Inhibitory Control and Drug Abuse Prevention: From Research to Translation

Inhibitory Control and Drug Abuse Prevention

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Michael T. Bardo  •  Diana H. Fishbein Richard Milich Editors

Inhibitory Control and Drug Abuse Prevention From Research to Translation

Editors Michael T. Bardo Center for Drug Abuse Research Translation University of Kentucky 741 S. Limestone, Lexington KY 40536-0509 USA [email protected]

Richard Milich Center for Drug Abuse Research Translation University of Kentucky Kastle Hall, Lexington, KY 40506 USA [email protected]

Diana H. Fishbein Transdisciplinary Science and Translational Prevention Program RTI International 5520 Research Park Drive Suite 210 UMBC Main Campus Baltimore, MD 21228 USA [email protected]

ISBN 978-1-4419-1267-1 e-ISBN 978-1-4419-1268-8 DOI 10.1007/978-1-4419-1268-8 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011922748 © Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

This book provides a general overview of the current knowledge base regarding behavioral inhibition and its etiology, from genetic and neurobiological underpinnings to social factors that influence its development. Importantly, it also focuses on how this research may be used to design more targeted and potentially more effective drug abuse prevention interventions, given the critical role that inhibition plays in pathways to drug abuse. The idea for this book originated from a symposium entitled “Neural and Behavioral Mechanisms of Inhibitory Control: Implications for Drug Abuse Prevention,” which was held at the annual Society for Prevention Research (SPR) conference in Washington DC in May 2009. Several of the speakers at that SPR symposium (Jentsch, Fillmore, Lejuez, Yurgelun-Todd, and Lynam) have contributed chapters to this book based on their work. SPR has been instrumental in advancing the translation of basic neurobehavioral research into practice and policy. All too often, basic research and prevention practice occur in a parallel fashion, rather than in an integrated cross-cutting fashion. It is important for multilevel and comprehensive research to be based on two-way communications so that basic researchers understand the problems in the field of prevention, and that practitioners are apprised of new advances in the laboratory. In the absence of an understanding of etiological mechanisms in inhibitory dyscontrol, preventive interventions are likely not to exert as beneficial effects as are possible. The overall goal for such translational efforts is to enhance and more appropriately target evidence-based interventions. This goal was epitomized in 2010 when the theme selected for the SPR conference held in Denver, Colorado was entitled “Cells to Society: Prevention at all Levels.” This book reflects an attempt to build on that theme. This book is intended for a wide audience, including researchers, practitioners, policy scientists and makers, and trainees at the graduate and advanced undergraduate levels. The contributors across the chapters represent experts chosen from an array of disciplines, including genetics, neuroscience, psychiatry, psychology, sociology, family studies, and health communication. We are grateful for

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their substantive contributions and appreciate their effort to provide the types of insight that are needed to move the field forward. Hopefully, some of this written word will translate into improving the lives of adolescents at risk. Lexington, KY Baltimore, MD Lexington, KY

Michael T. Bardo Diana H. Fishbein Richard Milich

Contents

Part I  Introduction   1  Translating Research on Inhibitory Control for the Prevention of Drug Abuse........................................................... Elizabeth M. Ginexi and Elizabeth B. Robertson

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Part II Neurobehavioral Approaches for Understanding Inhibitory Control   2  Animal Models of Behavioral Processes that Underlie the Occurrence of Impulsive Behaviors in Humans............................. Jerry B. Richards, Amy M. Gancarz, and Larry W. Hawk, Jr.   3  Monoaminergic Regulation of Cognitive Control in Laboratory Animals............................................................................ J. David Jentsch, Stephanie M. Groman, Alex S. James, and Emanuele Seu   4  Genetic and Environmental Determinants of Addiction Risk Related to Impulsivity and Its Neurobiological Substrates......... Michelle M. Jacobs, Didier Jutras-Aswad, Jennifer A. DiNieri, Hilarie C. Tomasiewicz, and Yasmin L. Hurd   5  Impaired Inhibitory Control as a Mechanism of Drug Abuse........................................................................................... Mark T. Fillmore and Jessica Weafer

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  6  Neuroimaging, Adolescence, and Risky Behavior................................. 101 John C. Churchwell and Deborah A. Yurgelun-Todd

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Part III Translating Research on Inhibitory Control to At-Risk Populations   7  Inhibitory Control Deficits in Childhood: Definition, Measurement, and Clinical Risk for Substance Use Disorders........... 125 Iliyan Ivanov, Jeffrey Newcorn, Kelly Morton, and Michelle Tricamo   8  Impulsivity and Deviance........................................................................ 145 Donald R. Lynam   9  Impulsivity and Adolescent Substance Use: From Self-Report Measures to Neuroimaging and Beyond................................................ 161 Matthew J. Gullo, Sharon Dawe, and Meredith J. McHugh 10  A Functional Analytic Framework for Understanding Adolescent Risk-Taking Behavior.......................................................... 177 Laura MacPherson, Jessica M. Richards, Anahi Collado, and C.W. Lejuez 11  Peer Influences on Adolescent Risk Behavior....................................... 211 Dustin Albert and Laurence Steinberg Part IV Translating Research on Inhibitory Control to Prevention Interventions 12  The Effects of Early Adversity on the Development of Inhibitory Control: Implications for the Design of Preventive Interventions and the Potential Recovery of Function......................... 229 Philip A. Fisher, Jacqueline Bruce, Yalchin Abdullaev, Anne M. Mannering, and Katherine C. Pears 13  Early Risk for Problem Behavior and Substance Use: Targeted Interventions for the Promotion of Inhibitory Control........................ 249 Nathaniel R. Riggs, Mark T. Greenberg, and Brittany Rhoades 14  Designing Media and Classroom Interventions Targeting High Sensation Seeking or Impulsive Adolescents to Prevent Drug Abuse and Risky Sexual Behavior.............................. 263 Rick S. Zimmerman, R. Lewis Donohew, Philip Palmgreen, Seth Noar, Pamela K. Cupp, and Brenikki Floyd 15  Self-Regulation and Adolescent Drug Use: Translating Developmental Science and Neuroscience into Prevention Practice.......................................................................... 281 Thomas J. Dishion, Joshua C. Felver-Gant, Yalchin Abdullaev, and Michael I. Posner

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Part V  Conclusion 16  Implications for Translational Prevention Research: Science, Policy, and Advocacy................................................................. 305 Anthony Biglan and Diana H. Fishbein 17  Future Directions for Research on Inhibitory Control and Drug Abuse Prevention.................................................................... 317 Michael T. Bardo, Richard Milich, and Diana H. Fishbein Index.................................................................................................................. 331

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Contributors

Yalchin Abdullaev, MD, PhD  Oregon Social Learning Center, Eugene, OR 97401, USA; Department of Psychology, Child and Family Center, University of Oregon, Eugene, OR 97403, USA; Department of Psychology, Robert and Beverly Lewis Center for Neuroimaging, University of Oregon, Eugene, OR 97403, USA Dustin Albert, MA  Department of Psychology, Weiss Hall, 1701 N, 13th St., Philadelphia, PA 19122, USA Michael T. Bardo, PhD  Center for Drug Abuse Research Translation, University of Kentucky, 741 S. Limestone, Lexington, KY 40536-0509, USA Anthony Biglan, PhD  Oregon Research Institute, Eugene, OR 97403, USA Jacqueline Bruce, PhD  Oregon Social Learning Center, Eugene, OR 97401, USA John C. Churchwell, PhD  The Brain Institute, University of Utah, Salt Lake City, UT 84108, USA Anahi Collado, BA  Department of Psychology, Center for Addictions, Personality, and Emotion Research, University of Maryland, College Park, MD 20742, USA Pamela K. Cupp, PhD  Department of Communication, University of Kentucky, Lexington, KY 40506, USA Sharon Dawe, PhD  School of Psychology, Griffith University, Mt Gravatt Campus, Brisbane 4101, Australia Jennifer A. DiNieri, BA  Department of Psychiatry, Mount Sinai School of Medicine, New York, NY 10029-6500, USA Thomas J. Dishion, PhD  Child and Family Center, University of Oregon, Eugene, OR 97401, USA R. Lewis Donohew, PhD  Department of Communication, University of Kentucky, Lexington, KY 40506, USA

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Joshua C. Felver-Gant, MS  School of Psychology, Child and Family Center, University of Oregon, Eugene, OR 97401, USA Mark T. Fillmore, PhD  Department of Psychology, University of Kentucky, Kastle Hall, Lexington, KY 40506-0044, USA Diana H. Fishbein, PhD  Senior Fellow and Scientist, Transdisciplinary Science and Translational Prevention Program, RTI International, Baltimore, MD 21224, USA Philip A. Fisher, PhD  Department of Psychology, University of Oregon, Eugene, OR 97401, USA; Oregon Social Learning Center, Eugene, OR 97401, USA; Center for Research to Practice, Eugene, OR, USA Brenikki Floyd, PhD  Virginia Commonwealth University, Richmond, VA, USA Amy M. Gancarz, BS  Research Institute on Addictions, University at Buffalo, Buffalo, NY 14203, USA Elizabeth M. Ginexi, PhD  Division of Epidemiology, Services and Prevention Research, Prevention Research Branch, National Institute on Drug Abuse, Bethesda, MD 20892-9589, USA Mark T. Greenberg, PhD  The Pennsylvania State University, University Park, PA 16802, USA Stephanie M. Groman, BA  Department of Psychology, UCLA, Los Angeles, CA 90095, USA Matthew J. Gullo, PhD  Institute of Psychology, Health and Society, University of Liverpool, Liverpool, L69 7ZA, UK Larry W. Hawk, Jr., PhD  Department of Psychology, University of Buffalo, SUNY Buffalo, NY 14260, USA Yasmin L. Hurd, PhD  Department of Psychiatry, Mount Sinai School of Medicine, New York, NY 10029-6500, USA Iliyan Ivanov, MD  Mount Sinai School of Medicine, New York, NY 10029, USA Michelle M. Jacobs, PhD  Department of Psychiatry, Mount Sinai School of Medicine, New York, NY 10029-6500, USA Alex S. James, BA  Department of Psychology, UCLA, Los Angeles, CA 90095, USA J. David Jentsch, PhD  Department of Psychology and Psychiatry and Bio-behavioral Sciences, The Brain Research Institute, University of California, Los Angeles, CA, USA; Interdepartmental Neuroscience Program, UCLA, Los Angeles, CA 90095, USA Didier Jutras-Aswad, MD  Department of Psychiatry, Université de Montréal, 1058, St-Denis, Montréal, QC, Canada, H2X 3J4

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Carl W. Lejuez, PhD  Department of Psychology, Center for Addictions, Personality, and Emotion Research, University of Maryland, College Park, MD 20742, USA Donald R. Lynam, PhD  Department of Psychological Sciences, Purdue University, West Lafayette, IN 47907, USA Laura MacPherson, PhD  Department of Psychology, Center for Addictions, Personality, and Emotion Research, University of Maryland, College Park, MD 20742, USA Anne M. Mannering, PhD  Oregon Social Learning Center, Eugene, OR 97401, USA Meredith J. McHugh, PhD  Neuroimaging Research Branch, NIDA/IRP, NIH, Baltimore, MD 21224, USA Richard Milich, PhD  Center for Drug Abuse Research Translation, University of Kentucky, Kastle Hall Lexington, KY 40506, USA Kelly Morton, MPA, MD  Department of Psychiatry, Mount Sinai School of Medicine, New York, NY 10029, USA Jeffrey Newcorn, MD  Department of Psychiatry, Mount Sinai School of Medicine, New York, NY 10029, USA Seth Noar, PhD  Department of Communication, University of Kentucky, Lexington, KY 40506, USA Philip Palmgreen, PhD  Department of Communication, University of Kentucky, Lexington, KY 40506, USA Katherine C. Pears, PhD  Oregon Social Learning Center, Eugene, OR 97401, USA Michael I. Posner, PhD  Department of Psychology, University of Oregon, Eugene, OR 97403, USA Brittany Rhoades, PhD  Prevention Research Center, Pennsylvania State University, University Park, PA 16802, USA Jerry B. Richards, PhD  Research Institute on Addictions, Buffalo, NY 14203, USA Jessica M. Richards, MA  Department of Psychology, Center for Addictions, Personality, and Emotion Research, University of Maryland, College Park, MD 20742, USA Nathaniel R. Riggs, PhD  Institute for Prevention Research, Keck School of Medicine, University of Southern California, Alhambra, CA 91803, USA Elizabeth B. Robertson, PhD  Division of Epidemiology, Services and Prevention Research, Prevention Research Branch, National Institute on Drug Abuse, Bethesda, MD 20892-9589, USA

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Emanuele Seu, PhD  Department of Psychology, UCLA, Los Angeles, CA 90095, USA Laurence Steinberg, PhD  Department of Psychology, Temple University, Philadelphia, PA 19122, USA Hilarie C. Tomasiewicz, PhD  Department of Psychiatry, Mount Sinai School of Medicine, New York, NY 10029-6500, USA Michelle Tricamo, MD  Child and Adolescent Psychiatry, New York-Presbyterian, New York, NY 10065, USA Jessica Weafer, MA  Department of Psychology, University of Kentucky, Lexington, KY 40506-0044, USA Deborah A. Yurgelun-Todd, PhD  Cognitive Neuroscience, Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA Rick S. Zimmerman, PhD  Department of Social & Behavioral Health, Virginia Commonwealth University, Richmond, VA, USA

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Part I

Introduction

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Chapter 1

Translating Research on Inhibitory Control for the Prevention of Drug Abuse Elizabeth M. Ginexi and Elizabeth B. Robertson

Abstract  Significant progress has been made in effective approaches to the prevention of drug abuse over the past few decades. Many of the existing evidencebased prevention programs promote social and emotional learning, an approach that addresses underlying causes of problem behavior while supporting academic achievement and promoting positive youth development. There is growing recognition that these programs may work, at least in part, because of their impacts on underlying neurocognitive systems during important developmental periods in childhood. The development of the neural systems that support self-regulatory functions is important for acquiring appropriate neurocognitive skills that affect risk for mental, emotional, and behavioral disorders. This edited volume reviews the state of our knowledge about the neurobiological and psychosocial processes involved in behavioral inhibitory capacity and provides insights into how these findings may be translated into interventions. Significant progress has been made in understanding effective approaches to the prevention of drug abuse over the past few decades, in part because of careful attention given to understanding basic, developmental processes involved in family risk, early childhood problem behaviors, and later transitions to drug use, abuse, and dependence (National Institute on Drug Abuse 2003). This basic etiologic research has laid the foundation for the creation and testing of many effective family-, school-, and community-based prevention programs that target key developmental risk and protective factors. The goal of these programs is to build new and strengthen existing protective factors and reverse or reduce modifiable risk factors in youth. A particularly influential perspective in prevention science has evolved out of the field of developmental psychopathology, a subdiscipline that emerged as the offspring of its two well-known parents: psychopathology and developmental E.M. Ginexi (*) Division of Epidemiology, Services and Prevention Research, Prevention Research Branch, National Institute on Drug Abuse, Bethesda, MD 20892-9589, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_1, © Springer Science+Business Media, LLC 2011

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psychology (Cicchetti 1984). The focus of this approach is to understand mechanisms of development and change. Central to the developmental psychopathology approach is the belief that the study of atypical development can inform our understanding of normal development and, conversely, the methods and approaches used in normative developmental science may shed light on the etiology and course of mental illness. It integrates the fields of child development, child psychiatry, epidemiology, neuroscience and additional social sciences with a focus on examining interactive processes between domains of development, including physiological, cognitive, neural, interpersonal, social, and cultural domains. Heavily influenced by this interdisciplinary life-course developmental lens, prevention scientists have focused on a wide variety of prevention approaches involving positive modification of various precursors of substance use, such as aggressive behavior, academic problems and failure, poor social skills, misperceptions of social norms, poor parent–child attachment, and inappropriate parental expectations and responses. Many of the existing evidence-based prevention programs promote what is termed social and emotional learning (SEL). The SEL approach involves addressing underlying causes of problem behavior while supporting academic achievement and promoting positive youth development. Through developmentally and culturally appropriate classroom instruction and application to everyday situations, SEL programing aims to build children’s skills to recognize and manage their emotions, appreciate the perspectives of others, establish positive goals, make responsible decisions, and handle interpersonal situations effectively (Collaborative for Academic, Social, and Emotional Learning 2003). The process of acquiring social and emotional skills is similar to learning academic skills in that they both are developed incrementally over time with interactive instruction that is tailored to specific childhood developmental stages and cognitive capabilities. Numerous successful, multiyear, school-based interventions promote positive academic, social, emotional, and health behaviors. There is a solid and growing empirical base for the many effective SEL programs that enhance children’s social–emotional competence (Greenberg et al. 2003). What is relatively new for the field of prevention science is the growing recognition that effective prevention programs may work, at least in part, because of their impacts on underlying neurocognitive systems during important developmental periods in childhood (Romer and Walker 2007). This may be especially true for interventions involving early childhood when it is critical to foster the development of cognitive systems that support learning and adaptive learning behaviors (Blair 2002). The skills associated with cognitive and behavioral readiness for school are highly dependent upon the development of the executive regulatory systems during the preschool years. Indeed, young children’s early school successes are built upon on a number of key skills, including emergent literacy, basic mathematics, and vocabulary knowledge. Importantly, the ability to regulate behavior and attention can signify whether or not children thrive in school above and beyond these academic skills (e.g., Duncan et al. 2007; Nigg et al. 1999; Shaw et al. 2003; Vitaro et al. 2005). Self-regulation refers to a broad construct that represents skills involved in controlling, directing, and planning emotions, cognitions, and behavior (Baumeister and Vohs 2004). It is critically important for functioning in the many varied social

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contexts within which children interact daily, including classrooms. Accordingly, strong self-regulation is related to effective classroom behavior and high achievement, while poor self-regulation is predictive of problematic school outcomes (e.g., Bierman et  al. 2008; Blair 2002; Blair and Razza 2007; Liew et  al. 2008; McClelland et  al. 2007). These basic competencies lay the foundation for the individual’s academic learning experience, and therefore, they set the stage for the developmental trajectories of children. Starting off on a positive trajectory can lead to increasing opportunities for success and growth in both academic and social skills. The hope is that prevention programs that are offered in early childhood can enable the development of self-regulatory capacities. Programs offered as children develop into adolescence and young adulthood can offer instruction and coping support in the ability to use more complex executive functions in diverse contexts even in the face of high emotional arousal or negative affect. Behavioral self-regulation is thought to involve multiple components of executive function brain processes that govern flexible, goal-directed behavior, including attention, working memory, and inhibitory control. The concept of inhibitory control in particular has received a great deal of focus recently as a feature of behavioral regulation that may play an important role in early childhood school adjustment and achievement. Inhibitory control refers to the cognitive-related ability to inhibit a strong dominant response in favor of a subdominant one. Neuroscientists and cognitive psychologists suggest that inhibitory control may play a central role in fostering self-regulation in children by creating a delay in responding that enables cognitive and behavioral flexibility. This flexibility involves the ability for deliberative strategy selection, considering alternative behaviors, task switching, and complex mental set switching (Barkley 2001; Diamond et  al. 2005; Miyake et  al. 2000). These skills are critically involved in preventing or modifying responses. As such, inhibitory control is believed to be a key internal resource for young children as they face a myriad of social and academic challenges. What makes inhibitory control an especially appealing focus for prevention intervention is its relevance to diverse populations of children. There appears to be not only measurable variability in inhibitory control (Cameron Ponitz et al. 2008, 2009), but there is also evidence of developmental malleability (Bierman et  al. 2008; McClelland et al. 2007). Importantly, much of this variability is due to early life experiences. For example, children who experience extreme adversity early in life, such as maltreatment or neglect, often exhibit increased levels of attention problems, emotion dysregulation, language delays, and disrupted inhibitory control (Bruce et  al. 2009; Lewis et  al. 2007; Pears et  al. 2008, 2009; Pears and Fisher 2005). Moreover, impaired inhibitory control is ubiquitously present in the clinical picture of both substance use disorder and many childhood behavioral disorders, and thus, may represent an important indicator of neurobiological risk for SUD (Ivanov et  al. 2008; Pardini et al. 2004). This lends support for the use of both universal and selective preventive interventions to promote self-regulation as a means of preventing school failure and early emotional and behavior problems (e.g., Blair and Diamond 2008). Executive control of cognitive, emotional, and behavioral actions is something that children must develop over time. Impairments in the control of action, or failure to develop sufficient levels of inhibitory control,

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are thought to contribute to behavioral problems that adversely affect social relationships, cognitive development and learning. Interventions that aim to build these skills and teach children how to inhibit or change their responses, particularly in emotionally arousing contexts, can foster positive academic and social developmental trajectories. A few extant universal early childhood preventive intervention programs explicitly promote the development of behavioral self-regulatory capacities. These include the Promoting Alternative Thinking Strategies or PATHS program (Greenberg and Kusché 1998), and the Tools of the Mind program (Diamond et  al. 2007). Research that is ongoing in this area holds great promise to improve our understanding of the malleability of the regulatory capacities of young children. Other existing prevention programs that do not explicitly target inhibitory control nevertheless promote skills, such as conscious strategies for self-control, attention, concentration, and problem-solving that may ultimately aid in the development of children’s self-regulatory capabilities. Numerous studies have shown that appropriately designed and implemented school-based interventions can improve selfregulatory control of thoughts, emotions, and behavior in people of all ages, even young children, and several curricula and training programs have been designed to promote self-regulation in prevention frameworks (Greenberg et al. 2003). Similar effects are being demonstrated in family-based preventive interventions for highrisk families. Specifically, a brief, motivational skills based parenting intervention called the family check-up has demonstrated improvements in positive behavior support in young children, which in turn, promotes inhibitory control and language development over time (Lunkenheimer et al. 2008). At present, there are few investigations elucidating neurocognitive variables in the context of drug abuse prevention programs. We believe that this is an important next step for prevention science. The preliminary evidence to date suggests that neurocognitive measures do serve as significant outcomes, moderators, and mediators of preventive interventions. For example, in terms of outcomes, Fisher and colleagues have demonstrated that a therapeutic psychosocial preventive intervention for foster preschoolers impacted the cognitive control and response monitoring among children who received the intervention (Bruce et al. 2009). With respect to moderator potential, Fishbein has shown that neurocognitive and emotional deficits among adolescents predicted the lack of behavioral change in response to the Positive Adolescent Choices Training prevention program (Fishbein et al. 2006). Finally, a nice example of a mediation effect comes from Greenberg and colleagues who have reported that improvements in inhibitory control resulting from the PATHS program mediated the improvements in children’s emotional and behavioral problems over time (Riggs et al. 2006). According to the National Research Council and Institute of Medicine (2009), collaboration between prevention scientists and basic and clinical developmental neuroscientists may result in better tests of causal mechanisms and theories of pathogenesis which may, in turn, be used to improve preventive interventions. Defining the genetically-based neural substrates of healthy, cognitive, behavioral, and emotional development and, in particular, understanding the plasticity of such

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substrates in the face of environmental interventions may provide an important basis for prevention research and for future study. Findings from prevention trials that suggest causal mechanisms also should generate hypotheses that can be tested and further elaborated by basic and clinical neuroscientists using animal models and other neuroscience-based approaches. The development of the neural systems that support self-regulatory functions is important for acquiring appropriate neurocognitive skills that affect mental health and risk for mental, emotional, and behavioral disorders across the life span. Specific opportunities to support healthy cognitive and behavioral development and to protect against environmental factors present themselves across the life-span, but particularly during the prenatal period, during early childhood, and during adolescence and emerging adulthood. Applications of developmental neuroscience to prevention also may help us to build much needed scientific and public health support for more wide-spread implementation of effective universal and selective preventive intervention approaches aimed at decreasing the population prevalence of mental, emotional, and behavioral disorders. The purpose of this edited volume is to review the state of our knowledge about the neurobiological and psychosocial processes involved in behavioral inhibitory processes and to provide insights into, and in some cases, examples of how these basic research findings may be translated into the practice of drug abuse prevention interventions. This volume is organized around three general themes. The first theme reviews basic neurobehavioral research findings on inhibitory control and drug abuse. Chapters in this theme emphasize laboratory studies using human volunteers or laboratory animals that document the latest research implicating a relation between inhibition and drug abuse at both the neural and behavioral levels of analysis. The second theme moves the topic to at-risk populations that have impulse control problems, including children, adolescents, and young adults. The third theme concentrates on prevention science as it relates to inhibitory control. Chapters in this theme are written by experts attempting to develop and improve prevention interventions by integrating evidence-based knowledge about inhibitory control processes. In the conclusion sections, Dr. Biglan touches on the policy and advocacy issues related to translating research on inhibitory control to drug abuse prevention, and Drs. Bardo, Fishbein, and Milich speculate about the innovative approaches that may be most useful for the practice of prevention.

References Barkley, R. A. (2001). The EF and self-regulation: An evolutionary neuropsychological perspective. Neuropsychology Review, 11, 1–29. Baumeister, R. F., & Vohs, K. D. (2004). Handbook of self-regulation: Research, theory, and applications. New York: Guilford. Bierman, K. L., Nix, R. L., Greenberg, M. T., Blair, C. & Domitrovich, C. E. (2008). Executive functions and school readiness intervention: Impact, moderation, and mediation in the Head Start REDI program. Development and Psychopathology, 20, 821–843.

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Blair, C. (2002). School readiness: Integrating cognition and emotion in a neurobiological conceptualization of children’s functioning at school entry. American Psychologist, 57, 111–127. Blair, C., & Diamond, A. (2008). Biological processes in prevention and intervention: The promotion of self-regulation as a means of preventing school failure. Development and Psychopathology, 20, 899–911. Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78, 647–663. Bruce, J., Martin McDermott, J., Fisher, P. A., & Fox, N. A. (2009). Using behavioral and electrophysiological measures to assess the effects of a preventive intervention: A preliminary study with preschool-aged foster children. Prevention Science, 10, 129–140. Cameron Ponitz, C., McClelland, M. M., Matthews, J. S., & Morrison, F. J. (2009). A structured observation of behavioral self-regulation and its contribution to kindergarten outcomes. Developmental Psychology, 45, 605–619. Cameron Ponitz, C. E., McClelland, M. M., Jewkes, A. M., McDonald Connor, C., Farris, C. L., & Morrison, F. J. (2008). Touch your toes! Developing a direct measure of behavioral regulation in early childhood. Early Childhood Research Quarterly, 23, 141–158. Cicchetti, D. (1984). The emergence of developmental psychopathology. Child development, 55, 1–7. Collaborative for Academic, Social, and Emotional Learning. (2003). Safe and sound: An educational leader’s guide to evidence-based social and emotional learning programs. Retrieved October 15, 2009, from http://www.casel.org Diamond, A., Barnett, W. S., Thomas, J., & Munro, S. (2007). Preschool program improves cognitive control. Science, 318, 1387–1388. Diamond, A., Carlson, S. M., & Beck, D. M. (2005). Preschool children’s performance in task switching on the Dimensional Change Card Sort Task: Separating the dimensions aids the ability to switch. Developmental Neuropsychology, 28, 689–729. Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., Pagani, L. S., Feinstein, L., Engel, M., Brooks-Gunn, J., Sexton, H., Duckworth, K., & Japel, C. (2007). School readiness and later achievement. Developmental Psychology, 43, 1428–1446. Fishbein, D. H., Hydeb, C., Eldreth, D. Paschall, M. J., Hubal, R. Dasa, A., Tarter, R., Ialongo, N., Hubbard, S., & Yung, B. (2006). Neurocognitive skills moderate urban male adolescents’ responses to preventive intervention materials. Drug and Alcohol Dependence, 82, 47–60. Greenberg, M. T., & Kusché, C. A. (1998). Promoting alternative thinking strategies, Book 10: Blueprint for violence prevention. University of Colorado: Institute of Behavioral Sciences. Greenberg, M. T., Weissberg, R. P., O’Brien, M. U., Zins, J. E., Fredericks, L., Resnik, H., & Elias, M. J. (2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning. American Psychologist, 58, 466–474. Ivanov, I., Schulz, K. P., London, E. D., & Newcorn, J. H. (2008). Inhibitory control deficits in childhood and risk for substance use disorders: A review. The American Journal of Drug and Alcohol Abuse, 34, 239–258. Lewis, E. E., Dozier, M., Ackerman, J., & Sepulveda-Kozakowski, S. (2007). The effect of placement instability on adopted children’s inhibitory control abilities and oppositional behavior. Developmental Psychology, 43, 1415–1427. Liew, J., McTigue, E. M., Barrois, L., & Hughes, J. N. (2008). Adaptive and effortful control and academic self-efficacy beliefs on achievement: A longitudinal study of 1st through 3rd graders. Early Childhood Research Quarterly, 23, 515–526. Lunkenheimer, E. S., Dishion, T. J., Shaw, D. S., Connell, A. M., Gardner, F., Wilson, M. N., & Skuban, E. M. (2008). Collateral benefits of the Family Check-Up on early childhood school readiness: Indirect effects of parents’ positive behavior support. Developmental Psychology, 44, 1737–1752. McClelland, M. M., Cameron, C. E., McDonald Connor, C., Farris, C. L., Jewkes, A. M., & Morrison, F. J. (2007). Links between behavioral regulation and preschoolers’ literacy, vocabulary, and math skills. Developmental Psychology, 43, 947–959.

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Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wagner, T. D. (2000). The unity and diversity of EF and their contributions to complex frontal lobe tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100. National Institute on Drug Abuse. (2003). Preventing Drug Use among Children and Adolescents: A Research-Based Guide for Parents, Educators, and Community Leaders, Second Edition. NIH Publication No. 04-4212(A). Bethesda, MD: Author. National Research Council and Institute of Medicine (2009). Preventing Mental, Emotional, and Behavioral Disorders Among Young People: Progress and Possibilities. Committee on the Prevention of Mental Disorders and Substance Abuse Among Children, Youth, and Young Adults: Research Advances and Promising Interventions. Mary Ellen O’Connell, Thomas Boat, and Kenneth E. Warner, Editors. Board on Children, Youth, and Families, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press. Nigg, J. T., Quamma, J. P, Greenberg, M. T., & Kusché, C. A. (1999). A two-year longitudinal study of neuropsychological and cognitive performance in relation to behavioral problems and competencies in elementary school children. Journal of Abnormal Child Psychology, 27, 51–63. Pardini, D., Lochman, J., & Wells, K. (2004). Negative emotions and alcohol use initiation in high-risk boys: The moderating effect of good inhibitory control. Journal of Abnormal Child Psychology, 32, 505–518. Pears, K., Bruce, J., Fisher, P. A., & Kim, H. K. (2009). Indiscriminate friendliness in maltreated foster children. Child Maltreatment, Prepublished June 5, 2009, DOI:10.1177/1077559509337891. Pears, K., & Fisher, P. A. (2005). Developmental, cognitive, and neuropsychological functioning in preschool-aged foster children: Associations with prior maltreatment and placement history. Developmental and Behavioral Pediatrics, 26, 112–122. Pears, K., Kim, H. K., & Fisher, P. A. (2008). Psychosocial and cognitive functioning of children with specific profiles of maltreatment. Child Abuse & Neglect, 32, 958–971. Romer, D., & Walker, E. F. (2007). Adolescent psychopathology and the developing brain: Integrating brain and prevention science. New York: Oxford University Press. Riggs, N. R., Greenberg, M. T., Kusché, C. A., & Pentz, M. A. (2006). The mediational role of neurocognition in the behavioral outcomes of a social-emotional prevention program in elementary school students: Effects of the PATHS curriculum. Prevention Science, 7, 91–102. Shaw, D. S., Gilliom, M., Ingoldsby, E. M., & Nagin, D. S. (2003). Trajectories leading to schoolage conduct problems. Developmental Psychology, 39, 189–200. Vitaro, F., Brendgen, M., Larose, S., & Tremblay, R. E. (2005). Kindergarten disruptive behaviors, protective factors, and educational achievement by early adulthood. Journal of Educational Psychology, 97, 617–629.

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Part II

Neurobehavioral Approaches for Understanding Inhibitory Control

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Chapter 2

Animal Models of Behavioral Processes that Underlie the Occurrence of Impulsive Behaviors in Humans Jerry B. Richards, Amy M. Gancarz, and Larry W. Hawk, Jr.

Abstract  In this chapter, we describe a systematic approach for measuring three separate behavioral processes in laboratory animals that may result in failure to inhibit maladaptive behavior: (1) insensitivity to delayed consequences, (2) poor response inhibition, and (3) lapses of attention. We have developed procedures to measure these behavioral processes in both rats and mice. These measures use the same testing apparatus to measure each process in the two species, and these procedures are similar to parallel procedures used to measure these processes in humans. We describe the results from studies that support the validity of these test procedures in two different strains of mice (C57BL/6NTac, and 129/SvEvTac), as consistent differences in behavior indicate that C57 mice are more impulsive than 129s mice. This systematic characterization of differences in impulsivity between C57 and 129s mice illustrates both the wealth of data that can be obtained using these procedures and the potential usefulness of these procedures for characterizing impulsive behavior in rodents and humans.

Introduction Inhibitory control, broadly defined, refers to factors that regulate the performance of inappropriate or maladaptive behaviors. Failure of inhibitory processes increases the probability of maladaptive “impulsive” behaviors, such as drug abuse. The term “impulsivity” has been used to refer to personality constructs, as well as to specific behavioral measures, both in natural and laboratory settings. Impulsivity has been studied in the context of personality theory (Eysenck 1993; Zuckerman 1994), clinical and behavioral psychology (Ainslie 1975; Milich and Kramer 1984; Rachlin and Green 1972), clinical and biological psychiatry (Linnoila and Virkkunen 1992; McCowen et  al. 1993), and behavioral economics (Kirby and Herrnstein 1995).

J.B. Richards (*) Research Institute on Addictions, Buffalo, NY 14203, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_2, © Springer Science+Business Media, LLC 2011

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Although impulsivity has been extensively studied in various scientific contexts, there is no widely agreed upon operational definition of this concept. As a personality construct, impulsivity has been conceptualized and measured in many different ways (Barratt and Patton 1983; Eysenck 1993; Tellegen 1982; Zuckerman 1994). In laboratory studies, impulsivity has been defined as an inability to wait or to plan, an inability to inhibit behavior resulting in a pattern of socially inappropriate behavior, or as insensitivity to negative or delayed consequences. The Diagnostic and Statistical Manual of Mental Disorders IV (APA 1994) includes a wide range of impulsive behaviors as key symptoms of many psychiatric disorders. These symptoms include impatience, difficulty waiting or delaying responses, frequently interrupting or intruding on others, Attention Deficit Hyperactivity Disorder (ADHD), failure to plan ahead (Antisocial Personality Disorder), excessive spending, sexual promiscuity, substance abuse, reckless driving and binge eating (Borderline Personality Disorder), and substance abuse, shoplifting, aggressiveness, gambling, fire-setting, and poor anger control (Impulse Control Disorders). Whether these apparently heterogeneous behaviors share some core features and common underlying processes or whether they represent separate deficits is not known. In the following sections, we describe three behavioral processes that give rise to impulsive behaviors, and the procedures designed to measure them in laboratory animals. As a pattern of observable behavior in the natural setting, impulsivity has been measured using checklists and surveys by parents, teachers, and other observers (e.g., Achenbach and Edelbrock 1979; Kendall and Wilcox 1979). In the laboratory setting, impulsivity has been operationally defined and measured with tasks measuring specific constructs, such as insensitivity to delayed consequences, inability to wait, or inability to withhold a prepotent response. It has been difficult to reconcile these various indices of impulsivity, and it is unlikely that these behaviors reflect a single underlying process (Milich and Kramer 1984). A key challenge to researchers has been identifying and separating the behavioral and neurobiological processes underlying the expressions of impulsive behavior.

Three Models of Impulsive Behavior Establishing valid animal models of impulsive behavior is problematic. In humans, impulsivity is most often measured using paper and pencil self-report instruments and rating scales based on the observations of parents and teachers. These types of measures cannot be used to measure impulsivity in nonhuman animals. It is not clear what “impulsive” behavior looks like in animals. Instead, animal researchers must develop laboratory tasks that measure behavioral processes thought to underlie failure of inhibitory control in humans. These behavioral tasks can then be used as operational definitions of impulsive behavior in animals. An important advantage of using laboratory-based models of impulsive behavior is that similar tasks can be used across species.

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We have identified several behavioral processes that may underlie impulsive behavior and that can be studied in both humans and nonhuman animals in the laboratory. The range of definitions and measures of “impulsivity” makes it difficult to speculate on the nature of the deficit(s) that make some individuals more likely to emit inappropriate or maladaptive behaviors. However, over the past few years, two behavioral processes have been identified that may underlie the occurrence of impulsive behaviors (de Wit and Richards 2004; Castellanos et  al. 2006; Sonuga-Barke 2002). These underlying behavioral processes are delay discounting and response inhibition, separable explanations for the occurrence of impulsive behaviors, such as drug abuse. According to the delay discounting approach, impulsive individuals exhibit stronger preferences for immediate rewards (e.g., taking a drug) over more delayed rewards (e.g., succeeding in work or school), even though the delayed rewards are larger. Similarly, when choosing between an immediate ­positive outcome (e.g., euphoria from a drug), and the possibility of delayed negative consequences (e.g., job loss, relationship problems), impulsive individuals are relatively less sensitive to the possibility of punishment. According to the response inhibition approach, individuals may fail to inhibit maladaptive behaviors because of a relative inability to suppress prepotent highly reinforcing behaviors, such as drug taking (e.g., during periods of intended abstinence). Both of these independent operational definitions of impulsivity have face validity and empirical support, but until recently few studies have examined the correlations between the two. Sonuga-Barke (2002) found that although ADHD children in general were impaired on both tasks, the two measures were not correlated. Instead, there appeared to be two subpopulations of ADHD patients, one of whom exhibited sensitivity to delay while the other had poor inhibitory control. Similarly, a recent factor-analytic study of a variety of personality and behavioral measures of impulsivity in normal adults revealed two components, an impulsive disinhibition component which included the response inhibition component (measured using the Stop Task) and impulsive decision-making component which included delay discounting. Performance on the delay discounting and Stop Tasks was clearly uncorrelated (Reynolds et al. 2006a). We conclude that delay discounting and response inhibition are separate behavioral processes each of which may underlie the occurrence of an impulsive behavior, such as drug use. A third behavioral process, lapses of attention, may also underlie impulsive behaviors. Although impulsivity is closely linked to attention, as in the case of children with ADHD, impairments in attention have rarely been studied as determinants of impulsive behavior. From our point of view, it seems likely that impairments of attention may lead to the occurrence of impulsive behaviors, such as persistent drug abuse. For example, relapse is one of the most persistent problems in substance abuse. Although many former drug users are able to abstain from using drugs for limited periods of time, an alarmingly high proportion return to using their drugs, even after extended periods of abstinence. It is likely that abstaining from drug use after heavy habitual use requires an active and sustained attention to maintain response suppression. A single lapse of attention to the goal of abstinence can result in renewed drug consumption. When viewed in this way,

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J.B. Richards et al. Impulsive Behavior:

Underlying Processes:

“Difficulty Awaiting Turn”

Behavioral Inhibition

Operational Definition:

Stop Task

Insensitivity to Consequences

Discounting Task

Lapses of Attention

Reaction Time Task

Fig. 2.1  See text for explanation

it seems surprising that the relationship between attention and impulsive behaviors has not been the focus of more research. Figure  2.1 summarizes our approach to understanding impulsive behavior and measuring impulsivity in humans and rodents. The DSM IV gives “difficulty awaiting turn” as an example of an impulsive behavior that may occur in a child with ADHD. According to our approach, at least three separable underlying behavioral processes could lead to the occurrence of this specific impulsive behavior. As is indicated in Fig. 2.1, the child may fail to wait because it is unable to inhibit the behavior of getting out of line. Alternatively, the child may be insensitive to the delayed consequences (positive or negative of waiting in line). Lastly, the child may fail to wait in line because he is unable to sustain attention to cues, both external and internal, that maintain appropriate waiting behavior. As is indicated in the bottom row of Fig. 2.1, each of these hypothetical behavioral processes can be operationally defined and measured using laboratory-based tasks. We propose that the failure to inhibit maladaptive behavior may, like “Difficulty Awaiting Turn,” result from any of these behavioral processes.

Delay Discounting Delay discounting refers to a preference for smaller, more immediate rewards over larger, more delayed rewards (Ainslie 1975; Herrnstein 1981; Logue 1988; Rachlin and Green 1972, 1989). This definition of impulsive behavior is based on the observation that organisms “discount” the value of delayed consequences, such that the value of delayed rewards or punishments is inversely related to the delay of their occurrence. According to this model, impulsive individuals discount delayed events more markedly. Consistent with this, discounting is more pronounced in drug users including opioid-dependent individuals (Kirby et  al. 1999; Madden et  al. 1997), cocaine users (Coffey et al. 2003), alcohol abusers (Vuchinich and Simpson 1998), cigarette smokers (Bickel et  al. 1999; Mitchell 1999), and individuals with

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unspecified histories of drug dependence (Allen et al. 1998) compared to control samples. The fact that delay discounting tasks have been developed for use with both humans and other animals makes it particularly useful for translational research of this kind. The effects of delay on reward value have been studied in humans (Green et al. 1994; Rachlin et  al. 1991; Richards et  al. 1999b), pigeons (Mazur 1987), rats (Bradshaw and Szabadi 1992; Richards et  al. 1997), and mice (Mitchell et  al. 2006). In all four species, the curves that result from the devaluation of reward value by delay are well described by the hyperbolic function of Mazur (1987):

V = bA / (1 + kD ), where V is value, A is the amount of the delayed reward, D is the delay to reward and k and b are free parameters. The value of k indicates more rapid devaluation of reinforcer value by delay and greater impulsivity. The value of b indicates a side bias that is independent of delay. The shape of the hyperbolic discount function is illustrated in Fig. 2.2. Fitting discount points to the hyperbolic discount equation to determine the value of k provides a quantitative measure of impulsivity. As is indicated in Fig. 2.2, organisms that discount the value of the delayed reward more steeply are considered to be more impulsive. Larger values of k indicate steeper discount functions, such as the one depicted by the dashed line in Fig. 2.2. We have developed an adjusting amount (AdjAmt) procedure that allows us to determine how much animals value delayed rewards (Richards et al. 1997). The AdjAmt procedure allows us to determine the value of a large reward at different delays in terms of a smaller immediate reward. As is illustrated in Fig. 2.2, the best fitting hyperbolic discount equation can then be determined for each subject and the value of k used as a quantitative measure of impulsivity.

100

Value

V=bA/(1+kD)

Fig. 2.2  Two hypothetical hyperbolic discount functions showing how the value of a reward decreases with delay. The dashed line indicates more rapid (impulsive) discounting of the delayed reward

Impulsive 0

0

1

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Delay to Large Reward (s)

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In rats we have used this procedure to characterize the effects of deprivation (Richards et al. 1997), reinforcer magnitude (Farrar et al. 2003), opiate agonists and antagonists (Kieres et al. 2004), dopamine agonists and antagonists (Wade et  al. 2000), chronic amphetamine (Richards et  al. 1999a), and lesions of the nucleus accumbens (Acheson et al. 2006) on impulsivity.

Delay Discounting Task The AdjAmt procedure is outlined in Fig. 2.3 and described by Richards et al. (1997). Test sessions consist of discrete choice trials plus a variable number of forced trials. Each trial is separated by an intertrial interval (ITI). During the ITI, all of the stimuli in the chamber are off (Fig. 2.3, panel 1). Illumination of the light above the center snout poke hole (Fig. 2.3, panel 2) signals the start of each trial. The first response (snout poke) to the center hole after the beginning of a trial results in the offset of the stimulus light above the center hole and the onset of the stimulus lights above the left and right water dispensers (Fig. 2.3, panel 3). Inserting the head into the water dispenser on the left always results in the presentation of the standard alternative, which is the delayed delivery of a fixed amount of water. Inserting the head into the dispenser on the right always yields the adjusting alternative, which is immediate delivery of a variable amount of water (the animals used in this procedure are water restricted). When the animal chooses the standard alternative, the lights above both the standard and adjusting alternatives are turned off and a tone turned on (Fig. 2.3, panel 4A). This tone remains on throughout the delay period. At the end of the delay period a fixed, large amount of water is delivered and the tone turned off for the remainder of the 30-s trial (Fig. 2.3, panel 4B). Note that when the rat chooses the delayed standard alternative, the ITI duration is adjusted so that it is equal in duration to the ITI following choices of the immediate adjusting alternative. This adjustment of the ITI is important because in ensures that the overall rate of reinforcement is the same for both the delayed and immediate alternative. When the animal chooses the adjusting alternative, water is delivered immediately and the stimulus lights above the left and right water dispenser apertures turn off for the remainder of the ITI (Fig. 2.1, panel 5). During each session the amount of water available on the adjusting alternative is systematically varied. If the animal chooses the standard alternative, the amount delivered on the adjusting alternative is increased by 10% on the next trial. If the animal chooses the adjusting alternative, the amount delivered on the adjusting alternative is decreased by 10% on the next trial. Forced trials are used to ensure that the rats are exposed to the consequences of choosing both the delayed fixed large amount of water from the standard alternative and the immediate adjusted small amount of water from the adjusting alternative. Choice of either the standard or the adjusting alternative on two ­consecutive trials is followed by a forced trial in which only the stimulus light above the required alternative is turned on after the central snout poke response and only responses to the illuminated side are reinforced.

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Fig.  2.3  A schematic illustration of the phases of the adjusting-amount procedure. Panels 1–5 indicate when the various stimuli were turned on during the different phases of the test procedure. A darkened stimulus marker shows that the stimulus is on. See text for explanation

The primary dependent measure is the indifference point, which represents the value of the delayed reinforcer. The indifference point is operationalized as the median amount of water available on the adjusting alternative during the last half of the test session. Forced trials are not included in this calculation. Smaller indifference points, indicating greater discounting of the delayed reward, are the primary measure of impulsivity on this task. The AdjAmt procedure is based on a procedure developed by Mazur (1987) for pigeons which adjusted the delay to the reinforcer. In contrast, the AdjAmt procedure

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adjusts the amount or magnitude of the reinforcer. More recently, alternative methods for measuring discounting in rodents have been developed which adjust the delay to reinforcement and use fixed small and large reinforcers (e.g., one food pellet or three food pellets). For example, Evenden and colleagues (Evenden and Ryan 1996) and Robbins, Everitt and colleagues (Cardinal et al. 2004; Winstanley et al. 2006, 2004; Cardinal et  al. 2000) utilize a procedure which progressively increases the delay across the session, whereas Carroll and colleagues (Perry and Carroll 2008; Perry et al. 2005, 2008) and Szabadi and colleagues (da Costa Araujo et al. 2009) increase/ decrease the delay within the session dependent on the rat’s responses. Presumably, all of these procedures provide valid measures of delay discounting.

Delay Discounting in C57 and 129s Mice We parametrically characterized delay discounting in the two strains of mice (C57BL/6NTac and 129/SvEvTac), on the delay discounting procedure, as these strains have been shown to differ in impulsivity. Each strain was tested using the adjusting amount (AdjAmt) procedure described above, providing discount functions for five delays (0, 1, 2, 4, and 8 s) to reward. These discount functions were fit to the best fitting hyperbolic discount functions for each mouse across the five delays. Fitting the hyperbolic discount function provided a quantitative measure of the rate of discounting to compare across strains of mice. Both strains of mice learned the task and generated characteristic hyperbolic discount functions. We found that C57 mice behave more impulsively on the task than 129s mice. Figure 2.4 shows the rate of discounting for C57 mice was greater than the rate of discounting for 129s mice and that C57 mice had larger k values than 129s mice

Fig. 2.4  Delay discounting functions for C57 and 129s mice

C57 S129

1.00

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Value Adjusted Amount (uL)

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0.75 0.50 0.25 0.00

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(indicating greater discounting). The pattern of behavior generated by C57 mice indicates that their behavior is poorly controlled by delayed consequences relative to 129s mice.

Response Inhibition Impairments in the ability to inhibit the expression of inappropriate behaviors are characteristic of several psychiatric disorders, most notably ADHD. Response inhibition has typically been operationally defined by the Stop Task which measures the ability to stop a motor response after its execution has been initiated (Logan 1994). Stop Task performance is impaired in children with ADHD (Brandeis et al. 1998; Jennings et al. 1992, 1997; Oosterlaan and Sergeant 1996, 1998a, b; Oosterlaan et al. 1998; Quay 1997; Rubia et al. 1998, 1999; Tannock et al. 1989, 1995). The ­psychomotor stimulant methylphenidate, which is used to treat ADHD, also ameliorates impairments on the Stop Task in ADHD children (Tannock et al. 1989, 1995). Cocaine abusers perform more poorly than control subjects on the Stop Task, ­suggesting that response inhibition may also play a role in substance abuse (Fillmore and Rush 2002). Thus, the Stop Task may be an important laboratory model for studying basic behavioral and biological ­processes that mediate impairments in impulse control relating to substance abuse. We have shown that psychoactive drugs produce similar effects on the Stop Task in rats and in humans. For example, alcohol increased Stop time without affecting Go time in both rats and humans (de Wit et al. 2000; Feola et al. 2000), and amphetamine decreased Stop times in both humans and rats whose initial Stop times were slow. The increase in Stop time after alcohol is consistent with an increase in impulsivity, whereas the decrease in Stop time after amphetamine is consistent with a decrease in impulsivity. These findings lend support to the use of the animal model to study the neurobiological basis of impulsivity.

Stop Task The Stop Task procedure that we have developed for rodents is modeled after the Stop Task procedure developed for humans by Logan (1994) and colleagues. The diagram in Fig. 2.5 provides a schematic representation of the apparatus and procedure. Each trial begins with the chamber’s center light illuminated. The animal is then required to place its snout in the center snout poke hole just below the center light and to hold it there for a varying time period, after which the center light is turned off. Following the offset of the center light (Go signal), the animal is required to remove its snout from the center snout poke hole and move to the right water dispenser for a water reward. The time elapsed from the offset of the center light (Go signal) to the animal breaking the photo beam in the right water dispenser is the Go reaction time (RT) measure. In order to induce the animal to make the

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Light On

Light Off !

Variable Hold Time

P= 0.25

Initiation RT

Stop Task

Tone !

Go RT

Stop Signal Delay

P= 0.75

Move RT

Change RT

Successful Stop

Stop Failure

Go Response

Fig. 2.5  Flow chart of the Stop Task procedure. See text for explanation

Go RT as fast as possible, the amount of water the animal receives for making the Go response depends upon the speed of the animal’s Go RT. On 25% of trials, the Go signal is followed by a tone that serves as a Stop signal. The Stop signal requires the animal to inhibit the Go response to the right water dispenser and emit a head poke response to the left water dispenser in order to get a water reinforcer. If the animal fails to stop the Go response (i.e., an interrupted photo beam in the right water dispenser), it does not receive any water on that trial. If the animal successfully stops the Go response, it receives a water reward when it interrupts the photo beam in the left water dispenser. The amount of water that the animal receives for making the change response to the left water dispenser is equal to the amount of water that it receives for the most recently reinforced Go response. The time elapsed between the presentation of the Stop signal (tone) and the animal breaking the photo beam in the left water dispenser is defined as the change RT measure. The elapsed time between the Go signal (offset of the center light) and presentation of the Stop signal (tone) is referred to as the stop signal delay. The stop signal delay adjusts in increments of 20 ms depending upon performance on the preceding Stop trial. For example, if the animal successfully stops, the Stop signal delay is increased by 20 ms on the next Stop trial. If the animal fails to stop, the Stop signal delay is decreased by 20 ms on the next Stop trial. Adjusting the Stop signal delay in this fashion allows the animal to be able to successfully stop the Go response approximately 50% of the time. In addition to the 50% inhibition point, the Stop signal delays at which the animals can inhibit the Go response 25 and 75% of the time are also obtained. These points

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are determined by using separate Stop signal delays. The 25% inhibition point is determined by increasing the Stop signal delay after every correct stop and decreasing the Stop signal delay only after three stop failures have occurred. The 75% inhibition point is determined by increasing the Stop signal delay only after three successful stops and decreasing the Stop signal delay after every stop failure. The 25, 50, and 75% Stop signal delays are pseudo randomly presented throughout the test session so that one Stop signal delay of each type is presented every three Stop trials. The Stop signal delay at which the animal fails to stop 50% of the time is used to estimate how long it takes the animal on average to stop or “inhibit” an ongoing response. This estimate is referred to as the Stop RT and is calculated by subtracting the average Stop signal delay from the mean of the Go RT. A similar calculation is used to determine the 25 and 75% Stop RTs. These points in conjunction with the 50% Stop RT are used to construct an inhibition function. Logan (1994) describes a similar procedure for constructing an inhibition function in humans. The slope of the inhibition function may be an important indicator of the behavioral processes that are involved in stopping. The primary measure of impulsivity is the Stop RT. Faster Stop RTs indicate better response inhibition and less impulsiveness. It is arguable that the Stop Task described above is best described as a change task because the animal is required both to stop the Go response and then perform an alternative response in order to receive reinforcement. Eagle and colleagues (Eagle et  al. 2008; Eagle and Baunez 2009) have developed a rodent Stop Task which does not require a specific response to be executed in order to receive reinforcement. In their task, the animal is required to refrain from making any response at all on Stop trials for a specified limited hold period. If the animal does not make a Go response during the limited hold period, it receives reinforcement. We would make two points about this procedure. First, although no specific response is specified, it is likely that the animal is doing something during the limited hold period that precedes reinforcement. The contingency of reinforcement imposed by this procedure can be described as differential reinforcement of other behavior. The point being that it is likely the rat learns to perform an alternative response of some sort during the limited hold period. When viewed this way, this procedure can also be considered a change task. Second, the limited hold period proceeding reinforcement imposes a delay to reward which most likely decreases the potential reinforcing value of Stop trials in comparison to go trials where the reinforcement occurs immediately. This delay to reward may bias inhibition functions obtained using this procedure and may be differentially affected by drugs and brain lesions that are tested using this procedure.

Stop Task Performance in C57 and 129s Mice Figure 2.6 shows the inhibition functions for C57 and 129s strains of mice. These inhibition functions are constructed from the Stop RTs at which the mice were able

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C57 129s

% Inhibition

75

50

25

0 200

300

400

500

600

700

SRT (ms) Fig. 2.6  Comparison of Stop Task performance in C57 and 129s mice. Table shows various performance measures on Stop Task Strain Dependent Measure

C57

129s

Mean

701 ± 38

639 ± 37

Standard Deviation

355 ± 40

226 ± 28*

Stop Reaction Time

536 ± 37

418 ± 30*

Stop Signal Delay

165 ± 7

221 ± 13*

Go Reaction Time

Note: Values are in milliseconds. *indicates 129s strain is different from corresponding C57 strain p < 0.05

to stop 25, 50, and 75% of the time. Typically, the Stop RT is defined as the time it takes the mouse to stop 50% of the time. Determining the additional 25 and 75% Stop RTs allows a more thorough characterization of the functional relationship between the Stop signal delay and length of time it takes the animal to stop (Stop RT). Figure 2.6 shows that the inhibition function for the C57 strain is shifted to the right compared to the 129s mice, indicating that they have slower Stop RTs. The longer Stop RTs indicate that C57 mice have impaired response inhibition relative to 129s mice. The Go RTs are not significantly different, while the Stop signal delays for C57 mice are significantly shorter which indicates that this effect is not due to differences in the Go RT. From these data, we would conclude C57 mice have impaired response inhibition in comparison to 129s mice.

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Lapses of Attention We propose that impairments of sustained attention may also lead to the occurrence of maladaptive behaviors, such as drug abuse. Simply put, in some instances, failure to inhibit maladaptive behavior may occur because of poor stimulus control. This idea has received little experimental attention. We have developed a novel experimental approach to measuring lapses of attention using RT tasks in both humans and rodents. The main idea of this approach is illustrated in Fig.  2.7. The top row of Fig. 2.7 shows individual RTs, corresponding to the elapsed time to make a response after the onset of an imperative stimulus (i.e., RT). The distribution of these RTs is portrayed in the histograms shown in the lower portion of Fig. 2.7. The distributions on the left and right panels differ only in the presence of four long RTs in the right panel, which we define as lapses of attention. These occasional lapses of attention cause the tail of RT time distribution to have a rightward skew and have a large impact on the mean, but not the mode, of the RT distribution. Leth-Steensen et  al. (2000) describe the positive skew of RT distribution as a behavioral characteristic in ADHD compared to normal children. They show that the slow responses, or lapses, cause the RT distributions of ADHD children to have

Elapsed Time to Response

Lapse Lapse

Lapse Lapse

Reaction Time (ms) Fig. 2.7  Lapses of attention as indicated by a hypothetical distribution of reaction times

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a greater positive skew than the RT distributions of age matched controls, and propose that this skew is an important empirical marker of periodic “lapses of attention” in ADHD children. Importantly, they also suggest that these lapses of attention can be differentiated from the ability to respond quickly. They argue that the peak (modal point) of the distribution of RTs is an indicator of the optimal speed of responding when the subject is attending to the task at the moment that the imperative stimulus is presented. They fitted a complex ex-Gaussian distributional model to the RT distributions, which provides independent measures of the peak and tail of the RT distribution. Although the ex-Gaussian curve-fitting is appropriate, it requires a large number of RTs and sometimes does not provide a good fit to the distribution [13% of the time in Leth-Steensen et al. (2000)]. The ex-Gaussian approach also makes assumptions about the theoretical distributions of RTs that may not be correct. We have developed a simpler, nonparametric approach for quantitatively characterizing the peak and rightward skew of the distribution of RTs using the mode of the reaction distribution and the average deviation from the mode (DevMod) of the individual RTs. We have found in rats that the psychomotor stimulant methamphetamine decreases lapses of attention and that rats with fetal ethanol exposure have greater lapses of attention (Sabol et al. 2003; Hausknecht et al. 2005). Similarly, this approach has been used to show that both amphetamine and bupropion decrease lapses of attention in healthy young adults (Acheson and de Wit 2008) and that stimulant treatment reduces lapses of attention in children with ADHD (Spencer et al. 2009). The DevMod approach starts with the observation that unlike the mean, the mode (defined as the most frequent RT) is not affected by a rightward skew of the distribution tail and therefore provides an estimate of response speed from those trials in which the subject was attending when the imperative stimulus was presented. The deviation of the individual RTs from the mode provides a measure of the tail of the distribution. A convenient and useful method for measuring the mean DevMod is to subtract the mode from the mean. If the distribution is skewed to the right, then the DevMod or difference between mean and mode metric is greater than zero. The larger the tail of the RT distribution the greater the positive value of the DevMod. That is:

MeanRT = Mode + DevMod.

Thus, the mode reflects response speed on the trials in which the subject attends to the stimulus. The difference between the mean and the mode reflects the degree to which the subject has “lapses of attention” (i.e., is not attending to the task). Following this logic, the mode of the RT distribution may be thought to reflect perceptual processing, decision making and motor speed, while the DevMod measure is thought to primarily reflect variability in responding due to momentary changes in attention, such as a lapse of attention. The use of attention tasks to measure impulsive behavior in animals is not novel. Premature responses that occur during the performance of the five choice serial RT task have been used as indicators of impulsive tendencies (Robbins 2007). Premature responses are responses that occur in the absence of the imperative stimulus. Both

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premature responses and lapses (very slow responses) indicate that the animals are not attending to the imperative stimulus. In the case of premature responses the failure of attention is actively expressed, whereas in the case of lapses the failure of attention is passively expressed. Both premature responses and lapses reflect poor stimulus control and both can underlie the occurrence of maladaptive behaviors.

Choice RT Task A two choice RT procedure is used to measure lapses of sustained attention. This task is implemented using the same apparatus used for the discounting and Stop Tasks described above. The animals are trained to hold their snout in the center snout hole until either the left or right stimulus light is turned on (Fig. 2.8). The amount of time required for the rat to hold its snout in the center snout poke hole before the onset of the imperative stimulus (left or right stimulus lights) is called the hold time. As described below, the hold time is determined individually for each animal. Once the hold time criteria is reached and the imperative stimulus is presented, the animal must put its head into the water dispenser below the imperative stimulus or the trial will terminate (the imperative stimulus turned off) and the trial is counted as an omission. After the presentation of the imperative stimulus, a head entry response into the water dispenser associated with the stimulus light is reinforced if the RT is shorter than a criterion RT. If the animal’s RT is longer than the criterion RT, it will not receive a water reward.

Fig. 2.8  Flow chart for choice reaction time procedure. See text for explanation

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The purpose of using a criterion RT is to selectively reinforce fast responses. The criterion RT for reinforcement is adjusted for each individual animal according to the following rules. For every two correct responses that are faster than the criterion time limit, the criterion is reduced. For every incorrect or slow response, the criterion time limit is increased. Adjusting the criterion RT in this manner results in each animal being reinforced on approximately three out of four trials when the correct side is chosen. Because of the adjusting nature of the procedure, the actual rate of reinforcement is the same for fast and slow animals. As was mentioned above, the onset of the imperative stimulus is contingent upon the animals holding their snouts in the center snout poke hole for a variable hold time period. An average hold time is specified for each individual animal at the start of each test session. The hold time is cumulative. This means that the animal is not required to hold its snout in the hole for the entire hold time in one continuous snout poke. Any pattern of snout poking that equals the criterion hold time is acceptable. For example, if the hold time is 4  s, the animal can meet this requirement by holding its snout in the hole for 2 s on the two different occasions (i.e., two snout pokes of 2 s duration meets the 4 s criterion). The average hold time is adjusted for each test session depending upon performance during the previous test session. If the animal completes more than a specified number of trials during the previous test session, the average hold time is increased. If fewer trials are completed on the previous test session, then the average hold time is decreased. As is shown in Fig.  2.8, the RTs produced by this testing procedure can be broken down into initiation and move components. However, initiation RT, defined as the latency to remove the snout from the center hole after the onset of the imperative stimulus, is our primary measure because it isolates the part of the RT that is most likely to reflect lapses of attention. Additional dependent measures are (1) premature responses, (2) omissions, (3) average hold time, and (4) mode. (1) Premature responses are defined as the animal pulling its snout out of the center hole before the onset of the stimulus light and inserting its head into one of the two water dispensers. Because individual animals have different hold time requirements, the premature responses for each individual are calculated as: premature responses divided by total time that the animal actually holds its snout in the center hole. This measure takes into account the total time that the animal has the opportunity to make a premature response. (2) Omissions are defined as trials in which 2 s elapses after the presentation of the imperative stimulus without a head entry into either the left or right water dispensers. It is important to note that the DevMod measure includes both omissions and shorter intervals that do not meet the arbitrary criterion for an omission. Another important dependent variable is (3) the average hold time, which reflects the animal’s ability to wait for the onset of the imperative stimulus. The direction and degree to which the distribution is skewed is determined by the DevMod measure. This is calculated by subtracting the modal RT from the mean RT. (4) The mode of the distribution is calculated using the HalfRange Mode method (Hedges and Shah 2003). Distributions with large positive skew, indicating the presence of lapses of attention, have correspondingly greater DevMod.

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Choice RT Performance in C57 and 129s Mice We have compared the performance of C57 and 129s strains of mice on the choice RT task. As is depicted in Fig. 2.9, the distributions of initiation RTs are different for the two strains of mice. The 129s strain produced an RT distribution with a clear mode, at about 150 ms, indicating that it took 150 ms to initiate the response to the imperative stimulus. In contrast, the mode for the C57 mice occurs immediately 0.06

C57 129S

0.04

0.02

0.00 0

100

200

300

400

Initiation RT (ms) Fig. 2.9  Reaction time distributions for C57 and 129s mice. Table indicates various performance measures on choice reaction task Strain Dependent Measure

C57

129s

Initiation Reaction Time Mean

375 ± 28

399 ± 51

Standard Deviation

563 ± 39

582 ± 76

Mode

62 ± 8

150 ± 15*

313 ± 27

249 ± 42*

Percent Correct

0.44 ±0.08 82.8 ±3.5

0.33 ± 0.11* 86.8 ± 5.0

Hold Time (s)

1.03 ± 0.23

3.09 ± 0.66*

Omitted Trials 10.6 ± 1.7 9.5 ± 4.3 Note: Unless otherwise indicated, values are in milliseconds. *indicates the difference between the 129s and C57 strains is significant (p <.05)

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after the onset of the imperative stimulus. This pattern of results indicates that initiation of the response by C57 mice was not under the control of the imperative stimulus, suggesting that C57 mice were not attending to the imperative stimulus. It is notable that despite the obvious difference in the shapes of the initiation RT distributions, there were no significant differences between the means and standard deviations of the distributions between the two strains. In contrast, there were significant strain differences in both the mode and DevMod measures. This highlights the importance of an analysis that quantitatively characterizes the shape of the distributions. Other measures that produced significant differences between the two groups were the hold time and premature response measures. The C57 mice had significantly shorter hold time durations than the 129s mice and had more premature responses. These results are consistent with the analysis of the RT distributions above indicating that the C57 mice were not attending to the imperative stimulus. Premature responses are widely interpreted as being indicative of impulsive behavior in humans and rodents (Robbins 2007). In the present case at least, it seems likely that the occurrence of these impulsive premature responses in C57 mice is the result of poor stimulus control (or attention to the imperative stimulus). The distribution of RTs for C57 mice indicated that the failure of stimulus control was actively expressed. We argue that both the active and passive expression of inattention may underlie the occurrence of maladaptive behavior. Taken together, these results indicate that C57 mice are more likely to emit impulsive behaviors (such as premature responses) than 129s mice and that this impairment may be due failures of attention.

Limitations and Future Directions Animal Models of Impulsive Behavior Require Extended Training A general problem with the animal models described above is that it requires many weeks or even months to train the procedures. It is arguable that extended training of animals on tasks that are designed to measure behavioral processes that underlie impulsivity do not reflect the spontaneity that may be part of the occurrence of impulsive behavior in humans. In reply to this, we would argue that impulsive behavior in humans is usually identified in circumstances in which they have extensive experience and training. However, as it is defined, behaviors that are labeled as impulsive often occur in situations, where past experience and training have exposed the individual to the consequences of their actions. The behavior would not be considered impulsive if the individual is naïve to the possible maladaptive consequences of their behavior. For example, the failure of a child with ADHD to wait in line is only considered to be impulsive when it is known that the child has a history of being trained to wait in line. However, the requirement of extensive training in animals is a procedural problem. For example, it would be difficult to train rats on the tasks described in this

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chapter during the 30-day period of adolescence in rats. This limitation precludes the use of these procedures to measure the behavior of adolescent rats. The requirement of extended training in animals is also an impediment to train the same animal on several different tasks, making it difficult to evaluate the degree to which they measure overlapping or separable processes (Sonuga-Barke 2002). Although it may be possible to train the same rodent on all three tasks, each task has been measured at a different age. In rodents, with their short life span, the difference of 6 months may make a large difference in performance. In contrast, all three tasks can be easily tested in human subjects. In human work, construct validity is enhanced by the consideration of a multimethod, multitrait matrix (Cronback and Meehl 1955) which involves testing same task on multiple tasks. The long duration of training required in animals prevents the development of construct validity using this method. An important direction for future research is to develop animal models that can more rapidly measure behavioral processes that underlie the occurrence of impulsive behavior.

