review the evidence for structural brain alterations in cannabis users, and then examine associations between structural brain changes and various cannabis use parameters, as well as cognitive deficits and psychiatric symptoms. Finally, we discuss these findings with a view to determining the extent to which long term heavy cannabis use may be considered a model for investigating pathophysiological processes in schizophrenia, and thus whether brain changes that may develop in cannabis users can be considered a biomarker for neuropsychiatric disorders.

Evidence for Structural Brain Alterations in Cannabis Users We have recently examined the evidence for structural brain alterations in cannabis users in a review of the extant literature.50 Here we summarise the findings of that review. We identified only 13 structural neuroimaging studies of samples where the primary substance used was cannabis, and where major psychopathologies were excluded. The main imaging modality utilised was magnetic resonance imaging (MRI) (eight studies), with three studies using computed tomography (CT), and two early studies using pneumo-encephalography and echo-encephalography, respectively (see Ref. 50). The MRI studies used either a region-of-interest approach (six studies) or voxel based morphometry (VBM; two studies).

27 Structural Brain Alterations in Cannabis Users

No significant differences were found in any of the studies on global measures of brain volume. More specific regional brain analyses demonstrated evidence of structural brain abnormalities, but these were not consistent across studies. Six studies reported specific regional structural alterations in regular cannabis users,37,45,51–54 while the remaining seven studies found no significant differences between users and controls.45,46,55–59 Alterations in hippocampal or parahippocampal volumes were the most consistently reported findings, but the nature of the findings was still mixed. Hippocampal volumes in cannabis users were found to be smaller,37,54 larger,45 or no different to controls.47,52,53 Of three studies that examined parahippocampal volume, two reported no change,46,59 while one found an alteration in grey and white matter composition.54 Two studies examined amygdala volumes, with one reporting reduced volume,37 and the other no change.53 Finally, there were a number of brain regions that were only investigated within a single study. For these structures, significant between-group differences were found for the precentral gyrus, thalamus, parietal lobule, fusiform gyrus, lentiform nucleus, and pons,54 but not for the cerebellum.52 As these findings are few in number, it is not possible to plausibly speculate on their meaning. Several recently published studies have investigated white matter fibre tracts in cannabis users using diffusion tensor imaging (DTI). One study of heavy cannabis users reported significantly increased mean diffusivity in the anterior region of the corpus callosum, where white matter passes between the prefrontal lobes.60 The data suggest impaired structural integrity of the corpus callosum fibre tracts with prolonged cannabis exposure, particularly as the authors reported an association with duration of cannabis use within the sample. A study of lighter adolescent cannabis users, using a VBM approach, found no evidence of white matter integrity differences between users and controls,61 while another pilot study in ten heavy cannabis users showed trends toward both increased mean diffusivity and lower fractional anisotropy in the anterior cingulate cortex.62 Differences in the methods of measurement used and the brain regions investigated, and small sample sizes of varying age and exposure to cannabis, may have contributed to the differences in findings across studies. White matter tractography investigations in cannabis users are only at a preliminary

217

stage of investigation and hold much promise for the future. The lack of consistent strong evidence for significant brain volume losses from the structural neuroimaging studies of human cannabis users is in sharp contrast to the neurotoxicity evident from the animal literature, as discussed above. However, the most implicated region from animal work, the hippocampus, is also the brain region most identified by the few human studies conducted. One major difference between the animal and human studies is in the dose and duration of cannabis exposure. When we consider factors that differed between human studies that did or did not find differences between users and controls, what stands out is the cumulative dose of exposure to cannabis. For example, in our study,37 the cannabis users had a similar exposure (most days of the week for 20 years) to that of Landfield et al.’s8 rodent study (THC five times a week for 8 months [approximately 30% of the rat life-span]). Both of these studies found significant dose-related reductions in hippocampal volume. The cannabis users within our study had the most extensive exposure to cannabis of all the studies of human cannabis users, and the most striking findings. We reported a 12% reduction bilaterally in hippocampal volumes, as well as an approximate 7% reduction in bilateral amygdala volumes. The left hippocampal volume reduction was dose-related, and associated with subclinical psychotic symptoms, even though our sample was carefully screened for DSM-IV psychotic disorders. Further analyses of the brain anatomical data obtained in our study suggest differences in cerebellar grey and white matter,63 while analyses of prefrontal cortical regions are ongoing.

Association Between Cannabis Use Measures and Structural Brain Changes The evidence for an association between specific cannabis use measures and human brain anatomical findings is mixed, and may relate to the heterogeneity of cannabis use patterns observed across the various studies.50 However, a general trend for an inverse relationship between indices of cannabis use and hippocampal or parahippocampal volume appears to exist.

218

Specifically, samples with greater cannabis exposure37,54 demonstrated reductions in hippocampal or parahippocampal volumes, while samples with a lower quantity or frequency of cannabis use exhibited no change,46,47,52,53,59 or even volumetric increases.45 Studies of heavy cannabis users37,54,60,62 were more likely to detect regional abnormalities that those of lighter cannabis users. Greater brain alterations with an earlier age of onset of cannabis use have been reported in some studies,53 but not others.54,59 As discussed above, in our study of very heavy long term cannabis users, we found that the reduction in left hippocampal volume was dose-related, correlating significantly with cumulative exposure to cannabis.37 The cannabis users of our study had one of the longest durations of exposure to cannabis of all the studies (mean 19.7 years, range 10–32 years). One other study with a similar mean duration of use (mean 22.6 years, range 12–33 years) reported no brain alterations, but the minimum duration of daily use in their sample was only 1 year.59 In contrast the minimum duration of near daily use in our study was 10 years. Further, a key difference between the Tzilos et al.59 study and ours was in the estimated episodes of use, and hence the cumulative dose of exposure to cannabis. Tzilos et al.’s sample reported an average of 20,100 lifetime episodes of use. Our sample had an average 62,000 estimated episodes of use over the lifetime. Thus, despite a similar mean duration of use, our cannabis users used more than three times as much cannabis, which may be the critical factor in explaining our finding of a dose–response relationship between hippocampal volume and cumulative cannabis use. In addition, Tzilos et al.59 acquired their images at a lower field strength and with a coarser spatial resolution (1.5 T with 3-mm-thick slices vs. 3 T with 1-mm-thick slices in our study), an important consideration given the small size and boundary definition of the brain structures investigated. Moreover, the region of interest measured in their study was less specific to the hippocampus relative to ours because they also included the parahippocampal gyrus, whereas ours was restricted to the hippocampus itself using well-defined boundaries. We propose that structural brain alterations in humans may only manifest after a significant period of heavy cannabis use at very high doses, akin to that of the animal studies where brain abnormalities were found.

N. Solowij et al.

Association Between Structural Brain Changes in Cannabis Users and Cognitive Deficits Few structural brain imaging studies of cannabis users have specifically examined the relationship between brain volumes and cognitive performance measures, and those that did found no associations.46,47,59 We too found no relationship between reduced hippocampal and amygdala volumes and performance on the Rey Auditory Verbal Learning Test (RAVLT),37 despite replicating the now consistently reported learning and memory deficits on this task (i.e., the number of words recalled across the five learning trials and in delayed recall). In our study, these measures correlated inversely with the duration of regular cannabis use (words recalled across the five trials; r = −0.61, p = 0.016) and the frequency of cannabis use (words recalled following 20 min delay; r = −0.58, p = 0.03). Performance measures on the RAVLT are likely to reflect the operation of numerous cognitive processes not necessarily related to hippocampal function. Volumetric measures may be less sensitive to correlations with cognitive task performance than measures of brain activation as obtained in functional imaging studies. Some functional imaging studies of cannabis users have found reduced left hippocampal blood flow and activation during verbal and visual learning tasks in cannabis users.39,46 Combining structural and functional imaging while employing tasks designed more specifically to probe memory functions mediated by the hippocampus may shed light on these relationships in cannabis users. We also administered select tests from the Cambridge Neuropsychological Test Automated Battery (CANTAB)64 to a larger sample of cannabis users that also included the participants of our imaging study, and overall, the cannabis users’ performance was significantly worse than controls on most of these visuospatial memory performance measures.65 Smaller left hippocampal volume correlated with poor performance on the Spatial Span test and smaller right amygdala volume correlated with poor performance on Spatial Recognition Memory. Preliminary analyses suggest associations between poor performance in cannabis users on select memory indices and certain prefrontal cortical volumetric measures.65 The pattern of associations in the control group differed from that in the cannabis users, with fewer associations overall in controls,

27 Structural Brain Alterations in Cannabis Users

and differing hemispheric involvement. Analyses of these data are still in a preliminary stage and will be reported in full elsewhere. It is therefore difficult to draw firm conclusions about their potential interpretation. However, associations between structural brain measures, and indices of both cannabis use and poor cognitive performance respectively, allow some preliminary hypotheses to be formulated, as we discuss further below. There is evidence that associations between specific brain regions and specific cognitive functions exist in the general population, but that these relationships are disrupted in schizophrenia, particularly in males.66 Differential structure–function relationships are evident in patients with chronic schizophrenia and healthy controls.67 These complex relationships mimic some of our own preliminary findings, and may be interpreted as aberrant associations between brain structure and function. Lower scores on neuropsychological measures of declarative episodic memory have been shown to correlate with reduced MRI volumes of the hippocampus in schizophrenia,68,69 and in a recent study, Hurlemann et al.70 reported an association between delayed recall on the RAVLT and bilaterally reduced hippocampal volumes in unmedicated individuals in a late prodromal state for schizophrenia, but not those in early prodromal states, nor in healthy controls. These findings suggest that progressive and interrelated structural-functional pathology of the hippocampus became evident as prodromal symptoms and behaviours accrued, increasing the risk for psychosis. Those in the late prodromal state showed a reduction of hippocampal volume of a similar magnitude to that observed in the cannabis users within our study. Our cannabis users also showed greater impairment on the RAVLT than any of the ultra-high risk individuals within Hurlemann and colleagues’ study,70 and this in itself may obscure a structure–function relationship.