Do the Human and Animal Tasks Measure the Same Behavioral Process? All three of the animal laboratory models of impulsive behavior considered in this chapter bear remarkable similarity to parallel human paradigms. Indeed, that is one of their strengths. However, there are some important differences, and these should be clearly articulated and considered. Among the tasks described above, the greatest differences between human and rodent models occur in the measurement of delay discounting. In humans, the delays and consequences are for the most part hypothetical. Human subjects are required to make judgments about imaginary delays and consequences. Furthermore, the hypothetical delays used with humans (days, weeks, months, or years) are much longer than those used in animals studies (usually much less than a minute). Attempts to develop laboratory tasks for human adults that involve real-time delays and rewards have not been particularly successful (however, see Shiels et al. 2009; Reynolds and Schiffbauer 2004; Reynolds et al. 2006b). One problem with developing real-time tasks in human adults is that they often do not discount when ITIs are used. In animal studies, the rate at which the animal can make choices between immediate and delayed rewards is held constant by imposition of an ITI. This ITI ensures that the delay to the next choice is the same after choosing either the immediate or delayed alternatives. Without the ITI, exclusive choice of the immediate alternative would result in a higher rate of reinforcement. Inter-trial delays are used in animal studies in order to ensure that the task is measuring the animal’s sensitivity to delay of reinforcement and not rate of reinforcement. This means that in tasks with it is, such as the AdjAmt procedure described above, the between trial inter-reinforcer interval is the same regardless of choice of the small immediate or large delayed reinforcer during the current trial. The discounting behavior of animals on delay discounting tasks with ITIs

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indicates that they are sensitive to the within trial delays to reinforcement and not the inter-trial delays. If the animals somehow understood that choice of either the small immediate or delayed large alternative resulted in equivalent overall delays to reinforcement they would probably have exclusive choice of the larger reinforcer. In an important study, Lane and colleagues (Lane et al. 2003) reported that many human adults did not discount and demonstrated exclusive choice of the delayed large reinforcer in a laboratory task when an ITI was imposed. In laboratory tasks with real delays and rewards, it is necessary to use short delays in order to have test sessions of reasonable duration. It may be that under these circumstances humans are sensitive to the overall rate of reinforcement and not the within trial delays imposed by the task. It is not clear if the difference in discounting between humans and animals performing real-time laboratory tasks represents a qualitative or quantitative difference in discounting between humans and animals. One possible explanation is that humans integrate decisions about reinforcement across a larger time window making them less sensitive to within trial delays and more sensitive to the overall between trial rates of reinforcement. On the other hand, both the real-time animal and hypothetical human tasks have empirical similarities in that they both produce hyperbolic-like discount functions. Furthermore, as we reviewed in the introduction, there are now many studies indicating that impulsive populations of humans, such as drug abusers discount hypothetical delayed rewards more steeply. Understanding the differences and similarities of delay discounting in human and animal subjects is an important area for future research. In contrast to the delay discounting task, the measures of response inhibition (Stop Task) and lapses of attention (choice RT task) used in humans and animals are relatively similar. Although both the stop and choice RT tasks are often used without explicit reinforcers in humans, there are many examples of these two tasks being used with explicit reinforcers (Leth-Steensen et al. 2000; Kuntsi et al. 2009; Oosterlaan and Sergeant 1998a; Stevens et  al. 2002). The use of reinforcers in animals (and humans) increases the internal validity of these two tasks. Failures of inhibition lead to the absence of reinforcement on the Stop Task and lapses of attention lead to slow RTs that are not reinforced on the choice RT procedure. In comparison to delay discounting, there are fewer studies that indicate a relationship between response inhibition and lapses of attention and the occurrence of impulsive behaviors. This is particularly true of lapses of attention which have only recently been considered as a possible cause of impulsive behavior. An important direction for future research is to determine the predictive validity of animal models that measure behavioral processes that underlie the occurrence of impulsive behavior.

Is Drug Self-Administration in Animals Impulsive? Another problem for establishing the validity of animal models of impulsive behavior is that behaviors that are routinely considered to be impulsive in humans

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may not be impulsive in laboratory animals. For example, in this chapter, we have on several occasions referred to drug abuse as an impulsive behavior. In humans, taking drugs despite knowledge of, or experience with, the negative consequences associated with abusing drugs is often considered to be impulsive. Indeed, human addicts often persist in abusing drugs even after they experience the negative consequences associated with drug abuse. These negative consequences have not been well modeled in animal self administration studies. That is, there are positive consequences to self-administer the drug, but no explicit negative consequences to self-administration in animals. Thus, there is no reason to predict that more impulsive animals should have a greater propensity to self-administer drugs because there are no explicit negative consequences associated with drug consumption. If there are implicit negative consequences of drug consumption in laboratory animals, it is unclear what these consequences are (i.e., the subjects are housed, fed, and watered independently of how much drug they consume) or, if the animals are capable of associating these negative consequences with drug consumption. Furthermore, whereas drug abuse is by definition maladaptive in human drug users, it is not at all clear that self administration of drugs by laboratory animals is maladaptive. Researchers have long argued that when drugs of abuse are viewed as reinforcers they act in the same way as natural reinforcers, such as food and water. It is not surprising then, that laboratory rats respond to produce IV injections of drugs of abuse – why should not they? In contrast, there are many explicit negative consequences for drug consumption in human drug users, such as legal, financial, and social/family costs. Although it is possible to construct animal models of self administration that include explicit negative consequences (Deroche-Gamonet et al. 2004; Economidou et al. 2009; Pelloux et al. 2007), commonly used laboratory models of drug consumption do not incorporate explicit negative consequences, and therefore it is questionable if drug consumption in these models would relate to impulsive tendencies. Future research examining the relationship between animal models of impulsivity and drug self-administration models in which drug consumption has negative consequences are needed.

Comparison of C57 and 129s Mouse Strains on Three Laboratory Models of Impulsive Behavior The C57 mice were found to be more impulsive than 129s mice on all three tasks. In comparison to the 129s mice, the C57 mice discounted delayed rewards more, had slower stop RTs, and exhibited impairments in sustained attention. These results indicate that all three tasks can be used together to test the genetic basis of impulsive behavior in mice. The strength of the three behavioral tasks we used to characterize impulsive behavior in C57 and 129s mice is that they have a strong conceptual basis, and a methodology that can be applied to humans, rats, and mice. The results of this study support the use of these procedures to identify heritable processes in mice that may contribute to impulsive behavior in humans.

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Other studies have also reported pronounced behavioral differences in these two strains of mice that are relevant to impulsivity. Specifically, C57 mice consume more alcohol (Crabbe et al. 1999), are more sensitive to the rewarding effects of both cocaine (Kuzmin and Johnson 2000; Miner 1997) and sucrose solutions (Bachmanov et al. 1996a, b) than the 129 strain. Compared to 129s mice, C57 mice are more active in the open field, more responsive to the motor activating effects of cocaine (Crabbe et al. 1999; Kuzmin and Johnson 2000; Schlussman et al. 1998), demonstrate larger startle responses to tactile and acoustic stimulation and demonstrate less prepulse inhibition (Crawley et  al. 1997). These behavioral patterns indicate that C57 mice are more active and reactive to environmental stimuli compared to 129s mice. A final line of evidence suggesting a consistent pattern of differences between the two strains comes from a study by Logue et al. (1998). These authors tested inbred mouse strains on a response inhibition procedure in which the mice were required to withhold a nose poke response for 1–8 s until an auditory stimulus was presented. The 129s mice were better able to inhibit their responses compared to C57 mice. The higher alcohol consumption, greater responsiveness to hedonic stimuli, higher activity levels, and diminished ability to learn to suppress nose poking in C57 mice are consistent with our results indicating that C57 mice are more impulsive than 129s mice. These studies demonstrate that it is possible to use laboratory models of impulsive behavior to measure impulsive tendencies in mice. In future research, the use of these tasks in mice will allow us to address this important issue by testing genetically modified mice in order to identify neurobiological and genetic factors that contribute to impulsive behavior. For example, important new information may be gained by testing mice in which the neurobiological substrates that mediate the effects of stimulant drugs have been altered.

Implications for Drug Abuse Prevention The factors that influence drug use in humans can be divided into two broad categories of reward-related and impulsivity-related factors (de Wit and Richards 2004). The majority of drug abuse research using human and animal models has focused on reward-related factors, while impulsivity-related factors have received less experimental attention. Research on reward-related factors focuses on understanding the reinforcing or hedonic qualities of drugs of abuse. Because the goal of research on reward-related factors is to study the reinforcing aspects of drug consumption in isolation, factors that may decrease drug consumption are minimized in these animal models. Research on reward-related factors suggests that decreasing the reinforcing and/or hedonic qualities of drugs of abuse and associated stimuli may be an effective prevention strategy. In contrast, the animal models discussed in this chapter focus on impulsivity-related factors that normally inhibit or limit the use of drugs. These factors may allow drug users to resist the reinforcing effects of abused drugs. This research asks questions about why human and nonhuman animals may choose to consume drugs despite negative consequences that make

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drug consumption maladaptive. This approach suggests that increasing the influence of unwanted negative consequences over drug taking behavior may be an effective prevention strategy. In this chapter, three behavioral processes were identified: delay discounting, response inhibition and attention. These processes may mediate the ability of the unwanted negative consequences to decrease drug taking behaviors. This suggests that behavioral treatments that target these behavioral processes (particularly during development) may be effective for decreasing drug abuse. Furthermore, behavioral interventions and pharmacotherapies that target these behavioral processes may be effective strategies for decreasing relapse to drug abuse. Recent studies provide some support for the use of stimulants, such as methylphenidate and amphetamine as a pharmacotherapy for cocaine abuse in adults with and without ADHD (Mooney et al. 2009; Levin et al. 2007; Konstenius et al. 2009; Castells et al. 2007). The effectiveness of these stimulant drugs, in treating relapse to drug abuse is generally attributed to partial agonist effects at the dopamine receptor that compete with the effects of drugs of abuse. However, it is also possible that the positive effects of these drugs are due to impulsivity-related factors. Laboratory studies have shown that treatment with methylphenidate and other stimulants decreases lapses of attention (Spencer et al. 2009; Leth-Steensen et al. 2000) and increases response inhibition (Tannock et  al. 1995) in individuals with ADHD, suggesting that treatment with methylphenidate and other stimulants may decrease drug abuse by decreasing behavioral tendencies that cause drug abusers to ignore the negative consequences of their actions. Consideration of the impulsivity-related factors described in this chapter indicates that behavioral interventions designed to improve sustained attention, ability to inhibit prepotent responses, and the delay of gratification would be effective in decreasing the occurrence of impulsive behaviors, such as drug abuse. In their review of impulsivity as a construct, Milich and Kramer (1984) concluded that while there is general agreement that impulsivity is of great importance in childhood behavioral problems, it is difficult to come to a general agreement about what the term impulsivity meant. With this in mind, an important contribution of this chapter for drug abuse prevention may be the identification of behavioral tasks that operationally define some of the processes that may lead to the occurrence of maladaptive behaviors, such as drug abuse. This suggests that performance on laboratory tasks designed to measure sustained attention, response inhibition, and delay discounting may provide a measure of the effectiveness of early childhood interventions that promote the development of behavioral regulation capacity (Chap. 1, this book).

Conclusion and Summary A basic tenet of our approach to understanding and developing human and nonhuman animal models of impulsivity is that there is no single underlying behavioral process that is common to the general expression of behaviors that are labeled as

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impulsive. Instead, we propose that impulsivity can be best defined at the behavioral level as a failure to inhibit the occurrence of behaviors that are maladaptive. According to this approach, maladaptive impulsive behaviors may occur because of a number of different underlying behavioral processes. In this chapter, we have identified three possibilities, delay discounting, response inhibition, and lapses of attention. It seems likely that there are other underlying behavioral processes that may also lead to the occurrence of impulsive behavior. The idea that different behavioral processes may contribute to impulsive tendencies is not new. For example, Barratt’s Impulsiveness Scale, version 11 (Patton et  al. 1995) has nine subscales (i.e., attention, motor impulsiveness, nonplanning impulsiveness, etc.), which are designed to measure different psychological process that contribute to impulsivity. If this kind of multiple process approach provides the best characterization of impulsive behavior, then it seems likely that no single behavioral task can adequately measure impulsive tendencies in human and nonhuman animals. In the comparison between C57 and 129s mice described above, it turned out that the C57 mice were more impulsive on all three behavioral tasks. However, it is certainly possible that comparisons of other strains of mice may reveal differences on only one or two of the behavioral tasks or that only a subset of processes has predictive validity in a particular situation. Preclinical research using animal laboratory models (and human laboratory models), of impulsive behavior needs to take into account that different behavioral processes may underlie the occurrence of impulsive behaviors. In conclusion, the underlying causes of impulsive tendencies in humans remain poorly understood. The present multiprocess model, with parallel procedures across species, is one approach for improving our understanding of impulsive behavior. Although further development and refinement are clearly needed, this model offers a truly translational approach to studying one of the thorniest but widely cited ­constructs in the drug abuse literature. If these procedures can be used to identify genetic and environmental factors that contribute to impulsive behavior, then we will be in a better position to prevent or manage these difficult behavioral tendencies. Acknowledgments  The research described in this chapter and preparation of the manuscript was supported in part by grants R01DA010588 and R21DA014183, Jerry Richards PI and by R01MH069434, Larry Hawk PI. This work could not have been accomplished without the help of a number of coworkers, including Ashley Acheson, Andy Farrar, Artur Kieres, and Kathy Hausknecht. We thank Becky Ashare for her comments on the manuscript.

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Chapter 3

Monoaminergic Regulation of Cognitive Control in Laboratory Animals J. David Jentsch, Stephanie M. Groman, Alex S. James, and Emanuele Seu

Abstract  The ability to engage in adaptive, optimized behavior depends upon coordinated activity of neural systems that mediate the ability to maintain representations of previous events and future plans (working memory), to maintain focus on relevant predictors of goals (attention), and to update central rule representations and behavior when conditions change. With defects in these individual component psychological processes, inflexible, “impulsive” behavior arises. In this chapter, we review the role for the monoamine neurotransmitters (dopamine, noradrenaline, and serotonin) in the mechanisms that underlie control over cognitive and behavioral processes. Available data gathered in laboratory animals indicate that each of these transmitters contributes in distinct, nonoverlapping ways to the elemental processes that compose the cognitive control network, underscoring the potential for highly behaviorally selective pharmacotherapeutics that target behavioral problems related to poor cognition and impulse control.

Introduction Cognitive control is a term that is often used to describe a collection of cognitive and psychological processes involved in the coordinated, adaptive regulation of thoughts, feelings and actions. Essentially, these are processes that permit one to engage in adaptive, flexible, volitional behavior, as opposed to rapid, habitual responses dependent solely on reinforcement history, drive and reflexes. This ­concept links well with diverse concepts of impulsivity or impulsiveness in as much as we view those phenomena as circumstances where individuals evidence ­relatively lesser ­control over their “impulses”: they act without thinking, their drive for a reward is J.D. Jentsch (*) Department of Psychology and Psychiatry and Bio-behavioral Sciences, The Brain Research Institute, University of California, Los Angeles, CA, USA and Interdepartmental Neuroscience Program, UCLA, Los Angeles, CA 90095, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_3, © Springer Science+Business Media, LLC 2011

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too great to resist, or they seem to have difficulty ignoring the urge to engage in immediately gratifying behavior, even if it is less than optimal over the long term. In their extreme, individual differences in the abilities that contribute to cognitive control and confer levels of impulsivity and impulsive behavior are considered pathological. Impaired control over impulses is diagnostically characteristic of ­attention deficit/hyperactivity disorders, bipolar disorder, drug use disorders, ­obesity, Borderline Personality Disorder, and other DSM Axis 1 and 2 conditions (Evenden 1999; Jentsch and Taylor 1999; Lesch and Merschdorf 2000; Robinson and Berridge 2003; Kreek et al. 2005; Kalenscher et al. 2006; Nederkoorn et al. 2006; Adinoff et al. 2007; Dalley et al. 2007, 2008; Groman et al. 2008; Iancu et al. 2008; Ivanov et al. 2008; Perry and Carroll 2008; Potenza 2008; Verdejo-Garcia et al. 2008; Crews and Boettiger 2009). With that in mind, pharmacological treatment of many of these disorders may be facilitated by uncovering molecular influences on pathways known to modulate those core psychological operations required for optimal cognitive ­control over impulsive thoughts, feelings and actions. Importantly, impulsiveness manifests itself in different ways across individuals, suggesting that it is a construct summarizing many processes that contribute to it. This is generally endorsed by the idea that tests of impulsivity rely upon different neural systems and neurotransmitters. While it is not yet clear how differences in cognitive abilities mediate specific forms of impulsive behavior, the relationship has been reported in humans (Cools et al. 2007; Romer et al. 2009) and laboratory animals (James et al. 2007). Among the various aspects of cognition that are probably relevant to impulsivity, working memory, cognitive and behavioral flexibility, and attention probably rank as most relevant. Working memory summarizes the ability to represent important concepts about one’s recent experiences and future plans in mind to guide decision making and adaptive behavior; therefore, poor working memory may associate with forms of impulsivity (Cools et al. 2007; James et al. 2007) by creating a cognitive milieu in which individuals are driven mostly by the immediately-present circumstances, with the past fading quickly from mind and the future being difficult to predict. Cognitive and behavioral flexibility, on the other hand, represent the ability to successively update the understanding of rules of the world and behavior when contingencies change; impairments in these domains of function lead to inappropriate behavioral or mental persistence, another form of impulsive behavior. A final cognitive process relevant to impulsivity is attention and its control; with deficient top-down influences over attentional orienting and focus, distractibility may result, leading to an inability to stay on task. In the following sections, the influences of monoamine neurotransmitters (dopamine, noradrenaline, and serotonin) on cognitive abilities thought to be relevant to impulsivity are reviewed. Each section details available information on working memory, cognitive and behavioral flexibility, and attention. Before turning to those sections, a brief review of the common laboratory procedures used for these assessments is worthwhile. Working memory is often assessed using delayed response tasks that emphasize the ability of animals to encode and maintain trial-specific ­information about stimulus presentations in order to guide a later behavioral response (­Goldman-Rakic 1987; Curtis and D’Esposito 2004). Cognitive flexibility is ­frequently

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measured using tasks that emphasize “set shifting” or the ability to update central representations of abstract task rules when they change (Brown and Bowman 2002; Robbins 2007). Yet other tasks measure the ability of animals to update, override, or stop a conditioned response when task contingencies dictate; these include reversal learning and the stop signal reaction time task (SSRT; Lee et al. 2007; Robbins 2007; Eagle et al. 2008). Finally, the ability to sustain, shift, and focus attention in a controlled fashion is usually measured using choice reaction time procedures (Bari et al. 2008; Jentsch et al. 2009). Together, these cognitive abilities (maintaining representations about recent events and about near term plans, exhibiting robust abilities to update cognitive sets and motor responses, and the control of attentional focus and orienting) come together to sustain “controlled” and optimized behavior. Indeed, more complex metrics of cognition, including decision making, rely to varied extent on combinations of these individual elemental processes, so this chapter will also discuss neurochemical influences on tests of decision making, as well.

Dopaminergic Influences The dopaminergic systems (Fig.  3.1) responsible for mediating cognition and reward include major afferent pathways originating from three midbrain nuclei: the ventral tegmental area (VTA), retrorubral nucleus, and the substantia nigra

Fig.  3.1  Schematic representation of the dopaminergic system. PFC, prefrontal cortex; VTA, ventral tegmental area; SN, substantia nigra

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(SN;  Cooper et  al. 2002). Neurons within the midbrain innervate prefrontal and orbital regions of the frontal lobe, and through these pathways are thought to influence multiple aspects of cognitive control. Moreover, many modern biological conceptualizations of psychiatric disorders include pathophysiology within the cortically-projecting dopamine systems as a source of cognitive impairment in the conditions (Robbins 2005; Arnsten 2006; Toda and Abi-Dargham 2007). Therefore, the following mechanistic studies of dopamine’s role in cognition have the potential to explain both the origins of psychopathology and to reveal molecular targets for cognitive enhancement therapeutics.

Working Memory Dopamine’s role in cognition was initially demonstrated by showing that the ability of animals to maintain task-relevant information in working memory was dependent upon dopamine levels within the prefrontal cortex (Brozoski et  al. 1979). Subsequently, studies in monkeys and rats have strongly implicated dopamine’s actions on its D1-like dopamine receptors in working memory (Sawaguchi and Goldman-Rakic 1991; Arnsten et  al. 1994; Williams and Goldman-Rakic 1995; Zahrt et  al. 1997; Durstewitz and Seamans 2002; Castner and Goldman-Rakic 2004; Goldman-Rakic et  al. 2004; Arnsten 2007; Vijayraghavan et  al. 2007). Consequently, a great deal of effort is focused on developing strategies for enhancing dopamine D1 signaling in psychiatric disorders thought to involve working memory deficits secondary to hypoactive cortical dopamine systems (GoldmanRakic et al. 2004). As discussed in the preceding sections, however, the ability to maintain information in memory is only one dimension of cognition that contributes to optimized behavior.

Cognitive Flexibility Though retention of information in the working memory networks of the prefrontal cortex is compromised after depletion of cortical dopamine, the ability to update central representations of task-related rules is not. Attentional set-shifting tasks measure the ability of individuals to flexibly update their understanding of relevant task rules when performance contingencies change. Though this ability depends upon the dorsolateral prefrontal cortex, like working memory does (Dias et  al. 1996), animals with neurotoxin-mediated loss of dopamine within the lateral frontal cortex exhibit what appears to be enhanced ability to update central sets (Roberts et al. 1994; Crofts et al. 2001). While dopamine lesions have little negative impact on the ability to update representations of task rules in set-shifting tasks, pharmacological studies do implicate dopamine in these cognitive abilities. Inhibition of dopamine degradation improves set

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shifting in humans (Apud et al. 2007) and in rats (Tunbridge et al. 2004). Furthermore, microinjection of dopamine D1 and D2 receptor antagonists into the rodent prefrontal cortex has been shown to impair set-shifting (Ragozzino 2002; Floresco et al. 2006; Haluk and Floresco 2009), demonstrating that some aspects of frontal-dependent cognitive control processes are mediated by both D1 and D2 receptor subtypes.

Behavioral Flexibility A relatively similar pattern of results emerges when examining dopaminergic influences on the ability to voluntarily modulate responding in reversal learning tasks, where rules about reward availability change unpredictably. In these tasks, dopamine depletion within the frontal lobes has not been found to produce any effect on reversal learning performance (Roberts et al. 1994; Crofts et al. 2001; Clarke et  al. 2007). Despite this, systemic administration of a D2 receptor antagonist impairs reversal learning performance in monkeys (Lee et al. 2007), and deletion of the D2 receptor gene causes similar deficits in mice (Kruzich et  al. 2006). Though D2 receptors clearly play a role in flexible updating of behavior in this task, it is not necessarily due to simple ability to stop prepotent responses (Eagle and Robbins 2003; Eagle et al. 2007; Bari et al. 2009).

Attention Control of attentional resources is another dimension of executive function thought to depend upon dopamine inputs to frontal regions. As mentioned above, dopamine depletion within the frontal lobe actually improved set-shifting performance; however, this “improved” ability to update task rules is now believed to depend upon impaired ability to maintain task-related focus (Crofts et  al. 2001), invoking the concept that dopamine does play a crucial role in attentional functions. Supporting that, performance of the five-choice serial reaction time task (5-CSRT) by rats is sensitive to D1, but not D2 receptor, activation within the frontal cortex (Chudasama and Robbins 2004). Notably, intracranial infusions into the mPFC of dopamine D1 receptor agonists have been reported to improve attentional accuracy in low performing animals, while antagonism impairs attentional accuracy in high performing animals (Granon et al. 2000).

Decision Making Dopamine depletion of the orbitofrontal cortex in rodents causes increased discounting of delayed rewards (Kheramin et  al. 2004) and levels of the dopamine metabolite DOPAC are increased in orbitofrontal cortex when rats are performing

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a delay discounting task (Winstanley et al. 2006), suggesting that dopamine in this brain region mediates the ability to value delayed outcomes optimally. Furthermore, systemic administration of drugs that increase dopamine output have been shown to increase preference for delayed or probabilistic rewards that are normally discounted (Winstanley et  al. 2003a; van Gaalen et  al. 2006; St Onge and Floresco 2009), and this effect is blocked by coadministration of either a D1 or D2 receptor antagonist (van Gaalen et al. 2006; St Onge and Floresco 2009). Therefore, in tasks where subjects must make dynamic decisions about rewards when the magnitude, predictability, and temporal delay to presentation of the reward is varied, dopamine D1 and D2 receptors play a role in optimized behavior.

Summary The reviewed data indicate that dopaminergic transmission participates in multifaceted ways in elemental psychological processes mediating cognitive control and adaptive decision making. Dopamine, acting via D1 receptors, appears to promote cognitive representational processes like working memory and focused, rule-guided attention, while dopamine acting on D2 receptors promotes adaptive changes in cognition and behavior. For these reasons, it is crucial to understand this system beyond individual receptors.

Noradrenergic Influences Most noradrenergic neurons that innervate the forebrain (Fig. 3.2) are localized in a small nucleus in the pons, the locus coeruleus (LC), and from there they project to all major cortical and subcortical regions, excluding the dorsal striatum (Foote et  al. 1983; Cooper et  al. 2002). Historically, the function of the noradrenergic system has been linked to arousal and wakefulness (Foote et al. 1980; Aston-Jones and Bloom 1981); this conclusion was supported by the observations that salient and arousing stimuli (rewards/punishments or predictive cues) induce phasic activation of LC-noradrenergic neurons (Aston-Jones and Bloom 1981; Rajkowski et al. 1994) and release of noradrenaline in target regions (Abercrombie et al. 1988; Brun et al. 1993). More recently, much experimental work has focused on investigating the role of this system in the voluntary control of goal-directed behaviors and cognitive processes, including attention (Aston-Jones and Cohen 2005). This work has been motivated in part by conceptualizations of LC function that highlighted a crucial role of the noradrenergic system in the modulation of adaptive behaviors, and in part by new experimental evidence showing that selective pharmacological manipulations of the noradrenergic system can affect different executive functions related to cognitive control, as well as different forms of impulsivity that are thought to result when top-down executive control processes are impeded.

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Fig. 3.2  Schematic representation of the noradrenergic system. PFC, prefrontal cortex; LC, locus coeruleus

Working Memory The role of noradrenaline in the mediation of working memory processes is supported largely by Arnsten’s behavioral pharmacological studies in rodent and primate models (Arnsten 2007). The modulatory activity of noradrenaline in prefrontal cortex appears to reflect an inverted-U function, wherein both sub- or supra-normal levels of neurotransmitter result in poor working memory performance (Brennan and Arnsten 2008). These differential actions of noradrenaline may be mediated by different receptor populations: under moderate levels of neurotransmitter­, activation of high-affinity alpha-2 adrenergic receptors strengthen delay activity, while under increased noradrenaline, such as during stress, increased activation of lowaffinity alpha-1 receptors would disrupt delay activity resulting in poor working memory performance (Wang et al. 2007). Accordingly, it has been shown that administration of alpha-2 agonists generally improves working memory performance and reduces distractibility (Arnsten et  al. 1988; Arnsten and Cai 1993; Franowicz and Arnsten 1998, 1999), while local or systemic administration of alpha-2 antagonists or alpha-1 agonists impairs performance in working memory tasks (Li and Mei 1994; Arnsten and Jentsch 1997; Mao et al. 1999).

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Cognitive Flexibility Single-unit recording studies have revealed how cognitive states vary with the activity of LC-noradrenergic neurons. During performance of a Go/No-Go reaction time task, LC neurons in monkeys were reported to exhibit two modes of activity: phasic and tonic. When monkeys were performing well, LC-neurons showed moderate tonic activity, along with strong phasic discharge in response to signals (target cues) but not distractors, while poor performance and distractability were associated with high levels of tonic activity and almost absent phasic activity (Rajkowski et al. 1994, 2004). Based upon these results, it has been proposed that phasic activity of LC neurons promotes the ability to effectively focus cognitive processes towards current task-relevant information, while tonic activity switches the balance in favor of flexible responding when task contingencies change (Aston-Jones and Cohen 2005). This account of LC function as regulating modes of behavioral performance is in line with experimental evidence showing an influence of the noradrenergic system on adaptive use and updating of task-related rules (cognitive sets). Lesions of ascending noradrenergic fibers or depletions of prefrontal noradrenaline impair the ability of rats to update on-line cognitive rules they have developed when a change in task contingency necessitates it (Tait et  al. 2007; McGaughy et  al. 2008). Moreover, an increase in noradrenaline levels, induced by inhibition of the noradrenaline transporter, generally improves performance in tasks measuring the updating of cognitive sets (Lapiz et  al. 2007). Therefore, this transmitter plays a broad role in adaptive control over attention and cognitive representations.

Behavioral Flexibility Noradrenaline also plays an important role in the volitional modulation of behavior. Noradrenaline reuptake inhibitors improve the ability to update behavior in a reversal learning task (Lapiz et al. 2007; Seu and Jentsch 2009; Seu et al. 2009) and to inhibit inappropriate responses in the 5-CSRT and SSRT (Robinson et al. 2008b), further supporting the role of noradrenaline in the regulation of adaptive behavior. Although additional studies are required to define which receptors mediate this effect, some evidence suggests that the alpha-2 and alpha-1 receptors may be involved (Sirvio et al. 1994; Steere and Arnsten 1997; Lapiz and Morilak 2006).

Attention In rats performing a test of selective attention (5-CSRT), lesion of ascending noradrenergic terminals impaired performance only when stimuli were presented at faster, unpredictable rates or when a distractor noise was presented immediately prior to the onset of the stimuli (Carli et al. 1983); similar results were obtained

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with noradrenergic deafferentation of the prefrontal cortex (Milstein et al. 2007). Moreover, recent work indicates that inhibition of the noradrenaline transporter enhances attentional function only when top-down control over effective attention orienting and response selection are possible (Jentsch et al. 2009). Together, these data suggest that, under conditions where task demands require flexible deployment of attentional resources, the role for noradrenaline is relatively prominent.

Decision-Making To date, few studies have investigated the effect of noradrenergic manipulations on measures of decision-making, such as delay discounting paradigms. In a recent report, systemic administration of the norepinephrine reuptake inhibitor atomoxetine was found to enhance the ability of rats to adaptively inhibit impulsive choices in a delay discounting task (Robinson et al. 2008b); however in a different study, the administration of another norepinephrine reuptake inhibitor desipramine produced mixed effects (van Gaalen et al. 2006), due perhaps to less pharmacological specificity. Alternatively, alpha-2 adrenergic agonists, which decrease synaptic noradrenaline, were found to increase delay discounting (van Gaalen et al. 2006), indicating that in certain contexts, stimulation of alpha-2 receptors can compromise cognitive control. Collectively, an increase in synaptic noradrenaline, acting through postsynaptic adrenoceptors of the non-alpha-2 subtypes, appears to produce effects consistent with enhanced decision-making.

Summary The reported evidence suggest a crucial role of the noradrenergic system in the modulation of different domains of cognitive control, with two different receptor populations, alpha-1 and alpha-2 postsynaptic receptors, mediating different component processes. The use of drugs that increase extracellular level of norepinephrine, such as norepinephrine reuptake inhibitors, seems a promising strategy for the improvement of cognitive control. Administration of alpha-2 agonists may be a valid alternative, particularly when maladaptive behaviors are the result of an underlying working memory deficit.

Serotonergic Influences The mesencephalon contains serotonin (5-HT) producing neurons within the dorsal and median raphé; these cells diffusely innervate nearly all regions of the brain (Fig.  3.3) and stimulate at least 17 different receptor subtypes. As a result of

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Fig. 3.3  Schematic representation of the serotonergic system. PFC, prefrontal cortex

o­ bservations derived from animal and human studies, 5-HT has been ­conceptualized as a ­neuromodulatory system whose activity is related to behavioral inhibition (Soubrié 1986). Alternatively, 5-HT has been proposed to influence sensitivity to negative stimuli and punishment (Deakin and Graeff 1991; Stutzmann and LeDoux 1999). A related view suggests that 5-HT may act as an error detection system for the prediction of future punishment, with phasic firing of 5-HT neurons progressing from the time of unconditional negative stimulus presentation to the time of conditional stimulus presentation as the predictive association is learned (Daw et  al. 2002). This error detection system would act in combination with the DA reward prediction error system (Schultz et al. 1997). With respect to cognitive control, the available data indicate that 5-HT plays a fractionated role, exerting a major influence over behavioral flexibility but, in comparison to other monoamines, 5-HT appears to have a relatively small role in regulating working memory and selective attention.

Working Memory In tests of working memory, 5-HT manipulations often have little effect. Agonists of 5-HT1A receptors do not produce delay-dependent impairments or improvements in delayed response tasks, irrespective of whether pre- or postsynaptic ­receptors

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are activated (Warburton et al. 1997). Similarly, global 5-HT depletion, along with 5-HT1, 5-HT2, 5-HT5-7 receptor antagonists, and 5-HT1A or 5-HT2 agonists, all fail to produce delay-dependent effects on working memory (Ruotsalainen et  al. 1997, 1998), though there is one report of 5-HT2A antagonists improving delayed match to position performance (Terry et al. 2005). Collectively, these results stand in stark comparison to the quite substantial role for dopamine and noradrenaline in working memory.

Cognitive Flexibility Thus far, there is little evidence that serotonergic manipulations affect the ability to update cognitive representations of task rules in the attentional set shifting task. Neither selective PFC 5-HT depletion nor global 5-HT depletion affect performance when tracking stimulus-reward contingencies requires a shift in attention to a new perceptual dimension (Clarke et  al. 2005; Lapiz-Bluhm et  al. 2009). Furthermore, systemic administration of a selective 5-HT reuptake inhibitor in rats fails to affect this measure of cognitive flexibility (Lapiz-Bluhm et al. 2009).

Behavioral Flexibility Reversal learning tasks have revealed a specific role for cortical 5-HT in the ability to withhold prepotent responding as task rules change. Selective PFC 5-HT depletion in marmosets results in deficits in the serial reversal of visual discriminations (Clarke et al. 2004, 2005, 2007), and 5-HT2A and 5-HT2C receptors appear to have opposing roles in the modulation of reversal learning (Boulougouris et al. 2008). Further, the reversal deficit is specific to response perseveration to the previously rewarded stimulus, rather than learned avoidance of the previously unrewarded stimulus (Clarke et al. 2007). Given that in the primate, reversal learning has been shown to be dependent on the integrity of the orbitofrontal cortex (Jones and Mishkin 1972; Dias et al. 1996), 5-HT innervation may exert an obligatory neuromodulatory effect on the function of this brain region as it relates to behavioral flexibility. Indeed, PFC 5-HT depletion also impairs performance in other orbitofrontal cortex-dependent tasks of behavioral flexibility, including object detourreaching, which requires an animal to inhibit its prepotent tendency to reach straight for a visible reward (Wallis et al. 2001; Walker et al. 2006). Serotonin depletion biases animals towards a response-emitting strategy that impairs performance when waiting, stopping, or altering behavior is required. This conclusion is consistent with increased levels of anticipatory/premature responding in choice reaction time tasks following global serotonin depletion (Harrison et al. 1997; Carli and Samanin 2000; Winstanley et al. 2004a, b); in this task, premature responses are failures to suppress response initiation during periods when ­responding

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is inappropriate. Other manipulations that reduce 5-HT efflux, ­including genetic manipulation of the 5-HT transporter, inactivation of the median raphé nucleus, and systemic administration of an agonist for the pre- and post-synaptically expressed 5-HT1A receptor, also modulate premature responding (Carli and Samanin 2000; Homberg et al. 2007a, b; Le et al. 2008). A considerable body of evidence implicates different 5-HT receptor subtypes in the modulation of anticipatory responding in choice reaction time tasks. Systemic 5-HT2C antagonists increase impulsive responding, and 5-HT2C agonists and 5-HT2A antagonists do the opposite, while 5-HT2B antagonists are without effect (Higgins et al. 2003; Winstanley et al. 2003b, 2004b; Fletcher et al. 2007; Navarra et  al. 2008). Within the medial PFC (encompassing the infralimbic cortex), 5-HT2A/C antagonism decreases premature responding (Passetti et  al. 2003; Winstanley et al. 2003b), and cortical efflux of 5-HT has been positively correlated with number of premature responses made (Dalley et  al. 2002). Mirroring the effects of systemic administration, microinfusion of a 5-HT2A antagonist into the nucleus accumbens decreases premature responding, while 5-HT2C antagonism increases it, indicating that not all effects of serotonin on cognitive control are dependent upon drug actions within frontal cortex (Robinson et al. 2008a). Thus, the effects of systemic 5-HT manipulations are likely due to modulation of processes related to response initiation (dependent on the striatum) and response inhibition/flexibility (dependent on fronto-cortical regions).

Attention While 5-HT depletion clearly influences prepotent, inappropriate responding ­during tests of selective attention, these effects are not accompanied by actual changes in attentional processes related to stimulus detection (Harrison et al. 1997; Carli and Samanin 2000; Winstanley et al. 2004a, b); as such, the influence of 5-HT in these tasks appears to be relatively confined to behavioral flexibility/inhibition rather than recruitment of attentional focus in the context of these tasks.

Decision-Making Experiments investigating the role of 5-HT in valuation of delayed rewards have yielded mixed results. Global 5-HT depletion does not affect delay discounting performance, and a 5-HT1B knockout mouse also shows no changes in discounting (Brunner and Hen 1997; Winstanley et al. 2003a, 2004a). However, global or dorsal and median raphé nuclei 5-HT depletion, and 5-HT1A receptor stimulation have also increased choosing of the smaller, immediate reward (Wogar et al. 1993; Al-Ruwaitea et al. 1999; Mobini et al. 2000a, b; Winstanley et al. 2005). The latter findings might be adequately explained in terms of the role of 5-HT in regulating response initiation

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as already described. For example, 5-HT1A agonists, which increase premature responding in other settings (Carli and Samanin 2000), were shown to paradoxically decrease responding for the large reward even when it was not delayed (van den Bergh et  al. 2006), possibly due to enhancing the tendency to initiate a “default” behavior (in this case, choice of the small, immediate reward in the face of an alternative choice often involving a long delay); initiation of these automatic behaviors may be invoked when outcomes are so delayed that animals no longer comprehend that their actions caused those delayed outcomes. Of relevance, when decision-making tasks do not involve a delay to reward, such as in probabilistic discounting tasks, central 5-HT depletion is without effect (Mobini et al. 2000a).

Summary The role of 5-HT in cognitive control appears to be biased towards the inhibitory regulation of response initiation, as opposed to the regulation of representational processes in working memory or of attention, and therefore therapeutics influencing the 5-HT system might be expected to strongly influence disorders of impulsive action. That being said, despite the size of the 5-HT receptor subtype family, work thus far has focused on the 5-HT1 and 5-HT2 families of receptors almost exclusively, due to limited availability of selective ligands; our understanding of the role of 5-HT in cognitive control will undoubtedly be refined as our ability to target other subtypes improves (Hatcher et al. 2005; Hille et al. 2008).

Conclusions and Implications for Prevention Interventions The results reviewed here clearly implicate monoamine systems in complex aspects of cognitive control and impulsivity, though they also underscore the very complex and independent effects of dopamine, noradrenaline, and 5-HT. Indeed, it could be argued that these very multidimensional neurochemical influences explain why psychiatric disorders, and indeed subclinical variation in these processes, involve quite distinct types of impulsive behavior. Unfortunately, the majority of our therapeutic interventions for disorders of poor cognitive control do not yet take advantage of knowledge regarding specific receptor actions involved in specific processes that may be more relevant to one disorder over another (e.g., more need to target working memory processes, as opposed to behavioral inhibition). Knowledge about the mechanistic basis of component processes related to cognitive control has broad implications for intervention research. It is likely that many or all genetic or environmental risk factors that influence vulnerability for increased impulsivity and poor cognitive function do so by direct modulation of these monoamine systems. Whether it be ADHD, substance use disorders or depression, problems with cognitive control likely involve genetic and epigenetic factors that directly modulate the crucial monoamine systems described above, and it is ­predicted that

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these influences are very specific (e.g., factors that impinge upon D1 receptors will affect working memory phenotypes more than set shifting, which will relate more to factors that influence D2 signaling). Again, this likely relates to the distinctive manner in which impulsivity and cognitive impairment present themselves and affect functioning in different disorders (via biologically-diverse etiologies). Because of these facts, it may be possible to generate biomarkers that predict, in a very specific manner, the quality and degree of risk a particular subject carries, as well as the mitigation of that risk by effective treatment. If identified, these ­biomarkers can serve as surrogate markers of efficacy in intervention-based research for laboratory use and for prospective, longitudinal studies. What is more, these biomarkers may be further explored in animal models to determine the ­influences of environ­mental and biological factors that may be captured when developing novel interventions. Ultimately, mechanistic research related to these biological mechanisms may have broad policy implications as studies in animal models can assist in precisely delineating how and when various factors increase or decrease the chances of setting a developing organism on the pathway to psychopathology. At the heart of this research will be animal-based research on the relationship between genetic and environmental factors, the molecular complexities of the monoamine systems and their relationships to cognitive control, impulsivity and behavioral disorders. Acknowledgements  This work was supported by the Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants UL1-DE019580, RL1DA024853, RL1MH083270 and PL1NS062410), by the Translational Center to Enhance Cognitive Control at UCLA (P50-MH077248) and by the UCLA Adolescent Smoking Cessation Center (Philip Morris USA).

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Chapter 4

Genetic and Environmental Determinants of Addiction Risk Related to Impulsivity and Its Neurobiological Substrates Michelle M. Jacobs, Didier Jutras-Aswad, Jennifer A. DiNieri, Hilarie C. Tomasiewicz, and Yasmin L. Hurd

Abstract  Drug addiction is a complex disorder characterized by significant individual variability. Several lines of evidence suggest that addiction risk is linked to a number of factors including genetics and adverse environmental events during development as well as behavioral traits such as reward sensitivity and impulsivity. Together, these factors appear to influence the initiation of drug use, the transition from controlled to compulsive use that is the hallmark of addictive disorders, the effectiveness of treatment, and subsequent relapse vulnerability. Several neurobiological candidates have been speculated to underlie addiction vulnerability. In this chapter, we review the potential contribution of genetic and environment factors to inhibitory control and addiction risk via their relationship to dopamine transmission aligned to frontostriatal neural circuitry. As insights grow regarding neurobiological features common to gene × environment interactions, improved targeted prevention and intervention strategies will be developed to decrease the risk of vulnerable individuals to addiction disorders.

Introduction Drug addiction is a devastating disorder characterized by impairments of reward, compulsive behavior, and inhibitory control. Initiation of drug use generally occurs during adolescence and leads to detrimental social, medical, and legal consequences during adolescence and into adulthood. While significant efforts have been appropriately focused on developing treatments for chronic abusers, prevention is another important complementary approach in addressing addiction. Prevention and early intervention strategies directed against the drug abuse cycle are contingent on being able to identify individuals at risk. Recent attention has focused on deficits in inhibitory control since this behavioral trait is common among drug abusers. Y.L. Hurd (*) Department of Psychiatry, Mount Sinai School of Medicine, New York, NY 10029-6500, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_4, © Springer Science+Business Media, LLC 2011

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Adolescence is a developmental period characterized by a susceptibility to failures of judgment (decision-making) and self-control that often leads to impulsive, risky behavior, supporting the hypothesis that impaired inhibitory control contributes to addiction vulnerability that is so prominent in teens. In addition to behavioral traits such as impulsivity, drug addiction risk has also been linked to a number of factors including genetic and environmental determinants during development. These factors can influence initiation of drug use, the transition from controlled to compulsive use that defines addiction, and even the effectiveness of treatment and subsequent relapse vulnerability. The goal of this chapter is to understand the contributions of genetics and environment to impulsivity that may impact addiction risk through effects on neurobiological systems integral to both. Dopamine (DA) and striatal function have been the major focus of addiction research (Fig. 4.1), but it is clear that multiple neural systems contribute to addiction, which is unsurprising given the complexity of this disorder. The body of data about these neural systems is limited, however, in terms of formulating more substantial conclusions regarding potential neurobiological links between impulsivity and addiction vulnerability. As such, we focus this review on DA transmission related to frontostriatal neuronal circuits that have been implicated as an interface for inhibitory control and addiction behavior.

Fig.  4.1  Neuroanatomical substrates of addiction. Dopaminergic neurons originating in the ventral tegmental area (VTA) make up the mesocorticolimbic pathway, which targets limbic structures such as the ventral striatum (VS), as well as regions of the cortical mantel such as the prefrontal cortex (PFC). Dopaminergic neurons in the substantia nigra (SN) make up the nigrostriatal pathway, which projects to the dorsal striatum (DS)

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Neurobiology of Impulsivity Impulsivity is a behavioral trait characterized by behavioral disinhibition, defined more specifically as acting in a sudden unplanned manner to satisfy a desire without adequate consideration for the consequences of such behavior. It has been proposed that at least two impulsivity-related components contribute to addiction risk. The first is the motivation to obtain and use rewarding substances, and the second is a rash, spontaneous need to use these substances once they are obtained (Dawe and Loxton 2004). Major advances have been made as to the elucidation of the underlying neurobiology of addiction and behavioral traits of impulsivity. The neurotransmitter DA, in particular, its regulation of the ventral striatum (nucleus accumbens), has long been implicated in drug addiction due to its critical role in reward. More recent studies have emphasized the essential contributions of the dorsal striatum (caudate nucleus and putamen) in compulsive behavior, as well as the prefrontal cortex (PFC), which induces strong top-down control of the striatum in order to mediate decision-making and goal-directed behavior. Numerous studies have also documented that distinct components of the PFC regulate different aspects of motivation and executive function in relation to decision-making. The current theories of impulsivity contend that motivational impulsivity (referring to the inability to wait for larger long-term rewards over smaller short-term gains) is mapped to circuits of the orbitofrontal cortex and the ventral striatum, specifically the nucleus accumbens (Cardinal et al. 2001). Executive (cognitive) impulsivity is instead more aligned with the dorsolateral and ventrolateral PFC and their projections to the dorsal striatum (Crews and Boettiger 2009), while motoric impulsivity underlying response inhibition is associated with the anterior cingulate and posterior ventromedial PFC and their innervation of the ventral regions of the caudate nucleus and putamen (Bussey et al. 1997; Parkinson et al. 2000). The organization of information flow through discrete striatal circuits is also highly relevant to impulsivity in the context of addiction disorders. Medium spiny projection neurons constitute the major cell population of the striatum and are segregated in relation to their efferent output and subsequent effects on behavior. Striatonigral neurons facilitate behavioral responses and constitute the “Go” (positive reward/choice) pathway, whereas striatopallidal cells are involved in suppressing inappropriate responses and constitute the “No/Go” (avoidance learning; inhibitory) circuit (Fig. 4.2). These striatal pathways are also distinct in regard to their expression of DA receptors. Striatonigral cells predominantly express DA D1 (DRD1) receptors, whereas striatopallidal neurons express high levels of DA D2 (DRD2) receptors. Primate and rodent studies have revealed that these striatal pathways are independently regulated by DA – elevation of DA enhances the probability of positive responses in a DRD1-dependent fashion, and DA decreases favor response inhibition that is mediated through DRD2 (Calabresi et al. 1997; Finch 1999; Schultz et al. 1998; Surmeier et al. 2007). Studies utilizing pharmacological DRD1 and DRD2 agonists/antagonists to differentially modulate activity and gene expression in the separate “Go” and “No/Go” striatal populations

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Fig.  4.2  General schematic overview of striatal efferent circuits based on current concepts of “Go” and “NoGo” striatal circuits. This dissociation has been based primarily on the dorsal striatal pathway, but similar organization is evident, particularly for NoGo, in the ventral striatal circuit. Dotted lines, inhibitory regulation; solid lines, excitatory regulation. Ctx, cortex; DYN, dynorphin; ENK, enkephalin; SNc, substantia nigra pars compacta; SNr, substantia nigra pars reticulata; STN, subthalamic nucleus; Thal, thalamus; VTA, ventral tegmental area. Dopaminergic projections to the striatum arise from SNc (to dorsal striatum) and VTA (to ventral striatum). Glutamatergic inputs from Ctx to striatum

strongly support the filtering and counterbalancing function of the direct and indirect striatal pathways in action selection and inhibitory control processes (Frank and O’Reilly 2006). Recent genetic-neurocognitive studies in healthy adults have substantiated a DRD1-Go-striatonigral and DRD2-No/Go-striatopallidal pathway model completely consistent with the functional organization of striatal pathways. Positive reward choice was demonstrated to be associated with variation in the DRD1 gene (Frank et al. 2007), whereas response inhibition in the choice to avoid negative consequences was shown to be associated with DRD2 gene (Frank et  al. 2007; Klein et  al. 2007). Events that lead to an imbalance of the DRD1- and DRD2-aligned striatal circuits could thus underlie inhibitory control deficit and addiction risk. The following sections evaluate the role of genetics and environment in relation to frontostriatal neural circuitry and its potential relevance to inhibitory control and vulnerability to addiction disorders (Fig. 4.3). The question being addressed here is whether inhibitory control mediates the relationship between addiction risk and frontostriatal circuits that are influenced by genetics and environmental factors. Since no clear evidence has been established for such a role of inhibitory control per se, it is important to assess whether or not certain behavioral disorders characterized by impulsivity share common genetic underpinnings with substance abuse disorders.

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Fig. 4.3  Schematic outline of the hypothesized role of inhibitory control in addiction risk

Genetics Given the high abundance of key DA regulatory molecules within corticostriatal circuits, it can be posited that individual differences in genes critical for DA neurotransmission would modulate corticostriatal function. DA tone in the cerebral cortex modulates top-down cognitive control and DA neurotransmission in the striatum is strongly linked to impulsive and addiction behaviors.

Dopamine D1 Receptor DRD1 gene has a strong cortical bias in its expression in the human brain and has been widely implicated in cognitive function (Goldman-Rakic et al. 2000; Williams and Castner 2006). D1 receptors in the striatum have been associated with reward choice. Very few genetic studies, however, have been conducted since sequencing of the DRD1 gene has failed to identify exonic mutations (Cichon et al. 1994; Liu et al. 1995; Ohara et al. 1993). The most frequently studied polymorphism of the DRD1 gene is localized in the 5¢-untranslated region (UTR), an A to G transition at 48 bases upstream of the coding start site (A-48G, rs4532), and this polymorphism has been examined in disorders related to inhibitory control deficits, as well as in several drug abuse populations. Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent, childhoodonset heterogenous disorder that is characterized by impulsivity, inattention, and hyperactivity. Children with ADHD often have significant impairments in working memory, planning and organization, inattention and impulsivity in addition to deficits in response inhibition (Willcutt et al. 2005). Individuals with ADHD also have a high rate of comorbid substance abuse occurring in approximately 20% of patients (Biederman et  al. 2008; Wilens and Upadhyaya 2007). Only a limited number of studies have evaluated the potential genetic contribution of the DRD1 to ADHD. The two polymorphisms investigated, rs4532 and rs265981, are both located in the 5¢ UTR of DRD1. A trend towards association between ADHD and both rs4532 and rs265981 genotypes was found in small sized samples of ADHD families (Bobb et al. 2005; Misener et al. 2004). Kirley and colleagues, however, failed to find an association between rs4532 genotype and ADHD in a small casecontrol sample (Kirley et al. 2002).

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One of the first studies to investigate the potential contribution of the rs4532 polymorphism to addictive behavior was conducted in 1997 by Comings and colleagues (Comings et al. 1997). They investigated the contribution of this polymorphism in three independent groups of subjects with varying degrees of ­compulsive and/or addictive behaviors: Tourette syndrome probands, tobacco smokers, and pathological gamblers. In all three groups, there was a significant association of rs4532 with addictive behaviors (Comings et al. 1997). A significant association was also observed between the rs265981 polymorphism in discordant sib pairs with pathological gambling (da Silva Lobo et al. 2007). Potential interactions between impulsivity and addiction in relation to polymorphisms of the DRD1 gene have been suggested by a few investigations. In a small group of French male alcohol-dependent patients, a significant association was reported for the rs4532 polymorphism and the sensation seeking score on the Zuckerman’s scale (Limosin et al. 2003). The rs4532 genotype was also associated with the level of novelty seeking, harm avoidance, persistence, and the degree of alcohol dependence in a large sample of Korean alcohol-dependent subjects (Kim et al. 2007). In addition to evidence suggestive of an association with impulsivity that may be relevant to substance abuse, DRD1 has been more extensively evaluated in multiple abuse populations irrespective of behavioral traits. In heroin abuse, a small study of African-Americans showed a significant association for the rs5326 genotype in heroin addicts; a haplotype consisting of rs686*A–rs5326*A was also significantly associated with heroin abuse in this population (Levran et al. 2009). However, no association has been found between the rs4532 genotype and methamphetamine abuse in a small Taiwanese sample (Liu et al. 2006). A recent study examining two polymorphisms, rs4532 and rs686, in a small sample of alcohol-dependent patients found a specific haplotype, rs686*A– rs4532*G, associated with alcohol dependence (Batel et al. 2008). The rs4532 genotype was also significantly associated with smoking a greater number of cigarette packs per day (Comings et al. 1997). In a large study of European- and African-American families, significant association was found between two DRD1 polymorphisms (rs686 and rs4532) and nicotine dependence (Huang et  al. 2008). Furthermore, a specific haplotype of rs265973*C–rs265975*T– rs686*A was associated with nicotine dependence in African-Americans (Huang et al. 2008). Studies using an in vitro reporter assay have recently revealed that the rs686/A allele has a higher level of expression compared to rs686/G allele variants (Huang et al. 2008), suggesting a new functional polymorphism affecting expression of DRD1. Further genetic studies are clearly needed to validate DRD1’s role with regard to potential top-down cortical impairment and/or striatal dysfunction for behavioral traits conferring risk, however, evidence to date does suggest that there may be a genetic contribution of DRD1 to some abuse populations relevant to impulsivity. Of particular interest, the recently suggested functional role for the rs686 polymorphism might allow for further investigation into potential regulatory mechanisms of DRD1.

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Dopamine D2 Receptor Of the two major subtypes of DA receptors, the DRD2 is most strongly linked with reward and numerous animal and human studies suggest that reduced D2 receptor function is a primary neurobiological disturbance underlying increased drug abuse vulnerability (Nader et al. 2006; Volkow et al. 2002). DRD2 mRNA is abundantly expressed in all DA terminal areas (Bouthenet et al. 1991; Meador-Woodruff et al. 1989) but unlike DRD1, it is less abundant in the cortex. The D2 receptor is highly abundant in the striatum and is predominantly expressed in the indirect striatopallidal pathway posited to underlie No/Go inhibitory control (Frank et al. 2007; Klein et al. 2007). In relation to impulsivity and addiction risk, genetic polymorphisms of DRD2 have been more frequently studied compared to DRD1. By far, the most studied functional polymorphism of the DRD2 gene is the TaqIA restriction fragment length polymorphism (rs1800497) on chromosome 11 at q22–q23, 10  kb downstream of the DRD2 gene. More recently, this polymorphism was localized within exon 8 of a neighboring gene, ankyrin repeat and kinase domain containing 1 (ANKK1) that is involved in signal transduction (Neville et  al. 2004). The rs1800497/TaqIA SNP causes a missense coding change (Glu713Lys) that may affect substrate binding specificity of the ANKK1 gene product (Neville et  al. 2004). However, it is clear that the rs1800497 polymorphism, specifically the A1 allele, is related to reduced D2 DA receptor binding affinity (Noble 2003; Noble et al. 1991) and also affects DRD2 mRNA translation, stability, and postsynaptic DRD2 receptor density in the striatum (Ritchie and Noble 2003). Two intriguing neurocognitive and functional magnetic resonance imaging (fMRI) investigations have shown a clear association between genetic mutations of the DRD2 gene and inhibitory control using a probabilistic learning task sensitive to DA manipulations (Frank et al. 2007; Klein et al. 2007). Both studies demonstrated that genetic mutations predictive of reduced DA D2 receptor density (those individuals carrying the rs1800497/A1 allele or the rs6277/C allele) are associated with reduced capacity to learn to avoid negative consequences (Klein et al. 2007; Frank et  al. 2007). Thus reduction in D2 receptor density appears to increase deficits in inhibitory control, thereby leading to enhanced vulnerability for addictiverelated behaviors. Moreover, a delayed discounting paradigm designed to test motivational impulsivity in healthy college students also showed a significant association for rs1800497 and greater delayed discounting, therefore indicative of greater impulsivity with mutation of the D2 receptor (Eisenberg et al. 2007). Of all the DA receptors, the role of the DRD2 in addiction has been the most strongly investigated since it is the major presynaptic DA autoreceptor (and most abundant DA receptor in the striatum) controlling the phasic DA activity that is crucial for reward (Dickinson et  al. 1999; L’Hirondel et  al. 1998; Mercuri et  al. 1997). The first genetic association of DRD2 with substance abuse was reported by Blum and colleagues in relation to rs1800497 and alcoholism (Blum et al. 1990). Since that initial study, several meta-analyses have confirmed this association, but

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the effect size was somewhat small (Munafo et al. 2007; Smith et al. 2008). Le Foll and colleagues performed a comprehensive meta-analysis on 40 case–control studies, representing approximately 5,300 cases and 4,000 controls (Le Foll et al. 2009). This meta-analysis detected a modest, but very significant effect of rs1800497 in alcohol-dependent subjects. The rs1800497 variant has also been proposed as a risk factor for nicotine addiction (Comings et al. 1996; Huang et al. 2009; Radwan et al. 2007) and is associated with more rapid smoking progression in adolescents (Audrain-McGovern et al. 2004), younger age of smoking initiation and fewer quit attempts (Spitz et al. 1998). With regard to the potential link of rs1800497 with psychostimulant addiction, several studies in Caucasian subjects have found a significant association with rs1800497 and heavy stimulant use (Comings et  al. 1999; Persico et  al. 1996). However, this was not replicated in a racially mixed population of European- and African-Americans (Gelernter et al. 1999). A significant association has also been reported between the rs1800497 genotype and the degree of heroin dependence (Lawford et  al. 2000; Shahmoradgoli Najafabadi et  al. 2005), amount of heroin consumed (Lawford et al. 2000), and levels of cue-induced craving (Li et al. 2006). Specific DRD2 haplotypes have also been reported to be highly associated with heroin dependence in both Chinese and Caucasian populations (Xu et al. 2004). Overall, there is significant convergence of data supportive of an important contribution of DRD2 to various addiction disorders that might be relevant to its functional modulation of discrete striatal circuits that mediate inhibitory control.

Dopamine Transporter Synaptic levels of DA are mediated by release, reuptake, and metabolic processes. The neuronal DA transporter (DAT) is a presynaptically localized protein responsible for the reuptake of DA (most abundant in the striatum) and thus provides one of the central means by which the actions of synaptic (and extrasynaptic) DA are terminated in the brain (Cragg and Rice 2004). The importance of DAT in impulsivity and addiction disorders is emphasized by the fact that psychomotor stimulant drugs elevate DA levels by blocking the DAT, thereby preventing the reuptake of DA released (via exocytosis) from storage vesicles (e.g., cocaine and methylphenidate), and/or by releasing DA via reversal of the DAT [e.g., amphetamines; see Amara and Kuhar (1993) and Hitri et al. (1994)]. For example, mice genetically lacking DAT have altered responses to cocaine and amphetamine (Giros et al. 1996). DAT is a particularly interesting candidate gene for ADHD as it is the primary target for the medications (methylphenidate and other psychostimulants) used to treat patients with ADHD (Seeman and Madras 1998; Volkow et al. 1998). DAT is encoded by the SLC6A3 gene containing 15 exons spanning 60 kb at chromosome 5p15.3 and contains a common 40 base pair variable number tandem repeat (VNTR) polymorphism in the 3¢-untranslated region (3¢-UTR) (Vandenbergh

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et al. 1992). This VNTR has repeat copy numbers ranging from 3 to 13 with the nine and ten-repeat alleles the most frequently occurring in the population (Gelernter et al. 1998). Homozygosity for the ten-repeat allele was reported to be associated with higher levels of DAT protein in the striatum (Cheon et  al. 2005; Heinz et al. 2000; Jacobsen et al. 2000), although these effects may be population dependent because other studies have also reported a decrease (Heinz et al. 2000) or no change (Martinez et al. 2001) in DAT binding sites in association with the DAT polymorphism. Several studies have reported a relationship between ADHD and the DAT 3¢-UTR VNTR polymorphism. Cook and colleagues reported an association with the ten-repeat allele and children with ADHD (Cook et al. 1995). This finding was replicated by several groups (Curran et al. 2001; Daly et al. 1999; Gill et al. 1997; Kirley et al. 2002; Waldman et al. 1998), but not by others (Holmes et al. 2000; Palmer et al. 1999; Roman et al. 2001). Functionally, children homozygous for the ten-repeat allele exhibit higher commission errors, impulsive responses and reaction time variability compared to those carrying the nine-repeat allele (Loo et al. 2003). An additional study by Bellgrove and colleagues (Bellgrove et al. 2005b) also found higher reaction time variability in children homozygous for the ten-repeat allele. Other studies, however, have reported fewer omission errors and no significant differences in reaction time or reaction time variability in an attention task in subjects carrying two copies of the ten-repeat allele compared to those carrying one copy (Oh et  al. 2003). While these results are conflicting, they do highlight the important role of the DAT 3¢-UTR VNTR polymorphism in cognitive impulsiveness. Despite the DAT protein being a primary target for psychostimulant drugs, studies thus far have largely failed to show significant associations of the DAT 3¢-UTR VNTR repeat polymorphism with cocaine (Gelernter et  al. 1994) and methamphetamine (Hong et al. 2003; Liu et al. 2004) abuse. The 3¢-UTR VNTR polymorphism appears to be more associated with cocaine-induced paranoia (Gelernter et al. 1994) and/or methamphetamine-induced psychosis in individuals with nine or fewer repeat alleles (Ujike et  al. 2003). DA neurotransmission is critical not only for psychostimulant reward, but also for nonstimulant drugs of abuse that mediate some of their reinforcing actions through indirect regulation of DA neurons. Equivocal results have been reported thus far in relation to heroin abuse and DAT 3¢-UTR VNTR polymorphism. Heroin-dependent subjects homogenous for the DAT 9 repeat genotype have increased risk for irritability and aggression behavior, but not heroin abuse (Gerra et al. 2005). However, a Russian study found a significant association of the genotype 9/9 with early opiate addiction (Briun et al. 2001). Recent examination of other variants in the DAT gene revealed a functional polymorphism of a six-copy 30-base pair VNTR located in intron 8 of the gene that was significantly associated with cocaine abuse (Guindalini et  al. 2006). Although relevant to cognitive function and ADHD, the accumulating data on the DAT gene appears to suggest a stronger association with psychosis-related traits than with direct stimulant or opioid abuse.

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Catechol-O-Methyltransferase In addition to DA reuptake into presynaptic terminals, metabolism of DA is critical for regulating synaptic levels of DA. The catechol-O-methyltransferase (COMT) gene encodes an enzyme that catalyzes the metabolism of the catecholamine neurotransmitters including DA and norepinephrine. A missense polymorphism encoding a methionine substitution for valine at amino acid 158 in the COMT gene results in three- to fourfold reduction in COMT activity (highest in the cortex), that in turn would lead to increased DA levels (Lotta et  al. 1995). It is now well documented that homozygous Met individuals have enhanced performance on working memory (Egan et al. 2001; Goldberg and Weinberger 2004; Joober et al. 2002) and attentional (Blasi et al. 2005) tasks as compared to Val/Met heterozygous and Val homozygous subjects. fMRI studies have shown that the COMT Val158Met polymorphism also influences the extent of activation of the PFC (Bertolino et al. 2004; Egan et al. 2001). Studies of the Val158Met polymorphism in association with ADHD and other impulsive disorders have been more limited. Two studies have examined the role of this polymorphism in cognitive tasks that directly affect PFC function. Both studies found that in ADHD children there was no association of neurocognitive function or executive function with this polymorphism (Mills et al. 2004; Taerk et  al. 2004). A third study found impairments in sustained attention in carriers of the Met allele (Bellgrove et al. 2005a). Based on the limited work in ADHD, it has been hypothesized that the faster clearance of DA associated with the Val allele of COMT may be advantageous to cognition in ADHD (Bellgrove et al. 2005a). Despite the importance of cognitive control in addiction, and the potential importance of COMT in PFC function, surprisingly relatively few studies have evaluated the COMT gene in drug abuse disorders other than alcohol. Of the limited studies, a significantly higher frequency of the Val allele (which presumably is associated with lower cortical DA levels and lower cognitive control) was observed in polysubstance abusers (Vandenbergh et al. 1997) as well as in heroin-dependent subjects in a family-based haplotype risk analysis (Horowitz et  al. 2000). A higher frequency of the Val allele was also found in methamphetamine (Li et  al. 2004) and cannabis (Baransel Isir et  al. 2008) users. Recently, however, in cocaine-dependent subjects of African descent, a higher frequency of the Met  allele was observed; a risk haplotype consisting of the Val158Met  allele and another polymorphism (rs737865) was also identified in this group (Lohoff et al. 2008). Thus, apparent low DA tone that would confer low cognitive control seems to be significantly associated with risk for methamphetamine and opioid abuse, but not cocaine, but these effects may be attributed to ethnic differences. Overall, although there is a strong association between COMT and executive function, the limited nature of the studies conducted in relation to impulsivity and addiction currently make any definitive conclusions impossible.

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Environmental Factors in Relation to Impulsivity and Addiction Risk While it is clear that an individual’s genetic makeup is an important determinant of addiction risk, genetic factors account for roughly half of a person’s addiction vulnerability, suggesting the involvement of additional factors. Indeed, environmental determinants such as drug exposure, stress, trauma, infection, and social contexts also influence addiction risk. Furthermore, when occurrence of negative life experiences take place during critical periods of neurodevelopment, they have the potential to impact the hardwiring of the brain, which may lead to long-term deficits in neuronal function and behaviors related to addiction vulnerability. We focus here on environmental insults related to drug exposure and adverse life events during the prenatal and adolescent periods because several epidemiological and clinical studies have documented impulsive behavior, cognitive impairment, and consumption of addictive substances in adult individuals exposed to such factors.

Developmental Exposure to Cannabis The neurobiological consequences of cannabis exposure during both the prenatal and adolescent periods are a subject of growing interest given their potential impact on subsequent behaviors and mental health outcomes in adulthood. While the extent of cannabis use by adolescents is relatively well documented, approximately 4% of women in the USA use marijuana (Cannabis) during pregnancy (SAMHSA 2009). One-third of D9-tetrahydrocannabinol (THC), the major psychoactive component of cannabis, undergoes cross-placental transfer upon cannabis smoking (Hurd et al. 2005), and recent evidence suggests that developmental THC exposure alters fundamental neurodevelopmental processes, particularly impairing brain regions enriched in DA neurons that are relevant to impulsive behavior. DA neurons are present in the human fetal brain at an early developmental stage (Verney et al. 1991) and prenatal cannabinoid exposure affects the maturation of the DA system. Indeed, in human studies, maternal cannabis use is selectively associated with disruption of DRD2, but not DRD1 mRNA expression levels in the limbic and striatal structures (Wang et al. 2004). Animal studies in which neurochemical alterations can be more definitely characterized have validated these neurobiological findings and, in addition, have helped to identify other affected genes (i.e., preproenkephalin) that are specifically expressed in striatopallidal neurons (Ellgren et al. 2007; Spano et al. 2007). This apparent neurobiological specificity of developmental cannabis exposure is also underscored by a lack of significant effect on the DRD1 receptor and other genes expressed in the striatonigral pathway (Spano et  al. 2007). As such the neuronal disturbances associated with developmental cannabis exposure are particularly intriguing considering the role of the No/ Go striatopallidal pathway in inhibitory control (Frank et  al. 2007). In humans,

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increased neural activity has been observed in the PFC of subjects with in utero cannabis exposure during a Go/NoGo task routinely used to assess inhibitory control (Smith et al. 2004). Adolescent cannabis exposure is also associated with a similar pattern of brain activation during inhibitory processing even after a few weeks of abstinence (Padula et  al. 2007; Schweinsburg et  al. 2008; Tapert et  al. 2007). In animals, it has been repeatedly observed that maternal THC exposure is predictive of impulsive behavior in offspring, first evident in childhood and persisting throughout adolescence and into young adulthood (Day et al. 1994; Griffith et al. 1994; Leech et al. 1999). Discrete neuronal disturbances in striatopallidal circuitry could thus be a potential substrate underlying the heightened impulsivity seen in individuals with developmental cannabis exposure. Given the selective disturbances observed in the striatopallidal pathway, a natural question that arises is whether there is any association of early cannabis exposure with substance abuse risk. Indeed, longitudinal human studies have reported a significant association between prenatal cannabis exposure and cannabis use in adolescents and young adults (Day et  al. 2006; Porath and Fried 2005). Animal studies have confirmed a causal relationship between prenatal cannabinoid exposure and increased long-term vulnerability for drug use behavior in later life (Wang et  al. 2004, 2006). Similarly, both in humans and laboratory animals, adolescents who use cannabis are at greater risk to use heavier drugs such as cocaine and heroin later in adulthood (Agrawal et al. 2004; Ellgren et al. 2007). Altogether, the extant animal and human literature underline the concrete and extensive impact of developmental cannabis exposure on impulsivity and addiction risk and suggests a neurobiological link with striatopallidal dysfunction.

Developmental Exposure to Other Substances Other drugs are frequently used by pregnant women and teens during these critical developmental periods. Of these, nicotine and alcohol are most prominent. In the USA, 16 and 11% of women consume cigarettes and alcohol during pregnancy, respectively (SAMHSA 2009). Prenatal cigarette and alcohol exposure may alter the developing brain via a number of different mechanisms including direct cytotoxicity, impairment of umbilical circulation and placental transport of essential nutrients, alteration of neurotransmitter production and hypothalamic–pituitary–adrenal (HPA) activity, and disruption of synaptogenesis. These processes have been shown to result in gross structural and functional alterations in the basal ganglia, corpus callosum, cerebellum, and hippocampus, as well as hemispheric asymmetry and significant variation in amounts of cortical gray and white matter (Huizink and Mulder 2006). Given the extent of documented brain damage, cigarette and alcohol exposure during pregnancy are associated with broader cognitive alterations (i.e., low IQ) when compared to cannabis. Executive function, including inhibitory control, is also affected by cigarette and alcohol exposure, and both have been shown to be associated with impulse control-related disorders such as ADHD and addiction.

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In contrast to cannabis exposure, however, it remains difficult to tease apart the specific contribution of discrete frontostriatal alterations and inhibitory control deficits to the increase in impulsivity and risk-related behaviors, given the broad cognitive impairments found in cigarette and alcohol exposed individuals.

Early Life Stressors Early life stressors, such as maternal separation, impoverished environment, social stress, physical stress, and diet can influence an individual’s risk for addiction (Caprioli et  al. 2007). Additionally, studies in non-human primates and rodents have linked early life stressors with long-term changes in behavioral measures of impulsivity. In one such study (Parker et al. 2005), young squirrel monkeys were carried through a mild stress paradigm (exposure to unfamiliar adult monkeys for 1  h once a week) throughout adolescence and then tested during adulthood in a response inhibition task known to reflect PFC-dependent cognitive function. Interestingly, stressed subjects showed enhanced response inhibition, or increased inhibitory control compared to nonstressed controls, suggesting that an early-life stressful experience can serve as a “stress inoculation,” which may increase an individual’s emotional resistance to later stressors. Rodent studies have also linked early life stressors with alterations in inhibitory control. Hellemans and colleagues showed that the rearing environment alters cognitive, but not motoric, impulsivity later in life (Hellemans et  al. 2005) as rats reared in isolation showed increased preference for the large delayed reward in a Go/NoGo/delay-to-reinforcement task, consistent with increased inhibitory control. In contrast, others have demonstrated that rats raised in enriched environments are less impulsive and have increased inhibitory control when compared to rats raised in isolation (Perry et  al. 2008). While experimental design differences may explain these incongruencies, the data overall suggest that chronic stress increases and mild stress decreases impulsive behavior later in life. Numerous studies have shown disruption in striatal DRD2 (King et al. 2009; Kosten and Kehoe 2005) resulting from early stress and other studies have also reported impairment of proenkephalin (Gustafsson et al. 2008). Taken together, early life stress impacts both impulsive behavior and addiction risk that might be relevant to dysfunction of the striatopallidal pathway.

Gene × Environment Interactions While an individual’s genes and their environment can contribute independently to disease, there is growing evidence that direct interactions between genetic and environmental factors may substantially impact the etiology of complex disorders including mental illnesses. Gene × Environment (G × E) is generally defined as individual differences influenced by genetics that are sensitive to specific environmental

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factors (Rutter and Silberg 2002). Thus, specific gene variants may only exert risk effects on a disorder where an individual is also exposed to a particular environmental risk factor. An example of G × E relevant to complex behavioral trait involves a functional polymorphism in the gene encoding the neurotransmitter-metabolizing enzyme monoamine oxidase. Individuals with the low activity monoamine oxidase A-genotype show antisocial behavior in adulthood only when adverse events such as maltreatment are experiences in childhood (Caspi et  al. 2002; Kim-Cohen et  al. 2006). This G × E interaction highlights the potential importance of early environmental intervention once genetic candidates and environmental contributors are better understood. To date, very few genetic candidates in association with a specific environmental condition have been identified that specifically modify impulsive behavior that could impact addiction vulnerability. Most investigations have thus far focused on ADHD and the DAT genotype, indicating a contribution of psychosocial adversity (Laucht et al. 2007), family environment (Sonuga-Barke et al. 2009), and prenatal nicotine exposure (Becker et  al. 2008). Similar results have also been reported for the DRD2 genotype and a variety of environmental factors to predict alcohol dependence (van der Zwaluw and Engels 2009). As a growing number of investigations begin to explore the complex interactions between G × E in relation to substance abuse, greater insights will be obtained as to the contribution of impulsivity trait to addiction vulnerability.

Application to Prevention Significant questions remain to be investigated in order to delineate the specific genetic and environmental contributions to impulsive behavior and their influence on addiction risk. The DRD2 and striatopallidal circuit are intriguing convergent candidates, raising the possibility that they may play a potential role in the prevention and treatment of impulsivity-related disorders, including addiction. Early identification of high-risk individuals based on neurobiological factors (e.g., specific polymorphisms) will be an attractive strategy that may ultimately lessen the impact, or potentially even the emergence, of such disorders (Young et  al. 2004). In practice, however, this strategy raises several important ethical issues, and utilization of genetic-based preventative treatments would require careful and controlled implementation. One promising application arising from a growing body of research regarding the genetic determinants of impulsivity would be to focus on its complex interaction with environmental factors. Indeed, the wide application of universal prevention strategies targeted to populations rather than individuals has proven to be minimally effective. An important area of application for G × E interactions is personalized medicine as it relates to prevention and treatment. The addiction field provides a good example of a disorder where such strategy could begin to be applied with more specificity to increase treatment efficacy. For example, research into the DRD2 and striatopallidal circuitry indicates that while certain individuals may be

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at higher risk of developing substance use disorders, others may be relatively protected, showing insensitivity to particular risk factors. Implementing specific environmental prevention and intervention strategies that aim to compensate for the poor inhibitory control that characterize high-risk subjects (i.e., carriers of DRD2 polymorphisms, individuals exposed to cannabis or chronic stress in early life) may allow clinicians to decrease the potential synergetic effects of environmental and genetic factors in such individuals. Increasing the specificity of these strategies and targeting high-risk populations that are more likely to benefit from such interventions may also aid in increasing the efficacy and decreasing the cost of treatment. While it currently remains difficult to intervene on the genetic substrates of behaviors, prevention and intervention strategies focused on early environmental experience and endophenotypes may ultimately decrease the risk of adverse outcomes in vulnerable individuals.

Glossary Exon  DNA region of the gene containing the sequence for the mature form of the messenger RNA including protein coding regions as well as 5¢ and 3¢ untranslated regions. Haplotype  A combination of alleles at multiple loci that are transmitted together on the same chromosome. Intron  DNA region within a gene that is not translated into protein. Missense coding change  A nucleotide mutation that alters the amino acid sequence of the protein. Polymorphism  Multiple alleles of the same gene within a given population. Variable number tandem repeat polymorphism (VNTR)  A short nucleotide sequence polymorphism organized as a tandem repeat.

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Surmeier DJ, Ding J, Day M et al (2007) D1 and D2 dopamine-receptor modulation of striatal glutamatergic signaling in striatal medium spiny neurons. Trends Neurosci. 30: 228–35 Taerk E, Grizenko N, Ben Amor L et al (2004) Catechol-O-methyltransferase (COMT) Val108/158 Met polymorphism does not modulate executive function in children with ADHD. BMC Med Genet 5: 30 Tapert SF, Schweinsburg AD, Drummond SP et al (2007) Functional MRI of inhibitory processing in abstinent adolescent marijuana users. Psychopharmacology (Berl) 194: 173–83 Ujike H, Harano M, Inada T et al (2003) Nine- or fewer repeat alleles in VNTR polymorphism of the dopamine transporter gene is a strong risk factor for prolonged methamphetamine psychosis. Pharmacogenomics J 3: 242–7 van der Zwaluw CS, Engels RC (2009) Gene-environment interactions and alcohol use and dependence: current status and future challenges. Addiction 104: 907–14 Vandenbergh DJ, Persico AM, Hawkins AL et  al (1992) Human dopamine transporter gene (DAT1) maps to chromosome 5p15.3 and displays a VNTR. Genomics 14: 1104–6 Vandenbergh DJ, Rodriguez LA, Miller IT et al (1997) High-activity catechol-O-methyltransferase allele is more prevalent in polysubstance abusers. Am J Med Genet 74: 439–42 Verney C, Zecevic N, Nikolic B et al (1991) Early evidence of catecholaminergic cell groups in 5- and 6-week-old human embryos using tyrosine hydroxylase and dopamine-b-hydroxylase immunocytochemistry. Neurosci. Lett. 131: 121-124 Volkow ND, Fowler JS, Wang GJ et al (2002) Role of dopamine, the frontal cortex and memory circuits in drug addiction: insight from imaging studies. Neurobiol Learn Mem 78: 610–24 Volkow ND, Wang GJ, Fowler JS et al (1998) Dopamine transporter occupancies in the human brain induced by therapeutic doses of oral methylphenidate. Am J Psychiatry 155: 1325–31 Waldman ID, Rowe DC, Abramowitz A et  al (1998) Association and linkage of the dopamine transporter gene and attention-deficit hyperactivity disorder in children: heterogeneity owing to diagnostic subtype and severity. Am J Hum Genet 63: 1767–76 Wang X, Dow-Edwards D, Anderson V et  al (2004) In utero marijuana exposure associated with abnormal amygdala dopamine D2 gene expression in the human fetus. Biol Psychiatry 56: 909–15 Wang X, Dow-Edwards D, Anderson V et al (2006) Discrete opioid gene expression impairment in the human fetal brain associated with maternal marijuana use. Pharmacogenomics J 6: 255–64 Wilens TE, Upadhyaya HP (2007) Impact of substance use disorder on ADHD and its treatment. J Clin Psychiatry 68: e20 Willcutt EG, Doyle AE, Nigg JT et al (2005) Validity of the executive function theory of attentiondeficit/hyperactivity disorder: a meta-analytic review. Biol Psychiatry 57: 1336–46 Williams GV, Castner SA (2006) Under the curve: critical issues for elucidating D1 receptor function in working memory. Neuroscience 139: 263–76 Xu K, Lichtermann D, Lipsky RH et al (2004) Association of specific haplotypes of D2 dopamine receptor gene with vulnerability to heroin dependence in 2 distinct populations. Arch Gen Psychiatry 61: 597–606 Young RM, Lawford BR, Nutting A et al (2004) Advances in molecular genetics and the prevention and treatment of substance misuse: Implications of association studies of the A1 allele of the D2 dopamine receptor gene. Addict Behav 29: 1275–94

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Chapter 5

Impaired Inhibitory Control as a Mechanism of Drug Abuse Mark T. Fillmore and Jessica Weafer

Abstract  Much research on drug abuse has sought to identify unique behavioral characteristics of individuals who abuse drugs to determine if such characteristics might actually contribute to the etiology of drug abuse. This chapter focuses on impulsivity and reviews several lines of research that point to the role of impaired impulse control in the development and maintenance of drug dependence. A fundamental aspect of impulsivity is an inability to inhibit inappropriate actions or behaviors. Impulsivity is examined as a deficit of inhibitory control and behavioral tasks that assess impairments of inhibitory control in the laboratory are described. Studies of the acute disruptive effects of alcohol on drinkers’ inhibitory control are reviewed, and several lines of evidence point to the specific vulnerability of this behavioral function. Evidence is also presented to show how acute impairment of inhibitory control might contribute to abuse potential of alcohol by promoting excessive “binge” drinking. The chapter concludes by discussing how deficient inhibitory control could represent a fundamental behavioral mechanism by which certain emotional and behavioral states precipitate excessive binge drinking, and how such information might benefit relapse prevention treatments for alcohol abusers.

Introduction Understanding the causes of drug abuse poses a special challenge for behavioral scientists in large part because it is an individual difference problem. While many people are exposed to alcohol and other drugs, only a small percentage of these individuals go on to develop habitual use and dependence on these substances. As  such, one approach to the problem has been to identify unique behavioral ­characteristics of individuals who develop problems with drug abuse M.T. Fillmore (*) Department of Psychology, University of Kentucky, Kastle Hall, Lexington, KY 40506-0044, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_5, © Springer Science+Business Media, LLC 2011

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and to d­ etermine if such characteristics might actually contribute to the etiology of the abuse. One characteristic that has become well recognized as a possible risk factor is impulsivity. Broadly defined, impulsivity refers to a pattern of undercontrolled behavior in which the individual lacks the ability to delay gratification and acts without forethought or consideration of potential consequences. Its role as a risk factor is based on findings from studies examining drug abuse in relation to impulsivity as a personality trait and as a central characteristic of psychopathology. For example, several studies have examined the link between DSM personality disorder clusters and drug abuse. The general finding from this research is that substance abuse disorders have a high comorbidity with antisocial, borderline, and histrionic disorders, which are all characterized by under-controlled, impulsive patterns of behavior (e.g., Grekin et al. 2006; Trull et al. 2004). It is also well established that externalizing disorders, such as attention deficit/hyperactivity disorder (ADHD) and conduct disorder, pose risk for developing substance abuse disorders (Barkley 2006; Flory et al. 2003). A hallmark characteristic of externalizing disorders, such as ADHD, is impulsive or undercontrolled behavior. Impulsivity as a trait dimension of normal personality is also associated with risk for alcohol abuse. Impulsive individuals tend to drink more frequently and in larger amounts (Goudriaan et  al. 2007). These individuals are also more likely to binge drink (Marczinski et al. 2007). Further, prospective studies have shown that impulsive characteristics often precede the onset of problem alcohol use, suggesting that trait impulsivity might also play a causal role in alcohol abuse. Longitudinal studies have shown that impulsivity predicts early onset drinking age and the development of heavy drinking and alcohol dependence in young adults (August et al. 2006). From the wealth of evidence linking impulsivity to alcohol and other drug abuse, a new question has emerged that concerns the specific mechanisms through which impulsivity operates to promote the abuse of alcohol and other drugs. One fundamental aspect of impulsivity that appears particularly relevant to drug abuse is an inability to inhibit inappropriate actions or behaviors. This chapter examines impulsivity as a deficit of inhibitory control and describes cognitive models of inhibitory control mechanisms and how these models are used in the study of alcohol abuse. The next section describes inhibitory mechanisms in the control and regulation of behavior and the laboratory methods for their assessment.

Behavioral Control Although it is important to characterize the behavioral correlates of drug abuse in terms of complex traits, such as impulsivity, there is also a need to identify specific behavioral mechanisms by which these traits might promote drug abuse. In particular, it is important to understand the basic mechanisms that underlie impulsive behavior. Several theories in cognitive neuroscience postulate that the control of behavior is governed by distinct inhibitory and activational systems (Fowles 1987;

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Gray 1976; Logan and Cowan 1984). Considerable research has focused on the a­ bility to inhibit inappropriate action. This mechanism of inhibitory control is thought to involve frontal lobe substrates that exert inhibitory influences over conditioned responses and reflexive behaviors. Studies in neuropharmacology and neuroanatomy have identified distinct neural systems that implicate separate inhibitory and activational mechanisms in the control of behavior (Jentsch and Taylor 1999; Lyvers 2000). The orbitofrontal and medial prefrontal cortex contain neural substrates that subserve many ongoing activities that control and regulate behavior. The ability to inhibit or suppress an action enhances the organism’s behavioral repertoire by affording it some control over when and where responses may be expressed. As such, the inhibition of behavior is an important function that sets the occasion for many other activities that require self-restraint and regulation of behavior. Not surprisingly then, deficient or impaired inhibitory control has been implicated in the display of impulsivity and disorders of self-control, such as antisocial personality, obsessive–compulsive, and ADHDs (Barkley 2006; Nigg 2006). A number of behavioral tasks have been used to assess deficits of inhibitory mechanisms. Stop-signal and cued go/no-go models evaluate control as the ability to activate and to inhibit prepotent (i.e., instigated) responses (Logan 1994; Miller et al. 1991). The tasks model behavioral control using a reaction time scenario that measures the countervailing influences of inhibitory and activational mechanisms. Individuals are required to quickly activate a response to a go-signal and to inhibit a response when a stop-signal occasionally occurs. Activation is typically measured as the speed of responding to go-signals and inhibition to stop-signals is assessed by the probability of suppressing the response or by the time needed to suppress the response. In these models, inhibition of a response is usually required in a context in which there is a strong tendency to respond to a stimulus (i.e., a prepotency), thus making inhibition difficult. The validity of these models is well documented. The models are sensitive to inhibitory deficits characteristic of brain injury (Malloy et al. 1993), trait-based impulsivity (Logan et al. 1997), and self-control disorders, such as ADHD (Tannock 1998).

Acute Effects of Alcohol on Inhibitory Control Several studies using behavioral control tasks have provided consistent evidence that moderate doses of alcohol selectively reduce the drinker’s ability to inhibit behavior at doses that leave the ability to activate behavior relatively unaffected (Marczinski and Fillmore 2003; Fillmore and Weafer 2004). For example, Fillmore and Weafer (2004) used a cued go/no-go task to test the impairing effect of alcohol on drinkers’ inhibitory control over their behavioral impulses. The cued go/no-go task presented go and no-go targets to which subjects had to execute a response (go) or to inhibit a response (no-go). The subjects’ inhibitory control was tested on two occasions: following a placebo and following an active alcohol dose that was ­sufficient to raise a drinker’s blood alcohol concentration (BAC) to 0.08%.

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Fig. 5.1  Effects of placebo and 0.65 g/kg alcohol on inhibitory control (left panel) and reaction time (right panel)

Figure  5.1 shows that, compared with placebo, alcohol impaired inhibitory control by increasing the likelihood that drinkers would fail to inhibit responses to no-go targets. By contrast, no effect of alcohol at this dose was observed on the ability of drinkers to execute the responses to go targets as measured by their speed of responding. Similar findings have been reported using the stop-signal task. This task requires subjects to respond as quickly as possible to a go stimulus, but to suddenly inhibit that response when a stop-signal is presented. Studies examining alcohol effects on performance of this task consistently report that the drug increases failures to inhibit, yet has little effect on response speed (e.g., de Wit et al. 2000; Fillmore and Vogel-Sprott 1999, 2000; Mulvihill et al. 1997). What is particularly remarkable about findings such as these is the robust impairment that is evident despite the relatively simple nature of the inhibitory response tested. Typically, the sensitivity to alcohol-induced impairment increases as a function of dose and task complexity (Maylor et  al. 1992). However, the impairing effects of alcohol on the ability to inhibit behavior are often observed at BACs at or below 0.08% (Fillmore 2003). The findings suggest that activities requiring a quick suppression of actions might be particularly vulnerable to the disruptive influences of alcohol. The findings might also provide some account for the long-standing observation that alcohol intoxication is often characterized by increased impulsivity and aggression. Indeed, alcohol-induced impairments of inhibitory mechanisms might actually exert considerable disruptive influence on higher-order, executive cognitive functions. Many fundamental cognitive and perceptual processes, such as inhibitory mechanisms, are considered to operate in a “bottom-up” fashion to exert increasing influence at each stage of higher-order attentional and cognitive functions. Thus, the alcohol-induced disturbances of basic

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control mechanisms, such as inhibitory processes, might actually result in much more pronounced impairments of the higher cognitive operations for which they serve (e.g., decision-making, planning, goal maintenance, etc.).

Alcohol Impairment and the Vulnerability of Inhibitory Control The notion that inhibitory mechanisms might be especially sensitive to the disruptive effects of alcohol has been the focus of several studies aimed at better characterizing this vulnerability. One fundamental question concerns the possibility that the act of completely inhibiting a prepotent action might be more difficult under alcohol than merely altering that action so that some alternative response could still be executed. Abroms et  al. (2003) used the cued go/no-go task to examine the degree to which alcohol impaired performance when subjects could alter their response instead of being required to completely suppress the response. The authors found that, at moderate BACs (i.e., approximately 0.08%), alcohol had little disruptive effect on the ability to alter a prepotent action, whereas the ability to completely suppress action showed pronounced impairment. Another study tested the possibility that alcohol-induced impairment of inhibitory control might differ depending on whether inhibitory control is required to prevent the premature engagement of a response versus the premature disengagement of a response (Marczinski et al. 2005). The study modified the cued go/no-go task so that in one condition subjects were required to engage responses to go targets (press a key) and in another condition they were required to disengage responses to go targets (release an ongoing key press). The results showed that at moderate BACs (approximately 0.08%) alcohol had no effect on the ability to inhibit the premature disengagement responses. By contrast, the ability to inhibit the premature engagement of a response showed marked impairment. Together, studies such as these show that minor differences in response requirements can have dramatic impact on the degree to which alcohol impairs behavioral control. Moreover, the findings further support the notion that the specific act of suppressing instigated or prepotent actions appears particularly vulnerable to the disruptive effects of the drug. Examinations of tolerance development to the impairing effects of alcohol also point to the vulnerability of response inhibition. The term tolerance refers to the observation that the intensity or magnitude of a response to a drug diminishes as a function of repeated administrations of the drug (Kalant et al. 1971). This effect can also be observed under a single administration of a dose of alcohol. Tolerance can develop during the course of a single drinking episode and is referred to as acute tolerance. As alcohol is consumed, BAC initially rises rapidly and begins to gradually decline. This rising phase of the BAC curve is referred to as the ascending limb of intoxication and the declining phase is referred to as the descending limb of intoxication. Acute tolerance can be observed by comparing performance or impairment during equivalent BACs on the ascending and descending limbs

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of intoxication (Kalant et al. 1971). Acute tolerance refers to the observation that the degree of impairment at equivalent BACs is typically greater on the ascending rather than descending limb of intoxication. One study examined acute tolerance to alcohol-induced impairment of inhibitory and activational mechanisms (Fillmore et al. 2005). Subjects received 0.65 g/kg alcohol and performed two tests on the cued go/no-go task: one during the ascending limb and the other during the descending limb of the blood alcohol curve. Both tests occurred at comparable BACs (approximately 0.07%). The study showed that alcohol impaired response inhibition and response activation during the ascending limb. Impaired response activation was evident by slowed reaction time to go targets and impaired response inhibition was evident by more failures to inhibit responses to no-go targets. The study also found that impaired response activation showed acute tolerance, as speed of reaction time returned to sober levels during the declining limb of the blood alcohol curve. By contrast, there was no acute tolerance to impairing effects on response inhibition. Response inhibition remained equally impaired on both limbs of the curve. Taken together, these findings highlight another vulnerability of inhibitory control by showing that inhibitory mechanisms appear to recover more slowly from the impairing effects of alcohol than activational mechanisms of control. Evidence for a possible lag in tolerance development to inhibitory versus activational mechanisms suggests that as blood alcohol declines, drinkers’ response inhibition might continue to be impaired, despite having an unimpaired ability to activate responses (see also Pihl et al. 2003; Schweizer et al. 2004). Evidence that acute tolerance results in such an “activational-bias” of behavior would have important implications for understanding some of the behaviorally disruptive effects of the drug. An activational-bias in a drinking situation could increase the likelihood of disinhibited behavior under the drug, especially in the presence of environmental cues that instigate responses that are normally suppressed. Thus, an activationalbias could increase the likelihood of aggressive actions or continued, “binge” drinking. The next section describes evidence implicating acute impairments of inhibitory control in the abuse potential of alcohol.

Inhibitory Control and Alcohol Abuse Although there is little dispute that reward mechanisms play an important role in abuse potential, the acute cognitive impairing effects of alcohol might also contribute to abuse by compromising mechanisms involved in the regulation and self-control of behavior (Fillmore 2007). In particular, inhibitory mechanisms likely play an important role in terminating alcohol use during an episode (Fillmore 2007; Lyvers 2000). Many drinkers report intentions to limit their alcohol use to one or two drinks only to fail and instead drink excessively (Collins 1993). Such accounts have fueled the notion that alcohol reduces control over consumption in some ­individuals. Terminating a drinking episode requires inhibition of ongoing ­alcohol-administration behaviors.

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Any impairment of normal inhibitory mechanisms resulting from an ­initial dose of alcohol could compromise the ability to stop additional alcohol administrations in a drinking situation, resulting in a “binge” episode. Thus, it could be that individuals who are more sensitive to the disinhibiting effects of alcohol might be at increased risk for engaging in binge-drinking behavior. Initial findings from laboratory studies have begun to provide support for this hypothesis. For example, Marczinski et al. (2007) compared sensitivity to alcohol impairment of inhibitory control in a group of binge drinkers (i.e., those who consume large amounts of alcohol in a relatively short period of time) and nonbinge drinkers. Both groups performed the cued go/no-go task in response to placebo and an active dose of alcohol (0.65  g/kg), administered in a counter-balanced order. Task performance analyses demonstrated that the two groups did not differ in degree of inhibitory control under placebo. However, in response to the alcohol dose, binge drinkers committed significantly more inhibitory failures than did nonbinge drinkers. Additionally, participants rated their degree of subjective stimulation in response to both doses. The groups did not differ in the level of arousal in response to placebo. By contrast, binge drinkers reported significantly greater stimulation in response to alcohol than did nonbinge drinkers. Thus, binge drinkers displayed a heightened sensitivity to both the disinhibiting effects of alcohol and to alcohol-induced arousal. The degree to which sensitivity to alcohol-induced disinhibition predicts alcohol consumption has also been examined. Weafer and Fillmore (2008) measured individual differences in impairment on the cued go/no-go task as the difference in inhibitory failures committed in response to a moderate dose of alcohol (0.65 g/kg) relative to placebo. Each participant then individually attended a follow-up session in which ad lib alcohol consumption was measured. Participants were given 90 min to complete an ostensible beer taste-rating task and were told that they could drink as much or as little of the beer as they liked. At the end of the 90 min, the amount of beer consumed was recorded. Regression analyses were conducted to examine the relationship between impairment of inhibitory control in response to alcohol and amount of beer consumed. The results showed that alcohol-impairment of inhibitory mechanisms significantly predicted consumption. Specifically, those who were more disinhibited in response to alcohol on the cued go/no-go task consumed greater amounts of alcohol when given ad lib access. Additional analyses also examined the degree to which baseline levels of disinhibition (i.e., those observed in response to placebo), trait impulsivity, and alcohol impairment of response activation predicted ad lib consumption. Results showed that none of these factors were significantly associated with consumption. Thus, the amount of beer consumed was predicted specifically by the degree to which an individual’s inhibitory control was impaired by alcohol. The findings from these two studies provide evidence in support of an association between alcohol-induced disinhibition and excessive alcohol consumption. Specifically, these results suggest that inhibitory control is most severely compromised in response to alcohol among those drinkers who frequently engage in heavy, binge drinking behavior. Additionally, binge drinkers report greater alcoholinduced stimulation relative to nonbinge drinkers. This increased the level of

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arousal in response to alcohol, coupled with a decreased ability to inhibit continued consumption, and could serve to increase the “activation-bias” in these individuals. Such a bias could potentially promote excessive drinking behavior, in that the ongoing act of alcohol consumption would be more strongly governed by mechanisms of behavioral activation than by inhibitory mechanisms, resulting in reduced selfcontrol over alcohol consumption. This research also highlights the importance of understanding how acute cognitive changes that occur within a drinking episode might contribute to abuse potential. Past research on the role of cognitive processes in addiction has usually concerned how the individual’s cognitive state can contribute to alcohol abuse by triggering drinking episodes. For example, studies have examined how expectancies, implicit cognitions, and memories concerning alcohol effects can operate as precursors to consumption (Roehrich and Goldman 1995; Stein et  al. 2000; Wiers and Stacy 2006). Similarly, studies that concern the role of disinhibition in alcohol and other drug abuse usually approach the problem from a trait perspective by considering disinhibition as an enduring stable attribute that is part of a personality construct, such as impulsivity (Sher and Trull 1994; Widiger and Smith 1994). By contrast, the studies summarized in this chapter examined changes in cognitive processes that occurred following initial consumption of alcohol. As mentioned above, drinkers often report intentions of having only a couple of drinks, yet they often fail and then go on to drink excessively. So, it is important to understand how cognitive functions might change once drinking has begun, and how such changes might contribute to such unintentional excessive use, such as binge drinking. For instance, Marczinski et al. (2007) found that binge and nonbinge drinkers did not differ in degree of behavioral inhibition or arousal while sober. Instead, the differences between the two groups only became evident after drinking had begun. Likewise, we also found that it was the degree to which alcohol impaired inhibitory control that predicted excessive, ad lib consumption, and not the drinkers’ sober levels of inhibitory control or their trait levels of impulsivity (Weafer and Fillmore 2008). Thus, the degree to which inhibition is initially disrupted by alcohol might play a more important role in binge drinking than the level of inhibitory control that the drinker displays while sober. Another intriguing aspect of these findings is that they run counter to what would be predicted based on basic principles of pharmacological tolerance to alcohol. From a pharmacological perspective, individuals who regularly consume large amounts of alcohol would be expected to be more tolerant to the acute effects of a dose of alcohol (i.e., less impaired) than lighter drinkers. However, the opposite was found in our studies, in that greater alcohol consumption was associated with increased sensitivity to the disinhibiting effects of the drug. Tolerance also does not appear to develop uniformly across mechanisms of behavioral control as might be expected from some general neural adaptation to the drug. Rather, our findings suggest that inhibitory control might in fact become more sensitive to the impairing effects of alcohol with repeated use, resulting in greater levels of disinhibited and potentially hazardous behavior. Although speculative at this point, such changes in alcohol sensitivity over the long-term could play an important role in the transition from social drinking to alcohol dependence.

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Trait Impulsivity and Alcohol Impairment of Inhibitory Control Given the evidence for an association between alcohol-induced disinhibition and binge drinking in healthy drinkers, it is important to consider this relationship in individuals characterized by heightened impulsivity and disinhibition (e.g., ADHD). Such individuals are at increased risk for alcohol abuse, and a potential contributing factor to this increased risk could be a greater sensitivity to the disinhibiting effects of the drug. Previous research has consistently shown that both children and adults with ADHD exhibit impairments on laboratory tasks of inhibitory control (e.g., Alderson et al. 2007; Barkley 1997; Lijffijt et al. 2005; Oosterlaan et al. 1998; Tannock 1998). However, sensitivity to alcohol-induced disinhibition in adults with ADHD has only recently been examined. Weafer et al. (2009) compared alcohol impairment of performance on the cued go/no-go task in adults with ADHD and a group of healthy controls. Both groups performed the task in response to placebo and two active doses of alcohol (0.45 and 0.65 g/kg). Figure 5.2 illustrates the mean inhibitory failures for both groups. As the figure shows, those with ADHD committed significantly more inhibitory failures in all dose conditions. Moreover, inhibitory control was significantly impaired in response to alcohol in the ADHD group, whereas the controls were not impaired under the drug. Similar findings have been reported in high sensation-seekers, who are also characterized

Fig. 5.2  Effects of placebo, 0.45, and 0.65 g/kg alcohol on inhibitory control in a group of adults with ADHD and comparison controls

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by high levels of impulsivity. Similar to those with ADHD, high sensation-seekers also displayed greater impairment of inhibitory control in response to alcohol than did low sensation-seekers on the same cued go/no-go task (Fillmore et al. 2008). Thus, laboratory evidence is emerging to support the hypothesis that impulsive individuals who are at-risk for alcohol abuse might exhibit an increased sensitivity to the disinhibiting effects of the drug. Heightened sensitivity to alcohol-induced disinhibition in highly impulsive individuals could lead to other problems while drinking. For instance, binge drinkers, sensation seekers, and those with ADHD are more likely than nonimpulsive individuals to engage in risky behavior while intoxicated, including unplanned and unprotected sexual activity, aggressive acts, and driving under the influence (Jonah 1997; Wechsler et al. 2000). Particular emphasis has been placed on the role of disinhibition in driving performance, and research has begun to examine associations between alcoholinduced disinhibition and simulated driving performance. Fillmore et al. (2008) found that individuals who were most disinhibited by a dose of alcohol also exhibited the most impaired simulated driving performance under the dose. Additionally, individuals with ADHD have been found to be more sensitive to the impairing effects of alcohol on simulated driving performance than controls (Weafer et  al. 2008), and epidemiological studies find that individuals with ADHD are also more likely to receive traffic violations or be involved in accidents (Barkley et al. 1993).

Future Directions and Considerations for Drug Abuse Relapse Prevention Traditional models of drug abuse emphasize the drug’s rewarding effects as reinforcing drug use to the point of physical dependence and addiction. However, the past several years have seen increased focus on the role of cognitive disturbances as acute reactions to drugs. There is considerable agreement among researchers that impulsivity plays an important role in the risk for developing substance use disorders. This chapter focused on one aspect of impulsivity, deficient inhibitory control, and evidence was presented to show how alcohol-induced inhibitory deficits can operate as a risk factor for abuse possibly by impeding the drinker’s ability to terminate alcohol use during an episode. Evidence for the involvement of impaired inhibitory control in alcohol abuse poses particular challenges for treatment development, as treatment researchers come to recognize that poor impulse control and impaired cognitive functions, in general, can undermine the efficacy of many behaviorally based treatments. Prevention strategies for alcohol and other drug abuse could benefit from a greater consideration of risks associated with impaired inhibitory control. Of particular benefit might be strategies aimed at relapse prevention. Treatment-outcome studies find that as many as 90% of treated alcoholics relapse to drinking within 1 year after treatment (e.g., Miller 1996). Such poor outcomes have prompted efforts to identify factors that trigger relapse and to develop effective cognitive-behavioral therapies (CBTs) to counter these influences.

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CBTs emphasize problem-solving techniques, drink refusal skills, and perhaps most fundamentally, an enhanced self-awareness of the emotional and behavioral states associated with relapse. It is the link between drinking and emotional and behavioral states for which consideration of inhibitory control could be of value to treatment development. For instance, binge drinking is associated with traits, such as negative and positive urgency, that reflect tendencies to act impulsively in response to respective negative and positive emotional states (Cyders and Smith 2008). Intense emotions serve the adaptive function of preparing the organism for action. Emotional states represent sympathetic nervous system activation that is governed by the amygdala and hypothalamic–pituitary–adrenal (HPA) axis whose many functions include the rapid release of glucocorticoids to increase energy for behavioral action. Evidence also suggests that such sympathetic arousal could have a reciprocal effect on other aspects of behavioral control, such as inhibitory mechanisms, to diminish their influence in favor of activational mechanisms (Tice et al. 2001). Impaired inhibitory control might be the “endpoint mechanism” by which certain emotional and behavioral states trigger relapse. If so, this cognitive deficit needs to be better understood in terms of its relation to specific emotions and behavioral states commonly associated with relapse. Evidence that emotional states could result in a temporary reduction of inhibitory control would be important in a reductionist sense because it would provide a link between emotional states and relapse in terms of a fundamental mechanism that underlies the regulation of behavior – inhibitory control. Such evidence would also be of benefit to treatment development. Techniques to increase self-awareness of impaired inhibitory control in the context of intense emotions could enhance the efficacy of CBT approaches, as well as the more recent mindfulness-based approaches used in relapse prevention (Witkiewitz et al. 2005). Impaired inhibitory control could also play an important role in specific relapse phenomena known as abstinence violation and limit violation effects. For many alcoholics, complete abstinence from alcohol represents an all-or-none view of treatment success. Consequently, any alcohol consumption, even a “slip” involving a single drink, represents a complete personal failure for the alcoholic. As such, the alcoholic might experience negative emotions, such as guilt, and binge drink to alleviate these negative feelings. This scenario is referred to as the abstinence violation effect and has been used to account for the common observation that when an abstinent alcoholic relapses, it often results in excessive binge drinking (Marlatt and George 1984). A parallel scenario that is relevant to heavy drinkers who are attempting to reduce or restrain their alcohol use is referred to as the limit violation effect (Collins et al. 1994). Here, the aim is to curtail one’s alcohol consumption to a set limit (e.g., no more than three drinks tonight). Exceeding this limit elicits feelings of failure and negative emotions such that the individual binge drinks to alleviate these negative states. Impaired inhibitory control might be most relevant to limit violation effects. Studies reviewed in this chapter suggest that terminating a drinking episode requires inhibition of ongoing alcohol-administration behaviors and the reallocation of attention away from alcohol-related stimuli. Any impairment of normal inhibitory mechanisms resulting from an initial dose of alcohol could compromise

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the ability to stop additional alcohol administrations in a drinking situation. Thus, even the limited consumption of two or three drinks could be sufficient to impair the drinker’s inhibitory control, such that the ability to refrain from taking the next drink, and violating one’s drink limit, is impaired. Moreover, such limit violations are likely further instigated by subjective rewarding effects of the initial drinks and by environmental factors that encourage continued drinking (e.g., party setting). This impaired inhibition account of the limit violation effect complements and extends the early accounts of the phenomenon that focused on the role of emotion. Whereas emotion-based accounts sought to explain why binge drinking might follow a limit violation (i.e., drinking to alleviate negative feelings of failure), the impaired inhibition account seeks to explain why the limit was violated in the first place. With regard to prevention, the inhibition-based account highlights the need to better understand the effects of alcohol on inhibitory control in problem drinkers and the relevance of inhibitory control to CBT strategies for alcohol abuse, such as the training of drink refusal skills. It is also important that relapse prevention strategies recognize that other behavioral states associated with relapse and binge drinking might also operate via changes in inhibitory control. Fatigue, stress, and periods of sustained mental exertion are all considered high-risk states for unrestrained drinking and relapse (Cummings et  al. 1980). Moreover, such risk might be mediated by impaired inhibitory control. Indeed, there is evidence that self-control involves energy expenditure and therefore can be depleted if sustained over a period of time (Baumeister et al. 1994). For example, studies find that cognitively demanding tasks can diminish one’s self-control as measured by their reduced ability to resist consuming alcohol or food (Muraven et al. 2002). The notion that self-control can be depleted by demanding situations could explain why drinkers often report relapsing in times of stress. However, the basic behavioral mechanisms responsible for this loss of self-control are unknown. It is possible that impaired inhibitory control might underlie the inability to restrain from drinking. That is, one’s inhibitory control might be subject to depletion from sustained periods of cognitively demanding activities. However, as with emotional states, little is known about how behavioral states involving cognitive demand or fatigue might compromise inhibitory control and thereby increase risk for unrestrained drinking or relapse. It is also important to recognize that alcohol is commonly used with other drugs of abuse, such as cocaine and other stimulants. There is considerable interest in understanding the tendency for some individuals to begin to use other drugs in combination with alcohol. This chapter points to the possibility that the acute disinhibiting effects of alcohol itself could play a role in the development of polydrug abuse. It is possible that the acute impairing effects of alcohol on the drinker’s inhibitory control could increase the risk of other concomitant drug use when these drugs are also readily available in the situation. Moreover, stimulant drugs themselves also can produce negative behavioral effects, such as impulsive responding in humans and laboratory animals (e.g., Evenden 1999; Fillmore et al. 2002). As a result, the combination of alcohol with other stimulants could lead to appreciable impairment of inhibitory control among polydrug abusers. Prevention strategies for

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polydrug abusers could benefit from recognition that even low doses of alcohol could reduce the one’s ability to refrain from using other drugs in the situation, and therefore pose considerable risk to the individual. Finally, it is important to consider how emotional and behavioral states might be especially detrimental to inhibitory control in individuals known to have deficient inhibitory control, such as those ADHD. As mentioned earlier, it is well-established that externalizing disorders, such as ADHD and conduct disorder, pose risk for developing substance abuse disorders (e.g., Barkley 2006; Molina et al. 1999) and a hallmark characteristic of externalizing disorders, such as ADHD, is disinhibited or undercontrolled behavior. Our evidence that these individuals are more sensitive to the disinhibiting effects of alcohol suggests that they might also be more vulnerable to the influence of emotional and behavioral states implicated in unrestrained drinking and relapse. The role of inhibitory control as a mediator between these states and unstrained drinking remains to be explored. Acknowledgment  This research was supported by Award Number R01 AA12895, R01 AA018274, and F31 AA018584 from the National Institute on Alcohol Abuse and Alcoholism and by Award Number R21 DA021027 from the National Institute on Drug Abuse.

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Chapter 6

Neuroimaging, Adolescence, and Risky Behavior John C. Churchwell and Deborah A. Yurgelun-Todd

Abstract  Neuroimaging and behavioral studies have proven to be critical in ­establishing normative developmental trajectories for brain and behavior relationships. These studies suggest that delayed maturation of neural systems during ­adolescence may lead to increased risk taking and result in negative consequences, such as substance abuse and addiction. Specifically, discontinuities in the development of prefrontal cortical functioning may foster a neural and behavioral landscape that can increase novelty seeking, lead to increased impulsive actions and choices, and set the stage for substance abuse. This chapter focuses on functional neuroimaging studies of adolescents, examines conceptual challenges related to understanding risky behavior and substance abuse within a neurobiological framework, and considers future directions, such as using neuroimaging to determine biomarkers for risk and resilience through development.

Introduction The implementation of functional magnetic resonance imaging (fMRI) techniques has been essential to advancing brain research. Two areas in which these techniques have been successfully applied include the neurobiological basis of development and addiction. However, fMRI investigations at the crossroads of these two areas are only now beginning to emerge. One critical aspect of both development and addiction is self-control (Ainslie 2001; Killgore et  al. 2001; Steinberg et  al. 2009; Yurgelun-Todd 2007). Loss of self-control can result in impulsive actions and choices, which may be supported by distinct and overlapping neural systems. Impulsive action is thought to depend on neural systems that suppress prepotent or premature responding. Conversely, impulsive choice is thought to engage neural systems that support information processing and response selection in terms of D.A. Yurgelun-Todd (*) Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_6, © Springer Science+Business Media, LLC 2011

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valuation of specific outcomes. Further, emerging evidence from laboratory animal studies suggests that different forms of impulsivity may be associated with different stages of substance abuse or an increased likelihood of drug self-administration (Diergaarde et al. 2008; Perry and Carroll 2008; Poulos et al. 1995). The prefrontal cortex appears to be critically involved in both forms of impulsivity and protracted development of this brain region during adolescence has been importantly implicated in risk-taking. Moreover, adolescence is a time of increasing autonomy involving new experiences and experimentation, and novelty seeking during this transitional period is thought to be a significant factor in establishing substance abuse behaviors. Given that adolescence is a period of high novelty seeking, a preference for ambiguity or the unknown may be associated with drug use (Segal et  al. 1980; Shukla and Kelley 2007). Thus, loss of self-control, through both impulsive actions and choices, in combination with increased novelty seeking, likely synergize in adolescent drug use and initiation (Chambers et al. 2003; de Wit and Richards 2004). To this end, we have provided a simplified model to suggest how these factors may be related and how they may contribute to adolescent ­risk-taking in general and substance abuse in particular (Fig. 6.1). In this chapter, we focus on fMRI studies that provide evidence for neural ­systems involved in impulsive action, choice, and novelty seeking. In doing so, we draw attention to several areas that appear to be important for understanding the ­neural basis of loss of self-control in adolescents. Specifically, while research in the domain of response inhibition has been somewhat articulated, novelty seeking and impulsive choice have received little to no attention in fMRI studies of adolescents. Also, it is not entirely clear whether adolescents show increased or decreased neural sensitivity to reward or loss and whether decreased or increased sensitivity drives novelty seeking (Barratt 1994; Doremus-Fitzwater et  al. 2010; Geier and Luna 2009). Lastly, we discuss how fMRI might be implemented in developing ­intervention strategies related to impulsive action and choice in adolescents.

Fig. 6.1  Possible relationship among factors related to response inhibition and adolescent substance abuse

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Self-Control, Impulsivity, Actions, and Choices Loss of self-control can lead to impulsive actions and choices. Impulsivity itself is a complex construct and has been measured using a variety of behavioral approaches (Bari et al. 2008; Mobini et al. 2002; Monterosso and Ainslie 1999; Rudebeck et al. 2006). Broadly, impulsivity has been defined as unplanned actions without regard for consequences (Moeller et  al. 2001). Impulsivity includes several dimensions, such as acting without thought, rapid cognitive decisions, and decreased future orientation (Barratt 1994). It has been suggested that various tasks may represent different dimensions of impulsivity and these dimensions may be supported by distinct or overlapping neural systems, a relationship that may be modulated by task demands (Chudasama et al. 2003; Dalley et al. 2004; de Wit 2009; Reynolds et al. 2008; Robinson et al. 2009). Impulsive action is often investigated using tasks that measure response inhibition, such as the Go/No-Go, stop-signal, and stroop tasks, whereas choice impulsivity has primarily been investigated using delay discounting (Dalley et al. 2004; de Wit 2009; Luna and Sweeney 2004; Reynolds et al. 2008; Robinson et al. 2009; Winstanley et al. 2004). The application of fMRI methods during the completion of response inhibition and discounting tasks has allowed investigators to clarify the neural systems involved with these functions. Findings from these studies are reviewed and discussed below.

Response Inhibition The concept of response inhibition is multifaceted and has been associated with various capacities, including emotional, cognitive, and behavioral control (Ivanov et al. 2008). Although adolescents are capable of response inhibition, they may be less able than adults to reliably and flexibly execute such responses (Luna et  al. 2009). Decreased response inhibition has been associated with adolescent substance abuse and corresponds to protracted development of the prefrontal cortex (Yurgelun-Todd 2007). A number of behavioral tasks have been used to test the inhibitory performance of adolescents while undergoing functional neuroimaging procedures and these investigations point to altered recruitment of the prefrontal cortex and several other cortical and subcortical structures. Moreover, there is substantial evidence to show a relationship among substance abuse, impaired performance on response inhibition tasks, and altered neural function. In the Go/No-Go task, participants are required to respond as quickly as possible to a regularly occurring signal that indicates a response should be initiated (Go) or inhibited for an infrequently occurring signal (No-Go). An early investigation showed a positive correlation between age and activation of the inferior frontal gyrus and a negative relationship between age and activation of the superior frontal gyrus (Tamm et al. 2002). Rubia et al. (2006) specifically contrasted neural activation in adults and adolescents performing a Go/No-Go task and showed that adolescents have less activity than adults in the inferior prefrontal cortex, orbitofrontal

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cortex, and caudate. However, adolescents compared to adults, show decreased functional connectivity within a fronto-striatal-thalamic network during a Go/ No-Go task, suggesting decreased functional integration and implicating a greater dependence on frontal structures (Stevens et  al. 2007). Consistent with this idea, Braet et  al. (2009) compared adolescents and adults on a Go/No-Go task and showed increased activation of the superior, inferior, and middle frontal gyrus, cingulate, insula, and parietal cortex during successful inhibition in adolescents. Similar to the Go/No-Go task, the stop-signal task requires inhibition of a prepotent response. However, in this task participants are required to interrupt an already initiated response based on a signal, such as an auditory or visual cue. Activation of the inferior frontal gyrus is thought to be critical for inhibition in the stop-signal task (Verbruggen and Logan 2008). Consistent with this observation, Rubia et  al. (2007) examined neural activity of adolescents compared to adults performing a stop-signal task and showed that adolescents exhibit decreased activation of the inferior prefrontal cortex during successful trials and less activity of the subgenual anterior cingulate cortex during failed inhibition. The cingulate cortex has been specifically implicated in the stop-signal task in relation to error detection of failed inhibition (Chevrier et al. 2007). Interestingly, increased harm avoidance, as measured by the temperament and character inventory (Cloninger 1994), has been found to be associated with increased anterior cingulate activity in adolescents performing a stop-signal task (Yang et al. 2009). This study suggests a relationship among response inhibition, sensitivity to negative outcomes in adolescence, and activity within the subgenual anterior cingulate cortex. In the stroop task, participants must inhibit an automated response to read the name of a color as opposed to naming the actual color the word is written in (e.g., RED written in black). Age-related increases in performance and magnitude of activity in the fronto-striatal system have been observed for this task (Marsh et al. 2006). Moreover, Adleman et al. (2002) scanned adolescents and adults while performing the stroop task and found that adolescents show less activation of the middle frontal gyrus compared to adults. Further, a study using diffusion tensor imaging (DTI), a neuroimaging method for determining white matter integrity, showed that decreased white matter integrity in adolescents is associated with performance on the stroop task in female adolescents and is also associated with impulsivity in both males and females (Silveri et al. 2006). The anti-saccade task involves inhibiting a prepotent response and has also been used with fMRI to examine the neural basis of age-related changes during response inhibition. In this task, subjects must inhibit a naturally occurring eye movement response to a visually cued location by averting their gaze to the location opposite of the cue. Compared to adults tested in this task, adolescents show increased activation of dorsolateral prefrontal cortex and decreased activation of the intraparietal sulcus, thalamus, cerebellum, and superior colliculus (Luna et al. 2001). This paradigm has also been used to examine neurobehavioral disinhibition using fMRI. Neurobehavioral disinhibition is a trait indexed by a composite measure of behavioral, cognitive, and emotional functioning that predicts substance abuse during early adulthood (Tarter et  al. 2003). Scores on this trait have been shown to be

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n­ egatively correlated with the activation of prefrontal regions in adolescents ­performing the anti-saccade task, suggesting a possible relationship among frontal function, neurobehavioral disinhibition, response inhibition, and risk for substance abuse disorder in adolescents (McNamee et al. 2008). Whether adolescents show increased or decreased activity across frontal systems for tasks that require response inhibition is still open. A number of factors may contribute to reported differences in activation observed in this region, including the type of task used, task parameters, task difficulty, and method of analysis. Decreased activation may be related to inferior cognitive and behavioral control, whereas increased activation may be due to increased effort required to perform these tasks (Luna et al. 2009; Marsh et al. 2006). A number of studies also indicate that substance abuse is related to impaired performance on response inhibition tasks. For example, alcohol, cocaine, and heroin use disrupts Go/No-Go performance (Lawrence et  al. 2009; Verdejo-Garcia et al. 2007), while cocaine and methamphetamine use disrupts stop-signal performance (Fillmore and Rush 2002; Monterosso et al. 2005). Moreover, cannabis use is associated with increased errors of commission on the stroop task and performance on the stroop predicts treatment compliance in cocaine abusers (Gruber and Yurgelun-Todd 2005; Streeter et al. 2008). fMRI studies using inhibition tasks have provided further insight into the relationship among behavioral disinhibition, substance abuse and neurocognitive function. For example, alcohol abuse has been associated with altered patterns of prefrontal activation in the Go/No-Go task (Li et al. 2009). Moreover, in adolescents, Anderson et al. (2005) showed that increased activation of the insula and inferior parietal lobule during inhibitory trials of the Go/No-Go task is associated with increased negative expectancies and decreased positive expectancies for alcohol use. Decreased activation of the anterior cingulate and insula have also been associated with error awareness in cannabis users performing a Go/No-Go task (Hester et al. 2009) and abstinent adolescent cannabis users show increased activation of dorsolateral, medial prefrontal, inferior and superior parietal lobules during inhibition trials of a Go/No-Go task (Tapert et  al. 2007). Additionally, adult cannabis users show less activation of the anterior cingulate, more diffuse activation of dorsolateral prefrontal cortex, and greater activation of the mid-cingulate compared to controls in the stroop task (Gruber and Yurgelun-Todd 2005). Decreased activation of cingulate, insula, and motor areas has also been observed in chronic cocaine users performing the stroop task (Kaufman et al. 2003). Preliminary data from our group also indicates that adolescent cannabis users show altered activation of frontal regions when performing a visual odd-ball task (Fig. 6.2). The visual odd-ball paradigm requires identification and response to a pseudorandomly occurring visual target among distracters. Thus, participants must maintain attentional vigilance and suppress responding until the signal that indicates a response should be made is presented. Specifically, when compared to healthy controls, adolescent cannabis users show less activation of the superior frontal gyrus during the viewing of the target stimulus. This finding suggests that substance abusing adolescents produce less activation in a region of frontal cortex thought to play an important role in response inhibition (Tamm et al. 2002).

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Fig. 6.2  fMRI data showing areas of reduced activation in adolescent cannabis smokers relative to nonsmokers during target response trials of the visual oddball task. The brain region encompasses superior frontal gyrus and posterior cingulate gyrus. (p < 0.0001, Max T = 4.23; x = 52, y = −2, z = 48)

Taken together, fMRI and behavioral studies of adolescents and substance abusers suggest that each of these groups has a disrupted capacity to engage in cognitive and behavioral response inhibition and that alterations in this capacity are ­associated with the integrity of prefrontal cortex. These studies thus provide critical support for the notion that the prefrontal cortex is essential for self-control during adolescence and that alterations in this region are similarly related to substance abuse. However, it is unclear whether decreased response inhibition and altered neural function result in substance abuse or occur because of substance abuse (Chambers et al. 2009). Although the capacity to withhold and initiate responses appropriately is essential to understanding impulsivity and self-control, how adolescents value choice options is also crucial.

Decisions, Reward, and Loss A separate body of research has examined the issue of how neural systems support decision-making during adolescence (e.g., see Table 6.1). Specifically, decision ­processes are usually separated into several stages which include the

× ×

×

Stroop Adleman et al. 2002 Marsh et al. 2006

Anti-saccade Luna et al. 2001

Slot machine task Van Leijenhorst et al. 2009

Delayed response two-choice task Galvan et al. 2006 Galvan et al. 2007

Two choice decision task Ernst et al. 2005 Eshel et al. 2007

×

Stop signal task Rubia et al. 2006

×

×

×

×

×

×

× ×

×

×

×

×

Decision making Anticipation/Outcome (continued)

Decision making Outcome Anticipation

Decision making Outcome Probability

Response inhibition

Response inhibition

Response inhibition

Table 6.1  Shows select neural regions that are associated with response inhibition and decision-making in a sample of functional neuroimaging studies of adolescents Prefrontal Orbitofrontal Cingulate Task cortex cortex cortex Striatum Amygdala Insula Functional role Go/No-go task Response inhibition Tamm et al. 2002 × Rubia et al. 2006 × × × Braet et al. 2009 × × ×

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×

Risk taking task Bjork et al. 2007

Orbitofrontal cortex

Cingulate cortex

×

Striatum

×

Amygdala

Insula

Decision making Anticipation/outcome

Decision making Risk taking

Functional role

Guessing game Decision making Outcome May et al. 2004 × × Prefrontal cortex includes the following regions: mesial prefrontal cortex, inferior prefrontal cortex, dorsolateral prefrontal cortex as well as the inferior, superior, and middle frontal gyrus

Monetary incentive delay task Bjork et al. 2004a, b

Prefrontal cortex

Task

Table 6.1  (continued)

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formation of a preference, response selection, and the experience of an outcome (Paulus 2007). Moreover, in the time prior to an outcome or between response selection and outcome, anticipation occurs (Knutson and Greer 2008; Schoenbaum et al. 2003). In this regard, much effort has been focused on how differences in neural activation in adolescents and adults contributes to different components of decision processes, a challenge that fMRI studies are well-equipped to handle when paired with sophisticated behavioral testing. A number of investigations examining reward related activity during decisionmaking using fMRI in healthy non-substance using adolescents have been carried out. While the following discussion is not exhaustive of fMRI studies of adolescent decision-making, the referenced studies highlight the manner in which fMRI has been used to evaluate different aspects of decision processes. The tasks used in these experiments have typically involved probabilistic or cued reward. For example, Eshel et  al. (2007) used fMRI to examine reward processing in adolescents and adults using a two-choice decision task. In this probabilistic monetary choice paradigm, adolescents were found to be modestly different from adults in selecting riskier (probabilistic) choices and showed decreased activity of the anterior cingulate and orbitofrontal cortex. However, no significant differences were observed between adolescents and adults for activation in the dorsolateral prefrontal cortex, striatum, or amygdala. In a separate study, Ernst (2005) compared adults and adolescents using the same task under conditions in which probabilities were held constant, but magnitude of outcome was manipulated and found decreased activation of the amygdala during reward omission and increased activation of the nucleus accumbens during reward receipt. In contrast, Bjork et al. (2004b) used a cued monetary incentive task to show that anticipation of reward was associated with decreased activity in the striatum and amygdala in adolescents compared to adults and that there were no differences in striatal activation for gain outcomes between the groups. In a separate cued monetary task, decreased risky behavior and the expectation of negative consequences as a result of risky behavior was associated with diminished nucleus accumbens activity during reward anticipation in adolescents (Galvan et al. 2007). Increased insula activity in adolescents also has been observed during uncertain outcomes of a passive slot machine reward task (Van Leijenhorst et al. 2009). Bjork et al. (2007) have also shown that, compared to adults, adolescents fail to activate mesofrontal cortex in a cued risk task. These intriguing investigations support the idea that altered prefrontal, striatal, amygdala, and insula activity is associated with probabilistic reward choice, cues that predict reward, reward anticipation, and reward/loss outcome in adolescents. Despite these valuable findings, the question of whether adolescents show increased or decreased neural sensitivity to rewards and losses is still unclear. Importantly, reward sensitivity has been related to novelty seeking (Bornovalova et  al. 2009) and novelty seeking has been critically linked to substance abuse (Zuckerman 1994).

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Sensitivity to Reward and Loss One of several critical questions for understanding adolescent impulsive action and choice is adolescents’ sensitivity to consequences or outcomes (Doremus-Fitzwater et  al. 2009; Steinberg 2004). Ideas about motivation during this developmental period have largely focused on whether adolescents show an increased or decreased sensitivity to reward or loss (Chambers et al. 2003; Spear 2000). More recently, the notion of adolescent sensitivity to reward and loss has taken on a contextual tenor (Doremus-Fitzwater et al. 2010; Ernst et al. 2008). For example, it has been suggested that adolescents show an increased response to positive or negative stimuli when presented separately. However, when both negative and positive stimuli are set against each other, as in probabilistic decision-making, the probabilistically riskier choices win out (Ernst et al. 2008). On the other hand, it has been suggested that adolescents may show a decreased response to negative stimuli and increased or decreased response to positive cues under different conditions (DoremusFitzwater et  al. 2010). Either increased or decreased sensitivity to reward may contribute to the risk of initiating substance use. Decreased reward sensitivity or a need for increased stimulation may lead to increased reward seeking and consumption of abused substances (Knutson and Greer 2008; Spear 2000; Steinberg 2004), whereas increased reward sensitivity may augment the reinforcing effects of abused substances and contribute to compulsive drug seeking (Dawe et al. 2004; Galvan et al. 2006). Behavioral and imaging evidence examining this issue is beginning to emerge. For example, Bornovalova et  al. (2009) showed that insensitivity to a change in reward magnitude is associated with increased sensation seeking/ impulsivity as indexed by a composite score on the impulsivity subscale of the Eysenck’s impulsiveness and Zuckerman’s sensation seeking scale. Further, Joseph et  al. (2009) used fMRI to demonstrate that, compared to low sensation seekers, participants high in sensation seeking show more activation in the insula and posterior medial orbitofrontal cortex and less/later activation of the anterior cingulate and anterior medial orbitofrontal regions to arousing stimuli. This finding is important because increased activation in both the insula and posterior medial orbitofrontal cortex may be related to arousal, whereas decreased activation in the anterior cingulate and anterior orbitofrontal cortex are likely related to modulation of emotional responses, suggesting increased responsiveness and decreased control in high sensation seekers. Understanding how adolescents process outcomes may also give some insight into whether this transitional period is associated with more habitual as opposed to goal-directed responding. For example, it has been argued that the hallmark of goal-directed behavior is sensitivity to both a change in outcome value and the instrumental contingency between actions and outcomes (Balleine and Dickinson 1998; de Wit and Dickinson 2009). fMRI studies of adolescents have specifically targeted responsiveness to reward anticipation or reward receipt (Bjork et al. 2004b; Geier and Luna 2009; Knutson and Greer 2008). However, investigations comparing adults and adolescents have been equivocal in regard to this issue. With respect to reward outcome, fMRI

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s­ tudies comparing adults and adolescents have demonstrated that adolescents show decreased activation of the amygdala and insula during reward omissions and increased response of the nucleus accumbens to reward receipt (Ernst et al. 2005; Van Leijenhorst et al. 2009), increased or exaggerated striatal response to reward outcomes (May et al. 2004; Van Leijenhorst et al. 2009), increased nucleus accumbens activation to reward with increasing reward magnitude (Galvan et al. 2006), and no differences in striatal activation between adults and adolescents for magnitude of rewards (Bjork et al. 2004b). Reward anticipation, on the other hand, has been shown to elicit less activation of the striatum and amygdala in adolescents compared to adults (Bjork et al. 2004b) and increased insula activation in adolescents in anticipation of uncertain outcomes (Van Leijenhorst et al. 2009). These studies largely support the position that adolescents show alterations in functional activation of neural systems involved in anticipation and receipt of reward and loss. How changes in neural systems that support anticipation and outcome processing contribute to risky behavior is still poorly understood. Nonetheless, it can be expected that fMRI studies focused on this area contribute to increasing resolution on the subject. A corresponding area of interest that has received little attention in fMRI studies of adolescent decision-making is impulsive choice. Most imaging studies to date have centered on neural activity in adolescents during reward and loss anticipation and outcomes over the short-run. However, delay discounting procedures aim to understand choice impulsivity based on how value decisions are made over longer time horizons.

Impulsive Choice and Delay Discounting of Rewards Individuals typically prefer immediate and certain rewards, thus added delays or increased uncertainty of outcomes diminishes the values associated with those options (Olson et  al. 2007; Tversky and Kahneman 1981). Intertemporal choice describes the evaluative process related to selection of immediate or delayed rewards and losses. For example, delay discounting typically involves the devaluation of delayed future rewards in favor of smaller immediate rewards and is thought to be one model of choice impulsivity (Cardinal 2006; Evenden 1999). Adolescents discount future rewards more than adults and this has been associated with decreased future orientation (Steinberg et al. 2009). To date, there have been no investigations using functional neuroimaging to determine the neural substrates of delay discounting of reward in healthy or substance abusing adolescents, although fMRI research in adults indicates an important role for frontal, cingulate, striatal, and parietal systems (Ballard and Knutson 2009; Hariri et  al. 2006; Kable and Glimcher 2007; McClure et al. 2004). Further, when comparing probabilistic choice to delay discounting of rewards, unique activity has been observed in cingulate cortex and the striatum for choice of delayed options, suggesting distinctions in structures necessary for probabilistic and delayed reward choices (Weber and Huettel 2008). Behavioral studies have also shown that maturation is a factor in delay discounting,

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but not probabilistic discounting and that there is an age-related relationship between delay discounting and white matter integrity (Olson et al. 2007, 2009). In general, substance abuse has been associated with increased discounting of delayed future rewards (Bjork et  al. 2004a; Mitchell 1999; Monterosso et  al. 2007). Additionally, age of first drug use has been associated with increased delay discounting in college students (Kollins 2003). Therefore, it is likely that both maturational level and drug use impact delay discounting. However, whether decreased valuation of future rewards results from drug abuse or whether discounting of rewards is a risk for drug abuse remains unknown (de Wit 2009). A recent longitudinal investigation suggests that adolescents who recognize the long-term consequences of drug abuse, in the face of certain and immediate pleasure from utilizing drugs, are more likely to abstain from risky behavior as indexed by sexual activity, alcohol, and tobacco use (Goldberg et al. 2009). This finding indicates that adolescents engaged in risky behavior may have a decreased appreciation regarding longterm negative consequences of present judgments about rewards. These investigations suggest that the manner in which adolescents process future outcomes is related to substance abuse and that the prefrontal cortex is a region of critical interest for understanding how future orientation relates to delayed reward choice. Another significant factor for impulsive choice in adolescents likely involves the way in which present versus future losses are discounted.

Impulsive Choice and Delay Discounting of Loss Xu et al. (2009) applied fMRI methods in adults to examine delay discounting procedures that included choices between small immediate and larger delayed reward as well as immediate small losses and larger future losses. They showed that distinct neural systems underlie discounting of reward and loss. Though no behavioral or imaging investigations have examined adolescent performance during intertemporal choice for losses, behavioral studies in adults suggest that immediate losses are preferred over delayed losses (Xu et al. 2009; Yates and Watts 1975). Evidence for a relationship between the prefrontal cortex and amygdala in response to loss comes primarily from fMRI studies of emotional regulation in adults. Several studies have shown increased activity of the lateral prefrontal cortex, orbitofrontal cortex, and anterior cingulate and decreased activity of the amygdala associated with cognitive regulation strategies during the presentation of negative visual stimuli (Banks et al. 2007; Phan et  al. 2005; Wager et  al. 2008). Further, upregulation of affective response to negative visual stimuli in adults recruits the amygdala and downregulation recruits orbitofrontal cortex (Eippert et  al. 2007). Adolescents may have a decreased capacity to modulate emotions in response to negative stimuli, as suggested by a positive correlation between age and prefrontal activation in adolescents exposed to fearful faces (Yurgelun-Todd and Killgore 2006). Animal studies have shown that connectivity between the amygdala and prefrontal cortex increases from postweaning to early adulthood, supporting a developmental component in ­prefrontal

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modulation of the amygdala (Cunningham et  al. 2002). Consistent with this idea, Hare et al. (2008) showed that adolescents have increased amygdala response during an emotional Go/No-Go task and that a failure of amygdala activity to habituate was associated with increased trait anxiety and decreased functional connectivity between the prefrontal cortex and amygdala. A decreased capacity to modulate emotional responses to negative stimuli in parallel with decreased future orientation may result in avoidance of immediate small losses and preference for delayed larger future losses in adolescents. The prefrontal cortex and amygdala are likely critical to processing such information. A failure to recognize greater long-term negative consequences of substance abuse may be indicative of suboptimal decision-making in adolescents engaging in risky behavior (Bolla et al. 2005; Goldberg et al. 2009). Thus, insight into the neural basis of loss discounting during adolescence may prove to be crucially valuable for understanding risky behavior and the initiation of substance use during this period. Although it still remains to be seen whether impulsive action and choice are completely independent, one approach may be to use tasks that measure either or both of these dimensions and determine whether they are behaviorally and neurally dissociable and whether a unique neural system emerges when both dimensions are present.

Overlap Between Impulsive Action and Choice Delayed gratification often involves both sustained choice and response inhibition and may be a model behavioral paradigm for using fMRI to examine an overlap between these two dimensions (Mischel et al. 1989). In the classic delayed gratification procedure, children are confronted with a choice between a small immediate or larger delayed reward. However, at any time during the delay they can select the smaller immediate reward, in which case they do not receive the larger reward. There have been no fMRI investigations in adolescents examining delayed gratification. However, an interesting investigation by Reynolds et al. (2002) examined the performance of rats in a delay discounting or delayed gratification procedure. In this study, rats were required to select either a small immediate reward or a larger delayed reward. In the delay discounting version, once subjects committed to a choice, changing their response did not elicit rewards. In the delayed gratification adaptation, subjects could change from the delayed to immediate choice during the delay period. It was demonstrated that rats show similar discounting functions using either procedure. Yet, the group tested using the delayed gratification procedure switched responses or “defected” from a response less than the delay discounting group. Based on these findings, they suggested that sustained choice required for delayed gratification might require increased behavioral inhibition and mirror real-life decision-making. Further, a longitudinal study showed that preschoolers performing a delayed gratification procedure who had the ability to direct attention away from a more desired reward had faster reaction times on a Go/No-Go task when tested during adolescence. However, the amount of time spent waiting during

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the delayed gratification procedure was unrelated to performance on the Go/No-Go task (Eigsti et al. 2006). This study suggests that delayed gratification procedures may provide insight into both impulsive action and sustained choice. Decomposing terms and neural systems that define impulsive choice and action are essential to understand risky behavior. However, understanding how constructs overlap and what neural systems integrate these functional architectures are also critical. Although future orientation and response inhibition contribute to cognitive and motor elements of impulsivity, altered motivational states during adolescence interact powerfully with these processes. One way in which altered motivational states might be apparent during adolescence is increased novelty preference.