Associations Between Cannabis Use, Structural Brain Changes and Psychiatric Symptoms There has been a growing literature reporting an association between cannabis use and the development of psychopathology, including both psychotic and

219

depressive symptoms. Cannabis use, particularly in adolescence, has been shown to increase the risk for the later development of schizophrenia,4,71 but there is less evidence for cannabis as an aetiological factor in the development of major depression or other mental disorders.71 While risk for psychosis subsequent to using cannabis may be genetically mediated, for example via a COMT gene polymorphism among other possibilities,72,73 associations between the development of psychotic or depressive symptoms and brain changes in cannabis users have not been rigorously investigated. We have reported an association between smaller left hippocampal volume in cannabis users and subclinical positive psychotic symptoms as measured by the Scale for the Assessment of Positive Symptoms (SAPS).37 Positive symptom scores correlated with cumulative exposure to cannabis.37 Our cannabis users were carefully screened for DSM-IV Psychotic Disorders and had never sought treatment for any psychological disorders. Yet the majority of the sample endorsed firm beliefs (scores on the SAPS ranged from Questionable to Mild) concerning ideas of persecution, reference, mind reading, sin and/or guilt, while some displayed bizarre clothing/appearance or reported bizarre social/sexual behaviour. Smaller left hippocampal volume was also significantly correlated with higher scores on the paranoid subscale of the Brief Symptom Inventory (BSI) (unpublished data, Solowij N 2008). Negative symptoms were also elevated in the cannabis users.37 We are currently further exploring associations between subclinical positive or negative symptoms and prefrontal cortical regional brain volumes. The cannabis users included in our study showed higher depressive symptoms than non-users, as measured by the Hamilton Depression Rating Scale (HDRS). However, they had never been diagnosed with a major depressive disorder, had never sought treatment for depression, and scored less than six on the HDRS. Although an association between depression and hippocampal volume reduction does exist, this is seen in the more persistent forms of major depressive disorder,74,75 which clearly does not apply to our sample. Further, depressive symptoms in our cannabis users did not correlate with volumetric measures of any brain region, including the hippocampus, and the relationship between left hippocampal volume and cumulative exposure to cannabis remained significant after controlling for depressive symptoms. One other study has reported an association between overall

220

brain white matter volume and depressive symptoms in adolescent/young adult cannabis users without diagnosable mood disorders.47 In examining potential associations between subclinical psychotic symptoms and cognitive impairment, we found that Spatial span total usage errors correlated positively with positive psychotic symptom scores, but there was no other relationship between cognitive measures and subclinical positive or negative psychotic symptoms. Skosnik et al.76–78 have found associations between cognitive (e.g., poor negative priming) and psychophysiological measures (e.g., P300 to affective stimuli; 20 Hz neural synchrony), and higher scores on the Schizotypal Personality Questionnaire, on which cannabis users generally obtained high positive syndrome scores. The relationship between cognitive dysfunction and psychiatric symptoms has not been well investigated in studies of long term cannabis users. A number of studies have demonstrated that acute administration of THC to healthy volunteers induces both cognitive impairment and transient positive and negative schizophrenia-like symptoms.49,73,79 Such symptoms are also exacerbated when THC is acutely administered to patients with schizophrenia.73,80 Henquet et al.73 reported that sensitivity to the psychosis-inducing and cognitive impairing effects of cannabis may be genetically mediated. In patients with schizophrenia, associations between positive psychotic symptoms and memory deficits, and volumetric measures of the hippocampus, the superior temporal gyrus, and the temporal lobe in general, have been demonstrated, as well as between negative symptoms, executive function, and prefrontal cortical measures.81 However, in a general population sample of young female twins, Simons et al.82 reported that slower speed of information processing correlated (weakly) with subclinical positive and negative psychotic symptoms (and was influenced by genetic factors), but there was no relationship between episodic memory (RAVLT performance) and any psychotic symptom dimension. The interrelationships between cognition and psychopathology, and indeed between brain structure and function, are complex and require further investigation in cannabis users.

Discussion Long term heavy cannabis use can result in brain pathophysiological and functional changes that resemble

N. Solowij et al.

aspects of schizophrenia. While the evidence is only beginning to accumulate from a small number of studies that have used rigorous methods to investigate structural brain alterations in cannabis users, the data suggest that such alterations are likely to occur when cannabis is used very heavily over a prolonged period within brain regions similar to those where alterations have been reported in patients with schizophrenia, especially medial temporal lobe structures. Structural abnormalities in schizophrenia are wide ranging, but prominent volumetric loss is evident in the hippocampus and amygdala, as well as in prefrontal cortical regions.81,83–86 Reductions in the amygdalo– hippocampal complex are also seen in unaffected relatives.85,87,88 The prodrome, or at-risk mental state, has been reported to be associated with smaller hippocampal volumes in some studies70,89,90 and changes in medial temporal and prefrontal regions have been reported to occur with the transition to psychosis.85,88–95 The left hippocampal volume in particular, has been proposed as a vulnerability marker for schizophrenia.96 It is important to note that the reduction in left hippocampal volume evident in the cannabis users within our study,37 was not only of a similar magnitude to that seen in patients with schizophrenia,83,97–99 but also correlated with subclinical psychotic symptoms and cumulative exposure to cannabis. In the literature that we have reviewed, structural alterations in cannabis users were most evident in hippocampal, parahippocampal and amygdala regions. Our own findings of significant hippocampal and amygdala volume loss in cannabis users suggest potential toxicity due to cumulative exposure to large doses of cannabis over many years. However, the structural neuroimaging studies of cannabis users have so far focused on a very narrow range of brain regions. Cannabis use may affect the morphology of other cortical (e.g., prefrontal cortex) and subcortical (e.g., the striatum) brain areas, where cannabinoid receptors are heavily concentrated.35 Alterations in prefrontal areas have often been reported in studies utilising fMRI,42 and in one study using magnetic resonance spectroscopy (loss of N-acetylaspartate/total creatine in the dorsolateral prefrontal cortex).100 Recent DTI studies have also suggested that cannabis use may affect the integrity of white matter fibre tracts in prefrontal regions.60,62 Despite the functional neuroimaging, DTI and MRS literature suggesting that cannabis exerts a detrimental effect on prefrontal brain areas, no structural neuroimaging study has specifically examined

27 Structural Brain Alterations in Cannabis Users

this possibility. Only three of the published studies reported prefrontal measures, with one study finding decreased global grey-matter percentage to be more pronounced in the frontal lobes,53 another finding alterations in the pre-central gyrus,54 and the third finding no volumetric alterations within the frontal lobes.52 Clearly, future structural studies are needed to more comprehensively explore the impact of cannabis use on prefrontal brain areas. A crucial question is the extent to which interrelated structural-functional aberrations involving the hippocampus, prefrontal regions, or indeed other brain structures in cannabis users, might reflect a vulnerability to schizophrenia.70 Our findings suggest that long-term exposure to cannabis constitutes a vulnerability to psychopathology by disrupting the structural integrity of brain regions that are also involved in psychotic (and affective) disorders. On the other hand, it may be that psychopathological symptoms are related to regional brain changes independent of cannabis use. However, we demonstrated that the severity of subthreshold positive psychotic symptoms also correlated positively with cumulative exposure to cannabis.37 It could be argued that the dose-related hippocampal reduction that we observed may reflect heavy cannabis use in response to pre-existing or developing psychotic symptoms. However, there is limited empirical support for long-term self-medication of subthreshold psychotic symptoms with cannabis, and stronger support for the induction of psychotic symptoms subsequent to cannabis exposure.29 As such, it seems more likely that prolonged heavy use of cannabis induces subthreshold psychotic symptoms and that both of these factors are associated with the hippocampal volume loss that we found. The positive and negative psychotic symptoms were clinically subthreshold, in the questionable to mild range of scores on the SAPS and SANS, and the cannabis-using participants were carefully screened for a current and past history of mental disorder. Furthermore, the fact that the mean age of our cannabis users was nearly 40 years suggests that these symptoms are unlikely to reflect a prodrome. Instead we propose that very heavy cannabis use over many years results in schizophrenia-like conditions in the brain, with the development of cognitive deficits, psychological symptoms, and alterations in brain structure, that resemble schizophrenia. This begs the question of why the cannabis users within our study did not develop schizophrenia? Do they represent a population who are somehow protected from developing the disorder?

221

If so, what are the protective mechanisms in these individuals that prevented the development of psychosis when their combination of cognitive deficits, subclinical psychotic symptoms and brain structural alterations resembled aspects of schizophrenia? It may be that the participants of our study were less genetically vulnerable to developing a psychotic disorder subsequent to cannabis use. That is, perhaps they were not homozygous for the COMT Val allele that is involved in dopamine regulation and has been associated with a significant increase in the relative risk of developing schizophrenia or schizophreniform psychosis when cannabis use commences in adolescence.72,73 Perhaps their genetic make-up allowed them to smoke heavily for many years, developing schizophrenia-like symptoms, cognitive deficits and brain structural alterations, but protecting them from conversion to psychosis. A further protective factor may have been the relatively late age of commencement of cannabis use in our sample. The mean age of onset of regular cannabis use was 20.1; the mean age that cannabis was first tried was 18.3, with less than fortnightly use in the first couple of years of use. This is in keeping with the COMT literature, where increased risk for psychosis was only evident in the genetically vulnerable group when cannabis use commenced prior to the age of 15 or was at least monthly by age 18.72 There is also evidence of greater adverse cognitive consequences when cannabis use begins during early adolescence (e.g., before age 16 or 17; see Ref. 44 for a review), and less likelihood of recovery of function after 28 days abstinence.24 Early onset cannabis users were found to have smaller whole brain volume, lower percentage of cortical grey matter, higher percentage of white matter and increased resting CBF, compared with late onset users in one study,53 but greater brain abnormalities with earlier age of onset were not apparent in our study, nor in two other structural imaging studies.54,59 There is good evidence accruing to suggest that the developing brain may be particularly sensitive to the effects of drugs and substance abuse, resulting in aberrant brain developmental processes.101 As cannabis is the most commonly abused illicit drug during adolescence,1 further investigation is needed to explore whether cannabis has specific and/or more detrimental effects on adolescent versus adult users, whether there is an age-dependent effect, as well as a progression of brain morphological abnormalities with continued cannabis use.