Novelty Preference Optimal decision-making requires a balance between exploration of possible options and exploitation of known options (Cohen et al. 2007; Daw et al. 2006). Exploration often involves uncertainty marked by probabilities of known outcomes and those with unknown outcomes (Camerer and Weber 1992; Huettel et al. 2006; Platt and Huettel 2008; Rushworth and Behrens 2008; Schultz et al. 2008). Novelty preference defines sensation seeking, which is a predisposition to search for ­“varied, novel, and complex sensations and experiences” (Zuckerman 1994). Exploration of ambiguous options (uncertainty marked by the unknown) in the form of novelty preference may provide the opportunity to obtain valuable outcomes (Crews et  al. 2007; Wittmann et  al. 2008). Further, novelty preference in adolescents may serve a significant evolutionary function by enhancing reproductive opportunities (Steinberg 2008). However, there is also evidence from research in nonhuman animals that indicates increased novelty preference is associated with enhanced self-administration of drugs (Cain et al. 2005). It has been suggested that increased novelty seeking during adolescence is mediated by increased reward sensitivity resulting from changes in reward related activity in the prefrontal cortex and striatum in tandem with delayed maturation of frontal cognitive function that results in decreased future orientation (Steinberg 2008; Steinberg et  al. 2009). Delayed maturation of frontal systems in adolescence is thought to contribute to disruptions in affective modulation, impaired decision-making, poor response inhibition, and shortsightedness (Ernst et  al. 2008; Fareri et  al. 2008; Galvan et  al. 2006; Luna et al. 2009; Yurgelun-Todd 2007). Alterations in a circuit composed of the prefrontal cortex, amygdala, and striatum has been proposed to underlie the execution of suboptimal decisions, increased approach behavior, and decreased avoidance behavior in adolescence (Ernst and Fudge 2009; Ernst et al. 2006). It has been shown that amygdala damage results in making more suboptimal choices in the Iowa Gambling Task, a presumed index of choice under ambiguity (Bechara et al. 1999; Brand et al. 2007; Brand et al. 2006; Hsu et al. 2005). Further, it has been shown that ambiguity preference is related to activity in the lateral frontal cortex (Huettel et al. 2006) and orbitofrontal cortex lesions produce indifference to

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­ambiguity, whereas healthy controls are averse to ambiguity (Hsu et  al. 2005). Increased insula activity in adolescents has been observed during uncertain outcomes of a passive reward task and anticipation of negative stimuli also activates the insula (Herwig et al. 2007; Nitschke et al. 2006; Van Leijenhorst et al. 2009). Moreover, increased activation of the anterior cingulate has been associated with intolerance of uncertainty in adolescents (Krain et al. 2006). Increased sensation seeking may correspond to both increased approach behavior and decreased inhibition (Steinberg 2004; Zuckerman 1994) and it would be expected that conditions involving ambiguity would activate approach systems, such as the striatum and attenuate inhibition systems such as the amygdala and insula, as has been proposed for adolescents (Ernst et  al. 2006; Gullo and Dawe 2008). Collectively, these studies suggest that prefrontal modulation of neural systems involved in reward and loss likely contribute to novelty seeking and impulsive choice and action in adolescents (Dawe et  al. 2004; Luna et  al. 2009; Luna and Sweeney 2004; Rubia et al. 2007; Verbruggen and Logan 2008). However, generally speaking, there is little evidence from fMRI studies of adolescents demonstrating the possible neural substrates of novelty preference. Thus, this remains an area of critical importance that is largely uninvestigated.

fMRI and Intervention Strategies Adolescence is a developmental period marked by decreased self-control. Impulsive actions, choices, and sensation seeking during this time can lead to increased risky behavior and vulnerability to substance abuse and rapid drug use escalation (Behrendt et al. 2009; Chambers et al. 2003; Silveri et al. 2004). Specifically, it has been suggested that neural systems harmed by substance abuse are also critical for optimal decision-making and impulse control (Dawe et al. 2004; Schepis et al. 2008). Thus, substance abuse in adolescence may create a negative feedback loop that disrupts normal functioning of neural systems necessary for self-control and thereby increase impulsive actions and choices (Cardinal 2006; Dawe et al. 2004; Jentsch and Taylor 1999), which may contribute to subsequent and persistent drug use. fMRI studies investigating the neural basis of self-control and impulsivity in adolescence are therefore critical for understanding risky behavior that can lead to substance abuse and the impact of substance abuse on neural systems in the still developing brain. Because adolescence is a period of developmental vulnerability, it provides a unique window of opportunity for implementing intervention strategies, particularly strategies aimed at self-control. In addition to other convergent methods, evidence from fMRI investigations of neural systems underlying impulsive action, choice, and novelty preference in adolescents provide important insights that can support the development of prevention and intervention strategies (Bardo et al. 1996; Gullo and Dawe 2008; Lopez et  al. 2008). Specifically, understanding the neural basis of impulsive action and choice may provide the opportunity to determine whether particular adolescents are especially vulnerable to substance use initiation (Fig.  6.3).

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Fig. 6.3  Bidirectional influences among neuroimaging, substance abuse in adolescents and intervention approaches

For example, neural biomarkers corresponding to different forms of impulsivity may index the best intervention strategy for an individual, and differentiation of subgroups may also help to predict the best course of action in terms of intervention (Mayberg 2003). It has been suggested that, given a standard neural model of response inhibition, longitudinal fMRI studies showing dysfunctions in this network might demonstrate whether particular adolescents are at-risk to initiate substance use (Ivanov et al. 2008). Further, intervention strategies aimed at motivating adolescents to focus on the magnitude of seemingly abstract rewards with longer time horizons might amplify those rewards in the short term and enhance the functional recruitment of mesolimbic reward systems in response to such choice conditions (Dawe et al. 2004). Moreover, intervention strategies designed to emphasize the long-term consequences of substance abuse may be beneficial through activation of limbic and cortical circuits associated with increased avoidance. Prevention programs aimed at enhancing social, cognitive, emotional, and behavioral skills are critical to normative development and success. These programs may have a salient influence on neural systems that support higher order executive functioning and in turn contribute to improvements in self-control. In particular, cortical systems, especially the prefrontal cortex, undergo significant changes during early development and into adolescence and young adulthood (Lenroot and Giedd 2006). Gray matter pruning during adolescence is thought to be importantly related to synaptic plasticity and learning during this period. Further, white matter changes that support brain-wide functional integration generally shows a linear increase over early development. Thus, early intervention may enhance neural development during this time and introduce changes that have a sustained influence and create resilience in the adolescent brain and ultimately behavior. Imaging techniques can be used to assess how neural systems are impacted by prevention programs and act instrumentally to understand the neural mechanisms that support enhanced self-control resulting from these programs, which may lead to further refinements in implementing successful prevention programs and to heading off initiation of substance abuse.

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Part III

Translating Research on Inhibitory Control to At-Risk Populations

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Chapter 7

Inhibitory Control Deficits in Childhood: Definition, Measurement, and Clinical Risk for Substance Use Disorders Iliyan Ivanov, Jeffrey Newcorn, Kelly Morton, and Michelle Tricamo

Abstract  This chapter examines how the behavioral trait of impulsivity, when present in childhood as feature of disruptive behavior disorders such as attention deficit/ hyperactivity disorder (ADHD) and conduct disorder (CD), may contribute to the development of substance abuse disorders in adolescence. We discuss the relationship between the constructs of impulsivity and inhibitory control deficits and review their clinical manifestations in youth with ADHD and CD. We also review evidence suggesting that high levels of impulsivity in children can be viewed as a predictor of later substance abuse, and provide information about the possible biological underpinnings of such risk factors. The hypotheses that biological factors could potentially mediate the transition from drug experimentation to drug abuse and dependence is important for the development of prevention and treatment strategies to combat different types of addiction. Clinical considerations related to biological and psychological therapeutic interventions for impulsive types of behaviors in childhood and adolescence are also discussed.

Introduction Adolescence is a developmental period during which a great number of young individuals engage in risky behaviors including experimentation with substances of abuse. However, only a relatively small percentage of those who engage in this behavior eventually develop a substance use disorder (SUD). A variety of childhood psychological conditions, including disruptive behavior disorders as well as mood, anxiety and trauma related disorders, have been associated with adolescent substance abuse and are thought to mediate the transition from drug experimentation to abuse. Since patterns of impulsive behaviors appear ubiquitously present in the clinical picture of both childhood behavior disorders and adolescent substance

I. Ivanov (*) Mount Sinai School of Medicine, New York, NY 10029, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_7, © Springer Science+Business Media, LLC 2011

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abuse, high levels of impulsivity may represent an important indicator of neurobiological risk for early onset substance abuse. Therefore, a more in-depth understanding of the neurobiological factors that confer risk for the development of addiction may substantially advance the development of new prevention and treatment strategies to combat SUDs. This chapter examines the relationship between inhibitory control deficits, one component of the larger behavioral phenomenon of impulsivity, and risk for development of adolescent substance abuse disorders.

Impulsivity and Inhibitory Control Definitions of Impulsivity Before examining the possible relationships between impulsive behaviors and the development of early onset substance abuse disorders it is important to clarify that impulsivity is an overarching construct that may include several more discretely defined behavioral components. In general, impulsivity is a behavioral trait that reflects a person’s interaction with the environment in general and more particularly an individual’s tendencies for responding to environmental stimuli. It has been broadly characterized as reflecting poorly conceived and/or prematurely expressed actions that are either inappropriate for a particular situation or carry a disproportionate amount of risk for harm, and which often entail negative or undesirable consequences (Winstanley et  al. 2006). However, this definition does not fully reflect the heterogeneous nature of impulsive behaviors, since impulsivity is present across a variety of cognitive, behavioral, and functional domains. It also does not account for the broad range of psychiatric conditions for which impulsivity is a prominent feature (e.g., mania; personality disorders; substance abuse disorders; and Attention Deficit/Hyperactivity Disorder, ADHD) nor does it indicate that impulsivity plays an important role in normal behavior (Evenden 1999). Review of the psychological literature indicates agreement that impulsivity is a composite construct, consisting of several independent factors. However, there is little unanimity as to what these factors are and how to best describe them. Eysenck and colleagues have identified four components termed narrow impulsiveness, risktaking, nonplanning, and liveliness within a two-factor (e.g., impulsiveness and venturesomeness) impulsivity construct (Eysenck and Eysenck 1977). The authors emphasized that although these components may be routinely thought of by lay persons as reflecting “impulsivity,” they are relatively independent and represent largely different behaviors. They further draw a distinction between impulsivity as part of the more normative, adaptive behaviors from the less adaptive, and potentially harmful behaviors that they called “pure” or “narrow” impulsiveness. Barratt (1994) has proposed a three-factor construct that is purportedly more closely linked to the concept of impulsivity. These factors include motor activation, defined as acting on the spur of the moment, attention or the inability to focus on the task at hand, and lack of planning, or lack of careful preparation and thinking

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ahead. Alternative views propose that impulsive behaviors could be considered functional in circumstances when they are accompanied by high levels of enthusiasm and motivation, and could entail some positive outcomes – in contrast to dysfunctional impulsivity, which is characterized by disorderliness and ignoring facts, which are likely to result in negative consequences (Dickman 1990). An example of a definition that manages to strike a balance between focusing on narrow constructs related to the behavior and becoming overinclusive is provided in a review paper by Moeller et al. (2001) under the title “biopsychosocial definition of impulsivity.” This definition describes impulsivity as a biological predisposition, which is identified as a persistent pattern of impulsive behavior rather than a single impulsive act. This characterization suggests that biologically distinct mechanisms may underlie impulsive versus planned maladaptive actions (e.g., aggression) and therefore such behaviors may respond to different biological treatments. Second, the authors consider specific features such as the rapidity and the lack of planning of the impulsive response. This suggests that impulsive behaviors occur before there is an opportunity to consciously process and reflect on possible consequences of one’s actions. This separates impulsivity from impaired judgment or compulsive behaviors, in which planning occurs beforehand. Finally, the authors suggest that acting without regard to the consequences of one’s actions implies that impulsivity often involves risks that may cause harm to both the individual and others, and are therefore distinct from the types of risk associated with sensation seeking. In summary, Moeller’s model defines impulsivity as a “predisposition toward rapid, unplanned reactions to internal and external stimuli without regard for the negative consequences of these reactions to the impulsive individual or to others” (Moeller et al. 2001, p. 1784).

Disordered Inhibitory Control as a Core Feature of Impulsivity The relevance of the above definition to the subject of this chapter is the notion that although impulsivity is not a unitary construct it could be distilled to more discrete components that could, in turn, be objectively measured. It is believed that inhibitory control may be a more narrowly defined construct that lies at the core of impulsivity (Buss 1975) The term “inhibitory control” denotes the ability to withhold inappropriate or premature response(s) (both motor and cognitive), and therefore deficits in “inhibitory control” will be presumably linked to high levels of impulsive behaviors. For the remainder of this chapter we will examine both the wider domain of impulsivity and the more narrowly defined and measurable construct of inhibitory control and their relation to childhood disruptive behavior problems and later substance abuse. We propose that underlying deficits in inhibitory control may be central to mediating the transition from childhood disorders specifically characterized by highly impulsive behaviors, such as ADHD and Conduct Disorder (CD), to the adolescent onset SUD. In these lines, inhibitory control deficits in childhood could be a marker for increased risk for subsequent addiction.

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Measurement of Impulsivity Self-Report Measures of Impulsivity Impulsive behaviors are often assessed by self-report. Questionnaires that systematically inquire about impulsive behaviors include Barratt’s (1959) Impulsiveness Scale, initially developed in 1959 (BIS-1), with subsequent modifications the latest of which came in 1995 (BIS-11) (Patton et al. 1995) and the Eysenck Impulsiveness Scale (Eysenck and Eysenck 1977, see Table 7.1). These scales have been widely used for the assessment of impulsivity in both healthy and patient population samples, for clinical and research practices. In addition to the Barratt and Eysenck’s scales, several other self-report instruments that assess personality traits and temperament also contain subscales with items that inquire about impulsive decisions, choices and behaviors. For instance, Cloninger’s “Temperament and Character Inventory” uses seven dimensions of personality traits with four temperament scales among which Novelty Seeking contains impulsivity items (Cloninger et al. 1994). Similarly, Zuckerman’s Sensation Seeking Scale contains items that assess impulsive sensation seeking (Zuckerman et al. 1964). Although not designed to assess impulsivity per se, these subscales could be used in addition to the above mentioned impulsivity questionnaires for more comprehensive assessment. The Barratt scale is recommended for use in individuals 12 years of age and above and the Eysenck scale has norms for youth ages 8–15 years. However, the usefulness of these instruments largely depends on the individuals’ level of maturity and ability to accurately self-assess their actions. Comprehensive assessments of impulsivity in adolescents should also include information gathered from questionnaires completed by caretakers and teachers. There are few well validated impulsivity scales specifically designed for the evaluation of preschool and school age children. Moreover, self-reports from young children have value predominantly in the assessment of mood and anxiety, but are much less reliable when ascertaining behavioral patterns (Cantwell et  al. 1997; Smith et  al. 2001). Information about behavioral disturbances in these age groups is collected mainly from parents/ caretakers/legal guardians and teachers. Some of the most widely used questionnaires include the Conners’ ADHD rating scales (parent and teacher long and short versions) (Conners 2000) and the Achenbach Child Behavior Check List (CBCL) (parent report) and Teacher Report Form (Achenbach 1991). The Conners’ scale provides, among other measures, a Restless-Impulsive Index and a DSM-IV-based Hyperactive-Impulsive score. The CBCL items are divided into eight subscales that are grouped within externalizing and internalizing symptom domains. The Externalizing symptom domain contains the Rule-breaking Behavior and Aggressive Behavior subscales that provide total scores, T-scores, and percentile scores. These scores can be used as a proxy to estimate the level of impulsivity in young children. Although the above instruments are not specifically designed to measure impulsivity per se, they contain a number of items that inquire in a more focused way about impulsive types of behavior. These items can be extracted from the instrument and could be selectively used to generate a second level index of impulsivity.

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Table 7.1  Measures of impulsivity and inhibitory control in youth Constructs Instruments Description Impulsivity 30 Item self-report questionnaire designed to assess   Self-reports Barratt general impulsiveness based on six first-order factors Impulsiveness (attention, motor, self-control, cognitive complexity, Scale perseverance, cognitive instability), and three secondorder factors: attentional impulsiveness (attention and cognitive instability), motor impulsiveness (motor and perseverance), non-planning impulsiveness (self-control and cognitive complexity). A total score is obtained by summing the first or second-order factors. The items are scored on a four point scale: rarely/never – 1, occasionally – 2, often – 3, almost always/always – 4 63 Item self-report questionnaire designed to assess the Eysenck personality traits of impulsivity, venturesomeness, Impulsiveness and empathy. Impulsivity and venturesomeness Scale are presumed to contribute to risk preferences, therefore these two are used as a measure of both the personality constructs in and of themselves and of risk preferences. The response format is YES/NO Inhibitory control Go/No-Go task Motor response inhibition

 

Stop-Signal Task

 

Cognitive inhibition task

Stroop Color Word ask

Participant is instructed to respond by pressing a button to the presentation of the Go signal (e.g., letters A, B, C) as quickly as possible and to withhold the response when a No/Go trial (e.g., letter X) appears. The more frequent presentation of the Go signal (75% of the stimuli presented) sets up a prepotent response tendency, which must be inhibited when a No-Go stimulus is presented; therefore the task measures the ability to inhibit a prepotent motor response. The number of commission errors (i.e., responses to No/Go stimuli) is used to estimate the extent of the inhibitory control deficit Similar to the Go/No-Go paradigm participant is instructed to quickly press a button after he sees a Go signal, again establishing a prepotent response. However, during some trials, an additional stop signal is presented after the Go signal, indicating that the participant must withhold the button press. The stopsignal reaction time (SSRT) (i.e., the time required for the stop signal to be processed) is used as an index of the response inhibition function The task consists of a series of color words – such as blue, green, etc., printed in different ink colors. Participant is required to name the color of the ink and ignore the semantic meaning of the word. This requires inhibition of the automatic response to read the color word and causes a delay in naming the color of the ink. The slowing of the reaction time and the number of mistakes for mismatched trials (i.e., word “red” printed in blue ink) are used to measure the so-called Stroop effect, which is also an index of the ability to inhibit automatic cognitive tasks such as reading (continued)

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Table 7.1  (continued) Constructs Instruments

Description

Eriksen Flanker Task

 

Decisionmaking tasks with risk

The task consists of rows of five arrows that could be either congruent (i.e., all arrows point in the same direction) or incongruent (i.e., the center arrow points to left/right whereas the flanking arrows point in the opposite direction). Participant has to respond to the direction of a left or right pointing center arrow and ignore the flanking arrows. Similar to the Stroop task, there is a tendency to respond to the distracting flanker elements, and the participant has to inhibit the propensity to respond to the direction of the flankers. The response times are usually elevated when there is target-flanker incongruity relative to the control condition, where target and flankers are congruent (that is, they all point in the same direction). Reaction time and number of mistakes are used as measures of cognitive inhibition Delay Discounting The participant is presented with a series of hypothetical task choices of monetary reward (e.g., $10.00) available after one of several time delays (e.g., 0, 7, 30, 90, 180 or 365 days). An alternative but lower amount of money ($0.01, $0.25, $0.50, and further incremental increases) is available at the end of the session. The Delay Discounting effect represents the rate of discounting delayed larger rewards in favor of more immediate smaller rewards. The task has been adjusted for use in children by substituting money with reward points Miami DoorThe participant is asked to choose to either open the Opening task next door or to stop playing. As the game goes on the subject has to learn that while in the beginning of the game the opening of doors is rewarded later in the game it will entail punishment and will require a change of strategy (i.e., quitting the game) in order to preserve maximum gains

The tasks presented in the table are laboratory measures most widely used to assess aspects of impulsivity in youth. The Barratt Impulsiveness Scale can be use in adolescents above 12 years of age; the Eysenck Impulsiveness Scale has norms for youth 8–15 years of age; motoric, cognitive, and decision making tasks that involve risk have been designed mainly for use in adults, however, all of the tasks above have been modified using animation so that they can be used in young children. For instance, one version of the Go/No-Go task uses images of Spiderman as “Go” signal and “Green Goblin” as No-Go signal; instead of arrows on the flanker task, it is possible to substitute images of airplanes or animated fish; in one version of the Delay Discounting Task, children are instructed to collect flowers instead of coins

Impulsivity self-report scales offer the advantage of collecting information on a wide variety of behaviors, and help ascertain whether these acts constitute a persistent pattern of behavior that is stable over time (Table 7.1). However, these measures reflect subjective reports of one’s behaviors and are susceptible to reporters’ bias.

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In addition, due to learning, they are also unsuitable for repeated use, thus limiting their utility in assessing efficacy of treatment interventions.

Laboratory Measures of Impulsivity In addition to self-reports, impulsivity can be evaluated by the use of behavioral laboratory measures. These impulsivity measures exhibit stability with repeated assessments, which makes them suitable for the detection of treatment effects. However, they do not incorporate social aspects of impulsivity (with the exception of those using virtual reality techniques) and do not measure long-term patterns of behavior. A number of laboratory measures of impulsivity have been developed and utilized to objectively assess impulsive actions, impulsive choices, or impulsive decision-making. The feature that is at the core of these measures is the ability to withhold premature actions or thoughts, which is frequently defined as inhibitory control. The various types of inhibitory control tasks may be divided into three categories – tasks that measure motor inhibition, tasks that assess cognitive inhibition, and tasks that require making choices to maximize wins and avoid losses. When used in young populations, these tasks may need to be age-adjusted, which necessitates simplification and clarification of instruction, as well as choosing age appropriate animation and incentives. For instance, cartoon characters could be substituted for letter symbols in the Go/No-Go task, or the monetary wins in reward tasks could be changed to collecting reward points. Motor Inhibition Tasks: The main principle in motor inhibition tasks is that they create a prepotent response tendency by the frequent use of Go trials, to which the participants must respond by a quick press of a button (Perry and Carroll 2008). This prepotent response tendency must be inhibited when a No-Go stimulus is presented. In widely used Go/No-Go tasks, this response is measured by presenting the Go stimuli three times more frequently than the No-Go stimuli. The number of responses on the No-Go trials, known as commission errors, is used to estimate the extent of the inhibitory control deficit. A related motor inhibition task is the Stop-signal task (Logan et  al. 1984), in which the participant is instructed to quickly press a button after they see a Go signal, again establishing a prepotent response. However, during some less frequent trials an additional stop signal (or distractor stimulus) is presented after the Go signal, indicating that the participant must withhold the button press. The stop-signal reaction time (SSRT; i.e., the time required for the stop signal to be processed) is used as an index of the response inhibition function (see Table 7.1 for detail). Cognitive Inhibition Tasks: Widely used cognitive inhibition tasks are the Stroop Color-Word task, the Eriksen flanker task, and the Reversal Learning task. The common feature of these tasks is the requirement to inhibit automatic responses, which causes a delay in responding and a correspondingly slower reaction time, as well as an increased number of mistakes. For example, the Stroop Color-Word task

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(MacLeod 1991) consists of a series of color words – such as blue, green, etc., printed in different ink colors (i.e., the word “red” printed in blue ink). The participant is required to name the color of the ink and ignore the semantic meaning of the word. In the arrow version of the Flanker task (Eriksen and Eriksen 1974), the participant has to respond to the direction of a left or right pointing center arrow, and ignore flanking arrows that point in the opposite direction. Both tasks require the subject to withhold the automatic response to either reading or to the distracting flanker elements, and to resolve this conflict in order to emit the correct response. This interference in the reaction time is also known as the “Stroop” or the “flanker” effect. Lastly, the Reversal Learning task (Clark et al. 2004) assesses the ability to adapt behavior to environmental changes and inhibit the impulses to respond in an already learned manner (see Table 7.1 for detail). Decision-making Tasks that Involve Risk or Punishment: Decision-making tasks that involve risk include the Delay Discounting task (Perry and Carroll 2008), Iowa Gambling task (Bechara et  al. 1994), Balloon Analogue Risk task (Lejuez et  al. 2002), Rogers Decision Making task (Rogers et  al. 1999), and the Miami DoorOpening task (Daugherty and Quay 2006). All of these instruments measure the propensity for risky decision making, as the participant is asked to maximize their gain by choosing between different rewards while avoiding punishment. The Delay Discounting effect represents the rate of discounting delayed larger rewards in favor of more immediate smaller rewards. The common principle in these tasks is that the subject has to choose between a strategy consisting of incremental monetary wins that will produce a greater final gain versus a strategy of bigger immediate wins paired with possible penalties that could result in a smaller final sum of money earned. Of these measures, the Door Opening task has been specifically developed for use in young individuals. In general, these tasks are designed to isolate cognitive components that underlie impulsivity, such as sensitivity to consequences, perseveration, and risk taking (see Table 7.1 for detail). In recognition of the multifaceted nature of impulsivity, it may be helpful to use multiple assessments that measure various behavioral, biological, social, and environmental dimensions and group them together to create a composite “impulsivity index” (Barratt 1994). Studies of dimensions of impulsivity have frequently utilized separate instruments combining self-reports and laboratory tests that are scored independently, and then use these scores to generate a combined score or index that will help characterize groups with high and low levels of impulsivity. Such an approach is exemplified in the work of Tarter et al. (2003), which focused on the role of inhibitory control deficits in mediating risk for adolescent substance use. In a sample of 275 males, the researchers examined three domains of inhibitory control: (1) behavioral response inhibition, determined by tabulating the number of ADHD, CD, and ODD symptoms endorsed during the K-SADS interview; (2) emotional dysregulation, assessed via the difficult temperament index from the revised Dimensions of Temperament Survey, and (3) deficits in executive cognitive functions, as assessed on a battery of six inhibitory control tasks. Scores in these three domains were used to derive a composite secondorder construct, which the authors termed neurobehavioral inhibition. The scores on

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this second-order construct were then used as a proxy measure of impulsivity to characterize the sample (also see below).

Inhibitory Control Deficits and Risk for Adolescent SUD Abnormalities in inhibitory control are associated with a number of psychiatric disorders, including antisocial and borderline personality disorders, bipolar disorder, substance abuse/dependence, ADHD, and CD (Moeller et  al. 2001). In addition, individuals diagnosed with any of these conditions have been shown to exhibit deficits on laboratory measures of impulsivity. This suggests that these diagnostic categories may share common neurobiological mechanisms that may produce patterns of highly impulsive behaviors. The notion that features like impulsivity could be familial is proposed by reports that siblings of substance users, like their parents, have often been characterized as impulsive and aggressive (Carbonneau et al. 1998). This finding also suggests that such a common feature could be biologically based and therefore at least partly constitutionally determined, and may continuously operate during the course of development. If true, these common mechanisms may produce impairments in inhibitory control that in turn could manifest as symptoms associated with several disorders. For instance, high impulsivity in childhood may be manifested as excessive talking, intrusiveness, and inability to wait their turn (all symptoms of ADHD), or alternatively as verbal or physical aggression that could be associated with CD. In contrast, impulsive behaviors in adolescence may present as drug experimentation and sexual promiscuity, which are often associated with drug addiction, bipolar, or personality disorders. It stands to reason that the persistence of biologically determined inhibitory control deficits may create possible pathways that mediate the manifestation of high impulsivity during development as symptoms associated with either childhood ADHD/CD and adolescent SUD. This theoretical model suggests several possibilities that could be potentially evaluated using findings from several different lines of research. In accordance with this model, for instance, are reports showing that individuals diagnosed either with ADHD or SUD exhibit higher impulsivity scores and abnormal response inhibition on inhibitory control tasks such as the Go/No-Go task compared to unaffected controls (Halperin et al. 1995; Dom et al. 2006). This model also posits that disorders characterized by deficits in inhibitory control will tend to co-occur at a higher rate in affected individuals. This means that patients with ADHD may have higher rates of comorbid substance abuse. Indeed, studies consistently show higher rates of adolescent substance use in individuals diagnosed with childhood ADHD compared to the general population (Lambert and Hartsough 1998; Molina and Pelham 2003). More concretely, as many as 75% of children with ADHD versus 31% of non-ADHD controls may be involved with drugs by young adulthood, and 46% of children with ADHD smoke tobacco prior to age 17 years compared to 24% of non-ADHD controls. Other reports show that up to 50% of

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Fig.  7.1  Relationship between inhibitory control deficits and disorders characterized by high impulsivity

children with ADHD may eventually develop substance abuse disorders (Davids and Gastpar 2003), particularly nicotine abuse and dependence (Rhode et  al. 2004). In addition, the well established relationship between childhood disruptive behavior disorders and adolescent onset substance abuse (Shrier et  al. 2003; Lynskey and Fergusson 1995; Gordon et  al. 2004; Dennis et  al. 2004) may be mediated by early inhibitory control deficits. Such a mechanism is proposed by the so-called “cascade model,” which is supported by findings that low levels of childhood reactive control appear associated with adolescent substance abuse (Martel et  al. 2009). It may appear that in contrast to later onset addiction, early onset substance abuse disorders represent somewhat independent conditions, associated with more rapid development of dependence, heavier drinking in adulthood, and higher rates of comorbid personality disorders, but also with better response to treatment and a more favorable outcome in later life (Fig. 7.1). Taken together, these findings support the notion that individuals with impaired inhibition develop a variety of clinical syndromes that could be conceptualized as discrete developmental presentations of broader underlying pathology. Longitudinal studies have linked childhood ADHD and CD to elevated risk for adolescent onset of substance abuse. (Moss and Lynch 2001), and further suggest that ADHD interacts with CD to produce higher risk for SUD than either condition alone, perhaps by increasing the severity of disruptive and impulsive behaviors (Thompson et al. 1996). The risk for SUD, however, is possibly mediated by neither ADHD nor CD per se, but rather some more discrete dimension of these syndromes – such as impulsivity. However, there are no longitudinal studies that specifically examine the role of this more discrete behavioral construct rather than the broader diagnostic

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categories. Studies of drug abusing individuals showing deficits in inhibitory control have been conducted predominantly in adults, who have been exposed to the effects of drugs for considerable time periods. Since a number of reports indicate that both acute and chronic substance use have a negative effect on both motor inhibitory control tasks and decision making tasks (e.g., Iowa Gambling task), it is possible that impaired inhibitory control in drug abusing persons is not necessarily a preexisting feature, but an acquired feature that develops as a result of long-term exposure to drugs. Preliminary evidence that antecedent deficits in inhibitory control may be linked to later substance abuse has been provided by Tarter et al. (1999, 2003 as described above) using a composite measure of impulsivity, named neurobehavioral inhibition. In this research, neurobehavioral inhibition scores were obtained at two developmental time points (ages 10–12 and 16 years) in a sample of 275 males, who were divided into high and low risk groups, based on the severity of inhibitory control deficits. The results indicated that the presence of executive function deficits and the severity of neurobehavioral disinhibition in childhood predicted the likelihood of SUD developing in young adulthood, and also that neurobehavioral disinhibition, in conjunction with parental SUD and the occurrence of psychosocial problems between early and mid-adolescence, identified a population of high-risk youth with a 0.92 probability of developing SUD before the age of 22. Although preliminary, these findings offer compelling evidence for the usefulness of isolating different behavioral and neuropsychological indicators of risk in the prediction of adolescent SUD outcome. Of interest also are the effects of biological agents that have been used for the treatment of impulsive behaviors. Medications that are considered first line treatments for ADHD in children include two classes of stimulants – amphetamines and methylphenidate – and the nonstimulant agents atomoxetine and guanfacine. Stimulants have consistently been found to decrease impulsive behaviors in children with ADHD and CD (Wigal 2009). However, a drawback of stimulants is the potential for abuse. Therefore utilization of stimulants for the treatment of a disorder that carries a risk for subsequent substance abuse has generated considerable controversy, as well as an interest in longitudinal projects that would follow youth with disruptive behavior disorders from childhood in order to evaluate whether there is increased risk for the development of adolescent substance abuse in relation to stimulant treatments. The majority of such studies have reported either no effect or some protective effect of the stimulant treatment (Barkley et al. 1990; Biederman et al. 1999; Barkley et al. 2003; Molina et al. 2007; Biederman et al. 2008; Mannuzza et  al. 2008). However, one study by Lambert and Hartsough (1998) found increased risk for the development of adolescent nicotine and cocaine abuse in children with ADHD treated with stimulants. One important distinction between these studies is that the Lambert group studied the effects of stimulant treatment on ADHD in association with other risk factors such as family history of addiction and the presence of comorbid CD. The remainder of the reports focused more exclusively on youth with ADHD and controlled for these additional factors. The distinct outcomes of these studies suggest the following possibilities: (1) adequate

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treatment of childhood impulsivity may attenuate the risk for the development of later substance abuse, and (2) this “protective” effect may be offset by the interaction between stimulants and other risk factors for addiction, as these factors could contribute to elevated risk by either exacerbating the persistence and the severity of impulsivity or by some other mechanisms. Alternatively, the short-term effects of stimulants, such as amelioration of impulsive behaviors, may be accomplished by temporary correction of some neurobiochemical abnormalities (e.g., low catecholaminemediated brain activity), whereas the long-term effects of stimulant administration could be related to some more permanent effects that may include altered neuronal structure and decreased availability of dopamine receptors in brain cognitive and reward areas, such as the prefrontal cortex (PFC) and striatum, respectively.

Neurobiological Basis of Impulsivity The link between impulsivity and brain abnormalities has been suggested by observations that individuals who suffered physical trauma to the PFC exhibited patterns of increased behavioral dyscontrol following the traumatic event (Bechara et al. 1994). It therefore seemed reasonable to suggest that some constitutional dysfunctions of the PFC may contribute to deficits in inhibitory control and therefore to the clinical picture of impulsive behaviors associated with ADHD, SUD, or other disorders. Reports from functional neuroimaging studies have documented differences in activation between ADHD and control children obtained during a Go/No-Go task in the right inferior frontal cortex and the right ventrolateral PFC, as well as the striatum and the left anterior cingulate cortex (Bush 2010). The notion that ADHD and non-ADHD youth process information differently potentially lines up well with the proposed neurobiological basis of risk for SUD in youth with ADHD and other disruptive behavior disorders. More specifically, fMRI measures in youth with either a positive family history of SUD or the early onset of antisocial behavior showed altered PFC activation during the Go/No-Go task relative to control adolescents (Rangaswamy et al. 2004; Schweinsburg et al. 2004). Findings from neuroimaging studies of inhibitory control in substance abusing individuals have been summarized in a comprehensive review by Dom et al. (2005), who reported that functional neuroimaging techniques (e.g., PET, fMRI) have consistently documented differential activation in the PFC in individuals with SUD compared to control groups on tasks of cognitive inhibition. These reports support the hypotheses that altered PFC function is associated with drug abuse; however the direction of this relationship (i.e., do PFC abnormalities precede or antecede drug abuse?) is unknown. Accumulating evidence further demonstrates that specific brain regions (e.g., right lateral PFC, ventral striatum) and networks (e.g., white matter tracts connecting regions within the PFC with striatum and subthalamic nucleus) may be linked to the process of inhibitory control. Understanding the neurobiological basis of inhibitory control deficits could potentially be used to link a neuropsychologically defined trait (i.e., inhibitory control deficit) to a discrete neurobiological characteristic (abnormal activation of a specific brain area/network)

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to create neurobiological models of risk for addiction (Aron and Paulus 2007; Garavan and Hester 2007; Goldstein et al. 2009). One such model relates to the observation that the PFC is consistently recruited for diverse cognitive functions, including neurobehavioral inhibition. This would suggest that different inhibitory control tasks will produce bilateral activation of the same PFC regions, such as dorsolateral PFC, the inferior frontal cortex, and the dorsal anterior cingulate cortex, which has been supported by review of the literature (Duncan and Owen 2000). The emerging picture is that implementation of inhibitory control is achieved via interactions among components of PFC and physiologically related structures like the basal ganglia, the subthalamic nucleus and primary motor regions. A model linking dysfunction in these regions during childhood with the risk for SUD is theoretically testable by conducting longitudinal neuroimaging studies (e.g., pre and postonset of drug use) to investigate whether differences in the magnitude and the patterns of activation of these regions in individuals at risk versus no risk prior to development of substance abuse may be associated with subsequent SUD. A competing model that borrows from the dual pathway model of ADHD (Sonuga-Barke 2003) distinguishes between two types of inhibitory control: (1) executive or low anxiety inhibition, which is characterized by deliberate suppression of immediate motor behavior in the service of a distal goal in working memory, mediated by frontal–striatal–thalamic neural loops, and (2) motivational or reactive inhibition, which refers to anxiety-provoked interruption of behavior in the context of unexpected, novel, or punishment-cue indicators, mediated by activation of the limbic system. This model aligns with the idea that initiation and maintenance of drug abuse may be related to persistence of negative emotional states. It may further predict that findings from longitudinal neuroimaging studies would show altered baseline patterns of interaction between executive and motivational networks, with subsequent changes in these activation patterns following the initiation of drug abuse. Finally, a more developmentally based model would propose that disturbances in PFC maturation may at least in part underlie the liability for SUD. The PFC is the last brain region to functionally mature, extending into the mid 1920s, while substance use initiation typically begins in early adolescence (Grant and Dawson 1998; DeWit et  al. 2000). It is possible that maturation-related suboptimal executive cognitive capacity, accompanied by a background level of insufficient behavioral control and emotional dysregulation, could elevate the risk for substance abuse in social contexts where addictive substances may be readily available, and where social norms and regulations are ineffective at mitigating consumption. In a comprehensive review of the literature, Spear (2000) describes an array of risk-related bio-behavioral characteristics (e.g., negative affect, behavioral under-control) that are specific to adolescent brain maturation. Following from this hypothesis, longitudinal neuroimaging studies could be helpful in mapping out normative patterns of development in regions of interest (e.g., PFC, basal ganglia, the subthalamic nucleus), identifying the time-course of this developmental process, and determining whether there are specific developmental periods during which the initiation of drug use is more likely to result in longer lasting negative alterations of brain processes.

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Clinical Considerations What are the day-to-day clinical implications of the above theoretical considerations? If one concludes that stimulant treatment of children with ADHD may have an attenuating effect on the development of later SUD, then a more proactive role of clinicians to reliably diagnose and potentially treat affected children would be warranted. The exception to this may be a subgroup of ADHD youth who may have additional factors that may exacerbate the addition risk – among these are comorbid CD and familial addiction. Therefore, physicians should diligently assess all possible addiction-related risk factors before starting treatment and, if indicated, carefully consider and weigh these risks against the potential benefits of the different treatments being considered. Of interest is the observation that stimulants in general (methylphenidate, amphetamines, cocaine, and 3,4-methylenedioxymethamphetamine or MDMA) share common neurophysiological mechanisms that result in increased secretion and a slowed down clearance of catecholamines (e.g., dopamine, norepinephrine) in the synaptic cleft. The net effect of the administration of these agents is enhanced catecholaminergic neurotransmission. As mentioned above, however, it has been observed that while methylphenidate and other amphetamines successfully attenuate the symptoms of impulsivity in children with ADHD, abuse of these same or other stimulant agents (e.g., cocaine, MDMA) has been linked to increased levels of impulsive behaviors in affected individuals. These differential effects of agents that have similar mechanisms of action suggests that (1) these compounds may have different effects during the course of individual development, a hypothesis which seems to be supported by observations that youth who have responded well to stimulants initially may become resistant to the treatment during adolescence, and/or (2) that these different effects are dose related, meaning that therapeutic or relatively low doses of these agents may improve impulsivity, whereas their escalating use and abuse will have a negative effect on impulsivity. This last notion is supported by reports documenting successful treatment of cocaine addiction by the use of therapeutic doses of stimulants (Grabowski et  al. 2001; Dackis et  al. 2005; Moeller et  al. 2008; Anderson et  al. 2009; Mooney et al. 2009). Since there is lack of evidence to either confirm or reject these hypotheses, clinicians should carefully consider all available treatment options, including the use of nonstimulant agents (e.g., atomoxetine, guanfacine) or stimulants with different pharmacokinetic and pharmacodynamic profiles (lisdexamfetamine). From a strict psychopharmacological perspective, these agents have different mechanisms of action and may have presumably little to no influence on the risk for later substance abuse, particularly related to their effect on levels of impulsivity. For instance, atomoxetine is hypothesized to affect catecholaminergic neurotransmission by binding to the norepinephrine transporter; guanfacine has an agonistic effect on the alpha-2 receptors, and lisdexamfetamine is a prodrug similar to amphetamines, but with slower brain uptake. Accordingly, the abuse potential for both guanfacine and atomoxetine (Heil et al. 2002) is considerably lower than that of stimulants and

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appears clinically insignificant. Lisdexamphetamine has shown abuse potential lower than that of amphetamine salts (Jasinski and Krishnan 2009), but its long-term effects are unknown. Additional clinical considerations may be related to the acute and chronic effects of stimulants on the central nervous system. While the acute increase in the release of catecholamines appears associated with improved impulsivity symptoms, animal studies suggest that the chronic effects of stimulant use may entail altered neuronal structure in the PFC and the striatum, decreased dopamine D2 receptor availability, and depletion of brain serotonin (Robinson and Kolb 1997; Jentsch and Taylor 1999; Everitt and Wolf 2002). Concerns about long-term effects may influence clinicians’ design of short and long-term treatment plans to offer the least exposure to stimulants and enforce periodic assessment of the efficacy of these treatments. A more developmentally sensitive approach may account for the chronological patterns of brain maturation, especially of the PFC. Adolescence is a period marked by significant neuronal reorganization in a variety of cortical regions (Crews et al. 2007), which appears to be disrupted or altered in individuals with ADHD (Shaw et al. 2009). These neurobiological processes also coincide with the development of behaviors characterized by impulsive decision making, propensities for sensation seeking, and engaging in potentially dangerous activities. These could contribute to the increased levels of drug experimentation, which may further lead to impaired functioning and the development of “substance abuse disorder” especially for individuals with constitutional risk factors (e.g., family history of addiction). Adolescence and early adulthood is also a developmental period when (1) symptoms of hyperactivity/impulsivity may remit while inattention cluster of symptoms have shown to remain stable (Spencer et  al. 2007) and when (2) mood and anxiety disorders may have their onset (Ramirez et al. 2009) Accordingly, the increase of misuse and diversion of ADHD medications, which is especially prevalent in older adolescents and college-age young adults, may be partially viewed as self-medication of latent ADHD and ADHD-related mood and anxiety symptoms that may have a negative effect on an individuals’ ability to keep up with academic demands (Norwalk et al. 2009). It is also possible that the maturation of the PFC could be related to improved abilities to engage in behavioral treatments that could in turn benefit the control of symptoms of both ADHD and SUD. The main goal of behavioral modalities is to enable individuals to achieve a more adequate self-control over impulsive behavioral patterns resulting in diminishment of adverse consequences and increased positive outcomes. Behavior-modification techniques focus on altering undesired behaviors through identifying their triggers and understanding their consequences. In young children such behavioral techniques are usually delivered through collaborative work between clinicians, parents, and teachers, and usually include contingency management in which successful inhibition of undesired behaviors is paired with positive reinforcement (e.g., reward points that children can exchange for real rewards) (Kaiser et al. 2008). Behavioral charts that target one particular type of behavior at a time are most widely used for preschool and elementary school youth. As individuals mature and enter adolescence they become more

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capable in engaging in both group and individual behavioral treatments that may implement the same principals, but further require abilities to self-reflect, process, and in result gain some understanding of the psychological motivations that may propel negative behaviors. In substance abuse treatment facilities, for instance, positive behaviors (e.g., showing up for scheduled treatment visits or producing negative urine toxicology) are linked to positive reinforcement (including monetary rewards) with the goal to further encourage and solidify abstinence (Ledgerwood 2008, p. 340–49; Vocci and Montoya 2009). Although the underlying biological mechanisms that could be potentially linked to behavior treatments remain poorly understood, the interplay between reward and executive control neuronal networks could be hypothesized to mediate the effects of these modalities. As individuals enter adolescence, the maturation of the PFC could be associated with improved or more efficient functioning of top-down executive control systems. That may further allow for suppression of competing signals from the mesolimbic motivational system that, when unopposed, could produce compulsive drug seeking and taking. Interesting as they may be, such hypotheses are mainly theoretical and have not been experimentally evaluated.

Conclusions In this chapter we examined the relationship between inhibitory dyscontrol, one component of the larger behavioral phenomenon of impulsivity, and risk for early onset SUD. Impaired inhibitory control is ubiquitously present in the clinical picture of both SUD and childhood disruptive behavior disorders, and therefore may represent an important indicator of neurobiological risk for SUD. In addition, available evidence links inhibitory control deficits to distinct networks, including PFC, striatum and the subthalamic nucleus, thus elucidating the neurobiological aspects of the phenomenon of impulsivity. Deeper understanding of the neurobiological aspects of inhibitory control may clarify the effects of available biological treatments on vulnerability for addiction and also chart future directions for treatment development. Recent advances in the field of biological research also offer great promise for the identification of biologically based vulnerabilities for substance abuse and for mapping out their longitudinal course to assess more comprehensively the role of biological factors in the development of SUD.

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Chapter 8

Impulsivity and Deviance Donald R. Lynam

Abstract  The chapter discusses the relations between various forms of deviant behavior (i.e., antisocial behavior, substance (mis)use, and risky sex) and impulsivity/disinhibition within the personality trait perspective. Although many theories and much empirical research support the importance of impulsivity/disinhibition in deviant behavior, a lack of conceptual and operational clarity introduces confusion. The chapter offers an organizing framework, the UPPS-P, that posits the presence of five distinct personality pathways to impulsive behavior: Negative Urgency (NU) which reflects the tendency to act rashly under conditions of negative affect (e.g., anger, distress); lack of perseverance (PSV) which reflects the inability to remain focused on a task in the face of boredom and/or distraction; lack of premeditation (PMD) which reflects the tendency to act without thinking; Sensation Seeking (SS) which reflects the tendency to enjoy and pursue activities that are exciting or novel; and Positive Urgency (PU) which reflects the tendency to act rashly under conditions of positive affect. The chapter presents information on the derivation of this model and research supporting its validity, including cross-age, cross-sex, and cross-cultural support for the structure of the UPPS-P and differential relations to different forms of deviant behavior and different types of psychopathology. It is concluded that the UPPS-P offers a useful tool in organizing extant research on impulsivity/disinhibition and in directing future research efforts. The chapter closes with a discussion of important future directions.

The Importance of Impulsivity Acting without thinking or regard to consequences and engaging in risky ­behavior, often lumped together under the terms “impulsivity,” “poor inhibitory control,” “­disinhibition,” or “undercontrol” are important components of many forms of ­psychopathology. Impulsivity or failures of inhibition are included in the diagnostic D.R. Lynam (*) Purdue University, West Lafayette, IN 47907, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_8, © Springer Science+Business Media, LLC 2011

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criteria of at least 18 separate disorders in the fourth edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-IV; American Psychiatric Association 1994). The DSM IV includes a subsection for ­impulse-control disorders not classified ­elsewhere, and several other disorders have a lack of inhibition at their core. For  example, to receive a diagnosis of Attention-Deficit/Hyperactivity Disorder, individuals­ must manifest six or more symptoms of hyperactivity–impulsivity (American Psychiatric Association 1994). One of the explicit symptoms of Borderline Personality Disorder (BPD) is “impulsivity in at least two areas that are potentially self-­damaging” (p. 654; American Psychiatric Association 1994). Additionally, impulsivity serves as the centerpiece in several etiologic theories of crime (Cleckley 1976; Gottfredson and Hirschi 1990; Lynam 1996; Moffitt 1993). Moffitt’s theory of life-course persistent antisocial behavior begins with deficits in executive functioning – the ability to plan, organize, execute, and inhibit behaviors. In the field of sociology, the seminal work by Gottfredson and Hirschi (1990), A General Theory of Crime, has as its explanatory core the concept of low self-control. Individuals lacking self-control are described by Gottfredson and Hirschi as “impulsive, insensitive, physical (as opposed mental), risk-taking, shortsighted and nonverbal” (p. 90). Finally, seminal descriptions of psychopathy include multiple aspects of impulsivity and lack of inhibition. In his seminal description of psychopathy, Cleckley (1976) included unreliability, inadequately motivated antisocial behavior, poor judgment, and failure to follow any life plan. The Hare Psychopathy Checklist-Revised (Hare 2003), the gold-standard assessment instrument, includes assessments of proneness to boredom, poor behavioral controls, lack of realistic, long-term goals, impulsivity, and irresponsibility. Similarly, impulsivity is a central piece of many etiologic theories of substance use and abuse (e.g., Wills et al. 1994). In their model of the development of substance use disorders, Iacono et  al. (2008) suggest that SUDs are part of a larger spectrum of traits, behaviors, and associated behaviors that reflect a general problem with inhibition. At the core of this disinhibition are traits, such as impulsivity, lack of perseverance (PSV), thrill-seeking, and inattention. Inhibition-related constructs are also central to models of risky sexual behavior. Pinkerton and Abramson (1992, 1995) proposed a sexual decision-making model in which risk-related individual differences like sensation seeking (SS) and impulsivity influence the ways in which risks associated with different types of sexual encounters are perceived and weighted. Extant research is consistent with these theories linking inhibition/impulsivity to deviant behaviors. Across several meta-analyses of personality and antisocial behavior, traits related to disinhibition and impulsivity have been found to be among the strongest correlates (Miller and Lynam 2001; Ruiz et al. 2008). In a longitudinal investigation, Lynam et al. (2009) found that impulsivity at age 13 was among the strongest predictors of arrests and convictions between the ages of 18 and 26. A number of studies have demonstrated that traits related to impulsivity/disinhibition are among the most robust prospective predictors of future substance abuse (e.g., Chassin et al. 1999; Elkins et al. 2006; Sher et al. 2000). Using data from a large, population-based study, Caspi et al. (1997) found that temperamental impulsivity/disinhibition at age 3 predicted substance use disorders in young adulthood. Hoyle et  al. (2000) meta-analyzed the relations

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between several structural models of personality and three measures of sexual risk-taking – multiple partners, unprotected sex, and high-risk sexual encounters. Across all three outcomes, traits related to disinhibition and impulsivity (e.g., sensation seeking, impulsivity, constraint, conscientiousness) bore the strongest relations. Given the pervasive importance of inhibition/impulsivity in theories of deviance and the large body of empirical research, it may be somewhat surprising to note the variety of conceptualizations of impulsivity and the inconsistencies among them. Investigators differ in how they define and measure impulsivity. At the broadest level are differences in the mode of assessment. Some researchers define impulsivity in terms of behavior; from this view, impulsivity is engaging in risky behavior (e.g., Barratt 1993). Others investigators define impulsivity operationally in terms of performance on one or more tasks thought to be analogs of real world processes; for example, several investigators have operationalized impulsivity in terms of selecting a small, immediately available reward over a larger, delayed reward (e.g., Bickel and Marsch 2001). Finally, other investigators define impulsivity in terms of characteristic traits possessed by the individual (e.g., Eysenck and Eysenck 1978). Very little research has successfully connected constructs across levels, e.g., task measures with self-reports. Even within a given realm, investigators differ in their definitions. Within the task literature, impulsivity has been operationalized using multiple variants of delay discounting tasks, continuous performance tasks, gambling tasks, stop-signal tasks, go/no go tasks, and executive functioning tasks. Similar issues exist within the trait literature. In some models of personality, impulsivity-related traits appear more than once. For instance, Eysenck and Eysenck (1978) include impulsiveness as a component of psychoticism, and venturesomeness and sensation-seeking as components of extraversion in their three-dimensional view of personality. Depue and Collins (1999) note, “impulsivity comprises a heterogeneous cluster of lower-order traits that includes terms such as impulsivity, sensation seeking, risk-taking, novelty seeking, boldness, adventuresomeness, boredom susceptibility, unreliability, and unorderliness” (p. 6). Research on inhibition/impulsivity is hampered by both the “jingle” and “jangle” fallacies (Block 1995). The “jingle” fallacy is present when two traits appear nominally the same but assess different constructs (e.g., impulsivity); in contrast, the “jangle” fallacy is present when two traits are nominally different but assess similar constructs (e.g., control and deliberation). Such divergence in conceptualizations makes comparison across studies difficult and hampers the accumulation of knowledge.

Development of the Original UPPS Model Although there have been very few attempts to integrate the various sorts of tasks with one another, several investigators within the trait literature have attempted to organize various terms within an overarching taxonomy. In one of the more recent attempts, Whiteside and Lynam (2001) suggested that the Five Factor Model (FFM) of personality, as assessed by the NEO-PI-R (Costa and McCrae 1992), might ­provide a way of organizing the various conceptions of impulsivity. The FFM, derived originally from studies of the English language, emphasizes five broad

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f­ actors or domains of personality with each domain comprising several underlying facets or traits. Whiteside and Lynam specifically argued that the FFM model includes four personality traits that represent distinct pathways to impulsive behavior. These four traits exist on three separate higher-order factors of personality. The first trait they identified was NEO-PI-R impulsiveness, which is situated on the Neuroticism factor and assesses an individual’s tendency to give in to strong impulses. Next, they suggested that excitement seeking, a specific trait on the higherorder factor of Extraversion, which measures an individual’s preference for excitement and stimulation, was another route to impulsive behavior. The third and fourth traits, self-discipline, and deliberation from the broader factor of Conscientiousness, were hypothesized to be negatively related to impulsive behavior. Self-discipline measures an individual’s ability to persist in completing jobs or obligations despite boredom and/or fatigue, while deliberation assesses an individual’s ability to think through the potential consequences of his or her behavior before acting. Using these four NEO PI-R traits as potential markers, Whiteside and Lynam (2001) administered a number of the most widely used measures of impulsivity/disinhibition to a large sample of students and then factor analyzed the results. This approach was meant to yield a summary and taxonomy of current perspectives on impulsivity-related traits. The factor analysis produced four factors with each one marked by a separate NEO PI-R facet. Urgency, referenced as Negative Urgency (NU) hereafter, reflects the tendency to act rashly under conditions of negative affect (e.g., anger, distress). Lack of PSV reflects the inability to remain focused on a task in the face of boredom and/or distraction. Lack of Premeditation (PMD) reflects the tendency to act without thinking. Sensation Seeking reflects the tendency to enjoy and pursue activities that are exciting or novel. Based on the results of the factor analyses, Whiteside and Lynam developed the UPPS Impulsive Behavior scale which independently assesses each of these four personality pathways to impulsive behavior. Intercorrelations among those scales ranged from 0.00 (between SS and PMD) to a high of 0.45 (between PSV and PMD). Several studies have since tested the four factor structure of the UPPS. In the first of these studies, Lynam and Miller (2004) used confirmatory factor analyses. The authors averaged two or three items within each of the four scales to create four parcels for each subscale, which were then used in a two-group (men and women) confirmatory factor analysis. Such a multigroup model simultaneously estimates the models in the two groups and allows direct testing of the similarity of structure across groups. The model specified four latent factors with each parcel relating to only one of the factors. No correlated errors of measurement were included. Several models were estimated and compared with one another. In the first model, all factor loadings were constrained to be equivalent across men and women. This model fit the data very well (c2 (214) = 451, RMSEA = 0.039, and a CFI = 0.96) according to standard fit criteria – RMSEA less than 0.05 and CFI greater than 0.90. Although an unconstrained model that allowed all loadings to differ across men and women fit better (Dc2 = 32 on 18 df; p < 0.05), this improvement was due entirely to the difference in loadings for a single parcel on PMD. When this loading was allowed to differ across groups and all other loadings were constrained to be equal, this mostly constrained model fit the data better than the fully constrained model (Dc2 = 11 on 1 df; p < 0.001) and as well as the fully unconstrained model (Dc2 = 21 on 17 df; ns).

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Factor analyses of French and German translations of the UPPS also provide support for the original four-factor structure. For example, within a French translation of the UPPS (Van der Linden et al. 2006) the original four-factor solution fit the data very well and significantly better than other models tested. D’Acremont and Van der Linden (2005) have also provided support for the four-factor solution across gender in a large sample of French adolescents. Kampfe and Mitte (2009) recently provided support for a four-factor structure of a German translation of the UPPS in two separate samples. Thus, the four-factor structure of the UPPS seems consistent across age, sex, and language.

Positive Urgency Smith and Cyders and colleagues (Cyders et al. 2007) have recently made a compelling case for the addition of a fifth personality pathway to impulsive behavior – Positive Urgency (PU), which reflects the tendency to act rashly when experiencing extremely positive emotion. Several findings suggested the existence of such a pathway, including heavy drinking and rash behavior during celebrations, the presence of enhancement motives for alcohol consumption, and the association between positive mood and risk taking. Observing that this trait seemed underrepresented in the original UPPS model, the authors developed a content-valid scale, demonstrated its unidimensionality, and recovered a five-factor solution from the original UPPS plus the PU measure. PU correlated quite modestly with SS, PSV, and PMD (rs ranged from 0.21 to 0.28), but more highly with NU (r = 0.37). Subsequent work by these authors has further validated PU as an important and independent personality pathway to impulsive behavior (Cyders and Smith 2007, 2010). The PU has been folded into the original UPPS scale to form the UPPS-P scale. Table 8.1 provides example items from the PU scale.

Table 8.1  Descriptions of UPPS-P scales and sample items Negative Urgency:  the tendency to act rashly under conditions of negative affect Sometimes when I feel bad, I cannot seem to stop what I am doing even though it is making me feel worse When I am upset, I often act without thinking I have trouble resisting my cravings Lack of Perseverance:  the tendency to give up on a task in the face of boredom, fatigue, and/or distraction I tend to give up easily Sometimes, there are so many little things to be done that I just ignore them all Once I get going on something, I hate to stop (reversed) Lack of Premeditation:  the tendency to act without consideration of the consequences I have a reserved and cautious attitude toward life (reversed) My thinking is usually careful and purposeful (reversed) Before making up my mind, I consider all the advantages and disadvantages (reversed) (continued)

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Table 8.1  (continued) Sensation Seeking:  the tendency to enjoy and pursue activities that are exciting and/or novel I generally seek new and exciting experiences and sensations I will try anything once I quite enjoy taking risks Positive Urgency:  the tendency to act rashly under conditions of positive affect When I am in great mood, I tend to get into situations that could cause me problems I tend to lose control when I am in a great mood Others are shocked or worried about the things I do when I am feeling very excited

Research Support for the UPPS-P Model In addition to the research on the internal structure (i.e., factor structure) of the UPPS-P scales, approximately 50 studies have examined the relations between the UPPS-P constructs and various forms of deviance. These outcomes include substance (mis)use (e.g., Cyders et  al. 2009; Verdejo-Garcia et  al. 2007; Whiteside et al. 2005), antisocial behavior (e.g., Anestis et al. 2009a, b), risky sexual behavior (e.g., Simons et al. 2010), and gambling (Cyders and Smith 2008; Whiteside et al. 2005). Virtually, all studies have found differential relations between the UPPS-P scales and a given outcome, underscoring the importance of distinguishing between the various personality pathways to impulsive behavior. There are several general conclusions that can be drawn from this body of research. First, lack of PSV typically bears the weakest relation to the negative outcomes. Its only consistent correlates appear to be inattention and poor school performance (Miller et al. 2003; Smith et al. 2007). This is consistent with the idea that it reflects a difficulty in completing tasks in the face of boredom, fatigue, and distraction. Second, sensation seeking, the tendency to enjoy and pursue activities that are exciting and/or novel, seems more strongly related to sampling deviant behaviors than to penetrating particularly deeply into them (e.g., Verdejo-Garcia et  al. 2007). For example, across two samples of undergraduates, Smith and colleagues (Cyders et al. 2009; Spillane et al. 2010) examined the relations between the five subscales of the UPPS-P and indices of cigarette smoking and alcohol consumption. In terms of cigarette use, SS distinguished between smokers and nonsmokers but did not predict the level of nicotine dependence. Similarly, although SS was related to drinking frequency, it was unrelated to the quantity used or to the number of problems experienced as a result of drinking. Several studies also suggest a more specific relation between SS and socially acceptable forms of risktaking, such as mountain climbing and bungee jumping (Cyders and Smith 2008; Zimmerman 2010). These results underscore the importance of the narrower view of SS in the UPPS-P relative to the broader conception contained in Zuckerman’s (1994) early formulations in which sensation seeking was defined as “the seeking of varied, novel, complex, and intense sensations and experiences” and “the willingness to take physical, social, legal, and financial risks for the sake of such experience” (p. 27).

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Third, both negative and positive urgency relate consistently to problematic levels of deviant behavior. Several studies have found that NU and/or PU are related to substance abuse (e.g., Fischer and Smith 2008; Smith et  al. 2007; Whiteside and Lynam 2003). Verdejo-Garcia et  al. (2007) compared a group of substance dependent individuals to a group of healthy controls on the four original UPPS scales. NU showed the largest difference between groups (Cohen d = 1.96), and a discriminant function analysis was able to correctly classify 83.3 and 80.6% of the substance dependent and control groups, respectively. Moreover, NU was the strongest predictor of addiction severity. Several studies suggest that the importance of NU and PU may extend to problem gambling as well as problematic substance use (e.g., Cyders and Smith 2008). Whiteside et al. (2005) examined the relations of the original four UPPS scales and an index of gambling-related problems in a sample recruited from psychological treatment centers, support groups for gambling and alcohol abuse, and hospital waiting rooms. Even after controlling for comorbid psychopathology (i.e., alcohol-related problems and symptoms of borderline and antisocial personality disorders) NU was significantly related to problem gambling. Fourth and finally, lack of PMD appears to be related to each type of deviance. It is one of the most consistent correlates of antisocial behavior and antisocial personality (Anestis et  al. 2009a, b; Lynam and Miller 2004; Whiteside and Lynam 2003). In the Whiteside et al. (2005) study described earlier, even after controlling for gambling problems, symptoms of BPD, and alcohol use problems, lack of PMD significantly predicted symptoms of antisocial personality disorder. This dimension has also been found to relate to alcohol, marijuana, and harder drug use (e.g., Lynam and Miller 2004) and to risky sex (e.g., Simons et al. 2010). This dimension may well represent the most general risk for deviant behavior. In addition to research examining the relations between dimensions of the UPPS-P and traditional concepts of deviance, research has also explored the relations between UPPS-P dimensions and several clinical disorders/problems, including disordered eating (e.g., Anestis et  al. 2007; Anestis et al. 2009b, Claes et al. 2005; Fischer et  al. 2004; Mobbs et  al. 2008), suicide and self-harm (Glenn & Klonsky 2010; Lynam et al. in press), and BPD (Bornovalova et al. 2010; Lynam et al. in press; Tragesser and Robinson 2009). Again, a fair degree of specificity is found between the various UPPS traits and outcomes. One of the most specific relations is the one observed between NU and bulimic symptoms. In a recent study using a large, clinical sample, Anestis et  al. (2009a, b) examined the relation between NU and bulimic symptoms. Even after controlling for 15 other variables (i.e., demographics, depression, anxiety, suicidality; levels of negative and positive affect, PMD, PSV, sensation seeking, and a global index of impulsivity), NU remained significantly related to bulimic symptoms. In fact, NU was the only significant predictor in the final model and provided an additional 8% increment in the variance accounted for. Underscoring BPD as a quintessential disorder of impulse control, both clinical and community samples have shown that NU and lack of PMD are both related to symptoms of BPD (e.g., Lynam et al. in press; Miller et al. 2003; Tragesser and Robinson 2009).

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Although only two studies have examined suicide and/or self-harm, the results are quite consistent. In a sample of over 1,000 undergraduates, Glenn and Klonsky (2010) explored the relations between the UPPS-P and nonsuicidal self-injury (NSSI; e.g., skin-cutting, burning). Both NU and lack of PMD distinguished between groups of self-injurers and noninjurers even after controlling for comorbid psychopathology, i.e., depression, anxiety, and alcohol abuse). Interestingly, scores from a performance measure of inhibition, the stop-signal task, failed to distinguish between groups. In a residential sample of drug abusers, Lynam et  al. (in press) examined the relations between the UPPS dimensions and suicidal behavior (SB) and NSSI. As in the study described previously, both NU and lack of PMD were most strongly and consistently related to multiple indicators of SB and NSSI. Most interesting in this study, however, was a significant and consistent interaction between the two UPPS dimensions. The  interactions was such that relations between NU and SB – NSSI were stronger among those low in PMD; in each case, the effect of NU was nonsignificant among those who premeditated more but significant and strong among those who premeditated less. The effects of the interaction were quite substantial, providing increments in the variance accounted for between 10 and 18%.

Using the UPPS-P to Integrate Research Findings The UPPS-P model has the potential of making several important contributions to our understanding of impulsive behavior. First, it seems to demonstrate clearly that the terms “impulsivity” and “disinhibition” lack specificity and reference not a single dimension or process, but rather a collection of separable traits. That is, the UPPS-P demonstrates that there is a collection of separable personality traits that contribute to behavior that seems ill-considered, risky, rash, and/or lacking purpose. Second, particularly with the work of Cyders, Smith, and colleagues, the UPPS model has drawn attention to affect-related traits that were relatively neglected previously. Third, and perhaps most importantly, the UPPS-P offers a framework or taxonomy that may help bring clarity to extant research and guide future research. An excellent example of how the UPPS-P might be used to organize extant research is provided in a meta-analysis by Fischer et  al. (2008) on the relation between bulimic symptoms and impulsivity/inhibition. Although previous studies on these relations had yielded mixed results, the authors reasoned this might be due to differences in which impulsivity-related traits were measured in these studies. These authors identified 50 studies with relevant effect sizes and then coded effect sizes based on which of the four original UPPS dimensions was assessed; these decisions were based on the original factor analyses reported by Whiteside and Lynam (2001) and on content analysis. Results from the meta-analysis supported the initial hypothesis of variation in effect size as a function of personality pathway. NU had the largest mean effect size (mean weighted r of 0.38), whereas PSV had the smallest (mean weighted r of 0.08). SS and PMD showed mean weighted effect sizes of 0.16. PU was not included in this particular study. These results are important.

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They illustrate the utility of making distinctions among various UPPS-P traits, underscore the importance of emotion-based predispositions to rash action, and suggest that future research further explore the role of NU and PU in bulimia. In an effort to facilitate future organizations of extant research, Table 8.2 provides correlations between the UPPS-P scales and several widely used measures of impulsivity: four facets from the NEO PI-R (Costa and McCrae 1992); four impulsivity Table 8.2  Correlations between UPPS-P scales and other commonly used measures PU

NU 0.69 0.49 0.56 0.22

PSV 0.36 0.75 0.37 0.03

PMD 0.34 0.36 0.72 0.25

SS 0.18 0.13 0.49 0.62

EASI inhibitory control EASI perseverance EASI decision time EASI sensation seeking

0.72 0.31 0.32 0.29

0.30 0.80 0.26 −0.01

0.22 0.44 0.63 0.25

0.13 −0.08 0.39 0.65

I7 impulsiveness I7 venturesome

0.63 0.13

0.35 −0.08

0.58 0.13

0.41 0.93

−0.31 0.37 0.11 0.42 0.37

−0.04 −0.15 −0.27 0.03 −0.11

−0.26 0.06 −0.19 0.24 0.04

0.07 0.52 0.25 0.66 0.30

0.51 0.07 0.54 0.29 0.58 0.43

0.41 −0.09 0.51 0.61 0.18 0.46

0.67 0.23 0.64 0.63 0.47 0.42

0.14 0.46 0.25 0.04 0.43 0.11

0.32 −0.11 0.23 0.45 0.27

0.08 −0.08 0.11 0.08 0.13

0.52 0.20 0.47 0.41 0.30

0.60 0.80 0.33 0.28 0.14

0.44 0.12 0.25 0.49 0.27

ZKPQ impulsive sensation seeking ZKPQ Impulsivity   ZKPQ sensation seeking

0.36 0.43 0.27

0.25 0.36 0.12

0.53 0.59 0.41

0.66 0.49 0.69

0.61 0.65 0.43

PRF impulsivity scale MPQ control scale TCI novelty seeking

0.50 0.44 0.33

0.47 0.47 0.34

NEO PI-R impulsiveness NEO PI-R self-discipline (R) NEO PI-R deliberation (R) NEO PI-R excitement seeking

C & W BIS total C & W BAS total   BAS raw response   BAS Fun Seeking   BAS Drive Dickman dysfunctional impulsivity Dickman functional imp. BIS 11   BIS nonplanning   BIS motor impulsiveness   BIS attentional impulsiveness SSS total SSS TAS   SSS ES   SSS disinhibition   SSS BS  

 

0.77 0.81 0.68

0.23 0.27 0.10

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subscales from the EASI-III Temperament Questionnaire (Buss and Plomin 1975); impulsivity and venturesomeness from the I7 (Eysenck et  al. 1985); Carver and White’s (1994) BIS/BAS scales; two impulsivity scales from Dickman (1990); the Barratt Impulsiveness Scale-11 (Patton et  al. 1995); Zuckerman’s Form V Sensation Seeking Scale (Zuckerman 1994); the impulsive sensation seeking scale from the Zuckerman–Kuhlman Personality Questionnaire III (Zuckerman et  al. 1993); the impulsivity scale from the Personality Research Form (PRF; Jackson 1984); the control scale from the Multidimensional Personality Questionnaire (MPQ; Tellegen 1982); and the novelty seeking scale from the Temperament and Character Inventory (TCI; Cloninger et  al. 1994). Correlations are presented for total and subscale scores; correlations were obtained from four different samples with Ns ranging from 180 for the SSS correlations to 390 for the EASI, BIS/BAS, and ZKPQ correlations. Within the table, small correlations (less than 0.30) appear in unshaded cells, moderate correlations (between 0.30 and 0.50) appear in lightly shaded cells, and large correlations (greater than 0.50) appear in more darkly shaded cells. There are several important findings in the table. First, there is fairly good correspondence, in terms of divergent and convergent correlations, between the four UPPS-P scales and the four EASI Impulsivity subscales. With the exception of the 0.56 ­correlation between PMD and NEO PI-R Urgency, the correspondence between the UPPS scales and the markers from the NEO PI-R is also quite good. Second, there are several other scales with high and rather specific relations to UPPS-P SS and PMD. For example, the correlations between UPPS-P SS and I7 venturesomeness (r = 0.93) and SSS V Thrill and Adventure Seeking (r = 0.80) are among the highest correlations in the table. The relatively high correlations for Carver and White’s BAS total and BAS fun-seeking scales suggest these scales are primarily assessing sensation seeking. Similarly, both Cloninger’s Novelty Seeking scale and the Control scale from the MPQ appear to be relatively specific indicators of a lack of PMD. Third, neither NU nor PU has many strong, unique relations with other scales suggesting a general underrepresentation in the field of these two personality pathways to impulsive behavior. Fourth, beyond the specific correlations already noted, most other impulsivity measures manifest strong correlations with more than one of the UPPS-P scales. Several represent blends of NU and PMD, e.g., NEO PI-R Deliberation, I7 Impulsiveness, and Dickman’s Dysfunc-tional Impulsivity. Others represent blends of UPPS-P PMD and SS, i.e., SSS V Total Score, ZKPQ Impulsive Sensation Seeking and ZKPQ Impulsiveness. Still others show different patterns of relations. Whereas the nonplanning subscale of the Barratt Impulsiveness Scale is strongly correlated with UPPS-P, PSV, and PMD, the total score from this inventory is strongly correlated with NU, PSV, and PMD. Finally, some impulsivity measures fail to manifest a single strong correlation with any of the UPPS-P scales. This is true for the BIS total score, BAS Reward Responsiveness, and BAS Drive scales of the Carver and White measure. It is also true for the Dickman Functional Impulsiveness scale, the Barratt Attentional Impulsiveness scale, and all SSS V subscales except for Thrill and Adventure Seeking.

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Summary and Future Directions Thus far, I have reviewed evidence that impulsivity and disinhibition are not unitary constructs. I have suggested that these broad constructs are, at best, artificial umbrella terms. I have discussed the development of a model that parses the various conceptions under these umbrellas. Support for the distinctiveness of these traits from each other, both in terms of their measurements and patterns of external correlates, has also been briefly reviewed. I believe strongly that the UPPS-P framework not only holds promise for integrating extant research, but also for guiding future research. I end this chapter with a few suggestions on what the most promising future directions might be. First and foremost is the integration of the UPPS-P model, which represents one modality for assessing problems with impulse control, with constructs from other modalities, especially behavioral tasks. Many investigators use laboratory tasks to measure variability in processes that are presumed to underlie or reflect impulsive behavior in the real world. There have been several recent attempts to identify laboratory task correlates of the UPPS dimension. For example, Zermatten et al. (2005) reported that performance on the Iowa Gambling Task was specifically related to a lack of PMD. Similarly, Lynam and Miller (2004) reported that a lack of PMD was the only ­significant correlate of performance on a delay discounting task. In contrast, the  recent study by Verdejo-Garcia et  al. (2010) failed to find a relation between any of the five UPPS-P dimensions and a measure of delay discounting. In yet another study, Gay et  al. (2008) examined the relations between the UPPS dimensions and three behavioral tasks – two Go/No-Go tasks and a recent negatives task to assess proactive interference in working memory. These authors found that NU was associated with errors in prepotent response inhibition, whereas PSV was associated with task-unrelated thoughts and errors due to difficulties overcoming proactive interference. These results are difficult to interpret for several reasons. First, there are too few studies; more studies are obviously needed. Second, although there have been a few theoretical attempts to organize the great variety of laboratory tasks (e.g., Dougherty et al. 2005), empirically based taxonomies have not been developed. Such taxonomies might be hard to develop given that most investigations employ a single task at a time and the absence of standard task versions. Thus, it is difficult to know what a given laboratory task is assessing, whether the task assesses something similar to or different from what is assessed by another task, and how a given task should be related to a given personality pathway to impulsive behavior. Within the framework offered by Dougherty et al. one might expect negative and positive urgency to be associated with difficulties in prepotent response inhibition, lack of PMD to be associated with tasks demanding delay of gratification, and lack of PSV to be associated with tasks requiring resistance to distractors. Which kinds of tasks should be related to sensation seeking is unclear. A second important future direction involves conceptually and empirically sharpening the definitions of the various UPPS-P traits and distinguishing them

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from other constructs. This is probably most necessary in the case of negative and positive urgency – the two traits least studied in the field. There are lingering questions about these two traits. How does NU relate to negative emotionality – the tendency to experience negative emotions in general? Does NU reflect a coping response or an affective short-circuiting of inhibitory control? Are negative and positive urgency conditional forms of “impulsivity” such that particular affective contexts are necessary to activate or observe the action of these traits? Is negative (or positive) urgency only responsive to negative (or positive) affect or will any type of strong affect do? Is the presence of a prepotent response, either through overlearning or contextual demands, a necessary condition? Some questions have begun to be addressed. Several studies have shown that the effects of negative or positive urgency are separable from the effects of negative or positive emotionality in general (e.g., Cyders and Coskunpinar 2010). For example, Lynam et  al. (in press) found that the correlation between NU and SB/NSSI held even after controlling for negative affectivity. The other questions have been less well-studied. A third future direction involves the search for the biological mechanisms that might underlie the various dispositions. Several mappings have been offered. For example, Bechara (2005) suggests that three of the original four UPPS dimensions may reflect the functioning of different regions within the ventromedial prefrontal cortex (VMPC). He offers that NU may reflect deficient functioning within the ­posterior regions of the VMPC, which includes the anterior cingulate and basal forebrain. In contrast, a lack of PSV is hypothesized to reflect problems in the lateral orbitofrontal and dorsolateral regions of the prefrontal cortex. Cyders and Smith (2008) hypothesize that the biological underpinnings of negative and positive urgency lie in the connections between the amygdale and orbitofrontal/VMPC. Although these hypotheses appear compelling, there are very few studies that directly examine the relations between the UPPS-P traits and biological processes. In one of the few studies, Joseph et  al. (2009) examined the neural correlates of several UPPS-P dimensions using fMRI responses to high and low arousal pictures. Comparisons of high and low sensation seekers (HSSs and LSSs) indicated that HSSs showed stronger fMRI responses to high arousal stimuli in brain regions associated with arousal and reinforcement (i.e., right insula, posterior medial, and orbitofrontal cortex), whereas LSSs showed greater activation in regions involved in emotional regulation (i.e., anterior medial orbitofrontal cortex and anterior cingulated). Additionally, NU was negatively related to activation in the regulatory regions as well. In addition to studying the biological underpinnings of the UPPS-P traits, future research should examine the potential moderating effects that environments might have on the relations between these dispositions to rash action and deviant behavior. Several studies now demonstrate that the influence of impulsivity-related traits may depend upon the context in which individuals find themselves. Using a composite index of “impulsivity” in a large, high-risk sample of boys from inner-city Pittsburgh, Lynam et  al. (2000) found that the effect of impulsivity on antisocial behavior depended strongly on the neighborhoods in which boys lived. In wealthier neighborhoods, impulsivity bore little relation to ASB. In more impoverished neighborhoods, however, there was a strong, positive relation between impulsivity

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and crime. This interaction was present in both concurrent and longitudinal analyses. Lynam et al. suggested that the neighborhoods’ SES stood as a proxy for the level of external control present in the neighborhood and that the interaction indicated that external control was particularly important when individuals lacked internal controls. Jones and Lynam (2009) recently replicated these findings in a community sample of young adults using separate measures of SS and PMD and direct assessments of perceived external controls. This kind of research may be particularly important in that it tells something of the processes underlying the various UPPS-P traits as well as suggesting potential avenues for environmental interventions.

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Cleckley, H. (1976). The Mask of Sanity (5th. ed.). St. Louis, MO: Mosby. Cloninger, C.R., Przybeck, R.R., Svrakic, D.M., & Wetzel, R.D. (1994). The temperament and character inventory (TCI): A guide to its development and use. St. Louis, MO: Center for Psychobiology of Personality. Costa, P.T. Jr. & McCrae, R.R. (1992). NEO-PI-R: Revised NEO Personality Inventory (NEOPI-R) and NEO Five-Factor Inventory (NEO-FFI) professional manual. Odessa, FL: Psychological Assessment Resources. Cyders, M.A., & Coskunpinar, A. (2010). Is urgency emotionality? Separating urgent behaviors from effects of emotional experiences. Personality and Individual Differences, 48, 839–844. Cyders, M.A., Flory, K., Rainer, S., & Smith, G.T. (2009). The role of personality dispositions to risky behavior in predicting first-year college drinking. Addiction, 2, 193–202. Cyders, M.A., & Smith, G.T. (2007). Mood-based rash action and its components: Positive and negative urgency. Personality and Individual Differences, 43, 839–850. Cyders, M.A., & Smith, G.T. (2010). Longitudinal Validation of the Urgency Traits Over the First Year of College. Journal of Personality Assessment, 92, 63–69. Cyders, M.A., & Smith, G.T. (2008). Clarifying the role of personality dispositions in risk for increased gambling behavior. Personality and Individual Differences, 45, 503–508. Cyders, M.A., Smith, G.T., Spillane, N.S., Fischer, S., & Annus, A.M. (2007). Integration of impulsivity and positive mood to predict risky behavior: Development and validation of a measure of positive urgency. Psychological Assessment, 19, 107–118. D’Acremont, M. & Van der Linden. (2005). Adolescent impulsivity: Findings from a community sample. Journal of Youth and Adolescence, 34, 427–435. Depue, R.A., & Collins, P.F. (1999). Neurobiology of the structure of personality: Dopamine, facilitation of incentive motivation, and extraversion. Behavioral and Brain Sciences, 22, 491–569. Dickman, S.J. (1990). Functional and dysfunctional impulsivity: personality and cognitive correlates. Journal of Personality and Social Psychology, 58, 95–102. Dougherty, D.M., Mathias, C.W., Marsh, D.M., & Jagar, A.A. (2005). Laboratory behavioral measures of impulsivity. Behavioral Research methods, 37, 82–90. Elkins, I.J., King, S.M., McGue, M., & Iacono, W.G. (2006). Personality traits and the development of nicotine, alcohol, and illicit drug disorders: Prospective links from adolescence to young adulthood. Journal of Abnormal Psychology, 115, 26–39 Eysenck, S.B.G., & Eysenck, H.J. (1978). Impulsiveness and venturesomeness in a dimensional system of personality description. Psychological Reports, 43, 1247–1255. Eysenck, S.B.G., Pearson, P.R., Easting, G., & Allsopp, J.F. (1985). Age norms for impulsiveness, venturesomeness and empathy in adults. Personality and Individual Differences, 6, 613–619. Fischer S., Anderson K.G., & Smith G.T. (2004). Coping with distress by eating or drinking: Role of trait urgency and expectancies. Psychology of Addictive Behaviors, 18, 269–274. Fischer, S. & Smith, G.T. (2008). Binge eating, problem drinking, and pathological gambling: Linking behavior to shared traits and social learning. Personality and Individual Differences, 44, 789–800. Fischer, S., Smith, G.T., & Cyders, M.A. (2008). Another look at impulsivity: A meta-analytic review comparing specific dispositions to rash action in their relationship to bulimic symptoms. Clinical Psychology Review, 28, 1413–1425. Gay, P., Rochat, L., Billieux, J., d’Acremont, M., & Van der Linden, M. (2008). Heterogeneous inhibition processes involved in different facets of self-reported impulsivity: Evidence from a community sample. Acta Psychologica, 129, 332–339. Glenn, C.R., & Klonsky, E.D. (2010) A multimethod analysis of impulsivity in nonsuicidal selfinjury. Personality Disorders: Theory, Research, and Treatment, 1, 67–75. Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Stanford University Press. Hare, R.D. (2003). The Hare Psychopathy Checklist--Revised, 2nd edition. Toronto, Ontario, Canada: Mulit-Health Systems. Hoyle, R.H., Fejfar, M.C., & Miller, J.D. (2000). Personality and sexual risk taking: A quantitative review. Journal of Personality, 68(6), 1203–1231.

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Iacono, W.G., Malone, S.M., & McGue, M. (2008). Behavioral disinhibition and the development of early-onset addiction: Common and specific influences. Annual Review of Clinical Psychology, 4, 325–348. Jackson, D.N. (1984). Personality research from manual. Goshen, NY: Research Psychologists Press. Jones, S., & Lynam, D.R. (2009). In the eye of the impulsive beholder: The independent and interactive influences of impulsivity and perceived informal social control on offending behavior. Criminal Justice and Behavior, 36, 307–321. Joseph, J., Liu, X., Jiang, Y., Lynam, D., & Kelly, T. (2009). Neural correlates of emotional reactivity in sensation seeking. Psychological Science, 20, 215–223. Kampfe, N., & Mitte, K. (2009). A German validation of the UPPS impulsive behavior scale: Further evidence for a four-dimensional model of impulsivity. European Journal of Psychological Assessment, 25, 252–259. Lynam, D.R. (1996). The early identification of chronic offenders: Who is the fledgling psychopath? Psychological Bulletin, 120, 209–234. Lynam, D.R., Caspi, A., Moffitt, T.E., Wikström, P.O., Loeber, R., Novak, S.P. (2000). The interaction between impulsivity and neighborhood context on offending: The effects of impulsivity are stronger in poorer neighborhoods. Journal of Abnormal Psychology, 109, 563–574. Lynam, D.R. & Miller, J.D. (2004). Personality pathways to impulsive behavior and their relations to deviance: Results from three samples. Journal of Quantitative Criminology, 20(4), 319–341. Lynam, D.R., Miller, D.J., Vachon, D.D., Loeber, R., & Stouthamer-Loeber, M. (2009). Psychopathy in adolescence predicts official reports of offending in adulthood. Youth Violence and Juvenile Justice, 7, 189–207. Lynam, D.R., Miller, J.D., Miller, D.J., Bornovalova, M.A., & Lejuez, C.W. (in press). Testing the relations between impulsivity-related traits, suicidality and non-suicidal self-injury: A test of the incremental validity of the UPPS model. Personality Disorders: Theory, Research, & Treatment. Miller, J.D., Flory, K., Lynam, D.R., & Leukefeld, C. (2003). A test of the four-factor model of impulsivity-related traits. Personality and Individual Differences, 34, 1403–1418. Miller, J.D. & Lynam, D.R. (2001). Structural models of personality and their relation to antisocial behavior: A meta-analytic review. Criminology, 39, 765–792. Mobbs, O., Ghisletta, P., & Van der Linden, M. (2008). Clarifying the role of impulsivity in dietary restraint: A structural equation modeling approach. Personality and Individual Differences, 45, 602–606. Moffitt, T.E. (1993). Adolescence-limited and life-course persistent antisocial behavior: a developmental taxonomy. Psychological Review, 100, 674–701. Patton, J.H., Stanford, M.S., & Barratt, E.S. (1995). Factor structure of the Barratt Impulsiveness Scale. Journal of Clinical Psychology, 51, 768–774. Pinkerton, S.D., & Abramson, P.R. (1992). Is risky sex rational? Journal of Sex Research, 29, 561–568. Pinkerton, S.D., & Abramson, P.R. (1995). Decision making and personality factors in sexual risk-taking for HIV/AIDS: A theoretical integration. Personality and Individual Differences, 19, 713–723. Ruiz, M.A., Pincus, A.L., & Schinka, J.A. (2008). Externalizing pathology and the Five-Factor Model: A meta-analysis of personality traits associated with antisocial personality disorder, substance use disorder, and their co-occurrence. Journal of Personality Disorders, 22, 365–388. Sher, K.J., Bartholow, B.D., & Wood, M.D. (2000). Personality and substance use disorders: A prospective study. Journal of Consulting and Clinical Psychology, 68, 818–829. Simons, J.S., Maisto, S.A., & Wray, T.B. (2010). Sexual risk taking among young adult dual alcohol and marijuana users. Addictive Behaviors, 35, 533–536. Smith, G.T., Fischer, S., Cyders, M.A., Annus, A.M., Spillane, N.S., McCarthy, D.M. (2007). On the validity and utility of discriminating among impulsivity-like traits. Assessment, 14, 155–170. Spillane, N.S., Smith, G.T., & Kahler, C.W. (2010). Impulsivity-like traits and smoking behavior in college students. Addictive Behaviors, 35, 700–705.

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Chapter 9

Impulsivity and Adolescent Substance Use: From Self-Report Measures to Neuroimaging and Beyond Matthew J. Gullo, Sharon Dawe, and Meredith J. McHugh

Abstract  The capacity to regulate emotional impulses and pursue appropriate long-term goals is an integral part of adaptive human functioning. Therefore, it is not surprising that this capacity consistently emerges as a core trait in biologically-based models of personality. Variously labeled as “impulsivity,” “sensation seeking,” or “constraint” amongst other terms, variations in this trait reliably predict the development of substance use problems in prospective studies. Notably, marked increases in this impulsivity trait appear during adolescence – a period of life when substance experimentation and abuse typically begins. In recent years, neuroimaging research has identified the orbitofrontal and anterior cingulate cortices as important neural substrates of trait impulsivity. Interestingly, these same brain regions undergo substantial development during the teenage years. Indeed, there is remarkable consistency in the time course of these neural changes with those at the level of personality, suggesting both the imaging scanner and the self-report questionnaire are tapping into the same underlying construct, albeit with a differing degree of precision. Despite its far greater precision, the scanner itself cannot be practically employed in large-scale prevention programs to identify teens at risk. However, in validating the biological basis of impulsivity, along with behavioral and self-report measures of the trait, neuroimaging research allows one to use these more cost-effective tools in primary prevention with greater confidence. Indeed, there is already evidence demonstrating the ability of such “blunt” tools to focus and improve prevention programs.

Introduction Numerous prospective studies have shown that children with poor inhibitory control (i.e., “impulsive”) are at greater risk of future substance use and abuse during adolescence, suggesting it to be an important marker of future risk

S. Dawe (*) School of Psychology, Griffith University, Mt. Gravatt Campus, Brisbane 4101, Australia e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_9, © Springer Science+Business Media, LLC 2011

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(Caspi 2000; Lynskey et  al. 1998). Interestingly, this is true irrespective of how inhibitory control is actually measured; that is, whether it is assessed using an electroencephalogram (EEG), behavioral tasks, self-report questionnaires, or ratings from a parent or teacher (Caspi 2000; Habeych et al. 2005; Lynskey et al. 1998; Tarter et al. 2003). Most commonly, prospective studies have employed self-report measures of personality to assess impulsivity. Indeed, every major model of personality includes a trait that reflects inhibitory control, suggesting that this vulnerability marker is a core dimension along which all human beings vary. However, we believe that biologically-based models have the greatest theoretical potential to account for the overlap in findings across neuroimaging, behavioral, and self-report measures of adolescent impulsivity. This issue will be the focus of the first section of the chapter. Adolescence is a life-stage characterized by heightened impulsivity. It is also a time of profound neurodevelopment, during which the brain exhibits a heightened sensitivity to rewards coupled with poor (prefrontal) inhibitory control (Galvan et  al. 2006). Interestingly, substance use and abuse typically commences during adolescence (Wright et al. 2007) and we believe this may largely be due to natural increases in impulsivity resulting from these neurodevelopmental changes (Gullo and Dawe 2008). This issue will be the focus of the second and third sections of the chapter where we will review evidence demonstrating that increased impulsivity in adolescence associated with substance abuse has been observed across all domains of measurement: neuroimaging, behavior, and self-report. An often overlooked issue in adolescent substance use is how, from a public health perspective, a greater understanding of neurodevelopmental and personality changes can be best utilized to enhance early intervention and prevention efforts. While functional magnetic resonance imaging (fMRI) and other neuroimaging technologies have greatly enhanced our understanding of both the pathophysiology of substance use disorders and adolescent brain development, such tools are too expensive and time-consuming to employ as part of primary prevention work. It is just not feasible to scan a school full of adolescent brains to identify those at risk of future substance abuse/dependence and then intervene. However, given the close relationships among brain function, impulsive behavior, and general personality, neuroimaging research can still have a substantive impact on primary prevention work through less direct means. Advances in neuroimaging technology have allowed more detailed investigation and validation of biological theories of personality and the tools used in this field. Furthermore, we will argue that the benefits of this validation run both ways. That is, in validating the proposed neural basis of less direct impulsivity measures, such as behavioral tasks and self-report questionnaires, a more practical and cost-effective means of risk assessment becomes available to the clinician “on the ground.” In other words, can we use cruder measures of “neurobehavioral risk” that still tap into the same underlying construct? This will be the focus of the final section of the chapter. To summarize, this chapter aims to (1) review biological models of trait impulsivity and approaches to its measurement, (2) discuss the consistency in findings among these different

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approaches and how this is most clearly demonstrated in adolescent development, (3) explore the practical implications of how this overlap in findings can be used to improve the prevention of substance use problems in adolescents.

Theoretical Models of Impulsivity and Its Measurement Every major theory of human personality includes a trait that reflects general inhibitory control, or impulsivity. Eysenck (1967) was the first to propose a biologically-based theory of personality. According to his original model, the causes of impulsive behavior were biologically-based, and resulted from low cortical arousal. He argued that increased cortical activity inhibited the activity of lower brain structures and that human beings naturally varied in their baseline level of cortical arousal. Eysenck (1967) labeled this biologically-based dimension of behavior extraversion, whereby individuals high in extraversion were characterized by low cortical arousal (“extraverts”) and individuals low in extraversion were characterized by high cortical arousal (“introverts”). He argued that because extraverts had low baseline cortical arousal, they were more likely to seek out further stimulation to achieve “optimal arousal.” Therefore, extraverts were predicted to be more likely to engage in impulsive, sensation-seeking acts, like hazardous substance use. Importantly, nonbiological models of personality also regard extraversion to be a core dimension of human personality, suggesting it to be something of a universal trait (Depue and Collins 1999; Digman 1990). Over the past 40 years, theoretical work on the biological basis of extraversion has advanced considerably. This began with the work of Jeffrey Gray, a student of H. J. Eysenck, who determined that extraverts were not merely drawn to stimulation or arousal of any kind, but rather to that which resulted from rewarding experiences (Gray 1981; Pickering and Gray 1999). Gray’s position, based primarily on laboratory animal research, was that sensitivity to reward stimuli (and motivation to approach it) lay at the core of extraversion (Gray 1981; Pickering and Gray 1999). In three studies, Lucas and Diener (2001) reported that this was also the case for human extraversion. Gray (1981) also challenged the hypothesis that cortical arousal was the primary biological determinant of extraversion and impulsive behavior. According to Gray (1981), an individual’s sensitivity to reward (and, by extension, level of extraversion) was mediated by the functioning of a brain system, which he referred to as the behavioral approach system (BAS). Neurobiologically, the BAS comprised key structures in the mesolimbic dopamine system (Gray 1981; Pickering and Gray 1999). Specifically, dopaminergic projections from the ventral tegmental area (VTA) to the nucleus accumbens were argued to be an important component of the BAS (Pickering and Gray 1999). Indeed, the mesolimbic dopamine system has been shown to play an important role in extraversion and reward-related behaviors, such as approach motivation and reward conditioning

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(Depue and Collins 1999; Pickering and Gray 1999). The applicability of this model to adolescent substance abuse is clear. Extraverts, due to their greater reward sensitivity, would be more likely to approach and consume alcohol and illicit drugs, and the evidence supports this notion (Cooper et al. 2000; Dawe and Loxton 2004; Sher et al. 2005). Based on Gray’s work, and that of others, Eysenck (1993) later conceded that impulsive behavior was probably not solely related to low cortical arousal and extraversion. Accumulating evidence began implicating other biological substrates, such as the dopamine and serotonin neurotransmitter systems and the orbitofrontal cortex (Cools et  al. 2008; Eysenck 1993; Gray 1981). In H. J. Eysenck’s theory, dopamine and serotonin were related to psychoticism, another trait in his “Big Three” model of personality (Eysenck 1993; Rawlings and Dawe 2008). Interestingly, this trait has also emerged as a predictor of impulsive acts, including adolescent substance use (e.g., Knyazev 2004). H. J. Eysenck’s revised position was that impulsive behavior could result from high extraversion as well as high psychoticism (Eysenck 1993; Rawlings and Dawe 2008). It is important to point out that the label “psychoticism” is now somewhat misleading. While it is true that H. J. Eysenck originally conceptualized the trait as reflecting “psychosis-proneness,” subsequent revisions to its operationalization and proposed biological basis suggest that it now more correctly reflects antisociality and impulsivity (Digman 1990; Rawlings and Dawe 2008). Interestingly, however, a trait similar to psychoticism has consistently emerged in other models of personality. Psychoticism, as it is currently operationalized, bears a close resemblance to Watson et al. (1994) disinhibition trait, (low) constraint in Tellegen’s (1982) multidimensional model of personality, as well as (low) conscientiousness and (low) agreeableness in the “Big Five” taxonomy (Depue and Collins 1999; Digman 1990; Watson et  al. 1994). Poor prefrontal inhibitory control and reduced serotonergic functioning have been implicated as common biological substrates across these traits (Depue and Collins 1999; Gullo and Dawe 2008). The orbitofrontal region of the prefrontal cortex, in particular, appears to be an important neurological substrate for this behavioral dimension (Eysenck 1993; Gray 1981; Horn et  al. 2003). Low constraint is associated with a pattern of orbitofrontal functioning suggestive of poor affect regulation (Brown et al. 2006; Silbersweig et al. 2007). For example, when neural responses to emotionally evocative versus neutral stimuli are compared, adults low in constraint show reduced metabolism in the orbitofrontal cortex (Brown et al. 2006; Silbersweig et al. 2007). Thus, low constraint is associated with reduced engagement of higher-level inhibitory control mechanisms which may result in more enduring responses in lower limbic regions associated with emotional processing (Hare et al. 2008). Given the strong similarity of these traits, and to avoid confusion, we will refer to this trait as “constraint” rather than “psychoticism.” Factor analytic studies have shown that Zuckerman’s (1991) (impulsive) sensation seeking and Cloninger’s (1987) novelty seeking traits also strongly tap into the constraint dimension of behavior (Depue and Collins 1999; Zuckerman et al. 1993).

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However, a notable point of distinction is the greater emphasis on reward-driven, disinhibited approach behavior in novelty seeking and sensation seeking (e.g., “I often try new things just for fun or thrills, even if most people think it is a waste of time”; Cloninger 1989). This is also the case in other trait measures, such as the I7 (Impulsiveness) scale (e.g., “Do you often buy things on impulse?” Eysenck et al. 1985) and the Barratt Impulsiveness Scale (e.g., “I spend or charge more than I earn”; Patton et al. 1995), but to a somewhat lesser extent. In this sense, they can be considered derivative traits, representing an interaction between the more fundamental dimensions of extraversion/reward sensitivity and constraint (see Fig. 9.1; Dawe and Loxton 2004; Depue and Collins 1999). Indeed, compared to the “purer” constraint traits (e.g., conscientiousness), these derivative traits show higher correlations with those from the extraversion domain (Depue and Collins 1999; Zuckerman et  al. 1993). Furthermore, data from biological studies also suggest an overlap, showing significant dopamine involvement in traits like novelty-seeking and sensation seeking – the key neurotransmitter system in extraversion (Leyton et al. 2002; Zuckerman 1991). In sum, evidence from both selfreport and biological studies suggests that these traits comprise elements of extraversion and constraint. While personality taxonomists may scoff at the “purity” of such derivative traits, they afford many practical and theoretical advantages to the substance abuse researcher. Numerous studies have shown that derivative traits like novelty seeking are stronger predictors of substance use problems than “purer” traits from the reward sensitivity and constraint domains (Sher et  al. 2000, 2005). Sher et  al. (2000) prospectively followed 457 first-year college students and found that while extraversion, novelty seeking, and (low) constraint all predicted the presence of concurrent substance use disorders, only novelty seeking and constraint predicted

Fig 9.1  Simplified structural model demonstrating conceptual relations among major personality traits. Note: BAS, behavioral approach system; BIS, behavioral inhibition system. Superscript “a” refers to Cloninger’s (1987) trait, not the harm avoidance subcomponent of Tellegen’s (1982) constraint. Superscript “b” refers to conceptual location of self-report measures of BIS, which are based on Gray’s (1981) earlier conceptualization of trait

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diagnostic status at 6-year follow-up. Furthermore, novelty seeking was a somewhat stronger predictor than constraint. The combined high reward sensitivity/low constraint trait, which Dawe and Loxton (2004) labeled rash impulsiveness, is also of greater theoretical relevance to addiction. Neuroscience research suggests addiction is the result of the combined effect of dysfunction in two biological processes (Goldstein and Volkow 2002; Jentsch and Taylor 1999). The first of these processes involves a heightened ­sensitivity to reward mediated by hyperfunctioning of the mesolimbic dopamine system, consistent with Robinson and Berridge’s (2001) theory of incentive sensitization. However, we have argued that this sensitization can be influenced by preexisting factors, and is not solely the result of chronic substance use (Dawe et al. 2004). Indeed, measures of trait reward sensitivity (but not constraint) predict stronger reward-related physiological responses to alcohol in nondependent young adults (Brunelle et al. 2004), as well as cue-elicited urge to drink in social drinkers (Kambouropoulos and Staiger 2004). Importantly, these same measures have also been shown to correlate with gray matter volume in the striatum, a key region of the mesolimbic dopamine system (Barros-Loscertales et  al. 2006). Therefore, adolescents high in reward sensitivity/extraversion may be at increased risk for substance misuse due to a heightened susceptibility to incentive sensitization of drug cues, caused by underlying differences in mesolimbic dopamine functioning (Dawe et al. 2004). The second key process in addiction involves deficient inhibitory control mediated by hypofunctioning of the orbitofrontal and anterior cingulate cortices (Goldstein and Volkow 2002; Jentsch and Taylor 1999). This is consistent with human and animal lesion studies that have identified these brain regions as critical to response inhibition and the ability to learn that a previously rewarded behavior (e.g., illicit drug use) now results in punishment (i.e., reversal learning; Fellows and Farah 2003). While it was originally thought that such dysfunction resulted solely from chronic substance abuse, we have previously reviewed evidence suggesting such inhibition differences may be preexisting and could confer risk for developing dependence (Dawe et  al. 2004). For instance, constraint and rash impulsiveness (but not reward sensitivity) have been associated with poorer performance on reversal learning tasks sensitive to the orbitofrontal cortex (Franken et  al. 2008; Gullo et al. 2010), as well as metabolic activity in this brain region during response inhibition (Horn et al. 2003). To summarize, neuroscience research has determined that addiction is characterized by heightened reward sensitivity within the context of poor inhibitory control, and prospective self-report studies have determined that the best predictors of adolescent substance abuse are derivative traits reflecting high reward sensitivity combined with low constraint (i.e., rash impulsiveness). We believe that rash impulsiveness measures, at the level of self-report, are tapping into stable behavior patterns arising from the same neurobiological substrates involved in addiction. An often overlooked determinant of impulsive behavior and substance use is neuroticism, or sensitivity to punishment. Within Eysenck’ s (1967) original model, neuroticism exists as a third orthogonal dimension relating to emotionality, with

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individuals high in neuroticism being more reactive to emotionally arousing events. Later, Tellegen (1982) proposed a highly similar negative emotionality dimension which he aligned with sensitivity to stress and aversive stimuli. The neuroticism/punishment sensitivity dimension identified by H. J. Eysenck and Tellegen also bears a close resemblance to Cloninger’s (1987, 1989) harm avoidance and Watson et al.’s (1994) negative temperament, as well as the neuro­ ticism trait in “Big Five” models (Digman 1990). This trait is highly heritable, and has been theorized to relate to individual differences in the functioning of the amygdala and septo-hippocampal defense system (Gray and McNaughton 2000). There is some evidence suggesting that neuroticism is related to the serotonin transporter gene linked polymorphic region (5-HTTLPR), which regulates the reuptake of serotonin at the synapse (Munafo et  al. 2009). Interestingly, the 5-HTTLPR genotype, as well as neuroticism itself, has been linked to amygdala activity (Munafo et al. 2008; Silbersweig et al. 2007). This is consistent with the proposed biological basis of neuroticism. High neuroticism/punishment sensitivity has been linked to substance use onset among adolescents (Elkins et al. 2006), as well as the presence of drug dependence in adult samples (McGue et al. 1999). In understanding the link between neuroticism/ punishment sensitivity and substance use, it is important to consider that avoidance of aversive stimuli can be achieved through approach to cues associated with relief from distress (Gray and McNaughton 2000). Preclinical studies have shown that rats bred to exhibit high reactivity to stress evidence higher levels of dopamine in the nucleus accumbens (a neurobiological substrate of reward sensitivity) and are more likely to self-administer amphetamine (Piazza et al. 1991). For adolescents with high levels of neuroticism/punishment sensitivity, substance use may present a salient source of temporary relief from distress, or a means of coping (Cooper et al. 2000). Furthermore, this vulnerability may be exacerbated by concurrent high levels of reward sensitivity and low constraint (Cooper et al. 2000). Individuals high in both reward sensitivity and neuroticism/punishment sensitivity also appear to be particularly prone to disinhibited behavior. Patterson et al. (1987) found that individuals high in both neuroticism and extraversion exhibited the most inhibition failures on a Go/NoGo behavioral task, primarily because they spent the least amount of time reflecting on their past errors. Patterson et al. (1987) argued that, in response to error-related negative feedback, the heightened arousal of punishment-sensitive individuals served to facilitate continued disinhibition. Another recent approach to explaining the role of neuroticism in impulsive behavior has been to examine it in combination with poor inhibitory control. The derivative trait, urgency, reflects the tendency to engage in impulsive behavior to alleviate negative affect (Whiteside et  al. 2005). Theoretically, this scale appears to correspond to high neuroticism and low constraint in broader personality models. Trait urgency has been shown to predict various behaviors characterized by impulsivity, particularly, borderline personality features (Whiteside et  al. 2005). However, despite its clear heuristic value, a number of studies have failed to find a predictive role for urgency in substance use after controlling for other impulsivity traits (e.g., Cyders et  al. 2009; Whiteside et  al. 2005). Clearly,

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more research is needed to discern the precise role of this trait in adolescent substance use (Sher et al. 2005). In sum, neuroticism/punishment sensitivity presents an additional trait-level risk factor for adolescent substance use. However, its role in the etiology of substance abuse is likely to be more complex. Neurobiological and behavioral evidence suggests that high neuroticism/punishment sensitivity may serve to heighten reward sensitivity and undermine reflection during moments of distress, ultimately serving to facilitate approach to substances for relief (Gray and McNaughton 2000; Gullo and Dawe 2008; Patterson et al. 1987; Piazza et al. 1991).

Personality Change During Adolescence and Its Biological Substrates Adolescence is a time of increased risk-taking and experimentation (Gullo and Dawe 2008). The tendency to act without consideration of future consequences is a hallmark of both the adolescent phase, as well as the personality trait of impulsivity. While personality traits are relatively enduring and stable across the lifespan, there are mean-level changes in impulsivity-related traits during adolescence and this likely conveys added risk for substance misuse. Studies that compare adolescents with younger children report an increase in personality traits related to impulse control. Canals et  al. (2005) found that scores on both H. J. Eysenck’s extraversion and psychoticism scales increase from late childhood (age 10 and 11 years) to middle adolescence (age 14 and 15 years). Donnellan et al. (2007) investigated personality changes from adolescence to adulthood in a sample of young people at age 17 years and again at 27 years. The largest effects were found in the mean level decreases in negative emotionality (Cohen’s d = −0.95) and increases in constraint (Cohen’s d = 0.56). There were also significant decreases of lesser magnitude in social dominance, one of two facets of extraversion (Cohen’s d = −0.29). Similar findings were recently reported by Lucas and Donnellan (2009) with the Big Five traits in a nationally representative sample of Australians (N = 12,618; age range 15–85). They found that, over the lifespan, levels of extraversion and neuroticism were highest at 15 years of age, while levels of conscientiousness and agreeableness were at their lowest. In a meta-analysis of 92 longitudinal studies of personality, Roberts et al. (2006) found that during adolescence, people generally exhibit more extraversion/social dominance, but do not start showing increases in constraint/conscientiousness until later on, during early adulthood. In other words, the evidence suggests that there is a “time-lag” in trait changes during development, with the typical adolescent being higher in extraversion and neuroticism, and lower in constraint. Thus, in summary, there is strong evidence for changes in personality during adolescence and the transition to adulthood. These changes are typically viewed within a functional maturity framework as such changes can be seen to facilitate fulfillment of key adult roles (Donnellan et  al. 2007). However, we have emphasized the role of

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biological processes in driving this change, as there is remarkable consistency in the extent to which these personality changes correspond with underlying neuro­ development (Gullo and Dawe 2008).

Biological Underpinnings of Adolescent Personality Change It is difficult to overstate the magnitude of change occurring within the brain during the transition from childhood to adolescence and then to adulthood. At a structural level, this period is characterized by widespread reduction in gray matter volume and increases in white matter volume, thought to primarily reflect processes of dendritic/synaptic pruning, myelination, and axonal reorganization (Giedd et  al. 1996; Gogtay et  al. 2004; Paus et  al. 1999). These developmental changes are generally considered to enhance neuronal processing and support the significant improvement in cognitive abilities seen during this period (Luna et al. 2004). Although significant structural changes occur throughout the adolescent brain, the rate of development varies between regions, with prefrontal cortical areas, linked to cognitive, emotional, and behavioral control, maturing at the slowest rate (Gogtay et  al. 2004). The delayed development of orbitofrontal and anterior cingulate regions, in particular, is thought to significantly contribute to the increase in impulsive behavior seen during this period (Galvan et al. 2006). That is, while such developments are crucial for adaptive adult behavior, this transitional phase may also leave the adolescent vulnerable to a variety of impulsive behaviors, including substance abuse. Galvan et al. (2006) examined developmental changes in brain activity during a behavioral task that involved a choice between two cues associated with varying amounts of reward. They found that while adolescents and adults exhibited a similar magnitude of orbitofrontal activation during these trials, the pattern of activation exhibited by adolescents resembled that of the children in their sample, being more diffuse and less focal than that observed in adults. Additionally, they reported greater magnitude of nucleus accumbens activation in adolescents compared to children. Their findings suggest heightened mesolimbic reward responses within the context of an immature, “child-like” orbitofrontal cortex. Eshel et  al. (2007) directly examined age-related differences in brain activity during “risky” decision making. They employed the wheel of fortune task, which involved choosing between one of two probabilistic reward options under high-risk or low-risk conditions. High-risk trials involved choices between a 10% chance of winning $4.00 versus a 90% chance of winning $0.50. On low-risk trials, participants made a choice between a 30% chance of winning $2.00 and a 70% chance of winning $1.00. They found that during high-risk trials, adolescents exhibited reduced orbitofrontal and anterior cingulate activation relative to adults in their sample. This is also consistent with the view of reduced orbitofrontal and anterior cingulate engagement by adolescents during risky decision-making.

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Adolescence is further typified by increased reactivity in mesolimbic reward circuitry (Ernst et  al. 2005; Galvan et  al. 2006), a pattern that may underlie the heightened reward sensitivity observed during this period at the self-report level (Donnellan et al. 2007; Lucas and Donnellan 2009; Roberts et al. 2006). Specifically, Galvan et  al. (2006) reported greater reward-related activation in the nucleus accumbens among adolescents relative to children. Additionally, Ernst et al. (2005) reported greater reward-related activation in the nucleus accumbens among adolescents relative to adults. Together these findings suggest that adolescents may experience rewards as more salient compared to children and adults. Recent evidence also suggests that this heightened limbic reactivity during adolescence may extend to regions associated with threat-processing and avoidance motivation, such as the amygdala (Gray and McNaughton 2000). For example, Hare et al. (2008) observed greater amygdala activity in adolescents relative to both children and adults during the processing of fearful faces. Furthermore, poor habituation of this fear-related amygdala activity was associated with reduced coupling of the orbitofrontal and amygdala regions. Hare et al. (2008) argue that the strength of orbitofrontal–amygdala coupling may underlie individual differences in the ability to control/regulate emotional responses, particularly during the adolescent years. This is consistent with self-report data showing higher neuroticism during adolescence (Donnellan et al. 2007). This pattern of immature orbitofrontal/anterior cingulate control mechanisms coupled with heightened reactivity of subcortical reward and punishment regions is consistent with mean-level changes seen in extraversion, neuroticism, and constraint during the adolescent years. Importantly, there is also evidence to suggest that these changes convey further risk for adolescent substance abuse. In a sample of 107 adolescents, Hill et  al. (2009) found low scores on constraint/inhibitory control were associated with reduced orbitofrontal volume. Critically, however, they also found that these volume reductions (associated with low constraint) were most pronounced in the offspring of alcohol-dependent individuals selected for genetic risk. Their findings thus suggest that low constraint in adolescence is associated with increased neurological vulnerability for substance use disorders.

Implications for Early Intervention and Prevention It is clear from the preceding discussion that there is significant overlap among self-report, behavioral, and neuroimaging measures of inhibitory control. The special case of adolescence further highlights this overlap, with developmental changes consistent across self-reported personality (Donnellan et  al. 2007), behavioral decision-making (Eshel et al. 2007), and patterns of neural activation (Galvan et al. 2006). Indeed, increases in impulsivity during adolescence are evident at multiple levels of observation. This consistency is encouraging, as it not only supports the proposed links among biology, behavior, and personality, but also provides the substance abuse researcher with a more diverse “toolkit” of measures with which

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Behavioral Task

Cost-effectiveness

Objectivity and Specificity

Fig. 9.2  Pragmatic relationship between different methods of assessing adolescent impulsivity

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to screen adolescents for risk and target for early intervention. An important practical consideration is that these tools vary greatly in terms of their costeffectiveness, objectivity, and specificity (see Fig. 9.2). When it comes to primary prevention efforts, we advocate the use of self-report impulsivity questionnaires as an efficient means of screening large groups of adolescents to identify those who are at risk and in need of intervention. A particular strength of using such trait indicators, aside from the cost, is that interventions can be truly preventative by identifying adolescents at risk before they have begun using alcohol/drugs. An exciting example of this approach was recently reported by Conrod et al. (2008). Using a self-report measure of personality, these researchers screened 2,271 Canadian high school students in grades 9 and 10 to identify those most at risk of early alcohol use and binge drinking. A total of 368 students met personality risk criteria (e.g., high in sensation seeking/rash impulsiveness, or high in anxiety sensitivity/neuroticism) and provided parental consent to participate in a randomized controlled trial. Based on personality scores, participants were then randomly allocated to a control group, or an intervention targeting that “risk” trait. For instance, the sensation seeking intervention specifically challenged cognitive distortions associated with reward-seeking and boredom susceptibility. The anxiety sensitivity intervention, by contrast, focused on challenging catastrophic cognitions. Irrespective of specific trait focus, all interventions comprised two 90-min group sessions incorporating psychoeducation, motivational interviewing, and cognitive– behavioral therapy. Compared to the control group, the personality-targeted interventions significantly delayed the onset and natural increase in drinking behavior over the next 6 months (Conrod et al. 2008). Specifically, adolescents in any intervention group were 41% less likely to be binge drinking at 6-month follow-up. However, of greatest interest to the present discussion, the sensation seeking intervention was found to be particularly effective in delaying first use of alcohol among high

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sensation seeking adolescents (OR = 0.61). Similar benefits were also reported with regard to binge drinking rates. High sensation seekers receiving the targeted treatment were 45% less likely to binge drink at 6-month follow-up, and 50% less likely to binge drink at 1-year follow-up, compared to sensation seekers in the control group. Impressively, these effect sizes are approximately double those reported in any previous youth alcohol prevention program (Foxcroft et  al. 2002). While preliminary, these findings are very encouraging and speak to the potential of using self-report measures of impulsivity to focus prevention efforts. Indeed, similar successes have been also been demonstrated when public service announcements have targeted adolescents based on trait sensation seeking (Palgreen et al. 2001; see also Chap. 14 for review). Of course, the utility of such self-report measures depends critically on their ability to tap into a valid underlying construct that is relevant to substance abuse risk (e.g., rash impulsiveness). Eysenck’s (1967) notion of biologically-based differences in personality conferring risk for psychopathology has since been established through decades of behavioral and neuroimaging research. Furthermore, direct application of biologically-based personality theory to adolescent substance use has yielded encouraging results (Conrod et  al. 2008; Palgreen et  al. 2001; Chap. 14). However, it would be important to investigate whether the benefits of such interventions are indeed mediated by changes at the neurophysiological level. This could be achieved by replicating Conrod et al.’s (2008) study on a smaller scale, or having a small subgroup of the adolescent sample evaluated with neuroimaging. That is, while neuroimaging may be impractical when it comes to large-scale screening and treatment delivery, it can provide a biological account of how an intervention works in a way that questionnaires never could. A primary aim of the present chapter was to highlight the need to incorporate both approaches to understanding adolescent substance misuse and how best to reduce it. While H. J. Eysenck himself was confined to the use of the blunt, “low-tech” self-report questionnaire, insights from neuroimaging have since provided us a clearer account of what impulsivity is, biologically, and, in turn, renewed confidence in the utility of such tools to primary prevention efforts.

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Chapter 10

A Functional Analytic Framework for Understanding Adolescent Risk-Taking Behavior Laura MacPherson, Jessica M. Richards, Anahi Collado, and Carl W. Lejuez

Abstract  Adolescence is a unique period of growing independence and seeking of novel experiences, often in a peer context, that results in the exploration of new environments and the participation in a greater variety of behaviors not typically observed in childhood, including risk behaviors. Given the clear public health significance of engagement in risk behavior among adolescents, refining our models of mechanisms and processes underlying risk-taking has garnered increasing attention. Learning theory is one approach at the heart of many current models of risk-taking, but there have been few efforts to consider comprehensively the framework of functional analysis and the role of positive and negative reinforcement on risk-taking behavior. The current chapter addresses this gap outlining how functional analysis can serve as a framework to understand the proximal causes of risk behavior, but also how it can be conceptualized more broadly to consider distal influences and to integrate existing knowledge across a wide range of domains including personality and neurobiology. We conclude with a thorough consideration of the implications of this approach for prevention and early intervention.

Adolescent Risk-Taking Adolescence is a formative period of development bounded by the onset of puberty and culminating in the attainment of adult roles (Dahl 2004). Throughout adolescence there are significant and interrelated changes in biological, cognitive, social, and affective systems that underlie the acquisition of skills in a variety of domains as well as engagement in behaviors necessary for maturing into a well-functioning adult. In particular, adolescence is a unique period of exploration, growing independence and seeking of novel experiences, often in a peer context that results in the

C.W. Lejuez (*) Cognitive Neuroscience, Center for Addictions, Personality, and Emotion Research, University of Maryland, College Park, MD 20742, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_10, © Springer Science+Business Media, LLC 2011

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exploration of new environments and the participation in a greater variety of behaviors not typically observed in childhood. Simultaneously, adolescence, particularly early adolescence, marks a phase of development in which emotions are experienced more intensely and have a longer duration than in earlier childhood and adulthood (Silk et al. 2003; Steinberg et al. 2006). Such alterations in the intensity and modulation of affective experiences can also serve to motivate involvement in a novel set of behaviors beginning in early adolescence. Although changes in these systems are normative throughout this developmental period, the wider array of behaviors with more diverse outcomes observed in adolescence can also culminate in deleterious consequences. It is in this context that theorists and researchers have been particularly interested in understanding the development and function of risk-taking behavior. In their seminal work, Jessor and Jessor (1977) defined risk-taking as “behavior that is socially defined as a problem, a source of concern, or as undesirable by the norms of conventional society and the institutions of adult authority, and its occurrence usually elicits some kind of social control response” (p. 33). Focusing more explicitly on the potential consequences or outcomes of such behavior, risk-taking has also been conceptualized as behavior that involves some potential for harm or negative consequence to the individual, but that may also result in a positive outcome or reward (Byrnes et al. 1999; Leigh 1999). This latter view is crucial as it allows for consideration of a variety of factors that may affect an adolescent’s propensity to take risks including the potential gain from risks in terms of positive and negative reinforcement, and the corresponding opportunity costs for an unwillingness to take risks. It is well established that many types of risk-taking behaviors emerge, escalate, and often peak during adolescence. For example, there is a dramatic increase from early to middle adolescence in substance use (e.g., Windle et al. 2008), delinquency (Moffit et al. 2002), and other potentially health-compromising behaviors such as risky sexual activity and driving dangerously (DiClemente et al. 1996). Moreover, adolescence is the only period of human development during which the primary causes of morbidity and mortality are directly related to an individual’s overt actions and behaviors (e.g., driving recklessly, suicide) as opposed to a disease process (e.g., cardiovascular disease; Patton et  al. 2009). Although not all youth who experiment with risk behaviors will either experience deleterious consequences or progress to more problematic levels of such behaviors (Steinberg 2008), earlier age of risk behavior onset can often be prognostic of poorer health and emotional outcomes into adulthood (e.g., Brook et al. 2004; Colman et al. 2007; Sourander et  al. 2007). Given the clear public health significance of engagement in risk behavior among adolescents, refining our models of mechanisms and processes underlying risk-taking has garnered increasing attention. Learning theory is one approach at the heart of many current models of risktaking behavior including stress-coping (Wills et al. 2001), dual process models of cognition and also expectancy theory (e.g., Moss and Albery 2009; Goldman et al. 1999), as well as a wide range of models based broadly on social learning theory (Abrams and Niaura 1987; Maisto et al. 1999). However, through the incorporation

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of a wider array of constructs in such models, there subsequently has been limited attention to fundamental aspects of learning theory, including the framework of functional analysis and the role of positive and negative reinforcement on risktaking behavior. This approach provides an insight into the functional aspects of risk behavior and holds interesting implications for prevention and early intervention of risk-taking behavior.

A Functional Analytic Framework for Risk-Taking A functional analysis is an idiographic assessment of a specific behavior of an individual with a focus on the contextual factors underlying that behavior (Haynes and O’Brien 1990; Yoman 2008). It seeks to identify lawful and reliable relationships across the natural environment and observable behavior, with a particular focus on those current environmental variables maintaining the behavior for an individual that can be modified or manipulated (Sturmey 2007). In its most basic form, it includes a focus on the antecedents of behavior (A), the behavior itself (B), and the resulting positive and negative consequences of that behavior (C). Efforts to provide a more comprehensive framework include covert behavior such as thoughts and feelings. Although considered causal in some approaches to functional analysis, most behavioral approaches consider thoughts and feelings simply as behavior in the same manner as overt behavior, with no causal role ascribed to one behavior over another behavior independent of the context represented by the antecedent and consequences. In the following sections we consider antecedents, consequences, and covert behaviors as they relate to risk-taking. In an effort to broaden the scope of this approach, we will consider the role of environment both in terms of proximal and distal influences, while also attempting to integrate a wealth of available theory and data across environmental factors, personality, outcome expectancies, and biology. We will follow this review of research with a consideration of its application to prevention and early intervention.

Antecedents Antecedents constitute environmental or contextual factors that precede a certain behavior. Antecedents set the stage for a particular response to the extent that the behavior in question (in this case the risk behavior) results in some positive consequence in the past, whether it be through adding something positive or removing something negative (see later sections for a discussion of positive and negative reinforcement). For example, in previous cases when friends provided encouragement for drinking, the actual drinking behavior may have resulted in the enhancement of the situation or further approval from friends. Alternatively, when criticized by parents, drinking may have resulted in the removal of the

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emotional effects of that criticism. In both cases, the presence of these stimuli in the future will result in a high likelihood of drinking behavior recurring based on the past contingencies experienced. However, it is important to move beyond considering individual and proximal determinants of behavior in isolation. For instance, the popularity of the peer or the presence of a second supportive parent may mitigate the function of the drinking behavior, whereas the presence of other stressful events or a previous unsettling event may increase the influence of the antecedent in a particular case. Certainly these behaviors also have negative consequences that must be factored into the determination of whether the risk behavior will occur. The negatives of risk-taking often are perceived, at least by others, as being greater than the positives. However, factors such as the immediacy and certainty of the positives (discussed in greater detail later in the chapter) complicate this equation and under some circumstances can lead to greatly accentuated positives and mitigated negatives of risk behavior. Taken together, there are two important issues that impact the scope of antecedents within the functional analysis framework. First, the importance of this approach is to classify behavior not as purposive, but instead as a function of the context and past consequences for the behavior in question, and how this affects future behavior. Second, in addition to one’s own personal experience, the experiences of others can have a similar impact if the target individual observes or is provided information (“rules”) about such events, which may have particular relevance for the very first instance of involvement in a risk behavior. In considering the contextual role of antecedents, there is a wealth of research on environmental determinants of risk behavior. The most relevant antecedents for adolescent risk behaviors occur in the home, school, and neighborhood (Bronfenbrenner 1999; Brooks-Gunn et  al. 1993; Cicchetti and Lynch 1993; Wheaton and Clarke 2003), all of which have shown a direct effect on youth outcomes. For example, youths’ negative perceptions of home environments are related to self-reported depression, anxiety, stress, and behavioral problems (Field et al. 2002; Gauze et al. 1996). In addition, lower levels of parental monitoring, as well as greater familial stress and conflict, have been associated with substance use (e.g., Chilcoat and Anthony 1996; DiClemente et  al. 2001; Jacob and Leonard 1994; Lahey et al. 2008) and risky sexual behaviors (DiClemente et al. 2001; Sneed et al. 2009). Along with the home environment, school and neighborhood environments have been linked to adolescent risk-taking behavior. Risk-taking behaviors among adolescents tend to cluster in these environments (e.g., Mason et al. 1999) and effects of the school and neighborhood environments may be caused in part by a lack of attachment with school personnel, and/or perceptions of danger, instability, and absence of supportive resources in the neighborhood (Tyler et al. 2007). Within these contextual conditions, peer relations play a fundamental role in the initiation and progression of a variety of adolescent risky behaviors (e.g., Bates and Labouvie 1995; Chen et  al. 2000; Crandall 1988; DiBlasio and Benda 1992). Receiving the greatest attention to date is the influence of peers and peer behavior on adolescent substance use (Chassin et al. 2004). Peer-group modeling of substance use can create social pressure to engage in these behaviors, for which peers then

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provide reinforcement for conformity (Costa et  al. 1999). Likewise, difficult interactions with peers may result in social anxiety, perceived pressure to engage in a risky behavior such as substance use, and increased interpersonal conflict (e.g., Hundleby and Mercer 1987; Morris et al. 2005). Consequently, these stresses may enhance the appeal of substance use, particularly for teens who hold expectations that alcohol or drug use reduces social tension. Additionally, associating with deviant peer groups increases the likelihood that substances will be available (Costa et  al. 1999). Moreover, emerging research also suggests that these fundamental peer-related processes play a role in other adolescent risk behaviors such as binge eating (Crandall 1988), risky sex (DiBlasio and Benda 1992), dangerous driving (e.g., Arnett et  al. 1997; Chen et  al. 2000), and violent behaviors [e.g., Zimring (1998); for a comprehensive review see Prinstein and Dodge (2008)]. It is notable that while each of these environmental contexts, including peers, contributes a unique influence on adolescent development and emergence of risky behaviors, adolescents interact with each context in a transactional way (Sameroff 2000). For example, research suggests that when an adolescent experiences a negative home environment, the school environment may serve as a buffer against delinquency if the adolescent can connect with prosocial peers and supportive school staff (Kenny et al. 2002). Similarly, peer influences combine with family factors to determine adolescent substance use (Barnes 1990). Affiliation with deviant peers who model substance use may be more likely for teens whose parents are substance misusers or are less involved in monitoring their children (Patterson et  al. 1989). These interactive and transactional relationships capture the complexities of one’s environment across a variety of contexts, providing a more comprehensive picture of the role that environmental factors play in the engagement of a specific risky behavior by youth in a specific situation. As will be reviewed below, once an adolescent chooses to engage in a risk-taking behavior, positive and negative consequences can function to further modulate the probability that the behavior will be engaged in again.

Positive Consequences Positive Reinforcement From a behavioral perspective, a risk behavior is likely to occur if in the past the individual directly experienced, viewed, or heard of others experiencing positive consequences for such behavior. This fits well with the operant definition of positive reinforcement, which occurs when a behavior is followed by a stimulus that increases the probability of that behavior occurring in the future (Skinner 1957). Said differently, risk-taking may occur more frequently in the future because of prior experience with positive consequences associated with engagement in such behaviors. Further, factors such as personality traits and neurobiological processes may contribute to the variability in the extent to which positive consequences are reinforcing across individuals. Positive reinforcement-based

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processes have been most commonly applied to our understanding of risk-taking as they pertain to the early development of substance use behaviors (c.f., Glautier 2004; Koob and Le Moal 2008). Specifically, positive reinforcement of substance use engagement occurs at the acute pharmacological level including the euphoria resulting from the direct effects that substances exert on the brain reward system. This may lead to increased drug seeking, especially as the effects of tolerance require larger doses of the substance to experience the same positive effects. Although not as commonly considered, similar euphoric effects can result where no substance is present, such as in gambling, delinquent behaviors such as stealing or excessive speeding, and risky sexual behavior. Additionally, there may be secondary gains including others perceiving the adolescent in a positive light, such as increased popularity and social potency. For example, a youth might engage in a risky behavior such as staying out past one’s curfew to go to a party, which results in the development of new social opportunities that may increase the likelihood of further curfew breaking. Similarly, a youth who takes excessive risks such as trying new drugs or starting fights with older kids might develop a reputation of being fearless that leads to other risk behaviors to further that reputation and the gains that come along with it (Steinberg 2008). This framework for understanding risk behavior may be especially useful for considering the role of specific personality variables in the development and maintenance of risk-taking as each directly implicates the role of appetitive stimuli. Personality Over the past 30 years, there has been a proliferation of models of risk-taking spanning a wide range of key etiological factors (Fowles 1980; Sher et  al. 2000; Zuckerman 1983). Some of the most influential models include those focused on disinhibitory personality variables such as impulsivity (c.f., Evenden 1999) and sensation seeking (Zuckerman 1994), as well as more recent work in risk-taking propensity (Gottfredson and Hirschi 1990; Lejuez et al. 2002). Historically, personality has been considered biologically-based and largely static, and thus there has been little work that has examined factors that influence personality as it relates to risk-taking. However, seminal reviews and meta-analyses indicate that personality traits change over time both at a population and at an individual level (c.f., Caspi et al. 2005; Roberts and Mroczek 2008) suggesting the importance of understanding the interrelationship between personality variables and risk behavior. Thus, first we consider the contribution of operant processes of positive reinforcement and how they can be useful for understanding the relationship between disinhibitory personality variables and adolescent risk-taking. The appetitive set of personality models have focused on how risk-taking behavior may be motivated by the opportunity to gain access to novel or arousing stimuli with an inability to delay one’s access or without considering the consequences of such access. However, the appetitive-based aspects in personality models largely have occurred without reference to an operant framework including the specific process of positive reinforcement.

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Sensation Seeking A comprehensive definition of sensation seeking put forth by Zuckerman (1994), defines the construct as “the seeking of varied, novel, complex, and intense sensations and experiences, and the willingness to take physical, social, legal, and financial risks for the sake of such experience.” One reason sensation seeking is particularly relevant to adolescents is that the onset and development of this trait mimics the timeline of adolescence, namely, it manifests around age 11, increases from grades 7 to 9 (Donohew et al. 1994), peaks in middle adolescence, and generally begins to decline into older adolescence and young adulthood (Roth et al. 2005; Steinberg et al. 2008). In particular, recent work by Steinberg et al. (2005), with a sample of individuals aged 10–30, supported this general curvilinear trajectory of the development of sensation seeking with a self-report measure of this construct (although it is of note that the purported behavioral index of sensation seeking did not exhibit the same pattern). Multiple studies of adolescents have found that sensation seeking is predictive of risk behavior including substance use and risky sex (Crawford et al. 2003; Romer and Hennessy 2007; Wagner et al. 2001). In a study of African American adolescents, Xiaoming et al. (2000) found elevated sensation seeking scores among participants who had engaged in both risky sex and drug use, compared to those who did not engage in such behaviors. Finally, in a sample of adjudicated adolescents, Robbins and Bryan (2004) found that sensation seeking related to lower perceived risk, lower condom use, and a host of substance use-related measures. Together, this evidence suggests that sensation seeking is a trait particularly relevant to adolescents, and serves as a robust predictor and explanatory mechanism of risk behavior. Considered from a positive reinforcement perspective, the extent to which a risk behavior will be engaged in depends upon the level of arousal experienced by the individual when engaging in this behavior in the past or the extent to which observations or descriptions from others suggest the potential for novelty/arousal. This fits particularly well with substance use where one’s sensation seeking may be exacerbated by a particular sensitivity to the positively reinforcing effects of drugs and alcohol (Brunelle et al. 2004; Conrod et al. 1998). Moreover, sensation seeking has been linked to self-reported motives for alcohol use that involve the pursuit of the enhancement of positive affect in youth (Comeau et al. 2001; Cooper et  al. 1995), which may be consistent with developmental changes in reward salience in adolescence (Steinberg et  al. 2008). At an intuitive level, positive reinforcement may be more relevant to initiation of a risky behavior, such as substance use, because the value of that behavior for negative reinforcement may not be evident to the adolescent. Indeed, children in late elementary school (e.g., 10 years old) with no or little prior direct alcohol use experience hold positive outcome expectancies for alcohol use that are primarily comprised of expectations for positive reinforcement (e.g., more happy, friendly) as opposed to negative reinforcement (e.g., less nervous) from alcohol use (Dunn and Goldman 1998; Cameron et al. 2003). Later, we present a discussion of the neurobiological mechanisms involved in developmental changes in sensitivity to rewarding aspects of the

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environment and increases in novelty seeking that may also relate to the relevance of positive reinforcement processes in the onset of adolescent risk-taking behaviors. Trait Impulsivity Impulsivity has been long recognized as a multidimensional construct comprised of a number of facets (c.f., Evenden 1999). We will focus on two dimensions of impulsivity that are most relevant to a positive reinforcement framework of risk behavior involvement and that have seen a range of attention in the adolescent literature: (1) an inability to delay gratification or a heightened propensity to discount rewards as a function of delay (Green et  al. 1999; Mischel et  al. 1989); and (2) positive urgency, the tendency to engage in rash action in response to extremes of positive affect (c.f., Cyders and Smith 2008a). It is notable that while appetitive rewards may influence other aspects of impulsivity including an inability to focus on a present task (i.e., attentional impulsiveness), deficiencies in response inhibition, reduced task persistence (i.e., motor impulsiveness), and a lack of future planning (i.e., nonplanning impulsiveness; Patton et al. 1995; Eysenck et al. 1985; Whiteside and Lynam 2001); positive reinforcement is a less central process for these than it is for delay discounting and positive urgency. Delay Discounting Delay discounting of rewards is generally assessed as the extent to which a smaller more immediate reward would be selected over a larger delayed reward (Ainslie 1975). The inability to delay gratification and discounting of delayed rewards has generally been thought to have a developmental trajectory indicating an increased ability to delay rewards from childhood to adolescence, although differences between adolescents and adults are less well-established (Scheres et al. 2006; Steinberg et al. 2009). Among adolescents, the majority of the delay discounting literature has examined individual differences in ability to delay rewards across youth who are or are not engaging in various types or levels of risky behaviors, with often mixed results (e.g., Reynolds et  al. 2003; AudrainMcGovern et al. 2004a). For example, Reynolds et al. (2003) found that adolescent experimental smokers (those who tried cigarettes but did not progress in their smoking) discounted delayed rewards more than nonsmokers and regular smokers, with the latter two groups not differing from each other. These results suggest that delay discounting plays a specific role in adolescent smoking initiation, although these findings require replication. Similarly, social drinking college students who exhibited steeper discounting in a task using hypothetical money rewards also reported an earlier age of first alcohol use (Kollins 2003). In contrast, greater discounting of delayed rewards has predicted smoking progression in a community sample of adolescents (Audrain-McGovern et al. 2004a) and

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an ability to maintain abstinence among adolescent smokers receiving a smoking cessation intervention (Krishnan-Sarin et al. 2007). Although disparate findings across these studies may be due to differences in sample characteristics and measures used to assess delay discounting (e.g., self-report vs. behavioral), an important consideration is when and under what circumstances positive reinforcement processes may exert their greatest effects across the progression or cessation of an adolescent risky behavior. Indeed, more work is needed to examine the role of delay discounting in other forms of adolescent risky behaviors outside of those that comprise addictive behavior and for whom and under what contexts the rewarding aspects of alternative behaviors are discounted. However, as it stands, this construct provides a framework for examining appetitive stimuli and the extent to which those stimuli are reinforcing depending on the delay until their receipt compared to other appetitive stimuli. Further, the construct of delayed discounting provides a basis for identifying individuals who may be most vulnerable to engaging in risk-taking behaviors that are likely to result in an immediate positive reinforcer (e.g., the euphoric high that follows substance use) and conversely, the extent to which these individuals will discount the value of behaviors that are likely to result in long-term positive consequences (e.g., academic achievement as a means of obtaining college admission). Notably, a small body of work has also considered discounting of delayed aversive stimuli, fitting well within a negative reinforcement framework, which will be discussed later. Positive Urgency The concept of urgency originally grew out of the volume of literature indicating that impulsivity comprises multiple distinct constructs (e.g., Evenden 1999; Eysenck and Eysenck 1977; Smith et  al. 2007; Whiteside and Lynam 2001). Specifically, urgency referred broadly to an inability to regulate emotion and was identified in the seminal work of Whiteside and Lynam (2001), in which the authors factor-analyzed a variety of existing self-report measures of impulsivity informed by the five-factor model of personality. Notably, urgency was originally considered in terms of negative affective states, termed negative urgency (c.f., Cyders and Smith 2008a), which we discuss in further detail later in relation to the negative reinforcement framework. Subsequently, Cyders et al. (2007) developed an additional dimension of urgency, coined positive urgency, representing a trait-like propensity to act rashly when experiencing extreme positive emotion, a phenomenon not captured in other impulsivity dimensions (Whiteside and Lynam 2001). Although the empirical literature on positive urgency is only beginning to emerge, this construct has been shown to be related to alcohol use quantity, and related problems above and beyond other dimensions of impulsivity, as well as sensation seeking among older adolescents (Cyders et  al. 2007). More recently positive urgency was found to correspond with a range of risky behaviors among collegeage students in their freshman year, including mountain climbing and bungee jumping, as well as gambling behaviors, although relationships were only supported

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prospectively for changes in gambling (Cyders and Smith 2008b). In addition, increases in alcohol quantity and alcohol-related problems (Cyders et al. 2009), and risky sex and illicit drug use (Zapolski et  al. 2009) also were found across the college freshman year. Thus there is considerable promise for increasing our understanding of how the propensity to act impulsively in response to extreme positive affect can lead to involvement in adolescent risk-taking behaviors. Although Cyders and colleagues’ conceptual work on positive urgency is well-elaborated in terms of drawing together a wealth of potential evidence for how this trait may develop across the lifespan, it also requires translation to younger adolescents with empirical examinations of their hypotheses. However, their functional examination of a variety of risk behaviors provides considerable insight to the motivational role of extremes of positive affect in driving such behavior engagement, and further how positive affect regulation fits seamlessly within a positive reinforcement framework. Risk-Taking Propensity Although we offered a commonly accepted and widely applied definition for risktaking behaviors in the beginning, it is crucial to further elaborate our understanding of risk-taking propensity. Gottfredson and Hirschi (1990) outline two necessary conditions under which risk-taking can occur that are directly relevant to our conceptualization of risk-taking propensity. Specifically, they argue that risk-taking can only occur if an individual is (1) given the opportunity to take a risk but also that (2) he or she is prone to taking a risk when provided with an opportunity to do so. With regard to the latter condition, Gottfredson and Hirschi (1990) suggest that there is an underlying characteristic or quality that individuals possess that motivates involvement in a given risk behavior when the circumstances lend themselves to such behavior engagement. It is that latent characteristic that has been suggested to account for the shared variance observed across participation in a wide array of risky behaviors (Cooper et al. 2003). Moreover, risk-taking propensity likely exists on a continuum and thus some risk-taking is adaptive, and only at more extreme levels may become maladaptive (Bornovalova et al. 2009). Finally, it is crucial to note that such a propensity to take risks is not simply a static individual difference variable that motivates behavior, but is malleable within individuals and thus can be influenced by contextual factors in the way in which it is expressed and potentially in how it develops or changes over time (Caspi et al. 2005). Much of the empirical work on risk-taking propensity has utilized the adult and youth versions of the Balloon Analogue Risk Task (BART; BART-Y; Lejuez et al. 2002, 2007), a computer-based behavioral assessment of risk-taking propensity. In this task, larger prizes can be earned for increased risk-taking up to a point at which overly risky behavior results in the loss of accrued prizes. The task was developed to provide a controlled setting in which to model risk-taking in the natural environment, where risk-taking up to a certain point leads to positive consequences, with further excessive risk-taking leading to greater negative consequences that

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outweigh the positives. Risk-taking propensity assessed in this way captures the appetitive processes underlying a behavioral tendency to take risks in response to cues for potential reward with a probability for undesirable results, fitting well within a positive reinforcement framework. Data suggest that level of riskiness on the BART is related to current engagement in many adolescent risk behaviors (e.g., smoking, delinquent behaviors), and HIV risk behaviors (e.g., unprotected sexual intercourse, polysubstance use) in middle through older adolescents (Aklin et al. 2005; Crowley et al. 2006; Lejuez et al. 2002, 2003a, b, 2007) with recent evidence indicating a prospective role of increases in risk-taking propensity in the progression of a variety of risk behaviors in early adolescents (MacPherson et al. 2010a, b). The malleability of risk-taking propensity, in terms of response to contextual factors also is evidenced by work indicating alterations in risk-taking on the BART as a function of changes in potential magnitude of loss/reward (e.g., Bornovalova et al. 2009) and preliminary evidence from an experimental study in our laboratory indicating social factors such as presence of peers can increase an older adolescent’s propensity to take risks as compared to youth who are by themselves (Reynolds 2009). Thus in the positive reinforcement framework, the way in which risk-taking propensity is manifested requires contextualization by the immediate function of engagement in a given risky behavior and the balance of appetitive rewards and potential consequences in that moment. Neurobiological Factors As discussed above, there is some evidence that personality factors, which contribute to engagement in risk-taking behaviors, such as sensation seeking, follow a unique curvilinear developmental trajectory, such that sensation seeking increases from childhood to adolescence, and then decreases into adulthood. Although environmental and social factors may account in part for this developmental trend, there are also neurobiological changes that occur over the course of development that may contribute to the increase in disinhibition and risky behavior that is seen during adolescence. Specifically, adolescence is a developmental period characterized by significant changes in brain structure and function, which continue into early adulthood (for recent reviews, see Casey et al. 2008; Ernst and Mueller 2008; Steinberg 2008). Structurally, the proportion of white to gray matter changes over the course of adolescence, such that white matter increases and gray matter decreases, reflecting changes in myelination and synaptic pruning respectively (Gogtay et  al. 2006; Mabbott et al. 2006; Paus et al. 1999). These processes combine to provide faster communication, and more efficient neural coding over the course of neural maturation, with structural and functional maturation occurring at varying time points across brain regions (Ernst and Mueller 2008; Gogtay et  al. 2004). Specifically, research suggests that primary somatosensory regions and limbic regions implicated in reward, affective, and motivational processing are among the earliest regions to mature, while prefrontal regulatory regions responsible for providing top-down control over limbic activation do not reach full maturity until early adulthood.