222

Various other demographic, clinical, genetic and drug use factors may mediate the development of structural brain alterations and associated cognitive dysfunction and psychological symptoms in cannabis users. The majority of the published studies of brain structure in cannabis users comprise predominantly or exclusively of males,50 making generalisations to the general population unreliable. Where females were included, no study found an association between gender and cannabis-related brain structural changes. While the predominance of males in these studies may reflect the gender distribution of cannabis users in the community, further research with equal gender distributions, or of female samples, is warranted to investigate potential sex differences. There may be multiple moderators or mediators of adverse sequelae from long term heavy cannabis use, including gender, environmental factors, early neurodevelopmental insults and stress, that interact with cumulative exposure to high dose cannabis use to produce schizophrenia-like sequelae. Future longitudinal work assessing the emergence of hippocampal and other brain changes, as well as psychotic symptoms, with continued exposure to cannabis, and how these are related to polymorphic variations in susceptibility genes for psychotic disorders (e.g., COMT, BDNF), and multiple variations within multiple genes,102 will prove useful in better characterising these relationships. The cumulative evidence for neurocognitive dysfunction similar to that seen in schizophrenia and the development of subclinical psychotic symptoms in cannabis users, combines with the limited data from structural neuroimaging studies to support our proposition that chronic cannabis use may result in schizophrenia-like changes in brain structure and function. This is further supported by evidence that long term exposure to cannabis may result in lasting dysfunction of the endogenous cannabinoid system, as well as alterations in the functionality of a number of neurotransmitter systems – changes that resemble schizophrenia-like conditions in the brain.4,44,79,80,103,104 It clear is that heavy cannabis use in adolescence can increase the risk of later schizophrenia-like psychoses.4,71 In her recent review of the effects of cannabis on the brain, DeLisi34 stated the importance of establishing whether cannabis can cause brain abnormalities that place an individual at greater risk for developing schizophrenia-like symptoms. She concluded, on the basis of the literature that she examined, that

N. Solowij et al.

this is unlikely. With the addition of our most recent findings of hippocampal and amygdala volume reductions and associated subclinical psychotic symptoms in very heavy and chronic cannabis users,37 together with our and others’ appraisal of the literature on brain alterations, cognitive dysfunction and psychotic symptoms in cannabis users,27,29,30,44,50 we beg to differ. Very heavy use of cannabis over a prolonged period can result in subclinical psychotic symptoms, cognitive deficits and alterations to specific brain regions that are similar to those seen in schizophrenia. DeLisi34 further surmised that cannabis use may lead to schizophrenialike symptoms in individuals who already are at high risk for developing schizophrenia because of previous developmental or genetic insults to the brain. This does not apply to the participants of our study, who clearly were not at high risk for developing schizophrenia, as they had not developed this disorder, nor any other frank psychoses or other psychiatric disorders by the age of 40. DeLisi34 acknowledged that the neurochemical interactions between cannabis and the dopaminergic pathway may result in adverse consequences, a statement with which we do agree. However, she qualified this further by stating “particularly in genetically vulnerable individuals”. Overall, the literature to date in this area has supported such an assumption. Our findings suggest that everyone may be vulnerable to developing sub-clinical schizophrenia-like symptoms, cognitive dysfunction and brain alterations, if they use cannabis heavily enough and for a sufficiently prolonged period. This conclusion supports our premise that long term heavy cannabis use may be used as a model to investigate the pathophysiological processes involved in schizophrenia. The critical factor is in determining the parameters of cannabis use that lead to these structural and functional alterations in individuals who are, compared to those who are not, at high risk for the development of neuropsychiatric disorders, at various neurodevelopmental periods, and identifying the protective mechanisms that prevent the onset of such potentially devastating disorders. Acknowledgements This review was supported by funding from a National Health & Medical Research Council (NH&MRC) Project Grant (I.D.459111) (Solowij, Lubman, Yücel). Portions of the original research described here were supported by grants from the Clive and Vera Ramaciotti Foundation, the Schizophrenia Research Institute using infrastructure funding from NSW Health, and the University of Wollongong (Dr. Solowij). Murat Yücel is supported by a NH&MRC Clinical Career

27 Structural Brain Alterations in Cannabis Users Development Award (I.D. 509345). Valentina Lorenzetti is supported by the Endeavour International Postgraduate Research Scholarship (IPRS) and the Melbourne International Research Scholarship (MIRS). Dan Lubman is supported by the Colonial Foundation.

References 1. Substance Abuse and Mental Health Services Administration (SAMHSA). Results from the 2006 National Survey on Drug Use and Health: National Findings. Rockville, MD: Office of Applied Studies; 2006 2. Degenhardt L, Lynskey M, Hall W. Cohort trends in the age of initiation of drug use in Australia. Aust NZ J Pub Health 2000; 24:421–426 3. Australian Institute of Health and Welfare (AIHW) (2005) 2004 National Drug Strategy Household Survey: Detailed Findings. AIHW cat. no. 66. Canberra: AIHW (Drug Statistics Series No.16) 4. Cohen M, Solowij N, Carr V. Cannabis, cannabinoids and schizophrenia: integration of the evidence. Aust NZ J Psychiat 2008; 42:357–368 5. Gaoni Y, Mechoulam R. The isolation and structure of delta-1-tetrahydrocannabinol and other neutral cannabinoids from hashish. J Am Chem Soc 1971; 93:217–224 6. Heath RG, Fitzjarrell AT, Fontana CJ, et al. Cannabis sativa: effects on brain function and ultrastructure in rhesus monkeys. Biol Psychiat 1980; 15:657–690 7. Scallet AC, Uemura E, Andrews A, et al. Morphometric studies of the rat hippocampus following chronic delta-9tetrahydrocannabinol (THC). Brain Res 1987; 436:193–198 8. Landfield PW, Cadwallader LB, Vinsant S. Quantitative changes in hippocampal structure following long-term exposure to delta 9-tetrahydrocannabinol: possible mediation by glucocorticoid systems. Brain Res 1988; 443:47–62 9. Chan GC, Hinds TR, Impey S, et al. Hippocampal neurotoxicity of Delta9-tetrahydrocannabinol. J Neurosci 1998; 18:5322–5332 10. Lawston J, Borella A, Robinson JK, et al. Changes in hippocampal morphology following chronic treatment with the synthetic cannabinoid WIN 55,212–2. Brain Res 2002; 877:407–410 11. Harper JW, Heath RG, Myers WA. Effects of Cannabis sativa on ultrastructure of the synapse in monkey brain. J Neurosci Res 1987; 3:87–93 12. Meyers WA 3rd, Heath RG. Cannabis sativa: ultrastructural changes in organelles of neurons in brain septal region of monkeys. J Neurosci Res 1979; 4:9–17 13. Downer E, Boland B, Fogarty M, et al. Delta 9-tetrahydrocannabinol induces the apoptotic pathway in cultured cortical neurons via activation of the CB1 receptor. Neuroreport 2001; 12:3973–3978 14. Solowij N. Cannabis and Cognitive Functioning. Cambridge: Cambridge University Press; 1998 15. Ehrenreich H, Rinn T, Kunert HJ, et al. Specific attentional dysfunction in adults following early start of cannabis use. Psychopharmacology (Berl) 1999; 142:295–301

223 16. Pope HG Jr, Yurgelun-Todd D. The residual cognitive effects of heavy marijuana use in college students. JAMA 1996; 275:521–527 17. Pope HG Jr, Gruber AJ, Hudson JI, et al. Neuropsychological performance in long-term cannabis users. Arch Gen Psychiat 2001; 58:909–915 18. Gonzalez R, Carey C, Grant I. Nonacute (residual) neuropsychological effects of cannabis use: a qualitative analysis and systematic review. J Clin Pharmacol 2002; 42(11 suppl):48S–57S 19. Solowij N, Stephens RS, Roffman RA, et al. Cognitive functioning of long-term heavy cannabis users seeking treatment. JAMA 2002; 287:1123–1131 20. Kanayama G, Rogowska J, Pope HG. Spatial working memory in heavy cannabis users: a functional magnetic resonance imaging study. Psychopharmacology (Berl) 2004; 176:239–247 21. Solowij N, Battisti R. The chronic effects of cannabis on memory in humans: a review. Curr Drug Abuse Rev 2008; 1:81–98 22. Whitlow CT, Liguori A, Livengood LB, et al. Long-term heavy marijuana users make costly decisions on a gambling task. Drug Alcohol Depend 2004; 76:107–111 23. Fried P, Watkinson B, James D, et al. Current and former marijuana use: preliminary findings of a longitudinal study of effects on IQ in young adults. Can Med Assoc J 2002; 166:887–891 24. Pope HG, Gruber AJ, Hudson JI, et al. Early-onset cannabis use and cognitive deficits: what is the nature of the association? Drug Alcohol Depend 2003; 69:303 25. Dorard G, Berthoz S, Phan O, et al. Affect dysregulation in cannabis abusers: a study in adolescents and young adults. Eur Child Adol Psychiat 2008; 17:274–282 26. Degenhardt L, Hall W, Lynskey M. Exploring the association between cannabis use and depression. Addiction 2003; 98:1493–1504 27. Johns A. Psychiatric effects of cannabis. Br J Psychiat 2001; 178:116–122 28. Patton GC, Coffey C, Carlin JB, et al. Cannabis use and mental health in young people: cohort study. Br Med J 2002; 325:1195–1198 29. Moore TH, Zammit S, Lingford-Hughes A, et al. Cannabis use and risk of psychotic or affective mental health outcomes: a systematic review. Lancet 2007; 370:319–328 30. Wittchen HU, Frohlich C, Behrendt S, et al. Cannabis use and cannabis use disorders and their relationship to mental disorders: a 10-year prospective-longitudinal community study in adolescents. Drug Alcohol Depend 2007; 1(88 suppl):S60–S70 31. Arseneault L, Cannon M, Poulton R, et al. Cannabis use in adolescence and risk for adult psychosis: longitudinal prospective study. BMJ 2002; 325:1212–1213 32. Zammit S, Allebeck P, Andreasson S, et al. Self reported cannabis use as a risk factor for schizophrenia in Swedish conscripts of 1969: historical cohort study. BMJ 2002; 325:1199 33. Semple DM, McIntosh AM, Lawrie SM. Cannabis as a risk factor for psychosis: systematic review. J Psychopharmacol 2005; 19:187–194 34. DeLisi LE. The effect of cannabis on the brain: can it cause brain anomalies that lead to increased risk for schizophrenia? Curr Opin Psychiat 2008; 21:140–150