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Consistent with a positive reinforcement framework, this bias toward functionally mature limbic regions relative to prefrontal regions has been proposed as one mechanism by which potentially rewarding risk-taking behaviors become more reinforcing and therefore more prevalent among early adolescents (e.g., Casey et al. 2008; Steinberg 2008). From a positive reinforcement perspective, one neurobiological system that may play a key role in risk-taking during adolescence is the dopaminergic system, generally associated with reward processing, which undergoes important developmental changes beginning at the onset of puberty (Chambers et al. 2003; Spear 2000). Key structures within this system include limbic and paralimbic regions such as the amygdala, nucleus accumbens, orbitofrontal cortex, medial prefrontal cortex, and superior temporal sulcus (Chambers et al. 2003; Nelson et al. 2005; Spear 2000). Individual differences in dopaminergic functioning in these regions have been associated with propensity to engage in risky behavior (O’Doherty 2004), and activation in mesolimbic dopaminergic regions (e.g., the nucleus accumbens) has been shown to increase immediately prior to making risky monetary choices (Kuhnen and Knutson 2005; Matthews et al. 2004; Montague and Berns 2002). Further, accumbens activation has been shown to positively correlate with the occurrence of subsequent risky decisions that have the potential to result in a monetary gain (Kuhnen and Knutson 2005). Taken together, research suggests that engagement in risky behaviors is associated with activation in reward circuits of the brain, providing the neural substrate by which risk-taking behaviors are positively reinforcing. Specific to adolescents, the trajectory of neural maturation from limbic to prefrontal regions may increase the reinforcing effects of risky behaviors even further. In terms of limbic functioning, nucleus accumbens activation in response to rewarding outcomes among adolescents is exaggerated relative to children and adults (Ernst et al. 2005; Galvan et al. 2006). For example, Ernst et al. (2005) examined neural response to omission and receipt of rewards among 16 adolescents (mean age = 13.3; ±2.1 years) and 14 adults (mean age = 26.7; ±5.0 years) on a monetary reward task. Findings revealed increased activation in the left nucleus accumbens during reward receipt among adolescents as compared to adults, suggesting that increased sensitivity to rewarding outcomes among adolescence may be one mechanism driving increased risk-taking and novelty seeking among adolescents relative to adults. However, this project did not study how this mechanism differs between adolescents and young children, nor did it examine the role of cortical regions responsible for top-down regulatory control over limbic functioning. A subsequent study examined neural responses to reward across development among a sample of 37 participants, aged 7–29 years (Galvan et al. 2006), hypothesizing differential activation in both cortical and subcortical regions among adolescents relative to children and adults. Consistent with previous literature, adolescents showed exaggerated reward-induced activation of the nucleus accumbens relative to adults and children. However, adolescents showed a pattern of orbitofrontal activation that was similar to children, such that prefrontal activation appeared more diffuse in children and adolescents relative to adults (Galvan et al. 2006). The finding of diffuse prefrontal activation in adolescents relative to adults

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is consistent with other literature that has suggested that adolescents engage prefrontal regulatory regions to a lesser extent than adults when making risky economic decisions (Eshel et  al. 2007) and that frontal activation during reward-risk conflict appears less active in adolescents compared to that in adults (Bjork et  al. 2007). In addition, there is evidence that functional connectivity between prefrontal and limbic subcortical regions is immature among adolescents relative to adults (Hare et al. 2008), thereby limiting the role of top-down regulatory control over limbic functioning during early adolescence. In sum, it appears that exaggerated neural activation in reward processing regions of the brain in response to reward cues, in combination with immature prefrontal functioning and weak functional connectivity between these regions, may explain why adolescence is a developmental period during which risk-taking behaviors may be perceived as particularly reinforcing, thus increasing the frequency of risk-behavior engagement. Additionally, it is important to note that dopaminergic regions associated with reward processing overlap considerably with networks implicated in social information processing (Steinberg 2008). For example, a recent fMRI study experimentally manipulated peer acceptance and rejection while adolescents engaged in a task (Nelson et al. 2007, as cited in Steinberg 2008). The researchers found that exposure to peer acceptance was associated with increased activation in brain regions involved in reward processing, including the ventral tegmental area, extended amygdala, and ventral pallidum (Nelson et al. 2007). These findings suggest that exaggerated activation in dopaminergic regions among adolescents and young adults may provide the neural substrate by which risk-taking behaviors become particularly reinforcing in the presence of accepting peers, and may at least partially explain why adolescents engage in many risky behaviors, such as drinking, reckless driving, and delinquency in groups (Steinberg 2007, 2008). Negative Reinforcement When considering the positive consequences of risk behavior, one must also consider the role of negative reinforcement. As discussed earlier, one’s previous experiences influence the role of future antecedents. In a similar manner, several individual difference variables affect the extent to which a risk behavior serves as a negative reinforcer. Negative reinforcement models emphasize that the motivational basis of behavior is the escape or avoidance of negative affective states (Baker et al. 2004; Solomon and Corbit 1974; Wikler 1965). Adolescents may engage in various risk behaviors in response to aversive stimuli including coping with negative feelings or experiences. To follow from the example of a youth engaging in curfew-breaking behavior discussed above in the positive reinforcement framework, it is also quite possible that an adolescent stays out past his or her curfew in order to avoid the ridicule from peers for not attending a particular party. Negative reinforcementbased models have seen particular attention in the substance use literature and are increasingly applied to a variety of risk-taking behaviors. Negative emotionality has predicted escalating trajectories of substance use and other risk-taking behaviors

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(Chassin et al. 2002; Colder et al. 2002) although it is not simply the presence of negative affectivity, but the way in which youth cope with or regulate that negative affect (Wills et al. 2001) that is most crucial to understanding risky behaviors in a negative reinforcement framework. Similar to our discussion above of appetitive personality traits and their fit within a positive reinforcement framework, the avoidance set of models has focused on efforts to avoid aversive stimuli with some focusing on the emotion-related aspects of such avoidance. Thus in the following section, we reconsider how the impulsivity constructs discussed above (delay discounting, trait impulsivity, and urgency) and the characteristic of risk-taking propensity may operate from a negative reinforcement perspective. Negative Urgency As we highlighted in our discussion of positive urgency, Whiteside and Lynam’s (2001) work identifying subfactors of impulsivity produced four key dimensions, one of which was originally termed urgency, but later specified by Cyders et  al. (2007), and Cyders and Smith 2008a as negative urgency. Negative urgency is defined as the tendency to act rashly in response to extreme negative affect or distress and serves as the negative affect regulation-based mirror to positive urgency. In line with a negative reinforcement framework, Cyders and Smith (2008a) argue that engagement in a rash action in response to extreme negative affect is immediately reinforced to the extent that the negative affect is reduced, and thus more likely to occur in the future. Additionally, if that rash action is a risky behavior in response to negative affective experience, then of course it too would be immediately reinforced. Youth who rely on avoidant coping styles (e.g., impulsive nonplanning) in the face of negative emotions are less able to effectively regulate their negative mood states, thus becoming vulnerable to the immediate relief promised by various risky behavioral alternatives (e.g., substance use). Given that early adolescence in particular is a phase of development marked by greater intensity of affective experiences (Steinberg et al. 2006), in line with a negative reinforcement model, adolescents who experience frequent or intense negative affect may be more likely to rely on avoidant coping mechanisms that alter emotions directly and operate quickly (e.g., acting out behaviors) rather than more plan-focused responses (Catanzaro and Laurent 2004; Pardini et al. 2004; Westen 1994). To date, there is a more extensive body of literature examining the role of negative urgency than positive urgency in relation to a wide array of risky behaviors, although the majority of this work has focused on adults and older adolescents. For example, negative urgency has been found to predict bulimic symptomatology and compulsive shopping (Billieux et al. 2008). Among older adolescents, negative urgency predicted changes in “drinking to cope” behaviors, such that greater increases in negative urgency were significantly associated with higher levels of negative reinforcement-driven drinking behavior (Anestis et al. 2007b). The ­construct has also predicted problematic gambling behaviors among college ­freshmen (Cyders and Smith 2008b). Finally, a self-report measure based on the four

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i­mpulsivity dimensions identified by Whiteside and Lynam (2001) was recently developed for preadolescents and provided preliminary evidence that negative urgency was distinct from the other traits and showed unique relationships with hypothesized risk behaviors (Zapolski et  al. 2009). In sum, initial evidence is emerging for the utility of negative urgency in the context of a negative reinforcement framework in the functional role it plays in risky behaviors. Neurobiological Factors Although numerous researchers and theorists have examined neural mechanisms underlying positive reinforcement processes that drive risk-taking in adolescents, a clear limitation in the current literature is the dearth of research regarding neural mechanisms underlying negative reinforcement processes in general, but also specific to adolescent risk-taking. This relative gap in the literature may be at least partially explained by the current absence of widely used fMRI tasks designed to capture negative reinforcement processes. Despite this limitation in the current work, it is possible that early maturation of limbic regions most frequently implicated in positive reinforcement processes may also play a role in negative reinforcement of risk-taking behaviors. Specifically, adolescents have been found to exhibit greater activation in the amygdala (a region frequently discussed in regard to fear processing and anxiety) in response to threatening social cues (e.g., evocative faces) relative to adults (Nelson et al. 2005). Although speculative, this finding may provide one neural mechanism by which risk-taking in response to peer pressure may be seen as negatively reinforcing to the extent that it reduces the perception of social threat; however, this hypothesis has not been directly tested, and more research is needed in order to clarify the neural basis of negative reinforcement processes in adolescent risk-taking behaviors.

Negative Consequences Although many intervention strategies are based on increasing awareness of or changing attitudes about the potential impact of negative consequences related to risk behavior, there is surprisingly little attention to understanding how negative consequences affect engagement in risk behavior. Two areas of relevance currently are the cost discounting dimension of impulsivity. Delay Discounting Beyond the abundant literature examining the discounting of delayed rewards in relation to risk behaviors, there also exists a nascent effort to understand discounting of delayed costs (Baker et  al. 2003; Holt et  al. 2008; Murphy et  al. 2001).

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Whereas in the positive reinforcement framework, delayed reward discounting focuses on the extent to which a smaller reward will be selected based on its immediacy over a larger, delayed reward and the opposite effect is expected in the context of punishment in examining delayed costs. Specifically, in a delayed-costs model, the focus is on the impact of the delay of an aversive stimulus (i.e., cost) and the extent to which an individual would choose that larger delayed aversive stimulus to allow for the removal of a more immediate aversive stimulus that is smaller in magnitude. To provide an example of this process, relevant to adolescence, consider risky sexual behaviors. A youth may “give in” to pressure for engaging in unsafe sex from a partner even though it may result in a serious long-term consequence. According to a delay discounting perspective, the risk behavior becomes more likely because the long-term consequence (e.g., unintended pregnancy, HIV) is discounted and therefore has less impact than the smaller but immediate negative consequences associated with the pressure to not use a condom. This process of discounting delayed costs may readily explain how certain risky behaviors are maintained because the effect of a punisher is reduced as a function of its delay in occurring from the time of the behavior. That is, although risk behaviors may be thought of as being motivated by the appetitive gains despite large negative consequences, it may also at times be the case that the delay to punishment may make the punisher largely ineffective and therefore even less powerful than even modest positive gains of risk behavior. Differences have been identified between adult smokers and never smokers on delayed cost discounting in one study (Baker et  al. 2003), although not another (Ohmura et  al. 2005); to date there is virtually no empirical examination of this construct among adolescents. However, efforts are currently under way to develop both behavioral and self-report measures of delayed cost discounting that inform theory and have practical implications at the level of prevention. Neurobiological Factors One neurobiological factor that has been examined in relation to delayed cost is the ventromedial prefrontal cortex (vmPFC). Researchers observed that individuals who suffer damage to the vmPFC tend to make decisions driven by immediate rewards despite negative future consequences, while maintaining normal levels of global intellectual functioning. Based on his observations of individuals with vmPFC damage, Damasio (1994) proposed the somatic marker hypothesis (SMH), which posits that individuals with damage to the vmPFC are unable to use emotionbased signals from the body (i.e., somatic markers) as a means of evaluating different response options. Most of the work in support of this hypothesis has been conducted using the Iowa Gambling Task [IGT; for detailed task description, see Bechara et al. (2000)]. Briefly, the IGT requires participants to select cards from four possible decks: two decks are associated with high rewards but high occasional losses resulting in long-term net loss (i.e., disadvantageous decks), while the other two decks are associated with smaller rewards, but also smaller losses resulting in an

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overall long-term net gain (i.e., advantageous decks). When the IGT was administered to individuals with vmPFC damage, researchers found that they tended to prefer the disadvantageous decks, thus mirroring their real-life tendency to make decisions that are disadvantageous in the long run (Bechara et al. 1994, 1997). Further, researchers found that when they assessed physiological responding to card selection by measuring skin conductance response (SCR), they found that over time, healthy participants began to exhibit pronounced anticipatory SCRs prior to selecting a card from a disadvantageous deck; however, individuals with damage to the vmPFC completely failed to exhibit this anticipatory response (Bechara et al. 1999). The authors concluded that individuals with vmPFC damage may be unable to generate emotion-based bodily responses that signal the threat of loss, which subsequently impairs their ability to make advantageous decisions. Given the trajectory of cortical development over the course of adolescence, vmPFC immaturity may underlie the tendency for some adolescents to make decisions driven by short-term avoidance of negative consequences, despite high magnitude long-term negative consequences. In studies examining performance on the IGT across age groups, findings consistently show age-related improvements in IGT performance from early adolescence to early adulthood, such that older participants select more cards from the advantageous decks than younger participants (Hooper et al. 2004; Overman et al. 2004). These findings suggest that the vmPFC continues to mature over the course of adolescence, which may explain the reduction in risk-taking behaviors that is seen as adolescents mature into young adults. However, these studies have not examined physiological responses to card selection (e.g., SCRs), as was done in the original IGT studies. As such, conclusions cannot be drawn regarding whether emotion-based bodily responses to risk play a role in the age-related improvements in decision-making that occur over the course of development.

Covert Behavior As noted above, covert behavior is not considered to serve a causal role in most behavioral approaches to functional analysis, but understanding covert behavior in the context of a risk behavior provides an important window into how an individual is experiencing a particular context. Social learning theory (e.g., Abrams and Niaura 1987; Bandura 1986) centers on the influence of environmental forces on behaviors, as well as learned beliefs about the consequences surrounding engagement in those behaviors. Modeling by family members, peers, and society is a critical influence on the development of these beliefs regarding the consequences (both positive and negative) of involvement in risk-taking behaviors. Such beliefs are often formed relatively early in life and predict onset and escalation of risky behaviors, such as substance use, in youth (e.g., Goldman et al. 1999). Of course actual involvement in risky behaviors also serves to shape and elaborate these beliefs through direct experience with the consequences of one’s own behavior

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(Goldman et al. 1999). These beliefs, commonly described as expectancies, are a person’s expectation that certain behavior will lead to a specific outcome (Bandura 1986; Brown et al. 1980) and provide an important perspective to consider the role of one’s thoughts about the functional value of risk behavior that fill well in both positive and negative reinforcement as well as punishment contexts. Expectancies have been linked to adolescents’ likelihood of engaging in risktaking behaviors (Randolph et al. 2006; Christiansen and Goldman 1983) and can help us to better understand the functional purpose that these behaviors may have as a means to social acceptance and reduction of negative affect or increase in positive affect. Expectancies may result from memories of a youth’s direct experiences as well as vicariously acquired memories (for example through media advertisements or peer socialization). An early study of alcohol expectancies found that children who had never consumed alcohol held similar expectancies as adult drinkers about its effects in lowering stress, providing pleasure, and affecting social interactions (Christiansen et  al. 1982). In middle school children, these positive expectancies have been shown to prospectively predict alcohol consumption (McCarthy et al. 2009). In addition, adolescent expectancies of the effects of alcohol consumption predict drinking initiation and dependence (Christiansen et  al. 1989; Brown et al. 1987). Similar findings exist for expectancies regarding adolescent smoking behavior (e.g., Hine et  al. 2005; Myers et  al. 2003). Furthermore, positive expectancies about its effects, as well as positive attitudes toward drinking, have consistently shown associations with increased alcohol use (Burden and Maisto 2000; Leigh 1989; Stacy et al. 1994). Similarly, Stacy et al. (1991) found that positive drug expectancies predicted adult drug use, even after taking into account adolescent drug use. Both peer group and parental modeling of substance use and expectancies typically precede adolescents’ initial smoking and drinking (Smith 1994), and influence their subsequent decision-making about involvement in these behaviors. For example, aversion to cigarette smoke decreases over time if a child’s parents are smokers (Hirschman et  al. 1984). This finding is consistent with research suggesting that alcohol expectancies for social situations become more positive as children progress into adolescence (Miller et  al. 1990). Additionally, adolescents perceive peers who smoke as “cool” and adult-like, confident, and outgoing in social situations (McGee and Stanton 1993; Sharp and Getz 1996). Favorable perceptions of peers who drink and smoke may lead adolescents to engage in such behaviors. Research suggests that in the early stages of substance experimentation, higher levels of peer approval and perceptions of peer substance use lead to an increased endorsement of positive alcohol expectancies (Martino et al. 2006). Positive expectancies towards substance use behavior (e.g., smoking will calm me down) have shown to increase the probability of engagement in the behavior, whereas negative expectancies (e.g., drinking will cause me to act foolishly) have shown to have the opposite effect (Zamboanga et  al. 2009; Smith et  al. 1995). A series of balanced placebo design experiments by Fillmore and Vogel-Sprott 1995, 1996 offered evidence that negative alcohol expectancies, specifically expectations of more impairment from alcohol consumption, not actual alcohol

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consumption, corresponded to poorer performance on a variety of tasks administered by the investigators. In contrast to this line of research, in general, positive expectancies have received greater research attention (Jones et  al. 2001) than negative expectancies. A possible reason for this research disparity is that positive expectancies, which tend to focus on positive consequences that immediately follow the behavior, are considered to be stronger motivators than negative expectancies for which the consequences are delayed punishers (Rohsenow 1983). For example, alcohol consumption reduces distress shortly after ingestion, but a hangover is experienced the morning after the drinking episode. Recent work in the substance use expectancy field has also increasingly focused on genetic factors (e.g., Hendershot et  al. 2009) and temperament/personality (e.g., Simons et  al. 2009) as they influence the learned associations between behavior and consequence. Further, future antecedents may result in potentially varied behaviors based on these preexisting factors and their effects on learned relationships. Finally, in line with a functional analysis framework that focuses on both the positive and negative consequences of behaviors, more research is needed to examine the balance of positive and negative expectancies as they operate together to influence adolescent risk-taking behavior engagement.

Prevention and Intervention To date, there are a limited number of risk-taking prevention and intervention programs that have been developed focusing on aspects of avoiding and altering antecedents, either within or in line with a functional analytic framework. Several approaches have incorporated comprehensive, multicomponent structures aimed to modify antecedents across multiple environmental contexts (e.g., home, school, and peer groups) as well as skill building modules that provide adolescents with adaptive ways to respond to antecedents within their environments that are not amenable to change. As one example, the Midwestern Prevention Project (MPP, also known as students taught awareness and resistance; Project STAR; Pentz et al. 1989; Pentz et al. 1997) is a comprehensive substance use prevention program for early adolescents that includes school, media, parent, community organizing, and health policy components (Wolfe et al. 2006). The program provides “resistance skills” training to students, parents, and teachers, while also supporting the development of a drug-free climate in both the school and community, as well as changes in local drug and alcohol related health policies. Parents are included in the program and provided with training in communication and supportive parenting skills. Students involved in the MPP have shown significantly lower rates of substance involvement relative to comparison groups (Johnson et  al. 1990), with larger effects than those seen for programs that provide school-based components alone (Tobler and Stratton 1997). As another example, the Linking the Interests of Families and Teachers (LIFT) program was designed to prevent antisocial behaviors among youth by improving

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the quality of their social interactions across environmental contexts (Eddy et al. 2000). Components include the “Good Behavior Game” (a school-based behavioral treatment), social and problem solving skills delivered to the children, and parent management training. LIFT is based on theory consistent with a functional analytic framework suggesting that aggression and noncompliance with adult requests in early childhood can lead to social rejection from both adults and peers over the course of development. As youth move into adolescence, these environmental antecedents can lead to greater affiliation with peers who have similar histories and problems, thus increasing their vulnerability to risk-taking behaviors that may function to increase social interaction and peer acceptance. Researchers found that LIFT was associated with reduced playground aggression, as well as increased family problem solving, all within the first year of the intervention (DeGarmo and Forgatch 2004; Reid et  al. 1999). Additionally, children exposed to the LIFT program were less likely to use alcohol or come into contact with police during early adolescence (Eddy et al. 2003) and exhibited lower rates of tobacco and illicit drug use over the course of 5th through 12th grade, and that the reduced rate of tobacco use was mediated by increases in family problem solving (DeGarmo et al. 2009). By targeting factors across environmental contexts that have the potential to serve as antecedents for substance use in the future, LIFT is a promising approach to improving the developmental trajectory of adolescents by reducing the risk of delinquency and substance use in the long-term. Moving to interventions, probably the approach most closely tied to a functional analytic framework is the adolescent community reinforcement approach (A-CRA; Godley et al. 2001). A-CRA is a multicomponent, substance use treatment program that was modified from an adult version of the treatment to meet the developmental needs of adolescent substance abusers (CRA; Meyers and Smith 1995; Meyers et al. 1999; Meyers and Godley 2001). CRA views problematic substance use as originating from, and being maintained by, environmental contingencies across a variety of life domains. To target these contingencies, CRA integrates cognitive, behavioral, and family therapies. In addition to utilizing the basic features of the adult version of the treatment, A-CRA promotes positive family relationships and includes two sessions with the adolescent’s parents who are encouraged to support the youth’s efforts in remaining abstinent (Godley et  al. 2001). A-CRA also promotes positive peer relationships by including a close friend or significant other in the treatment. Healthy social relationships are considered essential for adolescents to effectively cope with stressful situations and to counteract feelings of isolation experienced as a result of disassociating from interpersonal networks that are unsupportive of abstinence (Titus and Dennis 2006). A-CRA has been shown to improve substance use outcomes in adolescents enrolled in outpatient continuing care following residential treatment (Garner et al. 2007; Godley et al. 2002, 2007). Other intervention programs have focused on specific reinforcement processes fitting well within a functional analytic framework. Conrod et al. (2008) developed a novel set of personality-targeted interventions to reduce premature alcohol use and binge drinking in at-risk youth. Within this work, the authors targeted sensation

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seeking as one personality vulnerability related to risk-taking, with a focus on providing alternative behaviors that would serve the same functions as risk-taking in terms of potential consequences. Results of an initial trial indicated that this approach may be particularly beneficial for youth with high levels of sensation seeking, who are prone to seek out experiences that provide immediate positive (or negative) reinforcement, by providing healthy alternatives that could fulfill these functions. Although few other function-based programs targeting positive and negative reinforcement have been applied to adolescent risk behaviors, the framework outlined here suggests interesting directions for exploration. In line with a negative reinforcement perspective toward adolescent risk-taking, low distress tolerance, defined as failing to persist in goal-directed behavior when experiencing discomfort (thereby reducing physical or emotional distress in the short-term), should also be explored as a potential treatment target to reduce adolescents’ involvement in risk behaviors. Several studies have identified an association between low distress tolerance and negative outcomes including unsuccessful smoking cessation attempts (Brown et al. 2005), early substance use treatment dropout (Daughters et al. 2005; MacPherson et al. 2008), and dysregulated eating behaviors (Anestis et  al. 2007a); however, these findings are specific to adults. Regarding treatment, an intervention designed to increase distress tolerance among a sample of early lapsing adult smokers produced longer maintenance of smoking abstinence (Brown et  al. 2008). The approach, which combines selfmanagement skills, cognitive restructuring to cope with antecedents, and acceptancebased strategies, addresses discomfort experienced as a result of smoking withdrawal by directly confronting this feeling rather than by engaging in avoidant coping behavior (i.e., smoking lapses), which are negatively reinforced by eliminating withdrawal discomfort. The study by Brown et  al. (2008) provides preliminary evidence that a behavioral treatment can effectively reduce engagement in negative reinforcement behaviors. However, modifications to this intervention are needed for it to be more developmentally appropriate for youth and further to examine whether a distress tolerance-based intervention can effectively reduce adolescent risk-taking behaviors that are driven by negative reinforcement processes. Behavioral activation (BA), which has been primarily used for depression treatment, offers an alternative approach to simultaneously target positive and negative reinforcement processes (Jacobson et  al. 2001; Lejuez et  al. 2001). BA is a highly structured treatment that uses functional analysis to understand contingencies in the individual’s environment that maintain depressive behavior. For example, receiving sympathy from others regarding one’s depressive state constitutes positive reinforcement; avoiding responsibilities constitutes negative reinforcement (Hopko et  al. 2003; Jacobson et  al. 2001). The functional purpose of engaging in detrimental behaviors contributing to depression is examined and weakened by replacing them with alternative healthier activities that serve the same function. BA has been examined as a treatment for illicit substance users with elevated depressive symptoms, with the idea that reducing depressive symptoms may improve substance use outcomes (Daughters et  al. 2008). In adolescents, BA has also shown promise as an effective treatment for depression (Chu et al. 2009; Gaynor

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and Harris 2008), but has not yet been tested as a treatment for adolescent ­risk-taking. However, in a recent study, BA was integrated into a college orientation program to address common adjustment difficulties among incoming college students that may lead to depression and alcohol use (Reynolds et al. in preparation). Compared to a control group, BA produced significant reductions in problem drinking in the sample. This study offers preliminary evidence of BA as an effective treatment for risk-taking behavior through the selection and completion of alternative healthy behaviors. In conjunction with a small but growing literature based in operant principles indicates that engagement in alternative, rewarding, nonsubstance related activities contributes to improved outcomes in youth receiving brief alcohol use intervention (Murphy et  al. 2005, 2007) and leads to slower adolescent substance use progression (Audrain-McGovern et al. 2004a, b, 2009); these novel findings suggest the potential value of using a BA approach within prevention efforts as well. Taken together, multicomponent prevention and intervention programs that target the function of adolescent risk-taking have shown promising results for reducing such behaviors, with a notable focus on substance use. The programs are consistent with a functional analytic framework to the extent to which they focus on identifying the function of adolescent risk-taking within an environmental context and the associated positive and negative consequences, and provide alternative strategies for adolescents to fulfill that function in a more prosocial or less harmful way. Further, by incorporating important people who interact with the adolescent across different environmental domains, the extent to which risk-taking is either positively reinforced (e.g., through peer acceptance) or negatively reinforced (e.g., through the reduction of negative affect associated with familial conflict) can be directly targeted and reduced. Of note, many current treatment efforts based on covert processes such as expectancies have largely focused on cognitive interventions; however, there would be value in future work clearly linking expectancies back to environmental experiences and positive and negative reinforcement processes.

Summary Although most intervention approaches for adolescent risk behavior contain key aspects of learning theory, few specifically utilize a formal functional analytic approach as a means through which to understand these varied influences on adolescent risk-taking. As we have tried to establish here, functional analysis provides a rigorous framework for the consideration of risk behavior that can provide a clear understanding of proximal causes or risk behavior, but also can be conceptualized more broadly to consider more distal influences and for integrating existing knowledge across a wide range of domains including personality and neurobiology. Additionally, evidence suggests that working within a functional analytic framework may serve as a useful guide for developing and implementing

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effective prevention and intervention strategies that aim to reduce adolescent risk-taking behaviors by specifically targeting antecedents, positive and negative reinforcement processes, and covert behaviors that contribute to or underlie adolescent risk-taking. In moving forward, the ultimate contribution of a functional analytic framework requires both that the broader discipline of risk-taking research consider the benefits of this approach, as it is necessary that proponents of functional analysis work to integrate theoretical perspectives and existing findings across domains typically not considered in functional analytic-based approaches to behavior.

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Chapter 11

Peer Influences on Adolescent Risk Behavior Dustin Albert and Laurence Steinberg

Abstract  Moving beyond studies of age differences in “cool” cognitive ­processes related to risk perception and reasoning, new approaches to understanding ­adolescent risk behavior highlight the influence of “hot” social and emotional ­factors on adolescents’ decisions. Building on evidence from developmental neuroscience, we present a theory that highlights an adolescent gap in the developmental timing of neurobehavioral systems underpinning incentive processing and cognitive control. Whereas changes in brain regions involved in incentive processing result in heightened sensitivity to social and emotional rewards in early adolescence, cognitive control systems do not reach full maturity until late adolescence or early adulthood. Within this framework, middle adolescence represents a window of heightened vulnerability to peer influences toward risk-taking behavior. At a time when adolescents spend an increasing amount of time with peers, research suggests that exposure to peer-related stimuli sensitizes the reward system to the reward value of risky behavior. As the cognitive control system gradually matures, adolescents gain the capacity to exercise self-regulation in socio-emotionally challenging situations, reflected by an increasing capacity to resist peer influence.

Introduction Evidence overwhelmingly points to adolescence as a period of heightened risk taking in multiple domains, including experimentation with alcohol, tobacco, and drugs, unprotected sexual activity, and reckless driving (Reyna and Farley 2006). Although risk-taking behavior declines as youth transition into mature adult roles, the public health consequences of the adolescent spike in risky decision-making are severe. Motor-vehicle accidents are the leading cause of mortality for 15–20-year-olds and, despite extensive efforts to educate adolescents about the dangers of unsafe D. Albert (*) Department of Psychology, Weiss Hall, 1701 N. 13th St., Philadelphia, PA 19122, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_11, © Springer Science+Business Media, LLC 2011

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sex, rates of sexually transmitted diseases remain alarmingly high (Steinberg 2008). Although not all individuals who initiate substance use in adolescence will progress along trajectories of abuse and addiction, most adult addicts begin using substances as adolescents (Chassin et al. 2009). In sum, the most severe threats to adolescent health and well-being come not from natural causes, but rather from behaviorcontingent outcomes like automobile accidents, suicide and homicide, substance abuse, and sexually transmitted diseases. A long tradition of research in developmental psychology points to adolescents’ peer groups as important contributors to trajectories of risk-taking behavior. It is well known that one of the strongest predictors of deviant behavior in adolescence is affiliation with deviant peers, and this relationship is particularly strong for adolescent substance use and abuse (Chassin et al. 2009). Crime statistics indicate that adolescents typically commit crimes, ranging from vandalism and drug use to homicide, in peer groups, whereas adults typically do so alone (Zimring 1998). Furthermore, adolescents are at greater risk of being involved in an automobile accident when riding in a car with multiple adolescent passengers (Simons-Morton et al. 2005). Several possible explanations have been advanced to account for the association between deviant peer affiliation – or even the mere presence of peers – and adolescent risk-taking behavior. First, a literal account of peer influence suggests that peer groups socialize adolescents in specific risk-taking behaviors. Research from social learning approaches like Problem Behavior Theory (Jessor and Jessor 1977) delineates potential pathways by which modeling and reinforcement of deviant behavior may initiate adolescents into a culture of risk taking. Although the social learning perspective is consistent with extensive correlational evidence linking adolescent risk taking to deviant peer affiliation, a second approach suggests that most of this association may be accounted for by selection effects or confounding variables; that is, adolescents with inclinations toward risk-taking behavior are likely to find one another, and these shared personality dispositions account for the correlations in behavior between the individual and peer group (e.g., Jaccard et al. 2005). A third approach accounts for the more frequent presence of peers in adolescent risk-taking situations by arguing that adolescents merely spend more time with their peers than do adults, thus increasing the probability that risk-taking tendencies are expressed in peer contexts (Brown 2004). In the present chapter, we propose an alternative, albeit compatible, account based on experimental evidence that the mere presence of peers differentially biases adolescents toward increased risk-taking behavior (Gardner and Steinberg 2005). Specifically, we propose a dual systems model of neurobehavioral development that views adolescence as a developmental window wherein the presence of peers may “prime” a reward-sensitive motivational state that frequently overwhelms the adolescent’s immature capacity for inhibitory control (Steinberg 2008). Before presenting the rationale and evidence to support our model of peer influences on risk taking, we first provide a brief review of traditional decision-making approaches to understanding increased risk behavior in adolescence. We then describe a new class of dual process theories that contrast relatively automatic (“hot”) with more deliberative (“cool”) modes of processing risk information, highlighting

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the role of affective states as inputs to the risk evaluation process. In the final section of this chapter, we review behavioral and neuroscientific evidence pointing to relatively independent trajectories of development for two core systems influencing risk-taking behavior in adolescence. The first, referred to as the socio-emotional reward system, undergoes dramatic remodeling around the time of puberty, resulting in normative increases in sensation seeking and sensitivity to socio-emotional stimuli. The second, the cognitive control system, develops in a gradual, linear pattern, and supports improvements in self-regulation observed in late adolescence and young adulthood. We present a model of adolescent risk taking that highlights the window of vulnerability created by a maturational gap between these two systems. We conclude by discussing ongoing research exploring developmental differences in the influence of peer presence on the relative engagement of the two systems in decision-making situations.

The Decision-Making Framework Traditional decision-making approaches, including health-belief models (e.g., Becker 1990) and the theory of reasoned action (Azjen and Fishbein 1980), emphasize that individuals behave rationally in deliberately weighing perceived risks and rewards to arrive at a decision that reflects their underlying goals (Reyna and Farley 2006). Within this consequentialist framework, it is assumed that when individuals possess accurate information about their personal vulnerability to the consequences of risk behavior, and these risks outweigh the subjective value of the behavior, they should generate a risk-averse response (Loewenstein et al. 2001). In short, decisionmaking outcomes are determined by the relative value of subjectively perceived costs and benefits, and the individual’s capacity to accurately weigh these inputs against each other. It follows from this perspective that excessive risk-taking behavior in adolescence derives from one or more of the following factors: (a) inaccurate perception of vulnerability to risk; (b) a goal structure that overvalues the benefits of risk behavior; and (c) immature cognitive processing of cost and benefit information. Empirical work has largely failed to support these predictions. In contrast to the long-held assumption of adolescent invulnerability, adolescents perceive risks and their personal vulnerability to such risks at an equal or greater level than adults; indeed, adolescents appear to overestimate risk relative to adults (Fischhoff 2008). Moreover, adolescents report a level of risk aversion that is comparable to that reported by adults, which argues against an assumption of adolescent goal structures that favor risk taking (Reyna and Farley 2006). Finally, although risk taking in laboratory contexts appears to decline somewhat from childhood to adulthood, children and adults use probability and outcome information in a similar fashion (Levin et al. 2007), and adolescents show logical reasoning abilities comparable to adults (Steinberg and Cauffman 1996). In sum, adolescents appear to possess the information and cognitive maturity to make reasoned decisions about whether to engage in risk behavior.

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The Role of Affect in Decision Making Given the failure of traditional cognitive models to account for adolescent risk taking, several theorists have called for increased attention to the socio-emotional and contextual inputs to the decision-making process (e.g., Fischhoff 2008; Loewenstein et al. 2001; Reyna and Farley 2006; Steinberg 2008). These approaches typically draw upon dual-process models to make the distinction between relatively slow, “cool,” analytical processing and faster, “hot,” associative, emotionally-driven processing. Whereas the rational calculus of expected value may guide decision making in cool situations, such models have typically failed to account for decision making in hot contexts, where social and emotional factors must be considered. Pointing out that most laboratory studies of age differences in risky decision making have purposefully minimized socio-emotional and contextual factors, the present critique offers a simple and compelling answer to the question of why extant research has not consistently revealed differences between adolescents and adults that match the real-world evidence of heightened risky behavior in adolescence: We have been studying the wrong thing. Showing up with a group of friends to a Friday night party, many (if not most) adolescents are unlikely to engage in a cool deliberative process of weighing the costs and benefits of decision options and calculating the expected value of a risky choice based on known probabilities of positive and negative outcomes. Decision making relies upon not only cognitive inputs, but also feelings: the excitement of being with friends, the thrill of crossing parental or legal boundaries, and the fear of getting caught are all plausible affective contributions to an adolescent’s decision of whether to drink at the keg party. To ignore affect is to study something other than risky decision making. Research with adult populations has identified several pathways by which affect contributes to the decision-making process (for reviews, see Loewenstein et al. 2001; Winkielman et al. 2007). First, the anticipated emotional outcomes of behavioral alternatives contribute to cognitive assessments of their expected value (Loewenstein et al. 2001). The teenager at the keg party might imagine that joining her friends in drinking beer will lessen her social anxiety and increase her positive emotion, whereas abstaining will make her feel excluded and increase her anxiety. These anticipated emotional consequences contribute to her global evaluation of the desirability of the risky choice. Second, direct emotional responses to qualities of the choice alternatives – that is, anticipatory emotions – influence their evaluation, and motivate approach or avoidance behavior (Loewenstein et al. 2001). Research grounded in inferential models of the influence of emotion on cognition suggests that individuals adaptively consult their feelings as a source of information when making a judgment about a given target (e.g., “affect-as-information”; Schwarz and Clore 1983). According to the somatic marker hypothesis, such emotional guidance of behavior reflects subtle affective learning from prior experience with reinforcement and/or punishment outcomes associated with the target (Damasio 1994). Returning to the keg party, if our teenager had a prior negative experience drinking beer, she may respond with a degree of disgust to the smell of spilled beer around the keg, and this aversive emotion might influence her behavior either indirectly (by contributing to a negative evaluation of the desirability of drinking) or directly (through heightened avoidance motivation).

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A third class of affective inputs has variously been referred to as incidental emotion or background mood, and includes emotions elicited by factors not related to the decision itself (Loewenstein et al. 2001). Dating back to Zajonc’s seminal affective priming studies (Zajonc 1980), research on the interplay of emotion and cognition has demonstrated the influence of preexisting or experimentally elicited affective states on perception, memory, judgment, and behavior (Winkielman et  al. 2007). For instance, individuals surveyed on a sunny day rate their life satisfaction as higher than those contacted on a rainy day (Schwarz and Clore 1983), and experimental elicitation of positive or negative emotion is associated with corresponding shifts toward optimistic or pessimistic judgments about risk (Johnson and Tversky 1983). Importantly, recent work grounded in affective neuroscience suggests that emotions do not even need to be consciously felt to influence behavior. In a clever experiment that elicited unconscious positive or negative emotion by presenting masked happy or angry faces, participants who had viewed happy faces chose to pour and drink more of a beverage than those who had viewed angry faces, despite reporting no differences in subjective mood (Winkielman et al. 2005). The authors of this study argue that exposure to salient emotional stimuli, especially facial expressions, activates subcortical circuits (e.g., nucleus accumbens and/or amygdala) that project to other subcortical and cortical regions involved in incentive processing and reward valuation (see also, Winkielman et al. 2007). Thus, neural responses to emotional stimuli – whether consciously experienced or not – may modulate an individual’s sensitivity to unrelated incentive stimuli, biasing the individual toward approach- or avoidance-related behavior. Returning to the keg party one last time, our hypothetical teenager is likely bombarded with socio-emotional stimuli, perhaps in the form of a crowd of friends’ smiling faces. These positively-valenced stimuli in turn may sensitize her reward system to respond appetitively to the incentive value of the cup of beer she is subsequently offered. In effect, her immersion in a happy crowd might sensitize her to perceive the beer as more appealing. Despite our use of an adolescent party to illustrate these mechanisms, it is important to note that the models we have reviewed describe the influence of emotion on risk-taking behavior as observed in adult populations. It is therefore reasonable to question the models’ power to account for heightened risk-taking behavior in adolescence relative to adulthood. Two recent studies suggest that certain affective stimuli exert a greater influence on the risk-taking behavior of adolescents than adults. In an effort to directly test a dual-systems account of age differences in risktaking behavior, Figner and colleagues developed two versions of the same risktaking task (the Columbia Card Task), one of which was similar to traditional deliberative tasks (i.e., minimizing affective arousal), and the other designed to trigger affective involvement (Figner et al. 2009). Both tasks presented participants with the opportunity to turn over cards revealing variable monetary gains and losses, after being explicitly informed about the magnitude and probability of the outcomes occurring. Thus, the tasks were equivalent in terms of the expected value of choice behavior. However, in contrast to the deliberative (“cold”) version of the task, which required participants to choose the number of cards to turn over at the beginning of each trial, the affective (“hot”) version instructed participants to turn over one card at a time, and presented feedback on gains and losses with each card. Thus, the cold

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version encouraged participants to rationally determine the optimal choice, whereas the hot version provided affective feedback (e.g., rewards and punishments) to guide the decision-making process. Consistent with traditional decision-making research, adolescents (ages 14–19) and adults (ages 20+) showed no differences in risk taking on the cold version of the task. In contrast, adolescents took significantly more risks than adults on the hot version of the task. Interestingly, although the effect was larger for adolescents than adults, both groups took more risks on the hot than the cold version of the task, consistent with the research reviewed earlier demonstrating the influence of affect on cognition in adult populations. Furthermore, across the age groups, risk taking in the hot task was positively correlated with self-reported need for arousal and negatively correlated with information use, providing support for the assertion that the hot task indeed captured affective influences on decision making. A more direct demonstration of the privileged role of socio-emotional stimuli as an input to adolescent decision making comes from an experimental study of peer context effects on risk-taking behavior (Gardner and Steinberg 2005). In this study, adolescents (mean age = 14), youths (mean age = 19), and adults (mean age = 37) were tested on a computer driving task that mimicked the real-life experience of approaching a yellow light and deciding whether to stop and wait for the light to turn green again, or drive through the intersection and risk being hit by an unseen car. Peer context was manipulated by randomly assigning each group of three participants to play the game either individually (alone in the room), or with two same-aged peers in the room. When tested alone, the three age groups engaged in a comparable amount of risk taking. However, when tested with peers in the room, adolescents and youth showed a significant increase in risk taking, whereas adults did not. Specifically, adolescents scored twice as high on an index of risky driving when tested with their peers in the room, relative to when they were alone, whereas the college-aged group was approximately 50% riskier, and adults showed no differences in risky driving related to context. This experimental demonstration of heightened peer influence on risk taking in adolescence represents an important advance over prior studies correlating adolescents’ risk behavior with behavior reported by their peers, findings that are subject to alternative explanations like selection or opportunity effects. At least in this one study, the presence of peers appeared to motivationally bias adolescents toward riskier behavior in a manner which was not apparent for adults. In the remainder of this chapter, we present a psychobiological model of adolescent development that offers a plausible account for this maturational window of increased susceptibility to peer influence on risk taking.

A Social Neuroscience Perspective on Adolescent Risk Taking Developmental theories of risk taking must account for two distinct trajectories observed in real-world behavior. First, risk taking increases sharply from childhood to adolescence. Second, risk taking steadily declines from late adolescence through the early adult years. Building on the dual-systems approaches described above, and

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incorporating recent evidence from developmental neuroscience, we argue that these two trajectories are related to normative maturational processes occurring in the brain over the course of adolescence and early adulthood. Specifically, we propose that risk taking increases around the time of puberty due to changes in what we refer to as the brain’s socio-emotional reward system, resulting in increased sensitivity to social and emotional stimuli, and heightened motivation toward reward seeking. Furthermore, we propose that risk taking decreases in the transition to adulthood due to gradual maturation of the brain’s cognitive control system, which supports advancements in self-regulatory control over goal-directed behavior, as well as a decrease in reward sensitivity as individuals mature into adulthood. Because of the gap in maturational timing of the two systems, mid-adolescence represents a window of vulnerability to social and emotional influences toward risk-taking behavior that are relatively unchecked by an immature capacity for self-regulation. We now briefly review the neurobiological and behavioral evidence for this model; the reader is referred to the original presentation of the theory for a more extensive review (Steinberg 2008).

Development of the Socio-Emotional Reward System The emerging field of developmental neuroscience is quickly amassing evidence indicating dramatic structural and functional changes in the human brain occurring around the time of puberty. One of the most important of these developments is the remodeling of the dopaminergic system within limbic and paralimbic areas (including the amygdala, ventral striatum, orbitofrontal cortex, medial prefrontal cortex, and superior temporal sulcus), a network of interconnected regions that we refer to as the socio-emotional reward system. This network is centrally involved in the processing of social and emotional stimuli (e.g., face recognition, social judgments, social reasoning (Adolphs 2003)) and, importantly, includes neural circuits that mediate reward processing (Spear 2009). Moreover, there is considerable overlap within this network between regions showing activation in response to social stimuli and regions that are differentially activated in response to variations in reward magnitude (e.g., the ventral striatum and medial frontal areas; Steinberg 2008). Research with animal models points to a pattern of proliferation and pruning of dopamine receptors in the striatum and prefrontal cortex (PFC) during adolescence, a pattern which is more pronounced in males than females (Sisk and Foster 2004). Developmental changes in the mesocorticolimbic dopamine system, in particular, appear to parallel adolescent shifts in reward-related behavior (Spear 2009). Briefly, this system includes dopamine neurons projecting from the midbrain substantia nigra (SN) and ventral tegmental area (VTA) to the striatum, including the nucleus accumbens (NAcc) and PFC. Converging evidence points to adolescent changes in dopamine receptor density and subsequent neurotransmission in the striatum and PFC. Dopamine receptor binding in the rat striatum peaks in adolescence at levels that are 30–45% greater than levels observed in adulthood (e.g., Teicher et al. 1995).

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Furthermore, excitatory dopamine input to the PFC shows adolescent peaks in both rodent (Spear 2009) and nonhuman primate populations (Rosenberg and Lewis 1995). Despite evidence for lower basal levels of dopamine release in adolescent (relative to adult) rats, adolescent rats evince greater dopamine release than adults in response to certain reward stimuli (Laviola et al. 2001). Although the nature and implications of dopaminergic remodeling remain hotly contested, one account suggests that changes in the mesocorticolimbic dopamine system facilitate heightened sensitivity to rewards in adolescence, relative to childhood or adulthood (for discussion of alternative views, see Spear 2009). Supporting this account, recent functional neuroimaging studies of age differences in reward processing have shown increased activation in adolescents of reward-relevant subcortical regions (especially the NAcc) in response to reward receipt (e.g., Galvan et al. 2006). Note, however, that opposite results were found in an fMRI study of age differences in reward anticipation (rather than receipt), with adolescents showing decreased accumbens activation relative to adults (Bjork et al. 2004). Importantly, Galvan et al. (2006) also reported a significant correlation between accumbens activity and self-reported risk-taking behavior, providing convergent evidence that adolescent reward sensitivity contributes to a heightened propensity toward risky behavior. Furthermore, these neuroimaging findings are consistent with the observations from animal models of adolescence indicating elevated dopamine neurotransmission in frontostriatal circuits, described above (e.g., Laviola et al. 2001). Consistent with this neuroimaging evidence for heightened reward sensitivity following puberty, adolescents report higher levels of sensation seeking than children or adults, a pattern that appears more closely related to pubertal development than age (Martin et al. 2002). Moreover, this peak in sensation seeking is mirrored in adolescent rodents, who show a marked increase in novelty seeking behavior (Spear 2009). Further evidence for curvilinear developmental changes in reward behavior comes from a recent study that examined age differences in reward processing, risk taking, and psychosocial maturity in a large population of individuals (N = 935) ranging from 10 to 30 years old (for a complete review of study findings, see Steinberg et al. in press). This study provided evidence for peaks (followed by declines) in early-to-middle adolescence of self-reported risk preference (Steinberg et al. in press) and sensation-seeking (Steinberg et al. 2008), as well as behavioral indicators of reward sensitivity (on a modified version of the Iowa Gambling Task; Cauffman et  al. in press) and preference for immediate over delayed rewards (Steinberg et  al. 2009). In contrast to this curvilinear pattern of development observed for measures of reward processing, age differences on measures of psychosocial maturity not directly related to reward processing (e.g., future orientation, impulse control, strategic planning) evinced a pattern of linear maturation extending across adolescence and into early adulthood (Steinberg et al. 2009). We return to the latter findings in our discussion of the development of cognitive control. In sum, evidence is beginning to accumulate suggesting that ongoing maturation of dopaminergic systems in adolescence contributes to changes in reward-system functioning coincident with heightened reward sensitivity and sensation seeking. It should be noted, however, that this dopaminergic remodeling has not been directly linked to puberty-related gonadal hormones. Research with gonadectomized rodents

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demonstrates normative patterns of dopaminergic proliferation and pruning, ­indicating that such neural development is not steroid-dependent (Spear 2009). Pubertycoincident changes in dopaminergic systems may result from steroid-dependent processes (some of which are activated pre or perinatally), ­steroid-independent processes, or interactions between these processes (Steinberg 2008). Whereas there is little evidence for direct effects of gonadal hormones on dopaminergic remodeling, puberty-related increases in gonadal hormones have been linked to a proliferation of receptors for oxytocin within the limbic system, including such structures as the amygdala and NAcc (Spear 2009). Oxytocin neurotransmission has been implicated in a variety of social behaviors, including facilitation of social bonding and recognition and memory for positive social stimuli (Insel and Fernald 2004). This evidence for puberty-related increases in gonadal hormones and oxytocin receptors is consistent with changes in a constellation of social behaviors observed in adolescence. In addition to reporting a spike in interest in opposite-sex relationships, adolescents begin to spend more time interacting with peers, and report the highest degree of happiness when they are doing so (e.g., Csikszentmihalyi et  al. 1977). This behavioral shift toward peer affiliation appears highly conserved across species; adolescent rats also spend more time than younger or older rats interacting with peers, while showing evidence that such interactions are highly rewarding (Spear 2009). Moreover, recent developmental neuroimaging studies indicate that, relative to children and adults, adolescents show heightened activation within the socio-emotional reward system in response to a variety of social stimuli, such as facial expressions and social feedback (Blakemore 2008). Finally, consistent with adolescent reports of heightened emotional intensity, several recent studies have demonstrated puberty-related increases in ­emotional reactivity, as indexed by heightened startle reflex, pupillary reactivity, and cortisol and cardiovascular response (for a review, see Dahl and Gunnar 2009). Taken together, this evidence for puberty-coincident remodeling of the brain’s socio-emotional reward system and associated elevations in sensation seeking, reward salience, and sensitivity to social and emotional stimuli suggests a number of compelling possible answers to the question of why risk-taking behavior increases between childhood and adolescence. Based on the observed changes in adolescent reward system functioning, a first answer is simply that adolescents who have undergone remodeling of the dopaminergic system may be more responsive to the reward value of risky choices than their younger counterparts. Building on this foundation, we propose a second mechanism: Not only are adolescents potentially more responsive to rewards, but due to puberty-related increases in sensitivity to social and emotional stimuli, this inclination toward approaching risky rewards is exacerbated when adolescents are in the presence of their peers. Such an explanation is consistent with the experimental evidence reviewed above showing that adolescents took twice as many risks on a driving simulation task when seated inbetween two peers, compared to when they completed the task alone (Gardner and Steinberg 2005). Furthermore, empirical and theoretical work detailing the influence of affective states on decision making suggests plausible neurobiological mechanisms for how such a peer effect might be instantiated in the brain. Recall our earlier description of a study demonstrating increased consummatory behavior in response to subliminal presentation of positively valenced, emotionally ­expressive

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faces (Winkielman et  al. 2005). Pointing to the extensive structural overlap of ­neurobiological systems mediating processing of socio-emotional and incentive stimuli, the authors argued that positive emotional responses may sensitize incentive circuitry toward activation of approach responses to appetitive stimuli. Given evidence of puberty-related intensification of socio-emotional reactivity in adolescence, we suggest that adolescents are more likely than their younger counterparts to strongly activate such circuitry when in the presence of their peers, resulting in greater sensitization to the reward value of risky choices. At the conclusion of this chapter, we describe results from an ongoing program of neuroimaging research designed to test these hypotheses. First, we turn to the question of why risk taking declines as adolescents mature into adulthood.

Development of the Cognitive Control System In contrast to the relatively sudden changes in social, emotional, and reward processing that occur around the time of puberty, cognitive capacities supporting mature self-regulation appear to develop in a gradual, linear pattern over the course of adolescence (and often into early adulthood; Steinberg 2008). A growing body of evidence from cognitive neuroscience suggests that these improvements in cognitive control are supported by structural and functional maturation of a phylogenetically recent brain system that includes the lateral PFC, parietal association cortices, and parts of the anterior cingulate cortex, as well as enhanced connectivity between this system and limbic circuitry (for a review, see Casey et al. 2008). Recent advances in structural neuroimaging techniques have permitted identification of two broad patterns of brain development occurring over the course of adolescence. First, after peaking between ages 10 and 12, the volume of gray matter in the frontal and parietal lobes decreases in the teenage years, a pattern commonly interpreted as reflective of synaptic pruning, the process by which infrequently activated neuronal connections are eliminated (Giedd 2008). Importantly, among the last regions to complete the process of gray matter loss is the dorsolateral PFC (DLPFC), a region implicated as crucial for cognitive control by functional neuroimaging studies employing a variety of complex control tasks (Casey et al. 2008). A second feature of adolescent brain development is a whole-brain, linear increase in white matter that extends well into the twenties (Giedd 2008). Increases in white matter volume are thought to reflect myelination, the process by which axons are wrapped in an insulating sheath, thereby supporting greater integrity and speed of neuronal transmission. Studies employing diffusion tensor imaging (DTI), a technique for imaging white matter tracts and estimating their relative structural integrity, have provided further evidence for continued myelination over the course of adolescence (Giedd 2008). Evidence of a prolonged course of myelination of neuronal connections within cortical regions, and between cortical and limbic regions, has led to predictions of improved processing efficiency on complex cognitive control tasks, as well as advances in the coordination of cognition and affect supporting goal-directed behavior. Indeed, such behavioral developments closely ­parallel the timetable for biological maturation we have just described.

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Consistent with evidence for early adolescent synaptic pruning in the PFC, improvements in basic information processing and logical reasoning capacities thought to rely upon the PFC are still evident over the course of early-to-middle adolescence, but show few maturational advancements beyond approximately 16 years of age (Steinberg 2008). For instance, in the large-scale study of age differences in capacities contributing to risk taking that we described previously, no developmental improvements in relatively simple measures of working memory or verbal fluency were evident after age 16 (Steinberg et al. 2009). When developmental gains in basic cognition do extend into later adolescence, they are typically seen on more demanding tasks with strong processing efficiency requirements, which likely rely on enhanced inter-cortical connectivity (Steinberg 2008). In contrast to the relatively limited adolescent gains in basic cognition, developmental improvements in higher-order executive functions known to simultaneously recruit multiple subregions of the PFC are evident across the course of adolescence and into early adulthood, consistent with the enhanced neural connectivity provided by ongoing myelination. For instance, improved performance is evident in late adolescence on tasks assessing response inhibition (e.g., Luna et al. 2001), strategic problem solving (e.g., Luciana et al. 2009), and flexible rule use (e.g., Crone et al. 2006). Furthermore, consistent with gains in cortical–subcortical connectivity, improved coordination of cognitive and affective processes is also evident in late adolescence and early adulthood (Steinberg 2008). For instance, self-report and behavioral evidence indicates a pattern of linear growth in impulse control extending through adolescence and into the twenties (Steinberg et al. 2008). The proposed link between structural brain maturation and gains in selfregulatory behavior is further supported by convergent evidence from functional neuroimaging studies of developmental differences in the neural correlates of cognitive control. Imaging studies utilizing a variety of cognitive control paradigms (e.g., Go-No/Go, Stroop, flanker tasks, antisaccade) suggest that adolescents recruit the control network – especially the DLPFC – less efficiently than adults (Casey et al. 2008). In general, adolescents show stronger activation than children of the DLPFC while engaging in cognitive control tasks, consistent with structural maturation of the region in early adolescence (e.g., Luna et al. 2001). In contrast, between adolescence and adulthood, differences in activation appear to reflect a process of refinement in the recruitment and coordination of structurally mature regions, rather than gross differences in level of activation. Specifically, adolescents show increasingly focal engagement of task-relevant regions supporting cognitive control, a functional advancement that may reflect the increased integrity and efficiency of interregional connections resulting from ongoing myelination (Durston et al. 2006).

Interactions Between Reward and Control Systems To summarize thus far, we have presented evidence that dopaminergic remodeling coincident with puberty is associated with heightened sensitivity in the socioemotional reward system in early adolescence, whereas synaptic pruning and

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myelination over the course of adolescence likely support gradual improvements in the efficiency of the cognitive control system. Is adolescent risk taking then simply a matter of an overactive socio-emotional reward system dominating an immature control system? This idea of competitive dominance has been proposed as a mechanism contributing to risky or impulsive decision making in contexts as diverse as drug use, cost/reward valuations, social information processing, and adolescent risk taking (Steinberg 2008). For instance, fMRI studies have shown correlations between activity in the socio-emotional reward system and preference for immediate over delayed rewards (McClure et al. 2004) and risky decision making (Ernst et al. 2004). Furthermore, evidence from an experimental study utilizing transcranial magnetic stimulation demonstrated increased risk taking following disruption of activity in the right DLPFC, a region consistently implicated in studies of cognitive control (Knoch et al. 2006). Together, these studies suggest that risk taking may result from overactivity in the socio-emotional reward system, underactivity in the cognitive control system, or a competitive imbalance between the two systems. A second, complementary approach to understanding developmental changes in the interaction between the socio-emotional reward system and the cognitive control system is to focus on age differences in coordination between the two systems. That is, mature decision making does not necessarily reflect developmental changes in the relative dominance of one system or the other, but rather the extent to which the two systems are simultaneously recruited and engage in “cross-talk” to produce a response that effectively integrates bottom-up (i.e., socio-emotional) and top-down (i.e., cognitive control) inputs. Given the steady gains in connectivity between cortical and subcortical regions observed over the course of adolescence, we would expect parallel gains in the capacity to integrate emotion and cognition. Indeed, adolescents evince gradual improvement of capacities reflecting self-regulatory control of emotionallydriven behavior, including impulse control, planning, and future orientation (Steinberg 2008). However, improved coordination of emotion and cognition is not only reflected in the capacity to override emotional inputs, but also in the ability to adaptively utilize affective information to guide decision making. For instance, research using a variety of affective learning paradigms (i.e., variants of the Iowa Gambling Task) demonstrates steady gains over the course of adolescence in the extent to which emotional feedback guides mature decision making (e.g., Cauffman et  al. 2010). Most importantly for the present argument, this improved coordination of bottom-up and top-down processing should facilitate growth in the capacity to regulate the influence of peers in risk-taking situations. Consistent with this prediction, a recent study demonstrated gradual, linear improvement in resistance to peer influence through at least age 18 (Steinberg and Monahan 2007). Recent neuroimaging work also supports the proposed association between improved neural connectivity and parallel gains in resistance to peer influence. For instance, one innovative fMRI study assessed a group of 10-year-olds with varying degrees of self-reported resistance to peer influence on their responses to emotionally arousing video clips (i.e., angry vs. neutral biological motion; Grosbras et al. 2007). Children reporting a relatively high degree of resistance to peer influence demonstrated greater functional connectivity in their responses to the emotional scenes, such

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that activity in motor-perception areas (i.e., dorsal premotor cortex) was correlated with activity in cognitive control regions (i.e., DLPFC). A second study demonstrated that individual differences within a group of 12–18-year-olds (after controlling for age) in resistance to peer influence were correlated with the extent of structural connectivity between prefrontal and premotor areas (Paus et  al. in press). Although research relating age differences in structural and functional connectivity to maturation of behavior is in its infancy, these findings are nonetheless suggestive of the need to move beyond examining age differences in recruitment of specific regions and toward a consideration of developmental changes in coordinated neural activity.

Future Directions We are currently engaged in a program of research in our laboratory designed to test the neurobiological account of adolescent risk taking proposed in this chapter. Building on experimental work showing age differences in the degree to which peer presence facilitates risky behavior (Gardner and Steinberg 2005), we have attempted to bring peer context into the scanner (Chein, Albert, O’Brien, Uckert, & Steinberg, in press). Using an event-related fMRI design, we are examining age differences in neural activation at the moment of decision making in a variety of risk-taking and reward-processing tasks. To manipulate peer context, we measure task-related neural activation for each participant during two separate sessions. In one session, the participant completes the tasks while their peers are observing their performance from the scanner control room; in the other session, the participant completes the task with no observation. In each case, the participant is made aware of the condition. Consistent with our predictions, analysis of age-by-context differences in neural activation corresponding to risk-relevant driving decisions on the Stoplight Task (Steinberg et al. 2008) indicate that adolescents activate socio-emotional reward regions (e.g., medial PFC, ventral striatum) more strongly when making risky decisions while being observed by their peers than when they do so alone (Chein et al. in press). In contrast, adults show no differences in neural activation related to peer context, but instead show stronger activation than adolescents of cognitive control regions (e.g., lateral PFC), regardless of peer context. Future analyses will also explore age-by-context differences in functional connectivity between cognitive control and socio-emotional reward regions that may contribute to these observed differences in risk-taking behavior.

Conclusion Research efforts to account for developmental trajectories of risk taking in ­adolescence have arrived at an exciting new stage. Moving beyond laboratory studies­ of age differences in “cool” cognitive processes related to risk perception and reasoning, new approaches have begun to incorporate insights from a rich

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l­iterature describing the many pathways by which social and emotional factors influence the decision-making process. Combining these insights with emerging evidence from developmental neuroscience, we have outlined a theory that highlights a neuro-maturational gap between early adolescent remodeling of the socioemotional reward system and a gradual, prolonged strengthening of the cognitive control system. Within this framework, middle adolescence represents a window of heightened vulnerability to peer influences toward risk-taking behavior. At a time when adolescents spend an increasing amount of time with their peers, research suggests that peer-related stimuli may sensitize the reward system to respond to the reward value of risky behavior. As the cognitive control system gradually matures over the course of the teenage years, adolescents grow in their capacity to coordinate affect and cognition, and to exercise self-regulation even in emotionally arousing situations. These capacities are reflected in gradual growth in the capacity to resist peer influence. A full understanding of the neurobehavioral processes involved in peer resistance will be important for designing more effective inventions that target health-related risks such as drug use.

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Paus, T., Toro, R., Leonard, G., Lerner, J., Lerner, R., Perron, M., et al. (in press). Morphological properties of the action-observation network in adolescents with low and high resistance to peer influence. Social Neuroscience. Reyna, V.F., & Farley, F. (2006). Risk and rationality in adolescent decision making: Implications for theory, practice, and public policy. Psychological Science in the Public Interest, 7, 1–44. Rosenberg, D., Lewis, D.A. (1995). Postnatal maturation of the dopaminergic innervation of monkey prefrontal and motor cortices: A tyrosine hydroxylase immunohistochemical analysis. Journal of Comparative Neurology, 358, 383–400. Schwarz, N., & Clore, G.L. (1983). Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states. Journal of Personality and Social Psychology, 45, 513–523. Sisk, C., & Foster, D. (2004). The neural basis of puberty and adolescence. Nature Neuroscience, 7, 1041–1047. Simons-Morton, B., Lerner, N., & Singer, J. (2005). The observed effects of teenage passengers on the risky driving behavior of teenage drivers. Accident Analysis and Prevention, 37, 973–982. Spear, L. (2009). The behavioral neuroscience of adolescence. New York: Norton. Steinberg, L. (2008) A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28, 78–106. Steinberg, L., & Cauffman, E. (1996). Maturity of judgment in adolescence: Psychosocial factors in adolescent decision making. Law and Human Behavior, 20, 249–272. Steinberg, L., Albert, D., Cauffman, E., Banich, M., Graham, S., & Woolard, J. (2008). Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: Evidence for a dual systems model. Developmental Psychology, 44, 1764–1777. Steinberg, L., Cauffman, E., Woolard, J., Graham, S., & Banich, M. (2009). Are adolescents less mature than adults? Minors’ access to abortion, the juvenile death penalty, and the alleged APA “flip-flop”. American Psychologist, 64, 583–594. Steinberg, L., Graham, S., O’Brien, L., Woolard, J., Cauffman, E., & Banich, M. (2009). Age differences in future orientation and delay discounting. Child Development, 80, 28–44. Steinberg, L., & Monahan, K. (2007). Age differences in resistance to peer influence. Developmental Psychology, 43, 1531–1543. Teicher, M., Andersen, S., Hostetter, J. (1995). Evidence for dopamine receptor pruning between adolescence and adulthood in striatum but not nucleus accumbens. Developmental Brain Research, 89, 167–172. Winkielman, P., Berridge, K.C., & Willbarger, J. (2005). Unconscious affective reactions to masked happy versus angry faces influence consumption behavior and judgments of value. Personality and Social Psychology Bulletin, 1, 121–135. Winkielman, P., Knutson, B., Paulus, M., & Trujillo, J.L. (2007). Affective influence on judgments and decisions: Moving toward core mechanisms. Review of General Psychology, 11, 179–192. Zajonc, R.B. (1980). Feeling and thinking: Preferences need no inferences. American Psychologist, 35, 151–175. Zimring, F.E. (1998). American youth violence. Oxford, England: Oxford University Press.

Part IV

Translating Research on Inhibitory Control to Prevention Interventions

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Chapter 12

The Effects of Early Adversity on the Development of Inhibitory Control: Implications for the Design of Preventive Interventions and the Potential Recovery of Function Philip A. Fisher, Jacqueline Bruce, Yalchin Abdullaev, Anne M. Mannering, and Katherine C. Pears Abstract  Because of their exposure to severe early adversity and the heterogeneity of these adverse experiences, foster children are an important population in which to study the associations between stress, alterations in underlying neural systems, and subsequent developmental and psychosocial outcomes. Moreover, inasmuch as the US foster care population numbers over half a million children and the needs of these children are not always met by existing service delivery programs, there are scientific and public policy reasons to pursue this work. In this chapter, we describe the results of a research program involving young foster children that has focused on the following three areas: examining how variations in dimensions of early adverse experiences are associated with specific negative developmental and psychosocial outcomes; identifying neural deficits associated with these negative outcomes; and conducting randomized clinical trials of preventive interventions with dual goals to improve developmental and psychosocial outcomes and examine the potential for recovery of or improved functioning in the underlying neural systems. The specific focus of this chapter is our investigations of inhibitory control within the context of this larger research program. We provide an overview of the research program and guiding conceptual model, summarize the studies focused on inhibitory control, and discuss the implications of this work for science and public policy.

P.A. Fisher (*) Department of Psychology, University of Oregon, Eugene, OR 97401, USA and Oregon Social Learning Center, Eugene, OR 97401, USA and Center for Research to Practice, Eugene, OR, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_12, © Springer Science+Business Media, LLC 2011

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Introduction Human brain development is a lifelong process. Experience shapes the brain, and this is especially true in the early years of life, a period of rapid brain growth and development. Thompson and Nelson (2001) characterized experiences during infancy, childhood, and adolescence as playing a central role in the development of neural sensory and regulatory systems and many higher-order cognitive systems. To the extent that the environment is generally responsive and supportive, development is more likely to proceed on course. However, a positive environment does not guarantee a positive outcome. There are myriad genetic, epigenetic, and other biological influences that contribute significantly to health and well-being. For example, even the most optimal environment will not fully mitigate the effects of a genetic disorder that produces lower cognitive functioning (e.g., Rett syndrome or Down syndrome). Nevertheless, a supportive caregiving environment can greatly enhance the likelihood of attaining positive outcomes and achieving one’s full potential. Adverse experiences during development exert a strong negative influence on subsequent outcomes, and a burgeoning body of evidence suggests that a primary mechanism linking early adversity to compromised later outcomes is the impact of adversity on the developing brain (National Scientific Council on the Developing Child 2005; Shonkoff and Phillips 2000). Shonkoff et  al. (2009) suggested two pathways by which these effects occur. The first pathway involves the effects of early adversity as a global risk factor with a high degree of multifinality. As the number of adverse experiences accrues, the likelihood of a broad range of negative health and psychosocial outcomes increases. This conceptualization, characterized by McEwen (1998) as “allostatic load,” defines a dose–response relationship between cumulative level of early adversity and disease risk. Although a vast array of neuroendocrine, metabolic, and immunological mechanisms are involved in the regulation of stress under ordinary circumstances, chronically high levels of stress may ultimately overwhelm and produce damage to these systems, resulting in negative effects such as premature aging, physical illness, and mental health effects (Karlamangla et al. 2002; McEwen 2003; Repetti et al. 2002). Whereas allostatic load represents a variable-centered approach to examining the effects of early adversity, the second pathway by which early adversity affects outcomes is person centered. As Shonkoff et al. (2009) noted, specific events occurring during critical periods of development may increase risks for specific outcomes. For example, maternal infection with the influenza virus during pregnancy has been documented to significantly increase risk for schizophrenia (Brown 2006). Similarly, low birth weight, which may result from different prenatal events and influences, is associated with a host of negative outcomes (Hack et al. 2005; McCormick et al. 1990). The role of neurobiology in shaping these outcomes appears to be related to the extent to which adverse experiences coincide with the development (in terms of growth and canalization) of specific brain regions and the connectivity among these regions (National Scientific Council on the Developing Child 2005).

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Characterizing the Heterogeneity of Adversity Among Foster Children Foster children are especially well suited to the examination of the effects of early adversity on development. In addition to being exposed to levels of maltreatment that are sufficient to warrant out-of-home placement, these children have typically been exposed to many additional stressors, including adversity during the prenatal period and adversity after entering foster care. Moreover, the US foster care population is quite large. There are currently over 500,000 children in foster care, and approximately 300,000 children per year enter the foster care system (U.S. Department of Health and Human Services, Administration for Children and Families, Children’s Bureau, 2006a, b, 2007). Thus, understanding the associations between early adversity and subsequent risk in foster children is important not only from a developmental science perspective, but also from a public policy perspective. Disparities among foster children on physical and mental health outcomes have been extensively documented. Foster children have higher rates of medical illness, psychiatric diagnoses, drug and alcohol problems, and school failure (Administration for Children and Families, Office of Planning, Research and Evaluation n.d.). Moreover, in many individuals, these effects are not transient, but rather appear to be associated with lifelong risks for compromised adjustment. Kessler et al. (2008), for example, noted that adults who were maltreated as children showed significantly higher rates of liver disease, heart disease, and lung cancer, all of which may be mediated through health-risking lifestyles. Further evidence of high-risk lifestyles in this population can be derived from a study by Wilson and Widom (2009), who found higher rates of sexually transmitted disease in adults who had been abused as children. Similarly, Hee-Soon et al. (2003) found that adult alumni of the foster care system showed higher mortality rates than the general population. Finally, Zielinski (2009) found that maltreated individuals had higher rates of poverty and unemployment in adulthood. It is tempting to surmise that the negative outcomes among foster children must be limited to severely abused and neglected individuals whose entire childhood and adolescence were spent in the child welfare system, moving between chaotic and, at times, maltreating environments. Consistent with the allostatic load conceptualization, those with very high dose exposure to adversity over long durations appear to be at greater risk for poor outcomes. However, there is emerging evidence that long-term exposure to adversity is not a prerequisite for permanent alterations in the course of development. For example, Shirtcliff et al. (2009) examined immune function via the presence of herpes simplex virus antibodies in three groups of 9- to 14-year-old children: (1) physically abused children who remained in the homes of their biological parents, (2) children adopted from overseas ­orphanages in infancy and early childhood, and (3) a nonmaltreated comparison group. As was expected, the physically abused children had more herpes antibodies than the nonmaltreated children. In addition, and somewhat less expected, the adopted children also had high antibody levels that were comparable to the physically abused children. This

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was true even though the adopted children experienced early adversity (typically severe neglect) only in infancy and had subsequently been living in what most would consider optimal conditions. This finding and others documenting differential effects from different types of early adversity (e.g., Fisher and Gunnar 2010) have demonstrated the limitation of solely employing an allostatic load, dose–response model to study of the effects of early adversity in foster children; such a model might not adequately account for the heterogeneity of outcomes among foster children. Consequently, it is important to complement this sort of variable-centered approach with more person-centered methodology. Indeed, one quality that makes foster children so important to the study of early adversity is the heterogeneity of their adverse experiences. All foster children have experienced some degree of early adversity, but that adversity falls along a continuum, with some children experiencing chronic and continuously high levels of adversity and others experiencing milder, episodic adversity. For example, some children enter foster care at birth due to maternal behaviors (e.g., prenatal substance use) and environmental circumstances (e.g., domestic violence) during pregnancy, whereas other children enter care in infancy or early childhood due to extremely inadequate early environments. Among all of these children, their subsequent experiences can range from high quality parenting and a relatively stable environment to poor quality parenting and placement instability. Among children entering care in later childhood, some have experienced high levels of adversity beginning in infancy, whereas others have had adequate early care and experienced deteriorating conditions due to parental substance use, incarceration, or mental health issues. Given the diversity of experiences among foster children, empirical studies of the effects of early adversity in this population may be facilitated by organizing the available data into discrete categories.

Parameterizing Early Adverse Experiences Among Foster Children There are a number of dimensions along which it may be useful to study variation in foster children’s adverse experiences – a process characterized by others as “parameterizing” early adversity effects (Camras et  al. 2006). Some of these dimensions are only beginning to be investigated empirically. For example, although there has been a considerable amount of research in humans and animals on the effects of prenatal drug and alcohol exposure and there are high prevalence rates of prenatal drug and alcohol exposure among foster children, only a few studies (e.g., McNichol and Tash 2001; Smith et al. 2007) have examined the effects of such exposure on outcomes for foster children. Similarly, in light of recent evidence that epigenetic effects on gene expression may influence the likelihood of specific outcomes (e.g., risk for suicide in individuals who have experienced prior abuse; McGowan et al. 2009) and that these epigenetic effects can be transmitted intergenerationally, attention is turning to molecular (Kaufman et  al. 2004) and

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behavior genetic (Reiss and Leve 2007) studies of the effects of gene–environment interactions in foster children. In contrast to the developing areas of research on prenatal and epigenetic effects, a number of dimensions of early adverse experiences have been examined more thoroughly. The most salient of these is the types of maltreatment to which children have been subjected. Although physical and sexual abuse are often the focus of media accounts of foster children, neglect and emotional abuse are far more common reasons for children being placed in foster care. For example, one study found that over 80% of a sample of preschool-aged foster children had experienced neglect and that 90% had experienced emotional abuse (Pears et  al. 2008). The prevalence of physical and sexual abuse, though not insignificant, was much lower: 33 and 26%, respectively. Different maltreatment types appear to exert differential effects on outcomes. For example, Pollak and Sinha (2002) examined the processing of facial emotions in physically abused and nonabused children and found that physically abused children accurately detected anger (but not other emotions) in the presence of significantly less sensory input than nonabused children. Moreover, these effects appear to have an underlying neural basis. In a subsequent study, Pollak and TolleySchell (2003) found enhanced attentional allocation to angry faces (assessed via event-related potential data) among physically abused children compared to nonabused children. Neglect also appears to have specific effects on foster children’s development, such as alterations in the diurnal functioning of the hypothalamic–pituitary–adrenal (HPA) axis. The HPA axis is an important neuroendocrine system because of its role in responding to stress (Levine 1992) and because dysregulation of the HPA axis has been widely observed in individuals with anxiety and affective disorders (Erhardt et  al. 2006; Yehuda 2002; Young et  al. 2003). Typical daytime diurnal functioning of the HPA axis is characterized by peak activity approximately 30 min after waking, followed by rapidly decreasing activity during the morning and little activity in the evening. However, preschool-aged foster children have been observed to exhibit a blunted diurnal rhythm characterized by low morning activity that remains low throughout the day (Gunnar et al. 2006). These blunted diurnal patterns have also been observed in separate samples of foster children in Oregon (Bruce et al. 2009a) and Delaware (Dozier et al. 2006); in both samples, the blunted diurnal pattern was present in approximately 30% of foster children, which is three times higher than in the general population. Notably, Bruce et al. (2009a) reported that the foster children with the blunted diurnal pattern had experienced more severe neglect – but not other types of maltreatment – than those who did not show this pattern. Converging evidence that this pattern of HPA axis dysregulation is associated with neglect in particular comes from studies of children from overseas orphanages, which are typically severely neglectful but not abusive (Carlson and Earls 1997; Gunnar et al. 2001; Kertes et al. 2008; Wismer Fries et al. 2008). On the positive side, there is also evidence of plasticity in the HPA axis in response to therapeutic interventions. Specifically, a number of researchers

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(Bakermans-Kranenburg et al. 2008; Fisher et al. 2007a) have reported improved HPA axis functioning among foster children in randomized trials of therapeutic foster care programs. Another dimension along which foster children’s exposure to early adversity may vary involves their foster care experiences. A number of these dimensions, such as the age of foster care entry (James et al. 2004) and the time spent in foster care (Davis et  al. 1997), have been shown to be associated with different outcomes. In addition, one of the most well-researched and strong predictors of outcomes in this area is the number of unique foster care placements, a concept referred to as “placement instability.” Placement instability has been shown to increase the risk of mental health problems and related difficulties (Rubin et al. 2007; Ryan and Testa 2005). In addition, placement instability is persistent. In a study of preschool-aged foster children, Fisher and colleagues (Fisher et al. 2005, 2009) found a strong relationship between the number of unique foster care placements and permanency outcomes. First-time foster children had approximately a 90% chance of achieving a permanent placement. In contrast, foster children with five or more placements had less than a 10% chance of achieving a permanent placement.

Placement Instability and Inhibitory Control Deficits Placement instability also appears to have a specific effect on inhibitory control (IC): the ability to inhibit prepotent responses to salient but irrelevant stimuli while pursuing a cognitively represented goal (Kochanska et  al. 1996). Deficits in IC functioning have been associated with risk for a number of negative outcomes. For example, children with IC deficits exhibit problems with peer relations (BoothLaForce and Oxford 2008; Hughes et  al. 2000) and school performance (Ponitz et al. 2009). Among foster children, IC assessed during preschool has been shown to predict academic and social-emotional competence in early elementary school (Pears et al. in press). IC deficits have also been observed in individuals with attention deficit/hyperactivity disorder (ADHD), disruptive behavior disorders, and substance use disorders (Casey et al. 1997; Pears et al. 2007a; Toupin et al. 2000). As such, IC deficits may represent a common pathway to many of the mental health difficulties frequently observed in foster children. In two prior investigations, foster children’s placement instability has been shown to be associated with IC deficits. In a study of preschool-aged foster children, Pears et al. (2010) examined scores on a composite measure of IC. This study is particularly noteworthy because, rather than being limited to parent- or self-report measures, the composite IC measure included two laboratory tasks (day–night Stroop and dimensional card sort), two executive function subscales from the NEPSY (Korkman et  al. 1998), and two parent-report measures. A significant ­correlation was observed between the number of unique foster care placements and IC composite scores, with more placements being associated with lower scores.

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IC difficulties among foster children were also reported in a study by Lewis et al. (2007), who examined performance on a day–night Stroop task among children who were adopted following foster care. The children with placement instability performed significantly worse on the Stroop task, regardless of age and general cognitive ability. Interestingly, adopted children with placement instability also had significantly higher caregiver ratings of oppositional behaviors; however, IC difficulties did not mediate the association between placement instability and oppositional behaviors. It is important to note that both of the aforementioned studies were correlational. Thus, the directionality of the association between placement instability and IC difficulties is not clear. Instability might increase IC difficulties, but children with IC difficulties might also be at greater risk for placement instability. Bidirectional influences are also a strong possibility. It remains for longitudinal investigations of the development of IC difficulties among foster children to clarify these associations.

Underlying Neural Mechanisms of Deficits in Inhibitory Control In neuroimaging studies with children and adults, researchers have suggested that specific regions of the prefrontal cortex underlie IC (Casey et al. 1997). There is also evidence that the anterior cingulate cortex serves a role in recruiting additional IC to meet task demands (Botvinick et  al. 2001; van Veen and Carter 2002). Previous researchers have suggested that maturation of the prefrontal cortex, anterior cingulate cortex, and their circuitry is protracted, limiting top-down regulatory control until the mid-twenties, and perhaps rendering children and adolescents particularly vulnerable to negative environmental experiences (Davies et  al. 2004). Indeed, researchers working with children and adolescents have shown this to be the case with early adverse experiences (Carrion et al. 2001; De Bellis et al. 2000). To expand upon this line of research, we have used neuroimaging methods to examine IC deficits in foster children. In a recent pilot study (Bruce et al. 2009b), event-related functional magnetic resonance imaging (fMRI) was employed to examine brain activation during performance of a Go/No Go task measuring IC in 11 foster children and 11 demographically matched nonmaltreated children (ages 9–12 years). The Go/No Go task, which has been shown to activate the ventral prefrontal cortex, anterior cingulate cortex, and striatum (Durston et  al. 2002, 2006), measures the ability to inhibit a prepotent response by selectively responding to the target stimuli (go trials) while inhibiting responses to equally salient nontarget stimuli (no go trials). By subtracting brain regions activated during the go trials (or during a resting baseline period for between-group comparisons) from regions activated during the no go trials, it is possible to determine which regions are involved in IC. Interestingly, there were no differences between the foster children and the ­nonmaltreated children on behavioral performance during the task: Both groups of

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children made significantly more errors on the no go trials compared to the go trials. The two groups did not differ in terms of accuracy on these trials. In contrast to the behavioral results, the fMRI data revealed noteworthy results. Consistent with the prior studies that have used this task, both groups of children showed greater brain activation in the bilateral prefrontal cortex and insula, the anterior cingulate gyrus extending into the supplementary motor cortex, the left middle temporal gyrus, the left postcentral gyrus, and the left parahippocampal gyrus during the no go trials than during the go trials (see Fig. 12.1). However, there were several group differences in the patterns of brain activation during the task. In particular, the groups demonstrated different patterns of brain activation in response to incorrect no go trials. When incorrect no go trials were compared to the resting baseline period, the foster children showed significantly stronger activation than the nonmaltreated children in a number of brain regions, including the posterior midline regions of the brain involving precuneus and cuneus, the left prefrontal cortex (area 9), the supplementary motor cortex, the right parietal cortex (area 7), the left middle temporal gyrus, the left superior temporal gyrus (area 38), and the head of the caudate nucleus bilaterally (see Fig. 12.2). Additionally, the groups also ­differed in their brain activation during correct go trials as compared to the resting baseline period. Specifically, the nonmaltreated children showed stronger activation in the

Fig. 12.1  Whole brain images displaying brain regions with significantly stronger activation during no go trials compared to go trials for the whole sample. Note: Slices progress from the base of the brain at top left to the top of the brain at bottom right

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Fig. 12.2  Whole brain images displaying brain regions with significantly stronger activation during incorrect no go trials compared to the resting baseline period for the foster children compared to the nonmaltreated children. Note: Slices progress from the base of the brain at top left to the top of the brain at bottom right

anterior cingulate and the left prefrontal cortex compared to the foster children, and the foster children showed stronger activation in the posterior brain regions (e.g., the visual cortex) compared to the nonmaltreated children. Interestingly, all of the brain regions that were more active for the foster children during the incorrect no go and correct go trials are not typically correlated with task performance (Durston et al. 2002, 2006). Overall, the fMRI results show the expected pattern of brain activation for the nonmaltreated children but a more diffuse, less localized pattern of brain activation for the foster children. The results for the foster children can be interpreted in a number of ways. One possibility is that these results represent a neuromaturational delay. Previous research has shown that brain function becomes more localized over the course of development (Sowell et al. 2004; Velanova et al. 2008). Indeed, in a longitudinal study, Durston et al. (2006) found that brain activation during the Go/No Go task changed from late childhood to early adolescence, with activation decreasing with age in the brain regions that were uncorrelated with task performance and increasing with age in the brain regions that were correlated with task performance. Additionally, general neuromaturational delays have been shown among children with ADHD (Rubia et al. 1999; Shaw et al. 2007). If these results

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represent a neuromaturational delay, a separate question is whether these altered patterns of brain activation will remediate over time. It is possible that brain maturation occurs only under more optimal circumstances. Alternatively, this developmental change might occur regardless of the environmental conditions. The answers to these questions have clear implications for public policy and programming. A second possible explanation is that the foster children’s more diffuse pattern of brain activation reflects permanent alterations in the neural system underlying IC. Tarter et al. (2003) described “neurobehavioral disinhibition” as being consistent with an alteration in this system. Prior researchers have shown that neurobehavioral disinhibition occurs at high rates among foster children (Fisher et al. 2007b) and children with prenatal drug and alcohol exposure (Lester et al. 2009). Neurobehavioral disinhibition, which has been associated with oppositional defiant disorder, conduct disorder, ADHD, and depression (Tarter et  al. 2003), might help to explain the complex and difficult-to-treat problems often observed in foster children. Even if these results are reflective of a deficit rather than a delay, it might be possible to improve developmental and psychosocial outcomes with intervention. However, the intervention might need to focus on retraining the brain regions that have been altered by adversity rather than simply facilitating development. A third possible explanation is that the foster children were relying upon alternative strategies for performing the task (Durston et al. 2003). That is, they might have employed compensatory strategies rather than simply exercising control over their behavior. For example, the foster children might have recruited brain regions typically unrelated to task performance because of the increased involvement of working memory or sustained attention. This compensation might have allowed the foster children to perform behaviorally on par with the nonmaltreated children. If these results are reflective of a compensatory process, there are considerable implications for intervention. There is extensive evidence from neurorehabilitation following stroke and closed head injury that it may be possible for individuals to recover behavioral competencies despite damage to the brain regions known to control those competencies (Floel and Cohen 2006; Strangman et  al. 2005; Taub et  al. 2002). Perhaps similar approaches might be used to improve outcomes for foster children. In addition to the group differences observed in the brain regions activated during the Go/No Go task, an important difference was observed in the activation within the anterior cingulate cortex. Although both groups of children showed stronger activation in the anterior cingulate cortex during the no go trials, there was a group difference in the specific subdivision of the anterior cingulate gyrus activated. Specifically, as is shown in Fig. 12.3, the foster children appeared to activate the anterior ventral portion of the anterior cingulate gyrus more strongly, whereas the nonmaltreated children appeared to activate the posterior dorsal portion of the anterior cingulate more strongly. These results are particularly noteworthy given the meta-analytic review of the literature by Bush et  al. (2000), which showed that emotional tasks primarily ­activate the anterior ventral part of the anterior cingulate and that cognitive tasks ­primarily activate the posterior dorsal part of the anterior cingulate. Taken together, these results suggest that the foster children relied more on affective neurocircuitry

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Fig.  12.3  Brain image displaying specific subdivisions of the anterior cingulate gyrus with s­ ignificantly stronger activation for no go trials. Note: The foster children showed stronger activation in the anterior ventral portion of the anterior cingulate gyrus (shown in red), and the nonmaltreated children showed greater activation in the posterior dorsal portion of the anterior cingulate (shown in blue)

during tasks in comparison to nonmaltreated children, who relied more on cognitive neurocircuitry during the tasks. It is important to recognize, however, that the results from this study are preliminary and require further investigation and replication. Nevertheless, these results suggest the intriguing possibility that foster children experience emotional arousal even on tasks that are considered purely cognitive. Understanding the extent to which unexpected emotions come into play and the degree to which these emotions interfere with performance might assist individuals to adapt their approaches to the needs of foster children.

Neural Plasticity on Inhibitory Control Tasks in Preventive Interventions Thus far, we have provided evidence of the IC difficulties observed among foster children and the association between these difficulties and placement instability. The neuroimaging data suggest that the IC differences between the foster children and the nonmaltreated children may result from the differential patterns of brain activation among the foster children. An equally important area of ­investigation that we have been pursuing involves the extent to which therapeutic preventive interventions can improve the functioning of IC-related neural systems.

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To test for neural plasticity in the foster children, we have been collecting e­ vent-related potential (ERP) data, which provide information about the temporal sequence of cognitive processes, during the Flanker task. The Flanker task, which assesses specific aspects of cognitive control such as IC and selective attention, has been shown to activate the ventrolateral prefrontal and anterior cingulate cortex in neuroimaging studies with adults (Botvinick et al. 1999; Hazeltine et al. 2000). In the version of the task we employed (McDermott et al. 2007), the children were told to press the button that matched the color of a target circle. In the congruent trials, the target circle is flanked on either side by two circles of the same color. In the incongruent trials, the target circle is flanked by circles of a different color. In addition to measuring IC and selective attention, this task has the potential to assess children’s response to corrective feedback from the environment through the provision of performance feedback on a trial-by-trial basis. This feedback has allowed us to examine a feedback-locked ERP component called the feedbackrelated negativity (FRN), a frontocentral negative deflection peaking approximately 300 ms after negative performance feedback that is believed to be generated in the anterior cingulate cortex and is thought to reflect the activity of a large neural system of error detection that encompasses response monitoring and motivation (Luu et al. 2003; Miltner et al. 1997; van Meel et al. 2005). In a pilot study using the Flanker task with foster children (Bruce et al. 2009c), we examined behavioral and electrophysiological performance for three groups of children: (1) foster children randomly assigned to a therapeutic preventive intervention, (2) foster children randomly assigned to a services-as-usual condition, and (3) demographically matched nonmaltreated children. The study was designed to examine patterns of behavioral and electrophysiological performance on the Flanker task, to explore the effects of early adverse experiences on performance in foster children, and to examine the performance-related effects of a subsequent therapeutic preventive intervention. The therapeutic intervention in this study, Multidimensional Treatment Foster Care for Preschoolers (MTFC-P; Fisher and Chamberlain 2000; Fisher et al. 1999), is a comprehensive intervention that includes services and consultation in behavior management to foster parents, individual and playgroup-based treatment for foster children, and parent management training for biological, adoptive, or other permanent placement resources. MTFC-P, which has been evaluated over the course of a 10-year randomized efficacy trial, has shown positive effects on the success of subsequent permanent placements (Fisher et al. 2005, 2009), on child attachment security (Fisher and Kim 2007), and on caregiver stress (Fisher and Stoolmiller 2008). As is shown in Fig. 12.4, there was a significant group difference on the FRN. As was expected, the nonmaltreated children displayed a greater negative ­deflection after receiving negative performance feedback than after receiving positive performance feedback. In contrast, for the services-as-usual foster children, the amplitude of the FRN did not vary according to the type of feedback. The intervention foster children exhibited a robust FRN in response to negative performance feedback that was ­virtually indistinguishable from the FRN observed in the nonmaltreated children. These results provide preliminary evidence that therapeutic preventive ­interventions have the potential to positively impact foster children’s brain activity. However, several

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study limitations should be noted. First, the sample was only a small subgroup from the full sample. Second, at the time of the pilot study, the children had been enrolled in the randomized efficacy trial for various lengths of time. Finally, and perhaps most importantly, behavioral and electrophysiological performance was not assessed prior to the intervention. Thus, it is possible that the two groups of foster children differed in terms of their electrophysiological performance prior to the intervention, although the random group assignment should have decreased this possibility. Currently, we are attempting to replicate these results in a second randomized trial of a therapeutic preventive intervention for kindergarten-aged foster children (Pears et al. 2007b). This current study addresses many of the limitations of the Bruce et  al. (2009c) study by including preintervention and postintervention assessments with a larger sample. If the results of the pilot study are replicated in the current study,

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a number of conclusions can be drawn. First, some of the difficulties observed in foster children, especially in terms of disruptive behavior problems, can be assumed to result from a failure to attend to corrective feedback at a neural level rather than the willful disregard of the feedback provided by parents, teachers, and other adults. Second, this vulnerability, whether a manifestation of a neuromaturational delay or a permanent alteration in specific brain regions, can be assumed to be mutable in the appropriate environmental conditions. Moreover, systematic therapeutic preventive interventions can be assumed to facilitate these environmental conditions in foster care and the situations that antecede such care (i.e., reunification or adoption).

Summary and Conclusions In this chapter, we synopsized our research program involving the effects of early adverse experiences on IC, the neural basis of these effects, and the extent to which the underlying neural substrates of IC are amenable to intervention. Despite our findings, there is a need for additional research in this area in foster care and other populations affected by early adverse experiences. Our goal has been to illustrate that measuring the underlying neural systems of at-risk individuals in the context of developmental and preventive intervention studies has great potential to move science and public policy forward. Replication is especially important in work in this area. As such, new data are being collected in our research and that of other scientists that will further illuminate the ability of developmental neuroscience to provide more precise models of the vulnerabilities of at-risk populations. These data will also illuminate the neural substrates underlying the IC deficits associated with the mental health disorders frequently observed among children, adolescents, and adults who have experienced early adversity. Similarly, the current generation of studies will allow us to test the hypothesis that psychosocial interventions, particularly those that emphasize the context of the caregiver–child relationship, can affect behavioral outcomes and the neural systems associated with such outcomes. Whether the data from these studies ultimately fuel optimism about the ability of science to reverse the effects of early adversity in foster children remains to be seen; regardless, the next decade promises to be an exciting time in the study of the developmental neurobiology of early adversity and translation in prevention science and practice.

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Chapter 13

Early Risk for Problem Behavior and Substance Use: Targeted Interventions for the Promotion of Inhibitory Control Nathaniel R. Riggs, Mark T. Greenberg, and Brittany Rhoades

Abstract  Central to many neurocognitive models of childhood problem behavior is inhibitory control, which Kochanska and colleagues refer to as effortful impulse control (Kochanska et al., Child Development 67:490–507, 1996). Inhibitory control is defined here to include active processes of inhibition, effortful or willful control of emotions, thoughts, and actions, as well as self-regulation of both approach and avoidance (Rothbart, Temperament in childhood 59–73, 1989). Inhibitory control has also been considered a central component of executive cognitive function (ECF), which includes a variety of neurocognitive skills necessary for problem-solving and goal directed behaviors. This chapter has three primary objectives. First, we discuss research on brain development and neurocognition (ECF) relevant to childhood and adolescent problem behaviors including substance use. We expand our focus from substance use to early childhood behavior problems in general (e.g., conduct problems, attention deficit hyperactivity disorder (ADHD)) due to common neurological correlates, as well as putative relationships between childhood conduct problems and adolescent substance misuse (Clark et al., Drug and Alcohol Dependence 77:13–21, 2005). Next, we review the growing number of public health interventions that measure and/or target ECF, while highlighting those that specifically target inhibitory control. Finally, we discuss the implications and research directions for the future development and implementation of substance misuse interventions.

Brain Development and Health Behavior There is a great deal of heterogeneity in the time-course of brain development in the areas responsible for self-regulation of affect and behavior. Most areas of the brain responsible for processing of affect (e.g., limbic system) achieve structural

N.R. Riggs (*) Institute for Prevention Research, Keck School of Medicine, University of Southern California, Alhambra, CA 91803, USA e-mail: [email protected]

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and functional maturity by early childhood. Thus, young children are fully capable of experiencing and expressing a range of emotion. In contrast, the frontal cortex, which provides secondary processing of thought and emotion and modulates selfregulatory and goal-directed, problem-solving skills commonly referred to as ECF, does not reach complete structural or functional maturity until early adulthood. The implication of this protracted time-course of frontal (ECF) development is that childhood and adolescence are characterized as periods during which behavior is disproportionately influenced by strong emotional impulses and drives for novel experiences (Chambers et al. 2003). In addition, adolescence is a period of growing social pressure and drive for independence that creates increasing opportunities to engage in risky behaviors (Reyna and Farley 2006). Thus, adolescence is a stage in which youth engage in new levels of emotional stimulation and risk taking, while still having a less-than-fully developed set of ECF skills for inhibiting and regulating responses to affective experiences (Steinberg et al. 2006). There are several extant frameworks for the conceptualization of ECF. Many consider ECF as a multidimensional construct. For example, Guy and colleagues suggest that individual ECFs load onto two ECF subfactors: Metacognitive skills such as task completion, organization, planning, sequencing, self-monitoring, and working memory, and those responsible for behavioral regulation, which include inhibitory control, emotional control, and set-shifting (Guy et al. 2004). Diamond (2006) agrees with the conceptualization of ECF as a multidimensional construct, but argues that ECF is composed of three related but distinct dimensions of inhibitory control, working memory, and set-shifting. These components encompass a set of higher-order, top– down cognitive processes that are elicited when flexible, coordinated, goal-directed behavior is needed to solve a problem (Hughes and Graham 2002). However conceptualized, two things are evident regarding ECF. The first is that inhibitory control plays a critical role in any ECF framework. That is, the ability to inhibit prepotent, often impulsive behaviors appears to be a central ECF skill. The second is that it is unlikely that one can conceptualize the application of inhibitory control processes in isolation from other important and integrated ECF skills. For example, the ability to shift set from one task or category to the other, is presumably dependent upon inhibiting the perseveration on the previous task and shifting attention to the novel task. Similarly, attention and working memory require constant inhibition of irrelevant and/or competing stimuli. Therefore, we will discuss the general role of ECF in the etiology and prevention of public health outcomes, but highlight specific examples of inhibitory control where appropriate.

Executive Cognitive Function and Behavioral Health Outcomes Executive cognitive function has been linked with a number of behavioral health outcomes. In young children and adolescents, ECF proficiency has been linked to greater prosocial behaviors and fewer behavior problems (e.g., Carlson and Wang 2007; Rhoades et al. 2009). Although much of this research has been ­cross-­sectional,

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longitudinal studies have demonstrated a predictive relationship between early ECF and later behavioral competence. For example, children’s inhibitory skills in first and second grade were significantly related to fewer behavior problems 2 years later, after controlling for early behavior as well as general intelligence (Nigg et al. 1999; Riggs et al. 2004). Additionally, ECF deficits in “hard to manage” children at age 4 years predict negative behavior at age 5 years as well as the commission of both rule violations and preservative errors at age 7 years (Brophy et al. 2002; Hughes et al. 2001). Research conducted with adolescents suggests a role for ECF in serious health outcomes including violence and substance use/misuse. Tarter and colleagues demonstrated that “neurobehavioral disinhibition” (a construct encompassing both ECF and behavioral skills) at age 16 years predicted substance use disorder at age 19 years and was a stronger predictor of substance use disorder than actual substance consumption frequency (Tarter et  al. 2003). Raine (2002) reviewed the literature linking executive deficits with antisocial behavior in children and adolescent samples and notes that antisocial behavior is also strongly associated with ECF impairments. Included in this review is Moffitt’s work (Moffitt 1993), which suggests that antisocial and delinquent children have ECF deficits in a number of domains including, inhibitory control, concept-formation, reasoning, problem-solving, attention, planning, and organization. Nigg et al. (2006) have also demonstrated that independent of a number of parental and child covariates, poor response inhibition predicted alcohol-related problems, illicit drug use, and comorbid alcohol and drug use. Recently this behavioral research has been complemented by studies directly assessing neural indicators of executive processes. For example, the P300 brain wave has received some attention as a neural indicator of impulsivity, and has been linked to increased later risk for substance use in adolescents (e.g., Moeller et al. 2004). In addition, a growing number of functional magnetic resonance imaging (fMRI) studies have linked greater substance misuse with deficits/lesions in the orbitofrontal and ventromedial cortices, two areas of the frontal cortex implicated in the inhibition of immediate and inappropriate behaviors (e.g., Dom et al. 2005). In short, ECF proficiency is consistently related to positive youth outcomes, whereas ECF deficits have been linked to a range of negative health behaviors.

Executive Cognitive Function as a Potential Mediator in Prevention Trials The apparent role that ECF deficit plays in the development of problem behavior suggests its potential as an important mediator in public health interventions. There are three general categories of interventions that are informative when exploring ECF’s potential as a mediator of prevention outcomes. The first category includes programs that focus on promoting cognitive and social–emotional mediators such as self-control and social problem-solving, which are considered to have ECF ­foundations, but which were not based upon neurocognitive theory and thus, did not directly assess ECF. This includes such early childhood interventions as Second Step

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(Frey et  al. 2000), Al’s Pals (Geller 1999) and Interpersonal Cognitive Problem Solving (ICPS; Shure and Spivack 1982). One could speculate that preventive interventions that promote self-regulatory and problem-solving skills may also facilitate the collateral effect of strengthening ECF proficiency, yet such conceptual models of mediation have not been tested in intervention trials. The second category of interventions focuses solely on promoting ECF development, but has not assessed ECF as a mediator of behavioral outcomes (Blair and Diamond 2008; Tang and Posner 2009). Three such studies have focused on promoting ECF skills in preschool children (Diamond et al. 2007; Dowsett and Livesey 2000; Rueda et al. 2005) and two of these are short-term laboratory-based training studies. In the first study, 3-year-olds who had repeated exposure to tasks requiring both the manipulation of visual representations and response control showed enhanced inhibitory control compared to nonexposed comparison groups (Dowsett and Livesey 2000). The authors contend that experience with these tasks increased the acquisition of complex rules via demands placed on executive processes. In a second study, Rueda et  al. (2005) intensively trained 4–6-year-old children on a number of executive skills over a 5-day period. Computer-based training modules included a focus on anticipation, stimulus discrimination, conflict resolution, and Stroop-like exercises. Results demonstrated significant improvements following 5 days of training on ECF tasks that were not part of the training program. Diamond et al. (2007) applied conceptual approaches to ECF in the Tools of the Mind (Tools) preschool intervention. Tools uses 40 core activities that facilitate developmentally appropriate dramatic play to help improve ECF skills in 4- and 5-year-old children. Lessons promote skills including memory, attention, and selfregulatory private speech. Using a quasi-experimental design, results demonstrated that children in the Tools condition perform significantly better on two ECF behavioral tasks (dot incongruent and reverse flanker tasks) than did preschoolers receiving the district’s standard literacy program. In a study utilizing the family check-up (FCU), Lunkenheimer et  al. (2008), examined the effect of FCU on the development of inhibitory control in preschoolers. The FCU home visiting program focuses on using motivational interviewing and other techniques (promoting positive and interactive parenting practices) to provide a context for learning and development. In a randomized evaluation, program effects showing parents’ improved positive behavior support (controlling for child gender, ethnicity and parental education) were related to improvements in children’s inhibitory control skills from 3 to 4 years of age. Klingberg et al. (2005) investigated the potential to promote working memory skills in 7–12-year-old children with ADHD. Children in this study performed computer-based visuospatial and verbal working memory tasks over a period of at least 25 sessions lasting 40 min on average. In the intervention condition these tasks increased in difficulty with participant mastery, whereas task difficulty remained the same in the control condition. Results not only demonstrated enhanced working memory performance among participants in the intervention condition on tasks not part of the training program, but also response inhibition as well as complex reasoning­(Klingberg et al. 2005).

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These studies are among the first to demonstrate the capacity of early preventive interventions to promote executive development in young children. Surprisingly, there has been a dearth of programs focused on children in middle childhood and none of the above programs has shown replications that involve effects on ECF. Further, in order to confirm a meditational role for ECF in the prevention of problem youth behavior, direct effects on ECF skills must translate into program effects on behavioral outcomes and the above programs have not demonstrated this link. Very few programs can be classified into the third category, which includes public health interventions that are based on neurocognitive theory and have demonstrated a meditational link between intervention and behavior outcomes. To our knowledge, no substance use prevention programs have been developed in which a primary conceptual model is based in neurocognitive theory of change (Fishbein 2001). However, a small number of social–emotional development programs incorporate neurocognitive models of mediation and include measurement of ECF processes in their evaluation. The Promoting Alternative THinking Strategies (PATHS) curriculum is based in large part upon developmental models of brain organization. PATHS is designed to promote development in social functioning, self-control and emotion regulation, attention, communication, and problem solving (Greenberg and Kusché 1998). Among the important theoretical concepts incorporated into the PATHS program is “vertical control” which refers to higher-order processing and regulation of emotions and actions by the frontal lobes over the limbic system and sensory-motor areas of the brain. PATHS incorporates to neurodevelopmental research demonstrating that much of the functional integration of the frontal cortex and limbic system proceeds at a rapid rate during early and middle childhood and is believed to be directly related to impulse control and emotion regulation. Extensive outcome evaluation of PATHS has demonstrated reductions in externalizing and internalizing problem behavior, peer aggression, conduct problems, and hyperactivity, and improvements in emotion regulation, planning and frustration tolerance in PATHS students relative to controls (Greenberg and Kusché 1993; Conduct Problems Prevention Research Group 1999, in press). Central to this chapter, a recent study partially confirmed the underlying neurocognitive theory upon which PATHS was developed. Relative to control students, second and third grade children who participated in the PATHS curriculum demonstrated enhanced inhibitory control skills at 9-month posttest, which mediated reduced rates of both externalizing and internalizing behaviors at 1-year follow-up (Riggs et al. 2006). Thus, the intervention led to improved inhibitory control and this improved inhibitory control was “causal” in explaining subsequent reductions in both internalizing and externalizing problems. A second study demonstrated the capacity of ECF to mediate the link between early prevention and social–emotional development (Bierman et  al. 2008). Head Start REDI was designed as an integrated model of social–emotional (Preschool PATHS Curriculum) and language/emergent literacy enrichment interventions implemented within the existing framework of Head Start. Results of a REDI

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r­ andomized trial revealed program effects on teacher and observer rated ­social–emotional development as well as on two measures assessing ECF, the dimensional change card sort (DCCS) and a behavioral rating of task orientation. Further, changes in these two measures partially mediated intervention effects on teacher and observed improvements in social–emotional development. While no ECF-inspired intervention models have been tested in adolescence, Fishbein et al. (2006) assessed the role of neurocognitive, physiological (heart rate and skin conductance), and emotion perception measures in teens’ ability to process and respond to curriculum materials, under laboratory conditions, from a wellknown prevention program (PACT). Results showed that neurocognitive and emotional regulatory function significantly moderated the effects of PACT on change in behavior, such that children with greater baseline ECF benefited more from the intervention. Thus, neurocognitive maturity may be an important factor in responsiveness of teens to interventions.

Implications and Future Directions The study of ECF as a potential mediator in substance use prevention programs is promising, but still in its early stages. As a result, there are a number of future directions for this field. Here we discuss several issues that will require greater scholarly attention, including the use of developmental theory, issues in assessment, and considerations in diagnosis and classification. We conclude with a discussion of the potential of new interventions, based on ECF models to impact developmental processes related to early substance use and abuse.

Use of Developmental Theory We emphasize here the importance of developmental theory in the construction of public health interventions. Theory can guide intervention content and strategies to target conceptually-driven and developmentally-timed predictors of health outcomes. In addition, theoretically informed evaluation can provide the necessary logic model for carefully investigating the mediating mechanisms through which interventions have their ultimate effects, including the possibility that improvements in ECF or other factors are causal in the process. As we have previously discussed, there is enormous potential in the careful integration of developmental neuroscience and prevention research (Greenberg 2006). The role of development is especially critical in the application of neurocognitive theory to public health interventions. Here, the time course in the structural and functional development of the brain suggests the importance of timing in preventive interventions. The most rapid advances in the structural organization of the corticolimbic networks related to substance misuse begin in childhood and continue

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through late adolescence. Consequently, developmental theory suggests that ­prevention strategies focusing on behavioral and cognitive control should start in early ­childhood, and they should continue through adolescence. Promoting the neural integration of frontal, limbic, and reward systems during this period of peak neurodevelopment may exact long-term influence on behavior (Riggs and Greenberg 2009). However, more refined theory of what aspects of ECF might be targeted in middle childhood and early adolescence and by what methods will be necessary for advances in this area. Due to the importance of a developmental perspective, longitudinal research is critical in answering most questions. However, with few exceptions (Tarter et al. 2003), the current research linking ECF with substance misuse utilizes crosssectional designs. Although important in terms of demonstrating an association between neurocognitive function and substance misuse, further research is needed that examines the complex transactional linkages between early neurocognitive function and later substance misuse as well as the potential influence of interventions focused on improving ECF as a mediator of later substance use.

Issues in Assessment of ECF and Neural Activity An important scholarly and practical need is a better understanding of issues surrounding measurement of ECF and its component skills. Recent research utilizing direct assessment of differential activity in neural regions has opened up new avenues for understanding the relation between ECF and activation of specific brain regions. For example fMRI studies suggest the importance of the orbitofrontal cortex and the ventromedial prefrontal cortex in the inhibition of immediate and inappropriate behaviors, the processing of reward value and affective valence of stimuli, and response selection (Bechara and DeMasio 2002), all of which are important ECF skills. Other fMRI research has shown that when inhibiting a prepotent response, adolescents with ADHD produce differential activation in a number of areas of the brain (i.e., left ACC, bilateral frontopolar regions, bilateral VL cortex, left medial frontal gyrus) suggesting the role of these regions of the brain in inhibitory control (Pliszka et al. 2000; Schulz et al. 2004). Studies using fMRI and other imaging methodologies are essential in drawing conclusions regarding underlying neurological bases for ECF skills. However, they are not without limitation. It is very unlikely that even specific ECF skills such as inhibitory control result from activation or inhibition of a single neural region of interest (Kagan 2007). More likely, inhibitory control results from a complex interplay of frontal and limbic regions within neural structures and is implicated in pathways of activation for which a greater understanding of neurotransmitter activity will be important. In addition, inhibitory control may “occupy” neural regions common to other ECF domains. There is a need to conduct small intensive intervention studies with carefully selected populations to examine the influence of interventions on neural activity and subsequent behavior (Davidson et al. 2003).

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Currently, the most commonly utilized assessments of ECF are behavioral (­cognitive) tasks that draw upon presumed underlying neurological processes and which have a long history of research in clinical neurology and more recently in developmental and clinical psychology. However, behavioral tasks can only be considered presumed indicators of neurological functioning. Furthermore, ECF tasks often, if not always, draw upon more than one dimension of ECF. For example, the DCCS or Stroop tasks draw upon inhibitory control and set-shifting skills, and almost all ECF tasks draw upon working memory as participants are required to maintain in short-term memory task instructions. Finally, age-related differences in task difficulty, and the associated occurrence of floor and ceiling effects, contribute to differential sensitivity in identifying ECF variation across the life-span. This suggests the importance of identifying appropriate tasks during specific periods of development. Verbruggen and Logan (2008) maintain that one developmentally appropriate measure of inhibitory control for adolescents is the stop-signal reaction time (SSRT) task in which participants perform a “go task” that is occasionally followed by a “stop signal,” which instructs the participant to inhibit that response. Self-report surveys have also been utilized to assess ECF (e.g., Guy et al. 2004). The advantage of self-report is that it can be administered to a large number of participants (i.e., all students in a classroom or school) in a relatively brief period of time. Although they may be of potential use in large-scale intervention studies, there is little data indicating that individual differences in self-report are related to underlying neurological functioning. Also, they often do not correlate with behavioral performance perhaps due to differences between self perceptions and actual behaviors. In addition, self-reports of behavior suffer from a number of recall and demand biases, which are likely to be more pronounced in youth with ECF deficits. Thus, self-report measures require substantial validation before they can be used as “proxies” for understanding neurocognitive processes such as inhibitory control. The use of any particular ECF assessment methodology, to a large degree, will depend upon which methodology is most informative and feasible for the research questions of interest, given it has reasonable validity. If the investigation is a basic science investigation into the nature of the relationship between cognitive processes and underlying neural regions of interest, then studies utilizing direct assessments of neurological function will be of great value. Prevention researchers interested in promoting ECF as a mediator in public health interventions will have to make a judgment call with respect to which ECF assessment methodology they should employ. One question that needs to be asked is “what is the added value of demonstrating change in actual brain function through the use of costly imaging techniques?” We believe that this is of great value in carefully developed small studies that examine effects at the neural level that can clearly test theories from developmental neuroscience. However, it is likely that multiple regions of interest are involved in any one ECF domain and that ECFs “share” neural regions of interest, suggesting that direct neural assessments will provide a broad but nonspecific understanding of the phenomena. These considerations suggest the critical need for validation studies that utilize multiple ECF assessment modalities within the same sample of participants.

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Currently, little is known regarding the overlap among task-oriented, self-report, and direct assessments of ECF. Comparing these modalities within the same sample can potentially enhance the convergent validity of each method which will have implications for measurement in large-scale intervention studies. Specifically, the time-intensive nature of direct neurological and task-oriented assessments of ECF will likely prohibit ECF assessment for large samples within universal prevention studies, but might be considered with selected subsamples.

Issues in Diagnosis and Classification Models developed to account for the role of ECF in later substance abuse in most cases posit a series of developmental goals in early and middle childhood that mediate the relation between changes in ECF and the reduced risk of early substance use and abuse. Most formulations focus on how improvements in ECF and related skills of lowered impulsivity, and improved emotional regulation and social problem-solving reduce the risk for aggression. Aggression is a key factor as it is the only risk factor (other than parental alcoholism) that has consistently shown a relationship to the early initiation of underage drinking (Clark et al. 2005; Clark and Winters 2002). As Giancola (2007) has discussed, impairments in ECF skills such as self-monitoring, inhibition, and attentional skills may lead to misattribution in conflict situations, which then leads to ineffective problem solving and increased conflict. Peer conflict and aggression are then linked to affiliation with delinquent peers, which mediate early drug use (Giancola and Parker 2001). Recent concerns have been raised regarding diagnostic heterogeneity in research samples and the need to utilize more limited and careful diagnostic classifications regarding aggression to create further scholarly advances. Hodgins et al. (2009) raise a number of issues regarding differences in aggressive youth with and without anxiety, as well as confusion caused by not accounting for ADHD comorbidity in understanding neural correlates of aggression. In addition, others have shown clear differences in correlates and outcomes between youth exhibiting psychopathy vs. other forms of aggression, as well as differences in children showing reactive vs. proactive forms or aggression. To understand unique factors that may lead to early and risky substance use will require better sample specification as some sub-populations may show specific endophenotypes in both behavior and ECF that will be necessary to test refined models of intervention and development. For example, somewhat different pathways recently have been identified between reactive vs. proactive aggression, ECF skills, and risk for substance abuse (Ellis et al. 2009). The role for ECF in the prevention of problem behaviors must also be considered within the social contexts known to influence adolescent substance misuse, particularly in the context of peer relations (Hawkins et al. 1992). This is especially true for social contexts that elicit high emotional arousal as such contexts clearly make it more difficult for youth to successfully draw upon executive cognitive processes (Steinberg et al. 2006). However, very little is known with regard to how

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social contexts influence ECF development. In this regard, there is a need for ­studies that examine how inhibitory control and other ECF skills may be directly related to abilities such as being able to refuse drug opportunities in the context of peer friendships. We posit that the development and refinement of interventions that target ECF skills, and in particular inhibitory control, may be a key to improvements in outcomes in the next generation of adolescent preventive interventions. If interventions are designed to help youth make more accurate perceptions in conflict situations, or lead them to be able to inhibit high emotional reactivity in early stages of peer conflict, or help them to stop and consider the consequences when challenged to participate in deviant behavior, it is possible that such interventions will result in more general improvements in inhibitory control. Further, these improvements in inhibitory control may be an “active or causal” component in long-term reduction in early use. The refinement of these ideas, the development of culturally relevant models of intervention to practice such skills, and the theoretical testing of such models are the next steps in integrating our basic knowledge of developmental and neurological processes into theoretically informed intervention.

The Role of ECF in Health Interventions: New Directions Theory based on new knowledge regarding ECFs may be “translatable” to other public health interventions for problems that share underlying ECF correlates such as childhood obesity. Pentz and Riggs have used this rationale to translate components of two evidence-based programs for violence (PATHS) and substance use (STAR) prevention in developing an obesity prevention curriculum called Pathways to Health (Riggs et  al. 2007). Although program effects from the main trial are forthcoming, pilot studies have demonstrated significant concurrent and longitudinal associations among ECF and high calorie snack food intake, fruit and vegetable intake, and physical activity, as well as significant changes in positive attitudes toward self-regulation of appetitive behavior and positive changes in actual food choices and television viewing patterns (Riggs et al. 2007, 2010a, b). There also has been considerable interest in the concept of mindfulness, its relationship to ECF skills and its potential to reduce numerous health risks (Tang and Posner 2009). Mindfulness can be defined as paying attention moment to moment without judgment to whatever is going on in the mind and in the body – including thoughts, physical sensations, and emotions. In other words, mindfulness means being aware without judgment (Segal et al. 2001). With the considerable and growing interest in mindfulness research and intervention with adults (Davidson et al. 2003; Teasdale 2004), we believe that careful clinical trials of interventions focused on mindfulness training or the “potentiation of cognitive control” (Keating 2004) in youth are warranted. Reviews of the fields of yoga and mindfulness in children point to the potential of such interventions to improve such skills as inhibitory control, but research designs have been weak and thus the potential of this new area of research is speculative at present (Black et  al. 2009; Burke 2010). A recent

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r­ andomized trial of yoga with fourth and fifth grade students demonstrated ­significant effects on reductions in rumination and intrusive thoughts, and improvements in self-reported emotion regulation (Mendelson et al. 2009). It will be important for future studies to add measures of ECF either by cognitive testing or neural examination. Given clear theoretical formulations, carefully controlled research on mindfulness, as well as some martial arts traditions and yoga, creates an exciting agenda for impacting early substance misuse that would provide a theory-based prevention model, which would naturally lead to examination of neural mediation (Mind and Life Research Network 2009). In conclusion, accumulating evidence from neuroscience and developmental research highlights inhibitory control skills and ECF more broadly, as key contributors to children’s behavioral competence. As such, interventions aimed at promoting ECF are of great public health importance. Although there is increasing evidence that interventions designed to promote self-regulatory, problem-solving, and other related skills have the capacity to improve ECF in young children, very few studies have focused on ECF as a mediator of program effects on behavioral outcomes like substance use. Additionally, several critical issues surrounding the measurement of ECF remain unresolved. Although this field is still in its infancy, we believe that through the thoughtful integration of developmental neuroscience and prevention research, future research can extend and improve the development and implementation of public health interventions aimed at reducing a wide variety of adolescent problem behaviors.

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Chapter 14

Designing Media and Classroom Interventions Targeting High Sensation Seeking or Impulsive Adolescents to Prevent Drug Abuse and Risky Sexual Behavior Rick S. Zimmerman, R. Lewis Donohew, Philip Palmgreen, Seth Noar, Pamela K. Cupp, and Brenikki Floyd Abstract  In this chapter, we describe interrelated programs of research conducted by teams of researchers at the University of Kentucky over a period of more than 20 years; the programs of research include both media and classroom-based approaches that have resulted in successful interventions in both. The research conducted during this period has focused on increasing the effectiveness of televised public service announcements (PSAs) and classroom programs designed to prevent drug abuse and/or reduce risky sex. The results of these studies provide important direction for the design of prevention interventions targeting adolescents at-risk for engaging in risky behaviors. A crucial component of prevention efforts is the development of a more advanced science of persuasive communication that offers greater hope for success in altering behaviors and encouraging people to live healthy lives. Increasing the effectiveness of public health media campaigns and the effectiveness of school-based instructional programs to prevent risky behaviors continues to have considerable appeal due to their potential for reaching vastly greater audiences than normally are reached through other interventions, as well as their ability to be delivered directly to target audiences. The advances are due in part to the incorporation of more sophisticated theories and methodologies of designing prevention interventions. A central assumption of the research described in this chapter is that in order for health messages to be seriously attended, individuals must be capable of attracting and holding attention long enough for persuasive content, which may promote more rational decision-making (e.g., Ajzen and Fishbein 1980) to be processed (Donohew 2009; Donohew et  al. 1998). This scenario requires that the messages provide enough stimulation to generate a level of attention many implicitly assume is present all the time. R.S. Zimmerman (*) Department of Social & Behavioral Health, Virginia Commonwealth University, Richmond, VA, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_14, © Springer Science+Business Media, LLC 2011

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On the basis of research to date, we take the position that the human decision-making process may or may not be “rational.” Many individuals may be more likely to choose to be in situations or to engage in behaviors that are novel, reduce boredom, lead to disinhibition, or are thrilling or adventuresome. Beyond this, many of those who find themselves in these sorts of situations are less likely to act in ways that might be predicted by rational models of health-related behavior. Thus, changing their health behaviors is a formidable task. The results of these studies provide important direction for the design of preventive interventions targeting adolescents at-risk of engaging in risky behaviors. In the remainder of this chapter, we describe our research on the relationship of two individual difference variables, sensation seeking and impulsive decisionmaking (IDM) (or impulsivity), and risk taking behavior, especially substance use and risky sexual behavior. In the first part of the chapter, we describe and define the individual difference variables and a model that we have developed and tested (the Activation Model) about designing intervention messages for high- and lowsensation seekers. In the second part of the chapter, we describe a program of research that has tested this model by presenting: (1) developmental work; (2) studies that tested a variety of intervention messages in both laboratory and field settings that used knowledge of these individual difference variables to design messages; (3) tests of some of the meditational pathways that lead from the individual difference variables to risky behavior; and (4) two unsuccessful attempts to design interventions for impulsive decision-makers. Finally, we summarize where the last 25+ years of this program of research has left off, as well as prospective work that remains to be done.

Sensation Seeking, Impulsivity, Message Design, and Risky Behavior Need for Novelty and Sensation Attention to novelty in our ancient past probably was a fundamental survival behavior developed in the process of our evolution. When viewed as an adaptive behavior, approach to novel stimuli may have contributed to the survival of the species because it allows organisms to locate new sources of food and potential sources of danger (Zuckerman 1979). Although the attention value of novelty is no longer as vital to survival, it continues to have major implications for the human communication process (Donohew et al. 1998) because humans appear to have a need to explore new and unusual matters. In describing the phenomenon of novelty-seeking as a major driving force in human behavior, researchers (Bardo et al. 1996: 33) wrote: Numerous historical anecdotes are available to underscore the human attraction to novelty. Indeed, discovery of the “new world” may not have been possible had it not been for the

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innate human attraction to novelty. In the middle of the nineteenth century, Charles Darwin recognized that “it is human nature to value any novelty, however slight, in one’s own possession” (Darwin 1859).

The attention process is further impacted by individual differences in reactivity to intense and novel stimulation as reflected in the trait of sensation seeking (Zuckerman 1979, 1983, 1994; Zuckerman et al. 1993).

Sensation Seeking Probably the most widely-researched concept employed to describe novelty seeking is the concept of sensation seeking (Zuckerman 1979, 1983, 1994), which has been an integral component of the research described in this chapter. Zuckerman (1994) described sensation seeking as a trait defined by “the seeking of varied, novel, complex, and intense sensations and experiences, and the willingness to take physical, social, legal, and financial risks for the sake of such experience” (p. 27). High sensation seekers (HSS) are receptive to stimuli that are intense, novel and arousing; stimuli producing lower levels of arousal may be considered “boring” and cause the HSS to seek alternative sources of stimulation. Low sensation seekers (LSS) tend to reject stimuli that are highly intense, preferring the familiar and less complex. According to Zuckerman, sensation seeking and its opposite – sensation avoidance – may represent adaptation to a dangerous environment in which novel stimuli can be either sources of reward or threats to survival. He has proposed that the search for novelty (Cloninger et al. 1994, 1996; Zuckerman 1994) is a fundamental survival behavior, in which detection of novel stimuli leads to alerting the system to prepare for fight or flight responses (Franklin et al. 1988).

Impulsive Decision-Making Having considerable implications for designing health interventions, impulsivity, or an IDM style has been viewed by several personality researchers as a trait related to sensation seeking (Buss and Plomin 1975; Eysenck and Eysenck 1977, 1978; Zuckerman et al. 1993). In his review of the relationship between impulsivity and sensation seeking, Zuckerman (1994) concluded that “while [it is] not an equivalent or supra-ordinate of sensation seeking, [it] is a highly related trait, particularly in its non-planning and risk-taking aspects” (p. 96). In fact, as part of a broad personality test (i.e. the Zuckerman–Kuhlman Personality Questionnaire), one of the five factors composed of impulsivity and sensation seeking items is thus labeled “impulsive sensation seeking.” Indeed Zuckerman’s latest measure of sensation seeking is actually called the Impulsive Sensation-Seeking Scale (Zuckerman and Kuhlman 2000). There continues to be some discussion, however, of the extent to

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which sensation-seeking and impulsivity are independent, but correlated constructs, or components of one primary construct (cf. Donohew et al. 2000). Furthermore, rather than viewing impulsivity as a trait variable, Zimmerman and Donohew (1996) view IDM as being at one end of a continuum of decision-making styles that vary from a consistent, rational style to a consistently impulsive style. Although conceptions of impulsivity suggest that impulsive individuals act spontaneously, that is, without considering consequences, IDM also focuses on the cues that individuals use to make decisions. Zimmerman and associates propose that although rational decision-makers use beliefs about consequences of their actions, impulsive decision-makers use noncognitive cues, including affective and physiological cues (as opposed to merely ignoring consequences), to make decisions. Zimmerman’s 11-item decision-making style scale has been shown to be moderately correlated with Eysenck and Eysenck’s (1977) narrow impulsivity scale (correlations of 0.31–0.65 in three samples of 100–650 high school students) and more strongly related to risky sexual behavior (unpublished data). Internal consistency is comparable to that of the Eysenck scale (generally in the 0.7–0.8 range), with a similar 1-year test–retest correlation in high school students (r = 0.5). In the program of research to be described here, IDM has been a component of the classroom-based interventions but in recent years it has been added to the media campaign studies as well.

Activation Model of Information Exposure The interventions described in this chapter are guided by an activation model of information exposure (Donohew 2009; Donohew et al. 1980, 1988, 1998), which has been a major cornerstone for our program of research on persuasive communication and prevention. This model operates under the assumption that human beings are continuously involved in a search for stimulation, driven by pleasure centers of the midbrain (Donohew et al. 1980; Olds and Fobes 1981). Individuals, thus, are thought to operate most effectively at some optimal level of arousal, which is presumed to differ across individuals due to individual differences that exist in plasma catecholamine levels released from the sympathetic nervous system (Zuckerman 1979, 1983). Need for arousal serves as an important motivation guiding the extent to which individuals expose themselves to different kinds of information presented in various ways (Christ 1985). Studies guided by the activation model of information exposure (Donohew et al. 1980) were designed to test the hypothesis that an individual’s preferred level of need for novelty and sensation significantly affects the likelihood that a message will attract and hold his or her attention. Motivation for exposure to a message is partly due to the need for physiological stimulation rather than a cognitive need for information alone (Donohew et  al. 1989; Finn 1984). Subsequently, a central assumption of this model is that although individuals may have a cognitive reason for exposing themselves to a particular stimulus, such as a source of information,

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processes of which they are unaware may play a major role in the information to which they seek exposure. Thus, the form of the message (i.e., its formal features, such as novelty, movement, etc.) plays a significant role in attracting individuals to a source of stimulation, and it plays an even more important role in receivers’ continued exposure to the source. The level of stimulation required to trigger these responses varies according to the level of need for stimulation (or need for sensation) of the individual, which we have generally measured with successive versions or adaptations of Zuckerman’s sensation-seeking scale (e.g., Zuckerman 1994; Zuckerman and Kuhlman 2000; Hoyle et al. 2002). We have observed that because HSS are attracted to novel, dramatic, and stimulating messages (Donohew et  al. 1991; Palmgreen and Donohew 2006), manipulation of message sensation value can be a successful way of overcoming cognitive and attitudinal barriers to prevention messages by those engaged in, or who are likely to become engaged in, risky behaviors. In addition, making impulsive decision-makers more aware of their special risks, as well as focusing on the sensation-value of alternative behaviors, may enable individuals to reduce their risky behaviors while they persist in their IDM style. Given that the central assumption of this theory is that individuals differ in their levels of need for stimulation at which they are most comfortable, attention then will be largely a function both of that level of need as well as the level of stimulation provided by a stimulus source. From this it is deduced that if individuals do not achieve or maintain this state when exposed to a message, it is likely that they will turn away and seek another source of stimulation that helps them achieve the desired state. If activation remains within some acceptable range, however, individuals are most likely to continue exposure to the information. The theory has guided an extensive series of experiments and field studies on improving the effectiveness of public health campaigns (Donohew et  al. 1990, 1991, 1998, 2000; Lorch et  al. 1994; Palmgreen and Donohew 2006; Palmgreen et  al. 1991, 1995; Zimmerman et  al. 2007b). Using the technical language of the theory, this body of research suggests that messages with high sensation value are required to attract and hold the attention of individuals who are HSS whereas low sensation value (LSV) messages should be preferred by LSS. The sensation value of a message is defined by Palmgreen and Donohew (2006) as the ability to elicit sensory, affective, and arousal responses.

Relationship Between Individual Differences and Risky Behavior A substantial body of empirical research has connected sensation seeking and IDM with risky behaviors such as drug and alcohol use and a variety of early and/or risky sexual behaviors (Donohew et al. 1990, 1998, 2000; Langer et al. 1993; Palmgreen et al. 1995; Zuckerman 1994). Researchers have established in previous studies that HSS adolescents and young adults are more likely to engage in risky situations and those who are impulsive decision-makers are also more likely to become involved

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in risky behaviors (Donohew et al. 1990, 2000). HSS may more likely choose to be in situations or to engage in behaviors that are novel, reduce boredom, lead to disinhibition, or are thrilling or adventuresome. Beyond this, if impulsive decisionmakers find themselves in these sorts of situations, they may be less likely to act in ways that might be predicted by rational models of health-related behavior. Thus, a young person who is both an HSS and an impulsive decision-maker may be especially likely to exhibit risky behaviors (e.g., drink alcohol before having sex to reduce anxiety about sexual activity, which could likely affect the nonuse of a condom in order to increase the sensation of the experience). On the other hand, an individual who is an LSS and a rational decision-maker may be more likely to plan steps to avoid being in a situation where intercourse would occur, or might anticipate the need for a condom in case of a last-minute decision to have sex. Such a person also might be more likely to avoid alcohol use before a date with a possible sexual partner. Although either sensation seeking or IDM could be expected to increase the probability that individuals involved would be more likely to take significantly greater health risks, the combination of these two characteristics makes them prime targets for health interventions. Not only are individuals with HSS/IDM personality characteristics more likely to engage in risky behaviors, but a continuing program of research on communication and health has shown that this should be the prime target group for interventions designed to meet their specific personality needs. The use of appropriate interventions targeting this group is necessary in order to increase the likelihood of attracting and sustaining their attention and, ultimately, changing their behaviors. For example, in one HIV prevention study involving 2,949 ninth-grade students in 17 high schools in two Midwestern US cities (Donohew et  al. 2000), strong associations were observed between sensation seeking and IDM (either singly or jointly) and nearly all of the measured indicators of sexual risk-taking (i.e., had sex, number of lifetime sexual partners, used a condom, had unwanted sex under pressure, and used alcohol or marijuana before sex).

Research Findings Developmental Research Over the years, our research has indicated that individual differences in need for sensation play a major role in exposure to and comprehension of messages, in addition to attitudinal and behavioral intention responses to the messages (Donohew et al. 1990). Findings from our studies indicate that HSS individuals tend to tolerate or require more novel and intense messages for attracting and holding their attention (Donohew et  al. 1980; Palmgreen et  al. 1991; Palmgreen et al. 1995). Our initial research agenda provided the basic research that led to the more extensive projects to follow: (1) identified characteristics of adolescents and young adults more likely to become early drug users (Donohew et  al. 1990); (2) developed a profile of stimuli that attracted and sustained adolescents’ and young adults’

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attention (Donohew et al. 1994); and (3) employed formative research, and designed messages and interventions that have been highly successful in reaching the target audiences (Donohew et al. 1991; Lorch et al. 1994; Palmgreen et al. 1995).

Sensation Seeking, Drug Use, and Message Style The studies conducted by Donohew and colleagues indicated highly significant differences in alcohol and drug use between HSS and LSS beginning with the onset of puberty and continuing through young adulthood (Donohew et al. 1988, 1990, 1994). In a study of junior and senior high school students, HSS were twice as likely as LSS to report use of beer and liquor during the past 30 days, three times as likely to have used marijuana, and seven times as likely to have used cocaine. In the laboratory portion of the study, HSS were more likely to expose themselves to messages presented in a more novel (narrative) format than to messages presented in other formats.

Formative Research on Message Sensation Value Prior to designing messages or interventions likely to attract, hold the attention of, inform, and persuade audiences possessing characteristics similar to those described above, we first conducted extensive formative research that revealed characteristics of televised messages that have differential appeal among HSS and LSS young people. The responses of HSS or LSS focus groups to a selection of product advertisements and public service announcements (PSAs) demonstrated that HSS participants reacted more positively to more novel and intense messages (e.g., messages where someone is shot or where we view someone skydiving from their perspective). In addition to preferring more novel formats and unusual use of formal features (e.g., extreme close-ups and heavy use of sound effects), HSS participants also responded more positively to high levels of suspense, tension, drama, and emotional impact than did LSS participants. With the use of focus groups, made up of 50% HSS only and 50% LSS only, we were able to identify a number of characteristics of the videos that had differential appeal to the two groups (Donohew et al. 1990, 1994; Lorch et al. 1994; Palmgreen et al. 2001). HSS have greater preferences than LSS for messages that contain one or more of the following characteristics: (a) novel, creative, unusual; (b) complex; (c) intense (auditory and visual); (d) physically arousing (exciting, stimulating); (e) emotionally strong; (f) graphic; (g) ambiguous; (h) unconventional; (i) fast-paced; and ( j) suspenseful. Because of these preferences among message characteristics, HSS have been found to exhibit a greater preference than LSS for horror films, sexually explicit films, heavy metal as opposed to Top 40 rock music and music videos, graphic violence, offbeat and unconventional comedy, shows that violate social norms, and sports shows.

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The foregoing discussion suggests that the sensation value of a program or message, in other words, the degree to which its formal and content features elicit sensory, affective, and arousal responses, should be an important factor in determining the appeal of a message. Messages with high sensation value should be more attractive to HSS, whereas LSV messages should be more appealing to LSS. Although the first research reported here dealt with testing of messages in a PSA format, we later learned that characteristics found to be appealing to HSS in PSAs were appealing to HSS in an instructional setting as well, thereby making the instruction more effective. Messages employing the characteristics just described have been highly successful with appropriate target audiences in laboratory settings and more recently in a variety of field studies (see next section of this chapter).

Laboratory Study: Persuasion to Call a Hotline In one of the early laboratory studies, Donohew and colleagues designed an experiment to test the ability to differentially motivate HSS and LSS individuals exposed to messages that contained different levels of sensation value (Donohew et al. 1991). Based on the earlier formative research, we created two 30-second televised antidrug PSAs, which were developed using a variety of production features such that one included characteristics that theoretically would appeal to HSS and one included characteristics thought to be preferred by LSS individuals. To identify HSS and LSS, participants completed Zuckerman’s (1979) Sensation seeking Scale, and a median split on the sum of the 37 nondrugrelated items was employed to define LSS and HSS; participants were then randomly assigned to one of the experimental conditions (n = 165) or the control group (n = 42). HSS and LSS participants were shown HSV and LSV versions of a televised antidrug PSA. The behavioral intention of HSS to call a hotline was more affected by the HSV message (which was more dramatic and stimulating), whereas LSS were somewhat more persuaded by the LSV message (p = 0.057; Donohew et  al. 1991; Palmgreen et al. 1991). The most important result from a targeting perspective is the interaction effect between message sensation value and sensation seeking on an index of intent to call the hotline. As hypothesized, the HSV message was more effective with HSS in inducing participants’ intentions to call a hotline mentioned in the PSA, whereas the LSV message was more effective with LSS participants (p < 0.06, Palmgreen et al. 1991). HSS users of illicit drugs in the past 30 days showed the strongest impact on behavioral intention.