224 35. Iversen L. Cannabis and the brain. Brain 2003; 126: 1252–1270 36. Miettunen J, Tormanen S, Murray GK, et al. Association of cannabis use with prodromal symptoms of psychosis in adolescence. Br J Psychiat 2008; 192:470–471 37. Yücel M, Solowij N, Respondek C, et al. Regional brain abnormalities associated with long-term heavy cannabis use. Arch Gen Psychiat 2008; 65:694–701 38. Yücel M, Lubman DI. Neurocognitive and neuroimaging evidence of behavioural dysregulation in human drug addiction: implications for diagnosis, treatment and prevention. Drug Alcohol Rev 2007; 26:33–39 39. Block RI, O’Leary DS, Hichwa RD, et al. Effects of frequent marijuana use on memory-related regional cerebral blood flow. Pharmacol Biochem Behav 2002; 72:237–250 40. Eldreth DA, Matochik JA, Cadet JL, et al. Abnormal brain activity in prefrontal brain regions in abstinent marijuana users. Neuroimage 2004; 23:914–920 41. Bolla KI, Eldreth DA, Matochik JA, et al. Neural substrates of faulty decision-making in abstinent marijuana users. Neuroimage 2005; 26:480–492 42. Quickfall J, Crockford D. Brain neuroimaging in cannabis use: a review. J Neuropsychiat Clin Neurosci 2006; 18:318–332 43. Chang L, Chronicle EP. Functional imaging studies in cannabis users. Neuroscientist 2007; 13:422–432 44. Solowij N, Michie PT. Cannabis and cognitive dysfunction: parallels with endophenotypes of schizophrenia? J Psychiat Neurosci 2007; 32:30–52 45. Medina KL, Schweinsburg AD, Cohen-Zion M, et al. Effects of alcohol and combined marijuana and alcohol use during adolescence on hippocampal volume and asymmetry. Neurotoxicol Teratol 2007; 29:141–152 46. Jager G, Van Hell HH, De Win MM, et al. Effects of frequent cannabis use on hippocampal activity during an associative memory task. Eur Neuropsychopharmacol 2007; 17:289–297 47. Medina KL, Nagel BJ, Park A, et al. Depressive symptoms in adolescents: associations with white matter volume and marijuana use. J Child Psychol Psychiat 2007; 48:592–600 48. Fletcher PC, Honey GD. Schizophrenia, ketamine and cannabis: evidence of overlapping memory deficits. Trends Cogn Sci 2006; 10:167–174 49. Koethe D, Gerth CW, Neatby MA, et al. Disturbances of visual information processing in early states of psychosis and experimental delta-9-tetrahydrocannabinol altered states of consciousness. Schizophr Res 2006; 88:142–150 50. Lorenzetti V, Lubman DI, Whittle S, et al. Structural MRI findings in cannabis users: what do we know? Subst Use Misuse (in press) 51. Campbell AMG, Evans M, Thomson JLG, et al. Cerebral atrophy in young cannabis smokers. Lancet 1971; 298: 1219–1224 52. Block RI, O’Leary DS, Ehrhardt JC, et al. Effects of frequent marijuana use on brain tissue volume and composition. Neuroreport 2000; 11:491–496 53. Wilson W, Mathew R, Turkington T, et al. Brain morphological changes and early marijuana use: a magnetic resonance and positron emission tomography study. J Addict Dis 2000; 19:1–22.

N. Solowij et al. 54. Matochik JA, Eldeth DA, Cadet DA, et al. Altered brain tissue composition in heavy marijuana users. Drug Alcohol Depend 2005; 77:23–30 55. Co BT, Goodwin DW, Gado M, et al. Absence of cerebral atrophy in chronic cannabis users: evaluation by computerized transaxial tomography. JAMA 1977; 237:1229–1230 56. Stefanis, C. Biological aspects of cannabis use. In: Petersen RC, ed. The International Challenge of Drug Abuse. Rockville, MD: National Institute on Drug Abuse; 1976:149–178 57. Kuehnle J, Mendelson JH, Davis KR, et al. Computed tomographic examination of heavy marijuana smokers. JAMA 1977; 237:1231–1232 58. Hannerz J, Hindmarsh T. Neurological and neuroradiological examination of chronic cannabis smokers. Ann Neurol 1983; 13:207–210 59. Tzilos GK, Cintron CB, Wood JB, et al. Lack of hippocampal volume change in long-term heavy cannabis users. Am J Addict 2005; 14:64–72 60. Arnone D, Barrick TR, Chengappa S, et al. Corpus callosum damage in heavy marijuana use: preliminary evidence from diffusion tensor tractography and tract-based spatial statistics. Neuroimage 2008; 41:1067–1074 61. DeLisi LE, Bertisch HC, Szulc KU, et al. A preliminary DTI study showing no brain structural change associated with adolescent cannabis use. Harm Reduct J 2006; 3:17 62. Gruber SA, Yurgelun-Todd DA. Neuroimaging of marijuana smokers during inhibitory processing: a pilot investigation. Cogn Brain Res 2005; 23:107–118 63. Solowij N, Respondek C, Whittle S, et al. Cerebellar grey and white matter changes associated with cannabis use in schizophrenia and in healthy controls. Neuroimage 2008; 41:S83 64. Cambridge Cognition. CANTAB Eclipse Software User Guide. Cambridge: Cambridge Cognition Ltd; 2004 65. Solowij N, Respondek C, Battisti R, et al. Visuospatial memory deficits in long term heavy cannabis users: relation to psychotic symptoms and regional brain volumes. Int J Neuropsychopharmacol 2008; 11:242 66. Antonova E, Sharma T, Morris R, et al. The relationship between brain structure and neurocognition in schizophrenia: a selective review. Schizophr Res 2004; 70:117–145 67. Antonova E, Kumari V, Morris R, et al. The relationship of structural alterations to cognitive deficits in schizophrenia: a voxel-based morphometry study. Biol Psychiat 2005; 58:457–467 68. Kuroki N, Kubicki M, Nestor PG, et al. Fornix integrity and hippocampal volume in male schizophrenic patients. Biol Psychiat 2006; 60:22–31 69. Nestor PG, Kubicki M, Kuroki N, et al. Episodic memory and neuroimaging of hippocampus and fornix in chronic schizophrenia. Psychiat Res 2007; 155:21–28 70. Hurlemann R, Jessen F, Wagner M, et al. Interrelated neuropsychological and anatomical evidence of hippocampal pathology in the at-risk mental state. Psychol Med 2008; 38:843–851 71. Di Forti M, Morrison PD, Butt A, et al. Cannabis use and psychiatric and cognitive disorders: the chicken or the egg? Curr Opin Psychiat 2007; 20:228–234 72. Caspi A, Moffitt TE, Cannon M, et al. Moderation of the effect of adolescent onset cannabis use on adult psychosis

27 Structural Brain Alterations in Cannabis Users

73.

74.

75.

76.

77.

78.

79.

80.

81.

82.

83.

84.

85.

86.

87.

by a functional polymorphism in the catechol-O-methyltransferase gene: longitudinal evidence of a gene x environment interaction. Biol Psychiat 2005; 57:1117–1127 Henquet C, Rosa A, Krabbendam L, et al. An experimental study of catechol-O-methyltransferase Val158Met moderation of ∆-9-tetrahydrocannabinol-induced effects on psychosis and cognition. Neuropsychopharmacology 2006; 31:2748–2757 MacQueen GM, Campbell S, McEwen BS, et al. Course of illness, hippocampal function, and hippocampal volume in major depression. Proc Natl Acad Sci USA 2003; 100:1387–1392 Frodl T, Meisenzahl EM, Zill P, et al. Reduced hippocampal volumes associated with the long variant of the serotonin transporter polymorphism in major depression. Arch Gen Psychiat 2004; 61:177–183 Skosnik PD, Spatz-Glenn L, Park S. Cannabis use is associated with schizotypy and attentional disinhibition. Schizophr Res 2001; 48:83–92 Skosnik PD, Grishnan GP, Aydt EE. Psychophysiological evidence of altered neural synchronization in cannabis use: relationship to schizotypy. Am J Psychiat 2006; 163:1798–1805 Skosnik PD, Park S, Dobbs L, et al. Affect processing and positive syndrome schizotypy in cannabis users. Psychiat Res 2008; 157:279–282 D’Souza DC, Perry E, MacDougall L, et al. The psychotomimetic effects of intravenous delta-9-tetrahydrocannabinol in healthy individuals: implications for psychosis. Neuropsychopharmacol 2004; 29:1558–1572 D’Souza DC, Abi-Saab WM, Madonick S, et al. Delta-9tetrahydrocannabinol effects in schizophrenia: implications for cognition, psychosis, and addiction. Biol Psychiat 2005; 57:594–608 Gur RE, Keshavan MS, Lawrie SM. Deconstructing psychosis with human brain imaging. Schizophr Bull 2007; 33:921–931 Simons CJP, Jacobs N, Jolles J, et al. Subclinical psychotic experiences and cognitive functioning as a bivariate phenotype for genetic studies in the general population. Schizophr Res 2007; 92:24–31 Wright IC, Rabe-Hesketh S, Woodruff PW, et al. Metaanalysis of regional brain volumes in schizophrenia. Am J Psychiat 2000; 157:16–25 Velakoulis D, Wood SJ, Wong MTH, et al. Hippocampal and amygdala volumes differ according to psychosis stage and diagnosis: an MRI study of chronic schizophrenia, first-episode psychosis and ultra-high risk subjects. Arch Gen Psychiat 2006; 63:139–149 Lawrie SM, McIntosh AM, Hall J, et al. Brain structure and function changes during the development of schizophrenia: the evidence from studies of subjects at increased genetic risk. Schizophr Bull 2008; 34:330–340 Walterfang M, Wood AG, Reutens DC, et al. Morphology of the corpus callosum at different stages of schizophrenia: cross-sectional study in first-episode and chronic illness. Br J Psychiat 2008; 192:429–434 Boos HBM, Aleman A, Cahn W, et al. Brain volumes in relatives of patients with schizophrenia: a meta-analysis. Schizophr Res 2006; 81:41

225 88. Fusar-Poli P, Perez J, Broome M, et al. Neurofunctional correlates of vulnerability to psychosis: a systematic review and meta-analysis. Neurosci Biobehav Rev 2007; 31:465–484 89. Pantelis C, Velakoulis D, McGorry PD, et al. Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI study. Lancet 2003; 361:281–288 90. Borgwardt SJ, Riecher-Rössler A, Dazzan P, et al. Regional gray matter volume abnormalities in the at risk mental state. Biol Psychiat 2007; 61:1148–1156 91. Pantelis C, Yücel M, Wood SJ, et al. Structural brain imaging evidence for multiple pathological processes at different stages of brain development in schizophrenia. Schizophr Bull 2005; 31:672–696 92. Pantelis C, Velakoulis D, Wood SJ, et al. Neuroimaging and emerging psychotic disorders: the Melbourne ultrahigh risk studies. Int Rev Psychiat 2007; 19:373–381 93. Sun D, Phillips LJ, Velakoulis D, et al. Progressive brain structural changes mapped as psychosis develops in ‘at risk’ individuals. Schizophr Bull 2007; 33:356–357 94. Walterfang M, McGuire PK, Yung AR, et al. White matter volume changes in people who develop psychosis. Br J Psychiat 2008; 193:210–215 95. Wood SJ, Pantelis C, Velakoulis D, et al. Progressive changes in the development toward schizophrenia: studies in subjects at increased symptomatic risk. Schizophr Bull 2008; 34:322–329 96. Seidman LJ, Faraone SV, Goldstein JM, et al. Left hippocampal volume as a vulnerability marker for schizophrenia. Arch Gen Psychiat 2002; 59:839–849 97. Copolov D, Velakoulis D, McGorry P, et al. Neurobiological findings in early phase schizophrenia. Brain Res Rev 2000; 31:157–165 98. Steen RG, Mull C, McClure R, et al. Brain volume in firstepisode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies. Br J Psychiat 2006; 188:510–518 99. Vita A, De Peri L, Silenzi C, et al. Brain morphology in first-episode schizophrenia: a meta-analysis of quantitative magnetic resonance imaging studies. Schizophr Res 2006; 82:75–88 100. Hermann D, Sartorius A, Welzel H, et al. Dorsolateral prefrontal cortex N-acetylaspartate/total creatine (NAA/tCr) loss in male recreational cannabis users. Biol Psychiat 2007; 61:1281–1289 101. Lubman DI, Yucel M, Hall WD. Substance use and the adolescent brain: a toxic combination? J Psychopharmacol 2007; 21:792–794 102. Henquet C, Di Forti M, Morrison P, et al. Gene-environment interplay between cannabis and psychosis. Schizophr Bull (advance online publication) 2008. Available at: doi: 10.1093/schbul/sbn108 103. Leweke FM, Giuffrida A, Koethe D, et al. Anandamide levels in cerebrospinal fluid of first-episode schizophrenia patients: impact of cannabis use. Schizophr Res 2007; 94:29–36 104. Murray RM, Morrison PD, Henquet C, et al. Cannabis, the mind and society: the hash realities. Nat Rev Neurosci 2007; 8:885–895

Contents to Volumes 1, 3 and 4

Volume 1 Part I Methodological and Technological Advances 1 Where Do We Stand in the Quest for Neuropsychiatric Biomarkers and Endophenotypes and What Next? Michael S. Ritsner and Irving I. Gottesman 2 Methodological and Statistical Issues in the Use of Biomarkers in Clinical and Research Studies Ryan J. Van Lieshout and Peter Szatmari 3 Challenging the Genetic Complexity of Schizophrenia by Use of Intermediate Phenotypes Assen Jablensky 4 Translational Medicine: Functional Biomarkers for Drug Development of “Cognitive Enhancers” in Schizophrenia Georg Winterer 5 Leveraging High-Dimensional Neuroimaging Data in Genetic Studies of Neuropsychiatric Disease Cinnamon S. Bloss, Trygve E. Bakken, Alexander H. Joyner, and Nicholas J. Schork 6 Proteomics as a New Tool for Biomarker-Discovery in Neuropsychiatric Disorders Thomas J. Raedler, Harald Mischak, Holger Jahn, and Klaus Wiedemann 7 Schizophrenia Endophenotypes as Treatment Targets Stephen I. Deutsch, Barbara L. Schwartz, Richard B. Rosse, John Mastropaolo, Ayman H. Fanous, Abraham Weizman, Jessica A. Burket, and Brooke L. Gaskins

227

228

Contents to Volumes 1, 3 and 4

Part II

Neuropsychological, Neurocognitive and Neurophysiological Domains

8

Neuropsychological Endophenotypes in Schizophrenia and Bipolar I Disorder: Yields from the Finnish Family and Twin Studies Annamari Tuulio-Henriksson, Jonna Perälä, Irving I. Gottesman, and Jaana Suvisaari

9

Is More Cognitive Experimental Psychopathology of Schizophrenia Really Necessary? Challenges and Opportunities Angus W. MacDonald, III

10

Intellectual Functioning as an Endophenotype for Schizophrenia Odette de Wilde

11

Emotion Recognition Deficits as a Neurocognitive Marker of Schizophrenia Liability Renata Schoeman, Dana J.H. Niehaus, Liezl Koen, and Jukka M. Leppänen

12

The Use of Neurocognitive Endophenotypes in Large-Scale Family Genetic Studies of Schizophrenia William P. Horan, Tiffany A. Greenwood, David L. Braff, Raquel E. Gur, and Michael F. Green

13

Neurocognitive Endophenotypes for Bipolar Disorder: Evidence from Case-Control, Family and Twin Studies Eugenia Kravariti, Fergus Kane, and Robin M. Murray

14

Trait and State Markers of Schizophrenia in Visual Processing Yue Chen, Daniel Norton, and Ryan McBain

15

Visual Scanning Abnormalities as Biomarker for Schizophrenia Patricia E.G. Bestelmeyer

16

Biomarkers and Endophenotypes in Eating Disorders Carolina Lopez, Marion Roberts, and Janet Treasure

17

Movement Abnormalities: A Putative Biomarker of Risk for Psychosis Vijay A. Mittal and Elaine F. Walker

Contents to Volumes 2, 3, and 4 Contributors to Volumes 2, 3, and 4 Index

Contents to Volumes 1, 3 and 4

Volume 3 Part III Possible Metabolic and Peripheral Biomarkers 28 Peripheral Biomarkers in Dementia and Alzheimer’s Disease Christian Humpel and Josef Marksteiner 29 S100B as a Potential Neurochemical Biomarker in a Variety of Neurological, Neuropsychiatric and Neurosurgical Disorders Patrick Wainwright, Jon Sen, and Antonio Belli 30 Can the Cortisol to DHEA Molar Ratio be Used as a Peripheral Biomarker for Schizophrenia and Mood Disorders? Peter Gallagher and Michael S. Ritsner 31 Neuroactive Steroid Biomarkers of Alcohol Sensitivity and Alcoholism Risk A. Leslie Morrow and Patrizia Porcu 32 Neuroendocrine Markers of Psychopathy Andrea L. Glenn 33 Mitochondrial Complex I as a Possible Novel Peripheral Biomarker for Schizophrenia Dorit Ben-Shachar 34 Peripheral Biomarkers of Excitotoxicity in Neurological Diseases Lucio Tremolizzo, Gessica Sala, and Carlo Ferrarese 35 Melatonin as a Biological Marker in Schizophrenia Armando L. Morera, Pedro Abreu-Gonzalez, and Manuel Henry 36 Peripheral Biological Markers for Mood Disorders Ghanshyam N. Pandey and Yogesh Dwivedi 37 The Diagnosis of Alcoholism Through the Identification of Biochemical Markers in Hair Nadia De Giovanni 38 Retinoic Acid Signalling in Neuropsychiatric Disease: Possible Markers and Treatment Agents Sarah J. Bailey and Peter J. McCaffery 39 Abnormalities of Inositol Metabolism in Lymphocytes as Biomarkers for Bipolar Disorder Galila Agam, Yuly Bersudsky, and Robert H. Belmaker

229

230

Contents to Volumes 1, 3 and 4

Contents to Volumes 1, 2, and 4 Contributors to Volumes 1, 2, and 4 Index

Volume 4 Part IV

Molecular Genetic and Genomic Markers

40

Pharmacogenomic Biomarkers in Neuropsychiatry: The Path to Personalized Medicine in Mental Disorders Ramón Cacabelos

41

Pharmacogenetics in Neurological Diseases Chantal Depondt

42

Gene Expression Changes and Potential Impact of Endophenotypes in Major Psychiatric Disorders Gursharan Chana, Janet Kwok, Stephen J. Glatt, Ian P. Everall, and Ming T. Tsuang

43

Molecular Genetics of Schizophrenia: Focus on Symptom Dimensions Michael S. Ritsner and Ehud Susser

44

Genetics of Mood Disorders Laura Mandelli, Alessandra Nivoli, and Alessandro Serretti

45

Endophenotype Strategy in Epilepsy Genetics Dalila Pinto, Dorothée Kasteleijn-Nolst Trenité, and Dick Lindhout

46

Epilepsy, Biomarkers, and Genes Danielle M. Andrade

47

Molecular and Imaging Genetic Markers in Panic Disorder Katharina Domschke and Jürgen Deckert

48

The Genetics of Obsessive–Compulsive Disorder Marco A. Grados and Rebecca Dang

49

Development of Biomarkers for Alcoholism and PolysubstanceAbuse Hiroki Ishiguro, Minori Koga, Yasue Horiuchi, Emmanuel S. Onaivi, and Susumu Higuchi

Contents to Volumes 1, 3 and 4

50 Role of Members of the Nur (NR4A) Transcription Factors in Dopamine-Related Neurodegenerative and Neuropsychiatric Disorders Daniel Lévesque and Claude Rouillard Contents to Volumes 1, 2, and 3 Contributors to Volumes 1, 2, and 3 Index

231

Contributors to Volumes 1, 3 and 4

Volume 1 Trygve E. Bakken M.Sc., Scripps Genomic Medicine and Scripps Translational Science Institute; Medical Scientist Training Program and Graduate Program in Neurosciences, University of California, San Diego, CA, USA E-mail: [email protected] Patricia E.G. Bestelmeyer, Ph.D., Post-Doc Centre for Cognitive Neuroimaging Department of Psychology, Glasgow, UK E-mail: [email protected] Cinnamon S. Bloss, Ph.D., Research Scientist, Scripps Genomic Medicine and Scripps Translational Science Institute, Scripps Health and The Scripps Research Institute, La Jolla, CA, USA E-mail: [email protected] David L. Braff, M.D., Professor, Department of Psychiatry, University of California, San Diego, CA, USA E-mail: [email protected] Jessica A. Burket, B.S., Mental Health Service Line, Department of Veterans Affairs Medical Center, Washington, USA Yue Chen, Ph.D., Director, Visual Psychophysiology Laboratory, McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA E-mail: [email protected] Stephen I. Deutsch, M.D., Ph.D., Mental Health Service Line, Department of Veterans Affairs Medical Center, Washington, Department of Psychiatry, Georgetown University School of Medicine, Washington, USA E-mail: [email protected] Ayman H. Fanous, M.D., Mental Health Service Line, Department of Veterans Affairs Medical Center, Washington, Department of Psychiatry, Georgetown University School of Medicine, Washington, USA Brooke L. Gaskins, B.A., Mental Health Service Line, Department of Veterans Affairs Medical Center, Washington, USA

233

234

Contributors to Volumes 1, 3 and 4

Irving I. Gottesman Professor, Departments of Psychiatry and Psychology, University of Minnesota, Minneapolis, MN, USA E-mail: [email protected] Michael F. Green, Ph.D., Professor, Semel Institute, University of California, Los Angeles, CA, USA E-mail: [email protected] Tiffany A. Greenwood, Ph.D., Assistant Adjunct Professor of Psychiatry, Department of Psychiatry, University of California, San Diego, CA, USA E-mail: [email protected] Raquel E. Gur, M.D., Ph.D., The Karl and Linda Rickels Professor and Vice Chair for Research Development, Departments of Psychiatry, Neurology and Radiology, Director, Neuropsychiatry Section, University of Pennsylvania Medical Center, Philadelphia, PA, USA E-mail: [email protected] William P. Horan, Ph.D., VA Greater Los Angeles Healthcare system & University of California, Los Angeles, CA, USA E-mail: [email protected] Assen Jablensky, M.D., D. Med.Sci., Professor of Psychiatry, School of Psychiatry and Clinical Neurosciences, The University of Western Australia, Director, Centre for Clinical Research in Neuropsychiatry, Australia E-mail: [email protected] Holger Jahn University of Hamburg, Department of Psychiatry, Hamburg, Germany Alexander H. Joyner M.Eng., Scripps Genomic Medicine and Scripps Translational Science Institute; Graduate Program in Biomedical Sciences, University of California, San Diego, CA, USA E-mail: [email protected] Fergus Kane, Ph.D. student at the section of General Psychiatry, Department of Psychiatry, Institute of Psychiatry, London, UK Liezl Koen, M.B. Ch.B., M.Med. (Psych), Department of Psychiatry, University of Stellenbosch, South Africa E-mail: [email protected] Eugenia Kravariti, M.A., M.Sc., Ph.D., Lecturer, NIHR Biomedical Research Centre for Mental Health, South London and Maudsley NHS Foundation Trust and Institute of Psychiatry, King’s College London, UK E-mail: [email protected] Jukka M. Leppänen, Ph.D., Assistant Professor, Department of Psychology, University of Tempere, Tempere, Finland E-mail: [email protected] Ryan J. Van Lieshout The Offord Centre for Child Studies, McMaster Children’s Hospital and Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada E-mail: [email protected]

Contributors to Volumes 1, 3 and 4

235

Carolina Lopez Eating Disorders Research Unit, Department of Academic Psychiatry, King’s College London, UK E-mail: [email protected] Angus W. MacDonald, III, Ph.D., Associate Professor, Departments of Psychology and Psychiatry, University of Minnesota, Minneapolis, Minnesota, USA E-mail: [email protected] John Mastropaolo, Ph.D. Mental Health Service Line, Department of Veterans Affairs Medical Center, Washington, USA Ryan McBain McLean Hospital, Belmont, MA, USA Harald Mischak Mosaiques Diagnostics and Therapeutics AG, Hannover, Germany Vijay A. Mittal, Ph.D., Postdoctoral Scholar, Department of Psychology, University of California Los Angeles, USA E-mail: [email protected] Robin M. Murray, Professor of Psychiatry, Institute of Psychiatry, King’s College London, UK E-mail: [email protected] Dana J.H. Niehaus, M.B. Ch.B., M.Med. (Psych.), D.Med. (Psych.), FC Psych., Department of Psychiatry, University of Stellenbosch, South Africa E-mail: [email protected] Daniel Norton McLean Hospital, Belmont, MA, USA Jonna Perälä, M.D., Researcher, National Public Health Institute, Department of Mental Health and Alcohol Research, Helsinki, Finland E-mail: [email protected] Thomas J. Raedler, M.D., Associate Professor, Department of Psychiatry, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada E-mail: [email protected]; [email protected] Michael S. Ritsner, M.D., Ph.D., Associate Professor of Psychiatry and Head of Cognitive and Psychobiology Research Laboratory, The Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa and Chair, Acute Department, Sha’ar Menashe Mental Health Center, Hadera, Israel E-mail: [email protected] Marion Roberts, Eating Disorders Research Unit, Department of Academic Psychiatry, Institute of Psychiatry, King’s College London, 5th Floor Bermondsey Wing, Guy’s Hospital, London, SE1 9RT ddi. 0207 188 0181 E-mail: [email protected]; www.eatingresearch.com Richard B. Rosse, M.D., Mental Health Service Line, Department of Veterans Affairs Medical Center, Washington, Department of Psychiatry, Georgetown University School of Medicine, Washington, USA Renata Schoeman, M.B. Ch.B., M.Soc. Sc., M.Med. (Psych.), FC Psych., Department of Psychiatry, University of Stellenbosch, South Africa E-mail: [email protected]

236

Contributors to Volumes 1, 3 and 4

Nicholas J. Schork, Ph.D., Director of Research, Scripps Genomic Medicine; Director of Biostatistics and Bioinformatics, The Scripps Translational Science Institute; Professor, Molecular and Experimental Medicine, Scripps Health and The Scripps Research Institute, CA, USA E-mail: [email protected] Barbara L. Schwartz, Ph.D., Mental Health Service Line, Department of Veterans Affairs Medical Center, Washington, Department of Psychiatry, Georgetown University School of Medicine, Washington, USA Jaana Suvisaari, M.D., Ph.D., Academy research fellow, National Public Health Institute Department of Mental Health and Alcohol Research, Helsinki, Finland E-mail: [email protected] Peter Szatmari Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada E-mail: [email protected] Janet Treasure Psychological Medicine Department, King’s College London, Institute of Psychiatry, London, UK E-mail: [email protected] Annamari Tuulio-Henriksson, Ph.D., Senior Researcher, National Public Health Institute Department of Mental Health and Alcohol Research, Helsinki, Finland E-mail: [email protected] Elaine F. Walker, Ph.D., Samuel Candler Dobbs Professor of Psychology and Neuroscience, Department of Psychology, Emory University, USA E-mail: [email protected] Abraham Weizman, M.D., Professor of Psychiatry, Research Unit, Geha Mental Health Center and the Laboratory of Biological Psychiatry at Felsenstein Medical Research Center, Sackler Faculty of Medicine, Tel-Aviv University, Ramat-Aviv, Tel-Aviv E-mail: Israel. [email protected] Klaus Wiedemann University of Hamburg, Department of Psychiatry, Hamburg, Germany Odette de Wilde, Ph.D., Academic Medical Center, University of Amsterdam, Department of Psychiatry, The Netherlands E-mail: [email protected] Georg Winterer, M.D., Ph.D., Associate Professor, Department of Psychiatry, Heinrich-Heine University, Duesseldorf, and Institute of Neurosciences and Biophysics, Juelich Research Centre, Juelich, Germany E-mail: [email protected]

Volume 3 Pedro Abreu-Gonzalez Professor of Biochemistry, Department of Physiology, School of Medicine, University of La Laguna, La Laguna, Santa Cruz de Tenerife, Canary Islands, Spain

Contributors to Volumes 1, 3 and 4

237

Galila Agam, Ph.D., Associate Professor, Psychiatry Research Unit and Department of Clinical Biochemistry, Faculty of Medicine, Ben Gurion University, Israel Sarah J. Bailey Lecturer, Department of Pharmacy and Pharmacology, University of Bath, Claverton Down, UK E-mail: [email protected] Dorit Ben-Shachar, Ph.D., Head of Lab, Laboratory of Psychobiology, Department of psychiatry, B. Rappaport Faculty of Medicine, Rambam Medical Center, Technion IIT, Haifa, Israel E-mail: [email protected] Yuly Bersudsky, M.D., Ph.D., Senior Lecturer, Faculty of Medicine, Ben Gurion University, Beersheva Mental Health Center, Beersheva, Israel Yogesh Dwivedi, Ph.D., Associate Professor, University of Illinois at Chicago, Department of Psychiatry, Chicago, IL, USA E-mail: [email protected] Carlo Ferrarese, M.D., Ph.D., Professor of Neurology, Director of the Department of Neurology and of the Neurology Residency School, University of Milano-Bicocca, Ospedale San Gerardo, Monza, Italy E-mail: [email protected] Peter Gallagher Research Associate in Psychiatry, School of Neurology, Neurobiology and Psychiatry, Newcastle University, Leazes Wing (Psychiatry), Newcastle upon Tyne, UK E-mail: [email protected] Nadia De Giovanni Istituto Medicina Legale, Università Cattolica S. Cuore, Roma, Italy E-mail: [email protected] Andrea L. Glenn, M.A., Doctoral Student, University of Pennsylvania, Philadelphia, PA, USA E-mail: [email protected] Manuel Henry Professor of Psychiatry, Department of Internal Medicine, Dermatology and Psychiatry, School of Medicine, University of La Laguna, La Laguna, Santa Cruz de Tenerife, Canary Islands, Spain Christian Humpel Associate Professor Dr., Laboratory of Psychiatry and Exp. Alzheimer’s Research, Department of General Psychiatry, Innsbruck Medical University, Innsbruck, Austria E-mail: [email protected] Josef Marksteiner, M.D., Associate Professor Dr., Laboratory of Psychiatry and Exp. Alzheimer’s Research, Department of General Psychiatry, Innsbruck Medical University, Innsbruck, Austria E-mail: [email protected] Peter McCaffery Professor, University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen, UK E-mail: [email protected]

238

Contributors to Volumes 1, 3 and 4

Armando L. Morera Professor of Psychiatry, Department of Internal Medicine, Dermatology and Psychiatry, School of Medicine, University of La Laguna, La Laguna, Santa Cruz de Tenerife, Canary Islands, Spain E-mail: [email protected] A. Leslie Morrow, Ph.D., Professor of Psychiatry and Pharmacology, Associate Director, Bowles Center for Alcohol Studies, University of North Carolina School of Medicine, USA E-mail: [email protected] Ghanshyam N. Pandey, Ph.D., Professor, University of Illinois at Chicago, Department of Psychiatry, Chicago, IL, USA E-mail: [email protected] Patrizia Porcu Assistant Professor of Psychiatry, Bowles Center for Alcohol Studies, University of North Carolina School of Medicine, USA E-mail: [email protected] Michael S. Ritsner, M.D., Ph.D., Associate Professor of Psychiatry and Head of Cognitive and Psychobiology Research Laboratory, The Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa and Chair, Acute Department, Sha’ar Menashe Mental Health Center, Hadera, Israel E-mail: [email protected] Gessica Sala, Ph.D., Post-doctoral Research Associate, Department of Neuroscience and Biomedical Technologies, University of Milano-Bicocca, Italy E-mail: [email protected] Jon Sen Specialty Registrar in Neurosurgery; Wessex Neurological Centre, Southampton University Hospitals, UK E-mail: [email protected] Lucio Tremolizzo, M.D., Ph.D., Neurologist and Post-doctoral Research Associate, University of Milano-Bicocca; Ospedale San Gerardo, Monza, Italy E-mail: [email protected]

Volume 4 Danielle M. Andrade, M.D., M.Sc., Assistant Professor, Department of Medicine, University of Toronto, Division of Neurology – Epilepsy Program UHN – Toronto, Western Hospital, Toronto, Canada E-mail: [email protected] Ramón Cacabelos Professor and Chairman EuroEspes Biomedical Research Center, Institute for CNS Disorders and Genomic Medicin, EuroEspes Chair of Biotechnology and Genomics, Camilo José Cela University, Coruña, Spain E-mail: [email protected] Gursharan Chana, Ph.D., Department of Psychiatry, University of California, San Diego, La Jolla, CA, 92093-0603, USA Rebecca Dang Johns Hopkins University, Baltimore, MD, USA

Contributors to Volumes 1, 3 and 4

239

Juergen Deckert, M.D., Full Professor and Chairman, Department of Psychiatry, University of Wuerzburg, Germany E-mail: [email protected] Chantal Depondt, M.D., Ph.D., Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles, Belgium E-mail: [email protected] Katharina Domschke, M.D., M.A., Head of Group “Genetics of Affective Disorders”, Department of Psychiatry, University of Muenster, Germany E-mail: [email protected] Ian P. Everall, M.D., Ph.D, FRCPsych., FRCPath., Department Of Psychiatry, University of California, San Diego, CA, USA Stephen J. Glatt, Ph.D., Department of Psychiatry and Behavioral Sciences, State University of New York, Syracuse, NY 13210, USA Marco A. Grados, M.D., M.P.H., Assistant Professor, Johns Hopkins University School of Medicine, Baltimore, MD, USA E-mail: [email protected] Susumu Higuchi, M.D., Ph.D., National Hospital Organization, Kurihama Alcoholism Center, Kanagawa, Japan E-mail: [email protected] Yasue Horiuchi, Ph.D., Department of Medical Genetics, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Ibaraki, Japan E-mail: [email protected] Hiroki Ishiguro, M.D., Ph.D., Assistant Professor of Department of Medical Genetics, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Ibaraki, Japan E-mail: [email protected] Dorothée Kasteleijn-Nolst Trenité Professor Medical Genetics, University of Utrecht; Department of Medical Genetics, University Medical Centre, Utrecht, the Netherlands; Professor Neuroscience, University of Rome “Sapienza”, Department of Neuroscience, Rome, Italy E-mail: [email protected]; [email protected] Minori Koga, Ph.D., Department of Medical Genetics, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Ibaraki, Japan E-mail: [email protected] Janet Kwok, B.Sc., Department Of Psychiatry, University of California, San Diego, La Jolla, CA, 92093-0603, USA Daniel Lévesque Associate Professor, Senior Scientist, Faculty of Pharmacy University of Montreal, Canada E-mail: [email protected] Dick Lindhout Department of Medical Genetics, University Medical Centre Utrecht, Utrecht, The Netherlands

240

Contributors to Volumes 1, 3 and 4

Laura Mandelli, Psy.D., Assistant Professor of Psychiatry, Institute of Psychiatry, University of Bologna, Italy E-mail: [email protected] Alessandra Nivoli, M.D., Associate Professor of Psychiatry, Institute of Psychiatry, University of Sassari, Via Luna e Sole 55, 07100, Sassari, Italy E-mail: [email protected] Emmanuel S. Onaivi, Ph.D., Associate Professor of Department of Biology, William Paterson University, Wayne, NJ, USA E-mail: [email protected] Dalila Pinto, Ph.D., Research Fellow. Department of Medical Genetics. University Medical Center Utrecht, Utrecht, the Netherlands; and Genetics and Genome Biology, The Center for Applied Genetics, The Hospital of Sick Children, Toronto, Canada. E-mail: [email protected] Michael S. Ritsner, M.D., Ph.D., Associate Professor of Psychiatry and Head of Cognitive and Psychobiology Research Laboratory, The Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa and Chair, Acute Department, Sha’ar Menashe Mental Health Center, Hadera, Israel E-mail: [email protected] Claude Rouillard, Ph.D., Professor, Department of Medicine, Faculty of Medicine, Laval University and Neuroscience Research Centre, Laval University Hospital Research Centre, Québec City, Québec, Canada E-mail: [email protected] Alessandro Serretti, M.D., Associate Professor of Psychiatry, Institute of Psychiatry, University of Bologna, Italy E-mail: [email protected] Ehud Susser, M.D., Senior Psychiatrist, Sha’ar Menashe Mental Health Center, Hadera, and the Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel E-mail: [email protected] Ming T. Tsuang, M.D., Ph.D., D.Sc., Behavioral Genomics Endowed Chair and University Professor, University of California; Distinguished Professor of Psychiatry and Director, Center for Behavioral Genomics, Department of Psychiatry, University of California, San Diego, CA; Director, Harvard Institute of Psychiatric Epidemiology and Genetics, Harvard Medical School and Harvard School of Public Health, USA E-mail: [email protected]

Index

A Adenine triphosphate (ATP), 171, 183 Alzheimer’s disease, 3–11, 17, 21,–23, 26, 30–32, 34–38, 40, 61, 94, 113, 194, 185, 188, 189, 227 Amygdala-Hippocampal Complex (AHC), 87, 92, 112, 220 Amyotrophic lateral sclerosis (ALS), 201, 208 Antipsychotic agents, 130 Antisaccade task, 72, 75, 77 Antipsychotics, 106–108, 134, 135, 171, 192 Anxiety, 21, 29, 133, 160, 172, 203, 216 Apolipoprotein E (APOE), 3, 5, 10 Attention, 22, 25, 29, 34, 37, 43, 44, 59, 61, 68, 74, 76, 98, 100, 104, 109, 123, 128, 131, 132, 135, 145, 146, 148, 154, 155, 160, 162, 171, 172, 175, 180, 189, 202, 203, 208, 216, 217 Attentional Deficit Hyperactive Disorder (ADHD), 145–147, 155, 160, 171, 172, 176, 177, 180, 181, 189–191 Autism, 145–163, 176

B Basal Ganglia, 33, 36, 37, 40, 75, 76, 87, 88, 90, 92, 1061–08, 113, 125, 151, 158, 175, 179, 183, 185–187, 189, 192, 203, 208, 210 Behavioral markers, 162 Biomarker, 3–11, 17, 18, 28, 30, 32, 33, 36, 37, 40, 41, 56, 58, 59, 61, 63, 64, 123–136, 171–192, 201, 202, 205, 207, 209, 210, 216, 227–229 Bipolar disorder, 59, 64, 74, 77, 79, 88, 171–192, 227–229 Brain, 4–11, 17–32, 34, 35, 37, 38, 40–45, 55, 57–62, 70, 71, 78, 87–91, 93–95, 97–102, 104–114, 123–127, 130–136, 145–163, 172–175, 177–179, 181–183, 186–188, 191, 192, 201–203, 205, 205, 207–209, 215–222, 227, 228 Brain Derived Neurotrophic Factor (BDNF), 67, 73, 78, 126, 135, 161, 214, 222 Brief Psychiatric Rating Scale (BPRS), 99

C Cambridge Neuropsychological Test Automated Battery (CANTAB), 215, 218 Catechol-O-methyltransferase (COMT), 55, 61, 67, 71, 73–77, 123, 132–135, 215, 219, 221, 222 Central nervous system (CNS), 17–19, 41, 69, 125, 132, 153, 163 Cerebral spinal fluid (CSF), 5, 7, 18, 24, 42, 87, 94, 95, 111, 112, 126, 127, 174, 201, 208–210 Cingulate Gyrus (CG), 7, 87, 94, 95, 103, 104, 130, 132 Cognition, 10, 25, 27, 31, 33, 34, 37, 40, 43, 44, 55, 59–62, 64, 102, 106, 134, 155, 157, 162, 174, 215, 220

Cognitive behavioral therapy (CBT), 201, 204, 207 Cognitive endophenotypes, 216, 229 Computer Tomography (CT), 18, 21, 24, 37, 55, 67, 69, 87, 111, 215, 216 Comorbidity, 181 Continuous Performance Test (CPT), 128, 131, 132 Corpus Callosum (CC), 87, 96, 97, 110, 111, 126, 158, 159, 185

D D-amino-acid oxidase activator (G72 or DAOA), 67, 73, 74, 135 Dementia, 3, 4, 6, 9, 17, 18, 21–23, 25–27, 29–32, 34, 37–41, 56, 60, 61, 78, 124, 149 Demyelinating disease, 17–20, 23, 41, 42, 45, 173 Depression, 23, 26, 29–31, 34, 38, 41, 100, 133, 171, 175, 181, 183, 186, 187, 189, 190, 202, 203, 205, 206, 215, 219 Diagnostic markers, 61, 67, 69, 70, 79 Disrupted in Schizophrenia 1 (DISC1), 75, 123, 126, 135 Dizygotic twins (DZ), 87, 111, 151 DNA-Deoxyribonucleic acid, 67, 78, 151, 157, 163, 229 Dopamine (DA), 18 Dopamine receptor D2 (DRD2), 106, 133 Dopamine receptor D3 (DRD3), 135 Dopaminergic neurotransmission, 69, 70, 73, 76, 123, 124, 131, 134 Dorsolateral Prefrontal Cortex (DLPFC), 33, 35, 55, 61, 74, 78, 123, 127–129, 131, 132, 157, 175, 179–181, 183–190, 201, 203, 206, 220 Dysbindin (DTNBP1), 71, 73–75

E Electroencephalography, 55 Endophenotypes, 67–79, 87–114, 145–163, 216, 227, 229, 230 Endophenotype strategy, 227, 229 Environment, 70, 78, 132, 133, 147 Epigenetic, 125, 229 Epilepsy, 59, 77, 88, 147, 229 Event-related potentials (ERP), 55, 62, 63, 67, 70–73, 75 P300, 62, 67, 70–73, 75, 78, 79, 220 Eye tracking movements, 69, 74

F Functional Magnetic Resonance Imaging (FMRI), 3, 5, 8, 11, 18, 20, 32, 36, 37, 40, 41, 55, 58, 59, 61–63, 67, 70, 72, 73, 75, 77, 123, 127, 130–132, 134–136, 171, 172, 177–181, 191, 201, 203, 215, 216, 220 241

242 G Gamma-amino-butyric acid (GABA), 68, 72, 73, 76, 171, 182–185, 187, 189, 190, 201, 208 Genetics, 30, 59, 69, 78, 88, 123, 132, 134, 149, 152, 154, 156, 158, 201, 207, 209, 210, 229 Genetic marker, 57, 59 Genome Wide Association (GWA), 72 Genomics, 149 Glucocorticoid Receptor (GR), 67 Glutamate, 19, 55, 61, 68, 73, 74, 171, 182, 183, 185, 187, 189, 190, 201–210 Glutamatergic neurotransmission, 72, 74 Gray Matter (GM), 6, 17–19, 23, 27, 30, 37–39, 42, 44, 87–95, 97, 98, 101–106, 109, 123, 125–127, 130, 131, 135, 173, 174, 176, 177, 183, 185–187, 189, 203, 209

H Hallucinations, 18, 31, 32, 34, 37, 38, 55–59, 76, 99–102, 105, 107, 109, 113, 128, 130 Hamilton Depression Rating Scale (HDRS), 215, 219 Heritability, 56, 89, 113, 125, 146, 147, 150, 151, 158, 207, 228 Hippocampus, 4, 7, 8, 36, 37, 40, 58, 59, 73, 75, 78, 87, 88, 90–93, 98–100, 106, 108, 111, 113, 123, 125, 176, 177, 183, 175–187, 192, 203, 208, 215–221 Hormone, 229 Hypofrontality, 59, 60, 72, 131

I Inositol, 171, 182, 189 Imaging genetics, 123, 132, 134, 149, 152, 154, 156, 158, 210

L Learning, 31, 36, 42, 45, 98, 146, 147, 157, 160, 215, 216, 218 Limbic-hypothalamic-pituitary-adrenal (LHPA), 202, 205 Lithium (Li), 171, 175, 176, 182–192

M Magnetoencephalography (MEG), 55, 62 Magnetic resonance imaging (MRI), 3–11, 17–25, 27–30, 37, 38, 41–45, 55, 57, 59, 68, 71, 75, 87–89, 94, 96–98, 101, 102, 105, 106, 108–113, 123–136, 145, 150, 152, 154, 155, 157, 171–174, 191, 192, 202, 203, 205, 209, 210, 215, 216, 219 Magnetic resonance spectroscopy (MRS), 3, 5, 7, 17–198, 23, 28, 40, 44, 45, 71–74, 123, 171, 172, 182–184, 186–188, 191, 201–203, 205, 209, 220 Magnetoencephalography (MEG), 3, 55, 62 Major depression, 100, 202, 205, 206, 219 Memory, 8, 10, 27, 29, 31, 36, 37, 40, 42–45, 55, 56, 58–62, 67, 71–76, 98–100, 105, 106, 108, 113, 123, 129, 131–136, 155, 157, 178–181, 216, 218–220 Metabolomics, 19, 20, 33, 35, 36, 40, 41, 55, 124, 174 Mismatch negativity (MMN), 67, 68, 72, 73, 75, 76, 79, 128 Mitochondria, 182 Monozygotic twins (MZ), 76, 78, 87, 99, 111, 123, 126, 150, 151, 154, 155, 157, 158 Mood disorders, 21, 23, 59, 174, 175, 177, 220

Index Movement abnormalities, 229 Multiple sclerosis, 18, 19, 41–45

N N-acetyl-aspartate (NAA), 7, 18, 19, 28, 40, 44, 73, 123, 135, 171, 182–185, 188–191, 202–206 Neural network, 61, 127, 130, 191 Neuregulin-1(NRG1), 77, 123, 135 Neurochemistry, 36, 227 Neurodegenerative disorder, 17, 21, 27, 28, 30, 32, 37, 125 Neurodevelopment, 88, 125 Neuron, 23, 57, 125, 127, 131, 134, 157, 182, 208 Neuropathology, 45 Neurophysiology, 55, 58, 62–64 Neurophysiological endophenotypes, 71, 79 Neuroprotection, 70 Neuropsychiatric disorders, 60, 62, 94, 201, 202, 210, 216, 222 Neuroscience, 21, 55, 58, 63, 88, 124, 131, 136 Neurosteroid, 134 Neurostructural Endophenotype (NSEP), 145–164 Nicotinic acetylcholine receptor (nAChR), 77 N-methyl-D-aspartate (NMDA), 201, 202, 208

O Obsessive-Compulsive Disorder (OCD), 77, 201–210

P Parkinson’s disease, 17, 18, 30–41, 43 Peripheral markers, 209, 227 Personality, 56, 87, 88, 98, 100–106, 108–113, 123, 220 Phenotype, 37, 64, 87, 89, 112, 132–145, 147–155, 157, 158, 160–163, 176, 230 Phenotypic variation, 229 Plasticity, 17, 78, 123, 124, 208 Polymorphism, 67, 74–76, 123, 133, 134, 145, 152–155, 158, 161, 208, 209, 219 Positive and Negative Syndrome Scale (PANSS), 55, 57, 59 Positron emission tomography (PET), 3–11, 17, 18, 20, 21, 28, 29, 32–37, 40, 41, 55, 59, 70, 73, 123, 124, 135, 171, 177, 215, 216 Prefrontal cortex (PFC), 87, 102, 103, 123, 126–129, 131–135 Prepulse Inhibition (PPI), 68, 71, 77, 135 Prognostic markers, 40 Proteomics, 227

R Regulator of G protein signaling 4 (RGS4), 123, 135 Rey Auditory Verbal Learning (RAVLT), 215, 218–220 Ribonucleic acid (RNA), 68, 70, 76

S Second-generation antipsychotic agents (SGAs), 135 Selective serotonin reuptake inhibitor (SSRI), 188, 189, 201, 202, 204, 207, 208 Seizures, 42, 59 Serotonergic neurotransmission, 69

Index Serotonin (5-HT), 31, 34, 41, 133, 145, 154, 161, 169, 201, 208 Scale for the Assessment of Positive (Psychotic) Symptoms (SAPS), 87, 109, 215, 219, 221 Scale for the Assessment of Negative (Psychotic) Symptoms (SANS), 87, 109, 215, 221 Schizophrenia, 55–64, 67–79, 87–114, 123–136, 145, 146, 174, 175, 215, 216, 219–222, 227–229 Schizotypal Personality Disorder (SPD), 87–89, 98, 100–106, 108–113, 123, 126 Side effects, 69, 76, 134, 188 Single Nucleotide Polymorphisms (SNPs), 123, 133, 135, 145, 153 Single photon emission computed tomography (SPECT), 3, 5, 7, 10, 17, 18, 20, 21, 28–30, 32–36, 41, 55, 59, 171, 177 Social cognition, 55, 59, 61, 62, 64, 155, 162 State marker, 58, 67, 70, 79 Stress, 68, 69, 148, 222 Striatum, 9, 36, 126, 128, 133–135, 179, 201, 203, 205, 208, 220 Stroke, 18, 21–23, 26–28 Structured Clinical Interview for DSM Disorders (SCID), 87, 99, 109 Suicide, 172 Suppression of P50, 161 Synapse, 73, 78, 203, 215 Symptoms, 17, 21, 26, 27, 29–36, 38, 40–42, 55–59, 61–64, 67–71, 75, 76, 79, 87, 88, 98–105, 112, 113, 124–126, 130–132, 134, 145, 147, 151–153, 155–158, 160, 161, 163, 172, 179, 181, 186, 187, 190, 201, 203, 205, 207, 208, 215–222 Symptom dimensions, 220

243 T Temporal lobe epilepsy, 59, 88 Testosterone, 208 Thalamus, 7, 26, 29, 33, 34, 37, 44, 87, 88, 90, 104, 105, 108, 109, 113, 125, 127, 151, 157, 175, 179, 181, 185, 187, 189, 201, 203, 205, 207, 217 The long serotonin transporter promoter region (5-HTTLPR), 133, 145, 154, 155, 158 Tolerance, 68 Toxicity, 41, 188, 220 Trait marker, 55, 58, 59, 67, 70, 77, 127, 131, 177 Transcranial magnetic stimulation, 58, 68, 71, 73, 74, 76, 229 Translin-associated factor X gene (TRAX), 123, 135

U ultra-high-risk (UHR), 68, 219 V Verbal memory, 44, 99, 100, 108, 113 Variable number of tandem repeat (VNTR), 133, 145, 155 Vascular dementia (VaD), 17, 18, 21–30 Voxel based morphometry (VBM), 3, 5, 6, 10, 18, 32, 37, 87, 88, 105, 123, 125, 126, 130, 135, 155, 171, 173, 176, 177, 203, 209, 210, 215–217

W Wisconsin Card Sorting Test (WCST), 73, 75, 76, 129 Working Memory, 31, 36, 37, 42, 45, 55, 60–62, 67, 71, 73–76, 108, 123, 129, 131–136, 157, 178–181