Field Test of Targeting and Motivation to Call a Hotline The next step was to test whether the procedures used to design and evaluate messages and program contexts in the laboratory could be implemented effectively

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with an actual televised campaign targeted at HSS young adults exposed to a drug abuse prevention campaign involving a combination of paid and free PSAs and a hotline (Palmgreen et  al. 1995). The campaign was carried out in January–June 1992 in Lexington, Kentucky, and included five different PSAs. Each spot concluded with an appeal to call a hotline for more information about exciting alternatives to drug use. Callers to the hotline received a full-color, 20-page guidebook, A Thrill Seeker’s Guide to the Bluegrass, which explained the concept of sensation seeking and its connection to drug use, and listed a wide variety of activities available in Lexington/Fayette County and surrounding areas. Over the course of the campaign, 615 purchased spots and 887 donated spots were televised. Information obtained in the precampaign survey (discussed later in this chapter) on television program preferences of HSS was used by a professional media buyer to guide placement of the campaign PSAs. Evaluation of the campaign was based on pre and postcampaign surveys, within-campaign surveys, and surveys of hotline callers. Data from these several sources converge on a conclusion that the campaign was successful in reaching the target audience of HSS with prevention messages. The combination of novel and highly stimulating messages was highly successful in motivating members of the prime target audience to call a telephone hotline featured in the PSAs. While the campaign was seen by both HSS and LSS young adults, hotline survey findings showed that 73% of those calling scored above the sensation seeking median of the population. This occurred despite earlier findings from this series of projects and others indicating that HSS watch less television than LSS and express considerably lower behavioral intentions to call a hotline to obtain substance abuse prevention information. It also should be noted that alcohol and other substance use were positively related to reported exposure to the campaign spots, and that 32% of the hotline survey respondents reported some use of illicit drugs in the past 30 days compared to 23% of the general population. Recall of the content of the messages also was considerably higher among HSS than among LSS, although the small number of LSS who also were drug users recalled the messages almost as well. LSS nonusers (the group least essential to target in a prevention campaign) showed the lowest recall.

Field Intervention Trials Messages and Behavior Change Two-City Time-Series Field Study on Substance Use Despite encouraging results indicating success in targeting the primary audience, HSS, and attracting them to call a hotline, many important questions remained, not the least of which stated, could such a campaign also change attitudes and marijuana use behaviors? Although a number of techniques had been found to be successful, there was little knowledge about the evolutionary process by which media messages began to change attitudes and behaviors in at-risk individuals.

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What are the causal lag periods involved? Are there more effective ways of designing and placing such messages? What amounts of expensive media time and space are needed to bring about desired change? The study designed to answer these questions employed a controlled time-series design in two matched U.S. communities (Palmgreen et  al. 2001). Two 4-month televised anti-marijuana campaigns targeted at HSS adolescents (but seen by HSS and LSS adolescents) were conducted in one county with one 4-month campaign in the comparison county. Personal interviews were conducted with 100 randomly selected teens per month in each county for 32 months. The cohorts followed were initially in the seventh through tenth grades. Regression-based interrupted time-series analyses indicated that all three campaigns reversed upward developmental trends in 30-day marijuana use among HSS (p < 0.002), with one campaign producing an estimated 26.7% decline in the relative proportion of HSS using marijuana over 12 months. As expected, LSS exhibited low levels of use and no campaign effects. Televised campaigns with high reach and frequency which employ PSAs designed for and targeted at HSS adolescents can affect significant reductions in substance use among members of this high-risk population.

Alcohol and Risky Sex: An HSV/IDM Curriculum This project involved 1,944 ninth-grade students in two Midwestern US cities in an experimental design using both a mass media campaign (radio) and a classroombased HIV prevention intervention aimed at reducing risky sexual behaviors, including alcohol use with sex (Zimmerman et al. 2008). The radio campaign was developed using focus and reaction groups, and then was implemented in one of the cities, to increase awareness and salience of HIV-related prevention issues; students in the other city did not receive the media campaign. Matched schools in both communities were randomly allocated to one of three conditions: Reducing the Risk (RTR), an already existing, evidence-based, skills-focused HIV prevention curriculum (Kirby et  al. 1991); a modified version of the Reducing the Risk (MRTR) curriculum developed in this study, which targeted HSS and IDM adolescents, though it was implemented in entire classrooms (and was developed on the bases of surveys, focus groups, and pilot-testing); or a no-RTR comparison group, in which students received their standard, information-focused HIV prevention curriculum. Among the changes made in the MRTR version of the curriculum was the addition of trigger films, music, talk shows, game shows, videotaping of roleplays, peer leaders to assist in class discussions, contests, prizes, and two young adults living with HIV as presenters. All programs involved formative research employing focus and reaction groups in their development, and all teachers were trained to administer the curriculum. In the subsequent years, booster classroom-based and/or media interventions were implemented in the respective cities. Students were followed-up over 3 years to

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assess the impact of the interventions on skills, behavioral intentions, and sexual risk-taking behaviors. Results of multivariate analyses controlling for clustering of individuals within schools showed that comparison-group participants had significantly greater odds of initiating sexual activity than participants in the two intervention groups (RTR and MRTR) combined, with an adjusted odds ratio of 2.4, p < 0.05. Disappointingly, however, there were no differences in outcomes between RTR and MRTR, either for the entire sample or for HSS and IDM analyzed separately. In the media city, two of the six test PSAs were randomly allocated per classroom at survey follow up. More than half of all participants reported hearing at least one of the test PSAs. The number of test PSA characteristics correctly recalled by students was significantly related to involvement in classroom activities in both the RTR and MRTR conditions. Those high in involvement recalled more test PSA characteristics than those reporting low classroom participation (p < 0.01). Nearly 90% of students in both classroom conditions correctly identified at least one test PSA characteristic. However, recall was unrelated to subsequent behavioral or efficacy outcomes. Analysis of results of unaided recall data collected from all students in the project (i.e., both media and nonmedia communities) indicates that students in the media community recalled more test PSA characteristics than students in the nonmedia community (p < 0.05), and that HSS were more likely than LSS to recall hearing PSAs related to HIV prevention in both communities.

Two-City Field Study to Assess a Safer Sex Campaign Another media study (Zimmerman et al. 2007b), similar in design to the two-city time-series study on substance use presented above, evaluated the ability of a safer sex televised PSA campaign to increase safer sexual behavior among at-risk young adults. The 3-month high-saturation campaign took place in Lexington, KY, with Knoxville, TN, as a comparison city. Independent, monthly random samples of 100 individuals were surveyed in each city for 21 months as part of an interruptedtime-series design with a control community. Five new PSAs were produced based on extensive formative research with the target audience; an additional six PSAs developed by the Kaiser Family Foundation and MTV were used in the campaign, as they also were extremely well-received by the target audience and covered key theoretical variables. Messages were especially designed and selected for the target audience (those above the median on a composite sensation seeking/IDM scale), though they were seen by HSS and LSS and impulsive and rational decisionmaking young adults. Data indicated high campaign exposure among the target audience, with 85–96% reporting viewing one or more PSAs. Analyses indicated significant 5-month increases in condom use, condom-use self-efficacy, and behavioral intentions among the target group in the campaign city with no changes in the comparison city. The results suggest that a carefully targeted, intensive mass media campaign using televised PSAs can promote safer sexual behaviors.

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Two-City Time-Series Field Study to Assess ONDCP Marijuana Initiative We evaluated the effects of the Marijuana Initiative portion of the U.S. Office of National Drug Control Policy’s National Youth Anti-Drug Media Campaign on high-sensation seeking and low-sensation seeking adolescents (Palmgreen et  al. 2007). Personal interviews were conducted via laptop computers with independent monthly random samples of 100 youths from the same age cohort in each of two moderate-sized communities over 48 months (April 1999–March 2003) of the campaign, including the critical first 6 months of the 9-month initiative. The start of the initiative was treated as an “interruption” in time-series analyses of the combined community sample. The Marijuana Initiative reversed upward developmental trends in 30-day marijuana use among high-sensation seeking adolescents (p < 0.001) and significantly reduced positive marijuana attitudes and beliefs in this at-risk population. As expected, low-sensation seeking adolescents had low marijuana-use levels, and the campaign had no detectable effects on them. Other analyses indicated that the portion of the initiative which included HSV messages depicting negative consequences of marijuana use was principally responsible for its effects on high-sensation seeking youths. Thus, substance use prevention campaigns can be effective within an approach using dramatic negative-consequence messages targeted to HSS.

Understanding Mediational Pathways Over the past several years, our research group has developed the multiple domain model (MDM), in which we have taken the Theory of Planned Behavior (TPB, Ajzen and Madden 1986) with its constructs of attitudes, norms, perceived behavioral control/self-efficacy, intentions, and behavior, and have added a number of contextual variable domains (social structure, social/cultural environment, and situational contexts), individual difference variables (such as those that are the focus of this chapter), and preparatory behaviors (from Bryan et  al. 2002) to more broadly describe the variables and processes leading to health related, or at the other end of the continuum, risky behavior (Zimmerman et al. 2007a). We have conducted a number of studies which directly or indirectly support this model. We briefly present the results of three of them here. In a partial test of MDM, we examined the relations among individual difference variables (sexual sensation seeking and sexual IDM), core constructs of the TPB (attitudes, norms, and self-efficacy), and behavior, with a large cross-sectional sample of young adults (n = 1,489, Noar et  al. 2006). Results indicated that the relationship between sexual sensation seeking and condom use was largely mediated by condom attitudes and that the relationship between sexual IDM and condom use was largely mediated by condom self-efficacy. Specifically, HSS had

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negative condom attitudes and impulsive decision-makers reported low condom self-efficacy; in turn, negative condom attitudes and low self-efficacy were each related to less condom use. In our second research example, we tested the complete MDM with a sample of 739 urban adolescents who had not had sex at the baseline survey, administered near the beginning of ninth grade (Zimmerman et al. 2010). Data were collected at two additional time points – at the end of ninth grade and the end of tenth grade. The relationship between sensation seeking and initiation of sexual activity was mediated by all three TPB variables (attitudes, norms, and self-efficacy); the relationship of IDM to initiation was mediated both through attitudes and self-efficacy. In our third example, we present results of an investigation that, while it did not test the MDM per se, was consistent with it. The focus of this study was the correlation between adolescents’ own level of sensation seeking, their friends’ sensation seeking, and their own and their friends’ substance use (Donohew et al. 1999). This focus is consistent with the MDM’s inclusion of norms as a potential mediator between sensation seeking and risky behavior. Individuals’ levels of sensation seeking were highly correlated with that of their friends. In addition, there were direct effects of friends’ sensation seeking on the adolescents’ own use of both marijuana and alcohol 2 years later. These three research examples suggest a variety of additional insights about designing successful interventions to reduce risky behaviors in HSS and impulsive decision-makers: (1) self-efficacy may be an especially important variable to change in impulsive decision-makers; (2) attitudes and norms may both be important variables to change in HSS; and (3) group-based interventions may be particularly appropriate for HSS and their friends.

Unsuccessful Attempts to Design Interventions for Impulsive Individuals Two very different kinds of attempts at understanding how to design interventions for impulsive individuals to date have not been successful. We describe each of these briefly in turn.

ISLE: Improving School Learning Environments One project funded by the National Institute of Nursing Research focused primarily on providing training for teachers on a variety of skills to be more effective in delivering evidence-based HIV, STD, and pregnancy prevention curricula to high school students. We conducted a series of experiments in six schools, randomizing schools semester by semester to a variety of training interventions; during one of

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the six semesters, we focused instead on improving content of the curriculum in ways that we thought might be more effective with impulsive adolescents. The evidence-based curriculum, Reducing the Risk, discussed earlier in this chapter, was used throughout this study (Kirby et al. 1991; Zimmerman et al. 2008). Based on a review of the relevant literature, we have come to believe that four elements within interventions might be especially effective for impulsive individuals: (1) presenting information with an emotional “hook” (for example, surprising students to learn that the person who was leading their class was HIV positive), so, consistent with the literature on state-dependent learning (cf. Overton 1991), later, when in a sexually and emotionally charged situation, the information and skills might be more likely to be recalled; (2) helping individuals identify themselves as impulsive; (3) considering ways of making choices of how not to wind up in risky situations (e.g., not being at your boyfriend or girlfriend’s house when no adults are there) since being impulsive may make it hard to make the safe choice once you’re in that situation; and (4) helping teach decision-making steps, specifically to notice what it feels like when you do risky, crazy things, so the next time you feel that way, you can notice the feeling and stop yourself. We dropped 3 of the 16 lessons in the curriculum that we had dropped in a successful adaptation of the intervention for Appalachian, Kentucky adolescents, and replaced those lessons with content related to the three elements we thought would be effective with impulsive individuals. For the emotional “hook,” we hired two young women between 18 and 20 who had been teen mothers, and worked with them to focus on parts of their stories we thought would be most useful in the curriculum; they presented their stories for most of a class session in the experimental schools. We combined the second and third components into a classroom session: we had students complete brief self-report scales measuring impulsivity in class, described what that was, and explained some of the consequences of being impulsive; we then had students participate in a number of activities to consider how to plan in advance not to get into situations that would be risky. For the fourth component, concerned with noticing how it feels when one is about to do something impulsive, we had students consider a variety of decisions they may have made in the past, concerning things such as what movies they wanted to see or what parties they might attend, and talked through times they had made impulsive decisions. We then had students talk through some of the ways they felt when they made these impulsive decisions and describe what they might look for the next time they were about to make another impulsive decision, so they could try to stop and move out of that situation. Results showed no significant differences in outcomes (attitudes about waiting to have sex, self-efficacy at refusing unwanted sexual advances, intentions to have sex) between the intervention group (n = 338 in two schools for whom the three lessons of the curriculum were changed as described above to target impulsive individuals) and the comparison group (n = 298 students in four schools which received the standard RTR curriculum). While the results were disappointing, we believe a more finegrained approach to each of the components we think might be effective in laboratory situations is likely to provide us with a better understanding of the mechanisms involved and of other potential skills that might be useful for impulsive individuals.

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Need for Cognition as an Important Message Processing, Targeting Variable Harrington et  al. (2006) presented an “extension of the Activation Model” proposing that individual differences in need for sensation and need for cognition be combined. Specifically, they suggest that both individuals’ preferences for levels of affective arousal and level of cognitive engagement will affect response to a message (attention and processing). Furthermore, the extended model suggests an elaborate set of hypotheses about the impact of the messages based on an interaction of individual differences (need for sensation and need for cognition), and message characteristics (message sensation value and a new construct introduced with the extended model, “message cognition value”). High cognition value messages are described by characteristics “such as presence of multiple arguments, complex arguments, presentation of problems to be solved and open endings.” In contrast, low cognition value messages are considered to have elements such as “a limited number of arguments, simple arguments, straightforward information, and closed endings” where conclusions are presented. Examples of hypotheses derived from the extended model are as follows: (1) HSS who are also high in need for cognition would be most affected by high sensation-value, high cognition value messages, with resulting adequate levels of attention, central processing, and favorable outcomes; and (2) HSS who are also high in need for cognition would be least affected by low sensationvalue, low cognition value messages, with no attention, no processing, and unfavorable outcomes. Some unpublished research by our research group had also suggested at least a moderate, negative correlation between need for cognition and impulsivity, suggesting that individuals high in need for cognition are likely to be less impulsive and those low in need for cognition may be a bit higher in impulsivity. Thus, Zimmerman and colleagues, who had been studying IDM, had hoped that need for cognition (the individual difference variable) and message cognition value (message characteristic) might be useful for gaining a somewhat better understanding of the kinds of messages that might be most effective with impulsive individuals (i.e., perhaps low cognition value messages). Unfortunately, the carefully and systematically laid out cognitive extension to the Activation Model did not pan out as hypothesized. Specifically, results of a series of experiments funded by the National Institute on Drug Abuse designed to create anti-marijuana messages that varied on level of message sensation value and message cognition value did not find that matching messages to the individual difference variables made a significant difference in the level of attention, kinds of cognitive processing, or outcomes as had been predicted. On the other hand, given the difficulty of successfully changing behaviors of impulsive individuals, our research group continues to look at other possible dimensions of messages, such as gain vs. loss frame and level of fear or threat that might impact message effectiveness for this important target group.

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Conclusion Over the last 20 years or so, we and our colleagues have conducted overlapping research programs investigating relationships between sensation seeking and impulsivity on the one hand, and risky behavior, particularly substance use and risky sexual behavior, on the other. We have shown strong relationships between these individual difference variables and these risky behaviors and have developed the Activation Model to help understand why certain kinds of messages are important for getting the attention and changing the behavior of HSS. Based on this model, we have been successful at designing large field tests of mass media PSAs which have significantly reduced marijuana use, increased condom use, and delayed initiation of sexual activity. In short, we have turned our understanding of HSS into less risky behaviors for thousands of adolescents and young adults. Our forays into school-based interventions and our attempts at understanding how to best design messages for impulsive decision makers have not yet yielded equally promising results. Some of the new directions that we are considering for the school setting include significantly more exciting and interactive communication channels, such as video games and other new media, as well as social networking sites on the internet, instead of traditional face-to-face teaching methods. Work, which we are considering for impulsive decision-makers, includes gaining a better understanding of their emotional responses and how to teach them to notice when those responses are happening. Another focus of our ongoing work with impulsives is to help understand how to work with them to change decisions about the situations they choose to be in, since once they find themselves in potentially risky situations they may continue to have difficulty in stopping their impulsive and risky responses. Acknowledgments  Research described in this chapter was supported by the following NIH grants: R01-DA03462 (Lewis Donohew, PI), R01-DA05312 (Lewis Donohew, PI; Philip Palmgreen and Elizabeth Lorch, co-PIs), R01-DA068924 (Lewis Donohew, PI; Philip Palmgreen, Elizabeth Lorch, and William Skinner, co-PIs), P50-DA005312 (Richard Clayton and Michael Bardo, PIs), R01-DA04887 (Lewis Donohew, PI, Richard Clayton, co-PI), R01-DA012371 (Philip Palmgreen, PI), R01-DA12490 (Lewis Donohew and Nancy Harrington, Co-PIs), from the National Institute on Drug Abuse; R01-AA10747 from the National Institute on Alcoholism and Alcohol Abuse (Rick Zimmerman and Lewis Donohew, Co-PIs); R01-MH061187 (Rick Zimmerman, PI) and R01-MH063705 (Rick Zimmerman and Phil Palmgreen, Co-PIs) from the National Institute of Mental Health; and R01-NR008379 from the National Institute of Nursing Research (Rick Zimmerman and Eric Anderman, Co-PIs). We would also like to thank Nancy Harrington, Derek Lane, and Eric Anderman for their careful reading of portions of the manuscript.

References Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall. Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22, 453–474.

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Chapter 15

Self-Regulation and Adolescent Drug Use: Translating Developmental Science and Neuroscience into Prevention Practice Thomas J. Dishion, Joshua C. Felver-Gant, Yalchin Abdullaev, and Michael I. Posner

Abstract  This chapter addresses the role of self-regulation in the development of adolescent-onset drug use. Specifically, we focus on the interface between peer influences, parenting, self-regulation, and drug use. Recent longitudinal analyses suggest that peer clustering into groups supportive of drug use is central to the etiology of adolescent onset and progression to young adult dependence. Longitudinal research also confirms that individual differences in adolescent self-regulation uniquely reduce progressions in terms of adolescent use and dependence, and serve as a protective factor for peer influences. We also explore the neurocognitive underpinnings of adolescent self-regulation by observing brain activation patterns associated with tasks with known properties in the attention network. We report an imaging study of adolescent marijuana users that revealed not only self-regulation deficits in users, but also specific neurocognitive activation patterns unique to earlyonset persistent drug use. These analyses revealed that more effort (and activation) was required in tasks assessing executive control of attention, suggesting less developed attention systems associated with self-regulation among known users compared with controls. We propose that future prevention efforts focus on interventions that reduce peer clustering into groups that support early drug use, and interventions that promote enhancement of self-regulation, in particular, building on recent progress in neurocognitive mindfulness-based intervention strategies.

Self-Regulation and Development Self-regulation can be a difficult construct to define because it encompasses several dimensions of regulation and functioning. A working definition of self-regulation is the ability to control or alter one’s thoughts and feelings within a given environment, in line with preferred standards (Vohs and Baumeister 2004). The focus of

T.J. Dishion (*) Child and Family Center, University of Oregon, Eugene, OR 97401, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_15, © Springer Science+Business Media, LLC 2011

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this complex set of abilities is to optimize long-term well-being (self, family, and community) over and above emotionally driven impulses for pleasure or for pain control. The perspective taken in this chapter is that self-regulation subsumes, but is not limited to, emotional regulation, inhibitory control, and effortful control of attention. It emerges in early childhood, continues to evolve through childhood and adolescence, and is refined during adulthood. New competencies are added to reinforce self-regulation over time, enhancing the automaticity of emotion regulation. One such ability is learning to avoid situations in which emotion regulation is tested, and in doing so, one regulates the environmental context in which one is functioning in a process often referred to as niche finding (Scarr and McCartney 1983). For example, individuals fearful of public speaking often select jobs and hobbies that do not require a constant demand to manage anxiety in social interactions. Similarly, an extrovert who enjoys social interaction avoids situations that are isolating. As one matures, new self-regulatory abilities are required; for example, discontinuing the use of an abusable drug requires avoiding drug-using peers and creating new social contexts and recreational activities that support efforts to stay sober. At the core of all facets of self-regulation is the cognitive ability to attend to environmental (e.g., social interactions, sights, sounds) and internal (e.g., emotions, impulses, urges) stimuli that empower action, thus optimizing long-term wellbeing. The key skill is to recognize the potential consequences of internally driven emotional reactions as well as the behavior of others, and, when possible, override impulses in favor of the common good. For studies of self-regulation that are based on imaging data, it is useful to develop an approach that can be measured simply and precisely, for example, in neurocognitive paradigms that tax the mind’s ability to resolve conflict within the context of competing stimuli. This aspect of attention, subsumed under executive control, contrasts with facets of attention, such as orienting and alerting, which involve the efficiency of the mind to use conditioned cues in directing attention (Posner and Rothbart 2007; Rothbart et al. 2004; Rueda et al. 2004). Evidence suggests that individual differences in the core component of attentional control have a genetic basis but develop and become increasingly refined in the context of being parented (Sheese et al. 2009). Evidence indicates that self-regulation in general, and attentional control in particular, define a child’s susceptibility to pathogenic parenting in early to middle childhood and in peer environments in middle childhood to adolescence (Belsky et al. 2007; Dishion and Patterson 2006). Put simply, early in development when children are less able to choose their social environments, the impact of harsh, punitive, or lax family environments is moderated by children’s ability to self-regulate. In some respects, this ability may emerge by contextual demand, such as in the case of the oldest child in an alcoholic family who acts as a parent by taking on some adult child care responsibilities when the parent is not fully functioning. Selfregulation is likely to be domain specific with respect to children who learn to not show emotion after misbehaving, lying, or committing violence despite the fear of harm often associated with physical aggression. It is noteworthy that self-regulation is the most frequent child-centered target in empirically supported treatment (see Weisz and Kazdin 2010) and prevention

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(e.g., Botvin 2000; Lochman et  al. 2003). However, much of the developmental literature does not address the measured targets of empirically supported interventions. In this chapter, we explore the role of self-regulation in the etiology of adolescent drug use and discuss its implications relevant to strategies designed to prevent early-onset adolescent substance use. It is an especially propitious time for empirical study of child and adolescent self-regulation because of the recent convergence of neurocognitive, developmental, and prevention science that is contributing to a new understanding of adolescent drug use.

Ecological Framework Susceptibility to Peer Influence Early-onset substance use is by and large a peer activity that occurs in contexts generally devoid of adult monitoring and supervision (Chilcoat et al. 1995; Dishion and Loeber 1985; Dishion et al. 1988; Friedman et al. 1985). It is well understood that susceptibility to peer influence is not simply a function of parental monitoring of youth, rather, some youth actively seek out peer settings in which drug use and other problem behaviors are the focus, and thus require more active supervision (Brody 2003; Capaldi 2003; Stoolmiller 1994). When parents of adolescents disengage from family management and monitoring and the adolescent is embedded into a drug-using peer group, escalations in drug use and problem behavior ensue (Dishion et  al. 2004). In one study, randomization of high-risk adolescents to a family intervention that promotes continued parental monitoring reduced drug use over a 3-year period, with the effect mediated by changes in monitoring (Dishion et al. 2003). The longitudinal progression from early-adolescence drug use to adult drug dependence was recently examined. The model, tested in a sample of 998 adolescents and their families enrolled in the Project Alliance study (see Fig.  15.1), showed that early-adolescence drug use (age 11–14 years, aggregated) and deviant peer exposure (age 11–14 years, aggregated) leads to drug use and association with drug-using friends at age 16–17, and further predicted substance dependence at age 22–23. This model was tested for alcohol use and marijuana use, including intensive measurement at each developmental period, with earlyadolescence drug use, deviant peer activity, and parent–adolescent conflict measured by aggregating youth reports at age 11, 12, and 13 years. Peer support of drug use at age 16 was measured by direct observations of friendship support for drug use, a videotaped interaction, interviewer impressions, and friends’ selfreported drug use. Interviewer impressions were measured because, as it turns out, many of the 16- to 17-year-olds seemed to be intoxicated during the friendship interaction task or blatantly discussed drug use in positive terms in the presence of interviewers. Parent–adolescent conflict was measured by direct observations, youth reports, and audiotaped narratives about the parent–adolescent

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Fig.  15.1  Adolescent drug use and deviant peer association predicts young adult substance dependence

relationship. Finally, at age 23, data about the youths’ frequency of drug use and symptoms of dependence on marijuana and alcohol were collected. Testing of the two models revealed a surprising convergence in the etiological pathways leading to adult alcohol and marijuana dependence. Generally, it was found that all three sets of constructs showed considerable continuity from early to middle adolescence. A feedback loop emerged in the alcohol and marijuana models between early-adolescence drug use and later selection of friends who support drug use. As such, youth who used drugs in early adolescence were likely to seek out friendships with others who engaged in the same behavior, over time. A similar result was found in a smaller sample of the Oregon Youth Study boys (Dishion and Owen 2002). In that study, a high correlation was found between drug use and friendships supportive of drug use among youth age 16–17 years. The correlation between these two constructs was so high that both drug use and the friendship construct could not be estimated in the same model. Setting adolescent drug use to zero revealed a very large effect for drug-using friends on later dependence; on the other hand, setting drug-using friends to zero revealed a high effect for adolescent drug use on young adult dependence. The latter model seems the most parsimonious to describe the progression of early-onset substance use to young adult dependence. Regardless, these models suggest that susceptibility to the peer drug-using environment is the most powerful dynamic indicator of a drug use progression culminating in early adulthood addiction.

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Self-Regulation and Drug Use Not all adolescents who develop friendships with drug-using peers escalate their substance use. Several investigators have discussed the role of self-regulation in the early onset of drug use (Wills et al. 1997) and addiction (Miller and Brown 1991). Gardner et al. (2006) examined the link between attention deficits and early-onset tobacco use and found that youth with attention deficits were more likely to initiate early tobacco use. These youth were later assessed on a widely used behavioral measure of attention, the Attention Network Task (ANT; Fan et al. 2002). The ANT is based on Posner’s tripartite model of attention (Posner and Petersen 1990) and measures three components of attention: alerting (vigilance to stimuli), orienting (directing attention), and conflict monitoring (executive control). (A more detailed description of the ANT appears later in this chapter.) Early-onset smokers actually performed better behaviorally on the executive control task (i.e., conflict monitoring, a proxy measure of self-regulation) than did their nonsmoking peers, suggesting that tobacco use may serve the secondary function of improving attention among vulnerable youth. The use of tobacco, the most commonly used early substance, is also predicted by social marginalization in childhood and early adolescence (Dishion et al. 1995; Ennett and Bauman 1994), suggesting an interplay between deficits in self-regulation and the social environment in the etiology of drug use. Self-regulation has not only been studied as a main effect on early-onset substance use, it has also been hypothesized to moderate the influence of substance-using peers (Wills and Dishion 2004). Previous research has revealed that self-regulation moderates the impact of deviant peers on escalation in delinquent behavior and in antisocial behavior (Gardner et al. 2008; Goodnight et al. 2006); however, this effect has yet to be tested relative to increased substance use. Recent work examined the role of self-regulation in the escalation of substance abuse from use to dependence among a sample of 16- to 18-year-old adolescents enrolled in Project Alliance. The model that was tested is summarized in Fig. 15.2. A construct of adolescent self-regulation was developed by using three indicators based on the Rothbart construct and measure of effortful attention control (Rothbart et  al. 2003): (a) youth report of attention control, (b) parent report of attention control, and (c) teacher report of self-control and task persistence. The convergence among the three indicators was quite high, greater than 0.35 with a sample of 800 youth. The contribution of the self-regulation construct to drug use at age 18 was tested further, controlling for previous drug use and for substance-using peers. Each substance was approached uniquely so that when predicting tobacco use, early tobacco use and tobacco use among friends were statistically controlled. The same strategy was used for alcohol use and for marijuana use. The models tested are summarized in Fig. 15.2. In the aforementioned models, each predictor and the interaction between selfregulation and peer drug use were entered. For all tested models, substance use (tobacco, alcohol, or marijuana) at age 18 was predicted by prior use, peer use, and the self-regulation construct. All the models fit the data well, however, only the

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Fig. 15.2  Adolescent self-regulation, substance use, and deviant peer association predict young adult substance

Fig.  15.3  Interaction of adolescent self-regulation and peer tobacco use, predicting escalating tobacco use

interaction effect between self-regulation and peer tobacco use was statistically significant. As shown in Fig. 15.3, youth with high levels of self-regulation were less vulnerable to the influence of tobacco-using peers. Adolescents with low levels of self-regulation showed an inverse effect and were highly influenced by tobaccousing peers in terms of escalating smoking behavior. Low levels of adolescent selfregulation were, as expected, prognostic of higher levels of use of all substances.

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Brain Mechanisms Underlying Self-Regulation Adolescence is a period of rapid brain development and physiological change secondary to pubertal development (Dahl 2004; Spear 2000). In particular, adolescence is marked by intensive development and neural maturation of frontal lobe circuitry of the brain (Gogtay et al. 2004), which in conjunction with the anterior cingulate cortex, form the scaffolding for the development of self-regulation. The covariation between the neurocognitive development of this area of the brain and adolescent drug use is not well understood. A critical consideration is the extent to which chronic substance use beginning in early adolescence disrupts the refinement of the adolescent brain, specifically areas relevant to self-regulation and inhibition. Among the general population, marijuana is the most frequently abused illicit drug; its onset occurs predominantly in adolescence, and levels are highest among adolescents at the 8th to 12th grade level (Johnston et al. 2008). Thus, the question of whether marijuana use perturbs the development of brain structures subserving self-regulation has significant implications. To examine the covariation between brain mechanisms underlying self-regulation and chronic adolescent substance use, we took advantage of a rare opportunity to neuroimage youth with known and exclusive marijuana use histories (Abdullaev et al. 2008, 2010). We examined two different sample groups of chronic marijuana users. First, we examined the previously described Project Alliance sample and found seven youth (age 11–18 years) with chronic marijuana use histories, and no history of other substance use. A control group from the same sample was similar, with the exception of marijuana use history. To buttress the sample, we recruited a second sample of chronic users (n = 7) and controls (n = 7) from the community through advertisements and flyers. Functional magnetic resonance imaging (fMRI) studies were performed using the Siemens Allegra 3.0 Tesla head-only MRI scanner. To measure self-regulation, we again used the ANT developed by Fan et  al. (2002). The ANT measures three components of attention: alerting, orienting, and executive control. Participants in the ANT task viewed a computer screen and determined in which direction the central (target) of five horizontally aligned arrows was pointing (i.e., left or right). The five arrows are presented either above or below the fixation point, pointing in the same left or right direction (e.g., =>=>=>=>=>) in the congruent condition (50% of trials), or with the central arrow pointing in the opposite direction of the four surrounding arrows (e.g., =>=><==>=>) in the incongruent condition (50% of trials). Trials also included a cue (either a central cue, spatial cue, or no cue at all), with cue types presented with equal probability. The central cue was indicated by an asterisk replacing the fixation point for 150 ms, and the spatial cue was indicated by an asterisk presented for 150 ms either above or below the fixation point. Spatial cues predicted the target location with 100% certainty. The subject’s task was to look at the central fixation point and press the left button with the left thumb if the central arrow pointed to the left, or press the right button with the right thumb if the central arrow pointed to the right. To determine alerting, orienting or conflict monitoring scores reflect subtractions of reaction times (RTs) between appropriate conditions.

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In the ANT, we used an event-related fMRI design with pseudorandom (predetermined unpredictable) order of different targets and cues within each block of trials (Buckner et  al. 1996) and the same interstimulus and intertrial intervals used in a previous ANT study (Fan et al. 2005). Five blocks of ANT were presented; each block consisted of 48 trials with different pseudorandom order and intervals. Responses were recorded with two buttons on an MRI-compatible button box. Reaction times were measured from the target onset to the button press. Percent of errors was computed as a measure of accuracy. Stimuli were presented and behavioral data (i.e., RT in milliseconds and accuracy as a percentage of errors) were collected using the Presentation program (www. neurobehavioralsystems.com) used within a Windows operating system. Stimuli were presented with a digital projector/reverse screen display system to the screen at the back end of the MRI scanner bore. Subjects saw the screen via a small tilted mirror attached to the birdcage coil in front of their eyes. The details of the data analysis and imaging protocols are provided in Abdullaev et al. (2008, 2010). We observed significant differences between the marijuana users and the control group relevant to the executive attention network of the ANT. There were no significant differences in the behavior or fMRI data for the alerting or orienting network between the two groups of subjects. Thus, the primary focus of this study was to examine differences between the marijuana users and the controls in the neurocognitive areas underlying executive control, otherwise known as the executive attention network (measured by incongruent minus congruent RT in the ANT). Behavioral differences between the groups of subjects indicated that marijuana users had more difficulty resolving conflict condition than did the control subjects, as determined by differential reaction times between congruent and incongruent conditions. The imaging data suggest that only the attention network associated with executive control, including the anterior cingulate and right frontal areas, was activated by both groups. However, the differences between the two groups are shown in Fig. 15.4, which contrasts users to controls on the ANT conflict task. The increased activation in right frontal areas and parietal areas on the part of the users suggests that they may require increased effort while orienting to or inhibiting conflicting information in incongruent trials. This, together with the increased time taken to resolve conflict (significantly longer RTs), indicates inefficiency within the executive attention system in this group. The two groups did not show any statistically significant differences in either the alerting or orienting components of the ANT, in either the behavioral or in the fMRI data. This means that the differences in attention-related behavioral and brain activity between the marijuana user group and control subjects were selectively related to the executive attention network. The right ventral lateral frontal area (specifically the right prefrontal region) was significantly more active in the users than in the controls. This increased activity suggests that despite poorer performance, the users exerted more effort during the tasks that require executive attention control (a proxy for self-regulation). This right prefrontal cortex activation has often been found in Go/No-Go tasks requiring response inhibition and executive attention control (Fisher et  al. 2011, this book; Garavan et  al. 1999;

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Fig. 15.4  Differential levels of activation between marijuana users and controls on task of executive control of attention. Group differences are particularly pronounced in right frontal and parietal regions

Konishi et al. 1999; Liddle et al. 2001; Rubia et al. 2001). Recent fMRI findings lend supporting evidence that marijuana users have stronger activation in the right prefrontal cortex relative to normal controls on a Go/No-Go task (Tapert et al. 2007). It should be noted that this finding has not always been consistently demonstrated (Jager et al. 2007; Nestor et al. 2010; Schweinsburg et al. 2008), possibly as a result of somewhat different tasks and unique neurocognitive demands on systems, such as memory. Two other differences appeared between users and controls during the ANT. Bilateral parietal areas were significantly more active in users (see Fig.  15.4). This could be related to higher levels of orienting in the users and is consistent with increased effort by them. Higher activation of the posterior temporo-parietal cortical areas in marijuana users was also revealed during a delayed verbal recall task (Jacobsen et  al. 2007). Although both users and controls activated areas of the anterior cingulate cortex, the peak of the activation in the user group was located slightly more toward the anterior ventral part of the anterior cingulate, whereas in the control group the peak was in the more posterior dorsal part of the anterior cingulate cortex. These subdivisions seem to correspond well to the functional subdivisions of the anterior cingulate cortex, on the basis of a meta-analysis of extensive literature in which the anterior ventral part was shown to be related to emotional control, whereas the posterior dorsal part was related to cognitive control (Bush et al. 2000). So the more ventral location of the anterior cingulate activation in the user group compared with more dorsal activation in the control group may indicate that more of the emotional control system is involved in users’ responses (Bush et al. 2000; Posner et al. 2006), although this finding did not reach statistical significance.

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The most pronounced difference between the user and the control groups, as expected, was found in the area associated with executive attention control in children and adolescents (Gonzales et al. 2001). Unfortunately, our study’s design did not allow for the determination of causal relations. Whether limited self-regulation characterizes those who end up being marijuana users, or whether marijuana use leads to poor regulation that in turn alters brain activity, remains unanswered from these data. It could be that the differences in the executive attention network of users compared with that of controls are the result of long-term use of marijuana during adolescence. However, given the main effect of poor self-regulation on adolescent drug use reported earlier in this chapter, the direction of effect is likely to be the reverse, that is, youth with poor self-regulation are the most likely to initiate early and persist through adolescence (Wills and Dishion 2004). Perhaps both are true, in that youth with poor self-regulation are prone to early and persistent drug use, and early and persistent drug use could disrupt the exercise and refinement of executive attention systems from early to late adolescence, when these systems are still developing (Dishion and Connell 2006; Galvan et al. 2006). Some evidence also suggests that patterns of brain activations differ between adolescents using alcohol and adolescents using marijuana (Schweinsburg et  al. 2005), suggesting that at least some of the described differences may be related to marijuana use. We plan to conduct a longitudinal study to examine these specific possibilities more directly.

Translation to Prevention Science Conceptual Model We asserted early in this chapter that underlying all empirically supported interventions used for treatment or prevention is the objective to foster and support an individual’s ability to self-regulate. The self-regulation of behavior and emotion is indeed one of the most important outcomes of successful childhood development. Self-regulation plays a central role in various normative and pathological developmental processes, including psychosocial functioning (Eisenberg et al. 2004), psychopathology (Rothbart and Posner 2006), coping (Compas et al. 2001), scholastic achievement (Duncan et al. 2007), and resilience (Dishion and Connell 2006). Broadly speaking, prevention interventionists seek to support all efforts that produce adults who are able to consciously and intentionally make decisions that are in the best long-term interests for themselves and their community. For example, as suggested earlier in this chapter, an adolescent’s ability to self-regulate can lead to less risky behavior later in life, including substance abuse. Although it seems obvious that the promotion of self-regulation is an important objective, it is not as obvious to identify how this goal is explicitly targeted. For example, family interventions that target problematic behavior often result in improvements in child self-regulation. Lunkenheimer et  al. (2008) found that in early childhood,

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randomization to the Family Check-Up resulted in improved positive behavior support by parents, as observed in the home and on videotape. Improved inhibitory control from ages two through four was also found. In terms of the discussion earlier in this chapter, it would not be surprising that changes in self-regulation were a collateral effect of improved family functioning, that is, the more predictable and contingent the family environment, the more likely that children have opportunities to manage their attention and regulate themselves. However, it could also be the case that some component of the Family Check-Up resulted in improvements in self-regulation for the parent, which was then modeled and internalized by the child. As such, improvements in family functioning and child behavior could be the collateral results of changes in self-regulation, and not the other way around. Future research benefits from measuring parent and youth self-regulation to study potential bidirectional effects as a result of intervention support. It is clear that to date, the study of parent self-regulation in family-centered research has been largely neglected. Although self-regulation is generally understood to be an important consideration in terms of prevention and intervention science, future research efforts must focus on elucidating the underlying mechanisms of how it exactly relates to behavioral outcomes of interest. It is hypothesized that interventions could be improved if they were to directly address the core skills underlying child and adolescent self-regulation. Translational research of this nature is critical to moving the prevention field forward by designing strategies with far-reaching effects on a variety of adolescent problem behaviors. If self-regulation is indeed a common factor in multiple problem behaviors, it follows that a strategy that promotes self-regulation may have broader, more extensive effects than strategies that simply attempt to reduce problems. The following section describes novel translational research that explicitly targets self-regulation through the use of mindfulness activities.

Mindfulness as a Promoter of Self-Regulation Self-regulation is often viewed as a secondary outcome of child-focused interventions. Given that self-regulation has important implications for many domains of functioning, however, interventions that specifically target self-regulation may produce better outcomes. Prominent among those interventions are mindfulnessbased interventions, which explicitly target self-regulatory strategies to effect positive change. Mindfulness, or “the self-regulation of attention so that it is maintained on immediate experience… [and] is characterized by curiosity, openness, and acceptance” (Bishop et al. 2004), has in recent years become increasingly popular as a method of psychosocial intervention (Brown et  al. 2007). Recent work on evaluating the effectiveness of mindfulness has demonstrated promising results, as evidenced by several meta-analyses detailing its ameliorative effects (Baer 2003; Grossman et al. 2004; Hofmann et al. 2010). Mindfulness interventions focus on

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teaching individuals strategies to disengage attention from internal and external experience that is unhelpful or that promotes suffering (e.g., ruminating about mistakes, focusing on unpleasant emotional experience, fruitless worrying about or planning the future), and instead focus nonjudgmentally on present experience. By resting attention on the immediate experience, individuals are able to behave in ways that are in their best short- and long-term interest; specifically, every momentby-moment decision is made with deliberate intention and care as opposed to functioning in an automatic, reactive fashion without attention paid to consequences. In this way, mindfulness interventions promote the self-regulation of behavior and can be construed as explicitly targeting self-regulatory mechanisms. Among its other salutary benefits, mindfulness training has been shown to improve self-regulatory and related attentional processes. Jha et al. (2007) examined the effect of a manualized mindfulness-training program, mindfulness-based stress reduction (MBSR; Kabat-Zinn 1990), on participants’ attentional capabilities when using the ANT (Fan et al. 2002). They found significantly improved ANT scores among those in the mindfulness group compared with scores among those in a waitlist control group. One study used fully randomized assignment either to a group trained in a version of mindfulness [integrated mind–body training (IBMT)] or to a control group given relaxation training. After only 5 days of training, the IBMT group showed performance in the ANT executive network that was superior to that of the control group (Tang et al. 2007). In another study examining the effects of mindfulness training on adults and adolescents with clinically diagnosed attentiondeficit hyperactivity disorder (ADHD), results demonstrated statistically significant improvements in ANT conflict monitoring scores and in self-reported ADHD symptoms (Zylowska et  al. 2008). Valentine and Sweet (1999) also found that mindfulness training improved various aspects of attention among participants relative to results among control subjects. Other study results have shown that mindfulness training improves the ability to attenuate the impact of emotionally distracting information on attention relative to relaxation training and to results among control groups (Ortner et al. 2007). Although tentative, an emerging body of research supports the claim that mindfulness interventions target self-regulatory processes. Integration of mindfulness practices into existing family- and childcentered intervention frameworks provides a promising example of how intervention science can explicitly target self-regulation as a salient outcome.

Family-Centered Interventions Although mindfulness training integrated with family-centered intervention has been investigated in only a handful of studies to date, it has demonstrated effectiveness as a family treatment modality. Results indicate improvement not only in standard child and family outcomes (e.g., symptoms reduction), but also in selfregulation. Coatsworth et al. (2010) compared a previously existing evidence-based

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family intervention, the strengthening families program (SFP; Molgaard et  al. 2001), with a mindfulness-infused SFP curriculum and a control group. Results indicated that the mindfulness-infused intervention outperformed both the original parent-training intervention group and a wait-list control group on measures of parent–child relationship (Coatsworth et  al. 2010). The researchers hypothesized that specifically targeting self-regulation of the parental relationship may have improved dyadic interactions and increased both parent’s and child’s ability to selfregulate their own behavior in service of a more harmonious relationship. In a study of a modified, family version of MBSR, Saltzman and Goldin (2008) demonstrated an improvement in the attentional subsystem involvement of the conflict monitoring and emotional reactivity in children. These studies and other calls for research (Cohen and Semple 2010; Dumas 2005) highlight the emerging evidence for the effectiveness of using mindfulness training in a family treatment modality that targets the specific need to address self-regulation.

Child-Centered Interventions As discussed earlier, the plethora of empirically supported prevention and treatment strategies for children and adolescents focuses on some aspect of self-regulation (Botvin 2000; Greenberg et  al. 1995; Lochman et  al. 2003; Weisz and Kazdin 2010). Treatments for child and adolescent anxiety and depression include many core components that emphasize critical aspects of self-regulation, such as identifying feelings, monitoring thoughts and feelings, exposure, and coping with unpleasant stimuli (Barlow 2004). Mindfulness-based interventions, which explicitly target features of self-regulation, have also been used to treat child and adolescent problem behavior. Modified versions of MBSR have been found to effectively decrease symptoms of ADHD in adolescents (Zylowska et  al. 2008) and significantly improve symptoms of anxiety and attention in children (Lee et  al. 2008; Semple et  al. in press). Napoli et  al. (2005) found that a mindfulness training program implemented in a public elementary school significantly affected selfreport measures of attention, anxiety, and social skills. In another study, adolescents with conduct disorder were able to self-regulate their aggressive behavior following mindfulness training in a public middle school (Singh et  al. 2007). Bootzin and Stevens (2005) found that adolescents who completed a modified version of MBSR had significantly fewer substance use problems than did noncompleters. All the aforementioned mindfulness-based interventions had significant treatment effects on children and adolescents with a variety of behavioral and mental health problems. Recent evidence has shown that the beneficial effects of mindfulness interventions include increased self-regulatory abilities and are directly related to underlying self-regulatory processes. As more research efforts target self-regulation, it behooves translational researchers to consider adopting mindfulness components into existing intervention frameworks.

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Summary and Conclusions Findings from the vast body of etiological research on adolescent drug use to date have made it clear that family mismanagement, poor relationships, and association with drug-using peers are core environmental risk factors. These findings are attested to by the effectiveness of interventions that target the family context to address adolescent drug use. Several investigators have found family interventions to be effective for reducing adolescent antisocial and delinquent behavior (Bank et  al. 1991; Forgatch et  al. 2005; Henggeler et  al. 1998) and substance use (Liddle 1999; Spoth et  al. 2001). In our work, randomization to the Family Check-Up intervention in middle school reduced substance use among high-risk adolescents, and the effects of this intervention were mediated by changes in parental monitoring (Dishion et  al. 2003). These family-centered intervention strategies can be summarized as environmental structuring in that they simply reduce the opportunities for substance use by mobilizing parents to monitor and structure their adolescent child’s lives. Although future work with family-centered interventions that improve the environmental context for adolescent development would benefit from strong effects on changing the peer environment, effects of family interventions on later substance use mediated by reduced peer exposure have yet to be reported in the literature. In general, there is ample evidence for a definitive link between child and adolescent self-regulation and early-onset substance use. Although early drug use emerges in the context of peer relationships, differences clearly exist in the tendency for children to seek out or resist unsupervised peer contexts, where substances are being used. Moreover, self-regulation appears to be a factor that reduces the likelihood of progressing to dependence on tobacco, alcohol, and marijuana. Our work and that of others suggests that self-regulation deficits associated with chronic use can be traced to the cerebral processing underlying executive control of attention, as shown in the context of cognitive fMRI tasks. When presented with competing stimuli, youth with a history of marijuana use work harder to direct their attention away from a prepotent response. Whether this self-regulation deficit is a cause or an outcome of adolescent drug use remains to be seen. Carefully conducted longitudinal research is needed that maps patterns of substance use with growth in adolescent neurocognitive development and self-regulatory patterns. Two questions require further study to evaluate the potential of a self-regulation focus in family-centered prevention of drug abuse. Both require multilevel analyses that not only target behaviors that are self-regulatory (e.g., “deals with distractions and stays on task”), but also investigate the underlying neurocognitive processes. Our fMRI work has shown that although behavioral differences between groups may be minimal, brain neuroimagery reveals differential functioning, suggesting that effortfully managing attention may be prognostic of self-regulatory failures down the developmental road. The first question concerns developmental sensitivity: Specifically, at what age should children’s self-regulation be targeted to have the most long-lasting effect?

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At first glance, considering the work of Rothbart and others, children age 36 months appear to be in a key developmental period in the development of effortful control of attention (Rothbart et  al. 2004). It may well be that targeting early childhood parenting environments may have the most enduring effects on selfregulation patterns that eventually reduce the risk of drug abuse. However, it is conceivable that children high in self-regulation in childhood could lose some of their advantage in adolescence when faced with the context of rewarding deviant peer activities, pubertal changes, and low levels of adult environmental structuring. Changes in brain development during adolescence suggest a need to support parenting practices in early adolescence as well, and we know that self-regulation is responsive to interventions during this period (Fosco et al. under review). Future longitudinal research that includes both behavioral and neurophysiological metho­ dology is therefore necessary to determine the most effective time period for intervention focused on promoting self-regulation, and what the implications of this intervention would be in terms of the developing child. The second question is perhaps more interesting with respect to translational research: How could the focus of existing family-centered interventions be revised for stronger effects on the developing self-regulation system? Three broad areas of improvement could be addressed to increase this potential and strengthen outcomes. Promotion of self-understanding, a joint and balanced focus on child and adolescent engagement in academic learning, and skill development and chores using family-centered interventions are likely to benefit children’s development of self-regulation. Relevant to the first area of improvement, longitudinal research by Eisenberg et  al. (1991, 2003) suggests that parent–child interactions that label emotional experiences in neutral nonjudgmental ways predict future prosocial behavior and emotion-related regulation. This effort, which requires parents to recognize their emotional experience from a stance of acceptance in the service of benefiting both themselves and their children, is also echoed among proponents of mindfulness interventions (Duncan et al. 2009). Given the central role of language and thinking in the self-regulation process, parents’ efforts to provide a conceptual framework that motivates children’s efforts to manage their short-term feelings and reactions in the service of long-term benefits are likely to be a key. One can imagine that highly contingent parents who do not promote emotional self-understanding in their child are less effective in the long run. One strategy that seems promising is to engage parents in a process of learning to label and regulate their own emotions and behavior through exercises, such as mindfulness training (Coatsworth et  al. 2010), or through exercises that fit with the PATHS intervention designed for children in schools as a universal intervention (Greenberg et al. 1995). Although all the components of empirically supported interventions for parenting are likely to be effective, those that emphasize parents’ discussion of self-regulation strategies, or simply awareness of emotional reactions, may have enhanced effects on the development of self-regulation. Second, parenting interventions that improve self-regulation might also include encouraging child and adolescent behaviors that exercise self-regulatory strategies, in particular, those that override momentary impulses that disrupt the development

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of key prosocial skills, such as completing academic tasks and completing and returning homework. Succeeding in school is a priority for development (Kellam 1990), and homework completion is a critical dimension of learning in school contexts. Academic engagement places high demands for the development of selfregulation. The amount of time that students are engaged in academic tasks differentiates antisocial from typically developing youth (Shinn et al. 1987), and homework skills most dramatically discriminate delinquent from nondelinquent youth (Dishion et al. 1984). Moreover, dysregulated behavior is one of the strongest predictors of peer rejection; social skills training that emphasizes prosocial interactions is likely to benefit children’s happiness and result in improved self-regulation. Third, completion of chores and other contributions to family well-being clearly contribute to the development of self-regulation. Research has shown that these skills are often missing for children with problem behavior (Patterson 1982). The ability to self-regulate is possibly the most important skill that is learnt ­during childhood and adolescence. When it is underdeveloped, it is strongly implicated in adolescent substance abuse. Overall, self-regulation is a robust predictor of long-term outcomes and future functioning. Interventions that specifically target the development and refinement of self-regulation, such as those that incorporate mindfulness skills, may be critical in the evolution of prevention science as a field. That said, future research is needed to determine in what way self-regulation plays a causal or mediational role in the interplay between intervention and outcomes. The ability to control and manage one’s thoughts, feelings, and behaviors hinges on self-regulatory skills. The field of prevention science must invest more effort to determine which interventions best support self-regulation and to more deeply understand the underlying processes related to the construct.

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Chapter 16

Implications for Translational Prevention Research: Science, Policy, and Advocacy Anthony Biglan and Diana H. Fishbein

Abstract  This volume presents the most recent findings regarding underlying mechanisms in inhibitory control, documents its central role in the onset and continuation of drug abuse, and discusses the approaches that apply basic science knowledge to practice. The current evidence fits well with the nascent movement to improve the conditions that harm child development and to provide a physical and social environment that fosters optimal functioning in our children and families. When conditions and teachings are conducive to developing selfregulation over inhibitions, young people are more likely to cooperate with others, exhibit prosocial behaviors and attitudes, delay gratification, learn from prosocial role models, and engage in adaptive decisionmaking. On the other hand, children who are exposed to circumstances, conditions, and experiences that are suboptimal or frankly deleterious, they are often directed toward a trajectory characterized by poor inhibitory control and, in turn, high-risk behaviors, such as drug misuse.

State of the Science Research over the last 20 years has converged in showing that most psychological and behavioral problems of childhood and adolescence are interrelated and stem in part from a common set of environmental conditions. Until recently, however, the biological substrates of these relationships have been unclear. This volume presents considerable evidence that the final common pathway between adverse environments and multiple problems is through deficits in inhibitory control subserved by neurobiological mechanisms. The development of inhibitory control and its supportive neural network is a product of both genes and environment (Jacobs et  al., Chap. 4 in this volume). Prevention scientists have primarily focused on manipulation of environmental

A. Biglan (*) Oregon Research Institute, Eugene, OR 97403, USA e-mail: [email protected]

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contexts to redirect developmental trajectories of children at risk. The common thinking is that the genetic part of the equation is relatively inalterable. The current science, however, suggests a very different scenario based on a rapidly growing body of literature from the fields of genetics, epigenetics, and proteomics showing that gene activity is experience-dependent. These findings provide compelling evidence for the notion that a change in environmental conditions has the potential to lead to a change in gene expression (Jacobs et al., Chap. 4 in this volume). Given that gene expression controls the molecular machinery of the brain, it stands to reason that environmental change has direct implications for brain development and function and, in effect, behavioral and mental processes. Such an interaction can produce effects for the better or for the worse, meaning that environmental conditions may either suppress or activate gene activities that support or, conversely, undermine neural processes requisite for inhibitory control to develop. Failure to provide a healthy social and physical environment may contribute to drug abuse and numerous other problems, including antisocial behavior, risky sexual behavior, and academic failure by perturbing or delaying development of self-regulatory skills critical to successful development. On the other hand, providing nurturing environments and social supports that optimize experiential conditions and perhaps negate or override adversities’ effects has the potential to reinforce the neural underpinnings of inhibition, thereby producing young people with strong self-regulatory skills. Childhood and adolescence provide critical windows of opportunity to intervene due to the high level of neuroplasticity during development (Churchwell and Yurgelun-Todd, Chap. 6 in this volume). Thus, as a matter of public health, preventive efforts to build environments conducive to maximizing the potential of our young people are warranted.

Effects of Deleterious Environments on Inhibitory Control During Developmental Phases The presence of severe and/or chronic stress and adversity has a direct effect on neural systems responsible for modulating inhibitory control, potentially impeding their development and function (Fisher et al., Chap. 12 in this volume). For individuals who have been negatively influenced by such experiences, their response to subsequent stressors may be compromised or dysfunctional due to the impact of these experiences on the development and integrity of underlying inhibitory control systems. As a result, precisely when an adaptive inhibitory response is required for any given challenging situation, individuals who have incurred “damage” to, or developmental delays in, otherwise supportive neural systems are less likely to show adequate self-regulatory controls. Inhibitory control, therefore, plays an important role in modulating reactions to stress and, therefore, may be the mechanism underlying the ability of protective factors to reduce risk and increase resiliency. Late childhood and early adolescence represent transitional periods during which profound transformations take place in the complexity of behaviors ­regulated

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by executive and emotional functions, as well as changes in socio-environmental context and demands. Throughout this period, multiple brain systems reorganize (e.g., synaptic pruning, dendritic myelination) due to interactions between physical maturation and new social experiences (Masten 2004). These changes and many others during this period result in more focused and effective activity within the frontal lobe and its circuitry in adulthood (Casey 1999; Luna et  al. 2001; Rubia et  al. 2000). The developmental literature suggests a sequential improvement of performance on executive cognitive tasks through childhood, adolescence, and early adulthood that coincides with growth in frontal lobe development (Bell and Fox 1992; Levin et  al. 1991; Thatcher 1991, 1992; Welsh et  al. 1991). Complex features of neurocognition, such as behavioral inhibition begin to surface in adolescence and do not coalesce fully until early adulthood when prefrontal-limbic networks consolidate (see Albert and Steinberg, Chap. 11 and Churchwell and Yurgelun-Todd, Chap. 6 in this volume). The time lag between pubertal maturation characterized by intense drives and feelings and development of neurobehavioral systems for self-control and affect regulation places adolescents at risk for numerous behavioral and emotional problems (Albert and Steinberg, Chap. 11 in this volume). Thus, early adolescence is characterized by increased vulnerability to behavioral maladjustments as a function of demands that may begin to exceed the capacity of an underdeveloped brain not yet prepared to execute strong inhibitory control. This vulnerability can be substantially heightened by the exposure to uncontrollable stressors that may result in damage or impairments in epigenetic processes and, in effect, the fronto-limbic neural circuitry that underlies self-regulatory behaviors. Thus, children already in a particularly demanding life course stage are placed at the threshold for heightened high-risk behaviors. On the other hand, as mentioned above, this vulnerable period of enhanced brain plasticity may also provide a window of opportunity for protective factors to attenuate the impairment otherwise incurred from major adversity. Protective factors may include strong family, extrafamilial, and neighborhood-wide supports, high IQ, nondeviant peers, or genetic advantages (e.g., the lack of a polymorphism in the serotonin transporter gene), among other experiential and biological conditions. Such conditions experienced during late childhood and early adolescence may have a profound impact on behavior due to enormous changes that occur in the brain. Given the prolonged development and organization of the prefrontal cortex throughout this development period, its functions may be most vulnerable not only to adversity (Fisher et al., Chap. 12 and Churchell and Yurgelun-Todd, Chap. 6 in this volume), but also to favorable psychosocial influences. The influence of protective environmental factors may facilitate the development of (or attenuate the damage to) physiological, emotional regulatory, and cognitive skills necessary for self regulation. Enhancing the ability to achieve developmental milestones through exposure to protective factors may impact future adjustments, even under conditions of adversity, as developmental trajectories begin to narrow (Brown 2005; Rutter and Sroufe 2000). Effective inhibitory control, for example, may involve an increased sensitivity to consequences, adaptive coping style, accurate attributions of emotional cues, or physiologic responses to acute stressors within normal range, providing insulation from a poor

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outcome, perhaps preventing behavioral maladjustments and enhancing resiliency (Cooper et al. 1988; Wills et al. 2002). Further research is called for to better understand neural processes subserving inhibition and factors that may insulate children from damage to their development. Such translational research proves vital for determining when to intervene and how best to construct developmentally appropriate intervention strategies to improve inhibitory control.

Building Inhibitory Control via Nurturing Environments Overwhelmingly, research in this area has focused on risk factors for drug abuse, such as disinhibition. Far less is known, however, about the protective mechanisms underlying differential child outcomes. Chapters in this volume highlight the growing literature on the development and efficacy of inhibitory control, which has strong implications for how to structure nurturing conditions to promote its development. Protective factors, such as parenting, functional family dynamics, and extrafamilial and community supports may attenuate the negative effects of deleterious environmental conditions that otherwise lead to disinhibitory responses, even for those at high risk (Glantz and Leshner 2000; Rutter 1991). For example, while peer networks and norms influence low-income African–American adolescents (Crosby et al. 2000; DiClemente et al. 1996), the quality of parental and extrafamilial relationships may be especially critical for the early adolescent tasks of selfregulation and other cognitive and social skills (Collins and Kimura 1997; Dishion et al. 1998; Wenz-Gross et  al. 1997). Numerous studies support the critical role of positive parenting and extrafamilial dynamics as key influences on the prevention of substance abuse (e.g., Kumpfer 1998; Resnick et al. 1997). Although the mediators of this effect have yet to be explored, the foregoing chapters suggest that both internal and external influences on inhibitory control may be at play. Parents are a primary source of socialization that may profoundly influence behavioral pathways. A lack of monitoring or low parental support in early adolescence precipitates “drift” into a deviant peer group, wherein a wide array of conduct problems, including drug use, may be reinforced (Chilcoat and Anthony 1996; Cohen and Rice 1997; Glendinning et al. 1997; Piko 2000; Steinberg et al. 1994). Conversely, effective parent management practices, reinforcement, warmth, and support for and involvement in the child’s achievement and behavior have been linked to greater child selfregulation and lower rates of drug use initiation. These practices may confer protection from the physiological and behavioral effects of a stressful environment (Baumrind 1987; Bremner 1999; Field et al. 1998; Maccoby and Martin 1983), thereby enhancing self-regulatory techniques (Johnson and Pandina 1991), suppressing drug use (Biglan et al. 1995; Baumrind 1991; Cohen et  al. 1994), and reducing susceptibility to peer pressure and deviant self-image (Mounts and Steinberg 1995). Strong, secure attachments to caregivers can buffer or prevent elevations of stress hormones in situations that usually elicit distress (Gunnar and Donzella 2002), thereby suppressing the effects of stressors on brain

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systems responsible for inhibition (Grant et  al. 2000). Supportive and involved caregivers, opportunities to observe an adult successfully cope, and reinforcements for functional coping have all been related to socialization of positive coping strategies, including inhibitory control (Hardy et  al. 1993; Kliewer et  al. 1994; Shek 1998). Favorable aspects of the home environment, in general, may be particularly effective in overriding or attenuating some of the adverse psychosocial effects of an adverse environment (El-Sheikh and Buckhalt 2003; Hussong and Chassin 1997; Johnson et al. 1990), potentially modifying relationships among stressors, inhibitory control, and resiliency outcomes (Hoffman et al. 2000; Vakalahi 2001).

The Role of Coercion Versus Positive Reinforcement Although adverse environments clearly contribute to inhibitory control problems, the precise environmental contingencies involved in these developments remain unclear. There is considerable evidence, however, that coercive family processes underlie the development of aggressive behavior (Patterson 1982) and depression (Biglan 1991). Specifically, these behaviors have been shown to be negatively reinforced. That is, they are shaped by their functional effect in reducing the aversive behavior of other family members; others’ aversive behavior is less likely immediately following aggressive (Patterson et  al. 1992) or depressive (Biglan 1991) behavior. To our knowledge, the biological substrates of these contingencies have not been studied. We suggest that studies that link coercive family processes to the development of inhibitory control processes could provide a more precise understanding of how control deficits develop. On the other hand, situations repeatedly arise (initially from family interactions and later in school and community settings) that may positively reinforce inhibitory behaviors, leading to a more adaptive trajectory. Inhibitory control involves a process in which the individual engages in an act of inhibiting one behavior in favor of another. The evidence indicates that behaviors considered expressions of inhibitory control become more likely in the context of high levels of positive reinforcement for these behaviors. Children are impulsive by nature; inhibitory control under normal circumstances and conditions develops over time into adulthood, contingent upon socio-environmental reinforcements. We suggest that inhibitory control might better be thought of as a process of developing “controlled,” prosocial, or “appropriate” behaviors in situations in which the highest probability behavior has thus far been an impulsive act. From this developmental view, young children do not learn to inhibit behaviors, such as crying or screaming when they do not get something they want. Rather they learn to delay gratification or to request the desired outcome as a function of repeated episodes in which patient and skilled adults prompt and reinforce such behaviors. Significant evidence, some of which is presented in this volume, indicates that reinforcements are only part of the equation. Genetic predispositions and integrity of neurobiological functioning play a significant role in the extent to which socializing experiences mold inhibitory skills.

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Inhibitory Control from an Evolutionary Perspective The lack of inhibitory control is typically characterized as a deficit. However, ­evolutionary psychology (see forthcoming Ellis and Bjorklund 2011) suggests a somewhat different perspective. Impulsive and risk-taking behavior, particularly during adolescence, may be an adaptation that has had survival value in threatening environments. From this perspective, the cascade of biological processes involved in “deficits” in inhibitory control actually make it more likely that young people growing up in a dangerous world will survive. Being prone to aggression, quick to take risks, and likely to procreate at an early age may make it more likely that one’s genes will survive in a world that places one’s physical survival at risk. Taking chances, experimenting with various behaviors in different social contexts, and seeking pleasurable activities may have a negative connotation in the use of the terms impulsivity and risk-taking. Yet, engagement in these behaviors provides invaluable learning experiences, newfound independence, and self-confidence to carry over into a successful adulthood. There is a threshold, however, at which point the lack of inhibition may reach extreme proportions, leading to disadvantageous decisions that are harmful to the actor and others. Placing the problem of inhibitory control within an evolutionary framework would encourage further exploration of the advantages that deficits in control confer in adverse environments. Such research also necessarily enhances our understanding of the ways in which inhibitory controls go awry, exceed thresholds, and persist well into adulthood in those who maintain a high-risk lifestyle. It would encourage research on the specific neurobiological and social pathways between adversity and gene expression. And, it would underscore the need to identify public health solutions to reduce the adversity.

Policies Favoring Nurturing Environments to Promote Inhibitory Control Scientists are appropriately cautious about overstating the implications of their work. However, it is fairly well accepted that inhibitory control problems and ­multiple psychological and behavioral problems develop in the context of nonnurturing environments. By filling in the biological details about the development of inhibitory control, this volume completes the account of how the most common and costly problems of childhood and adolescence develop and what we can do to prevent such development. These facts must inform public policy. We would argue that this evidence, coupled with substantial etiological and intervention evidence about multiple problem behaviors (Biglan et al. 2004; National Research Council & Institute of Medicine 2009), has direct and important implications for public ­policy: we need changes in public policy to help increase the prevalence of families, schools, workplaces, and neighborhood ­environments that are more nurturing.

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Those who doubt that science can influence beneficial public policy should consider the history of the tobacco control movement. The Institute of Medicine report on tobacco control pointed out that the reduction in cigarette smoking in the second half of the twentieth century was one of the most significant public health victories of the last 100 years (National Cancer Policy Board & Institute of Medicine and Commission on Life Sciences, National Research Council 1998). It resulted largely from changes in public policies that were driven by the creative use of the epidemiological evidence about the harmfulness of tobacco. A series of Surgeon General monographs from the National Cancer Institute (U.S. Department of Health and Human Services 1980, 1981, 1982, 1983, 1984, 1986a, b, 1988, 1989, 1990, 1994, 2000, 2004; U.S. Department of Health and Human Services & Pan American Health Organization 1992) marshaled the evidence on the aspects of the tobacco problem and influenced policies regarding (a) clear indoor air; (b) the marketing of cigarettes to young people; (c) the harmfulness of so-called low tar and nicotine cigarettes; (d) school policies regarding tobacco use; and (e) taxation of tobacco products. Within a period of 40 years, the prevalence of smoking has diminished by as much as 50%, despite a massive lobbying and disinformation campaign by the tobacco companies (U.S. v. Philip Morris et al. 2006). The tobacco control movement benefited from having a simple target – the prevalence of smoking. The prevalence of this simple behavior could be, and increasingly was, monitored: tobacco control efforts were shaped by their impact on prevalence. Progress on the myriad problems of youth would seem to be much more complicated and one might suppose it impossible to achieve the same kind of policy successes. However, substantial progress could be made if existing evidence were translated into a compelling picture of the solutions and how to direct them.

The Nature of Nurturing Environments All evidence converges on the need to increase the prevalence of nurturing environments. A nurturing environment (Biglan and Hinds 2009) is one that (a) minimizes biologically and psychologically toxic conditions; (b) models and richly reinforces prosocial, self-regulated behavior; (c) sets effective limits on antisocial behavior; and (d) promotes a psychologically flexible orientation marked by a pragmatic, values-driven way of behaving (Biglan et al. 2008). The concept of nurturing environments is simple and readily understood, yet true to the epidemiological evidence. Rather than advocating separately for policies that would affect diverse problems, such as antisocial behavior, drug abuse, and academic failure, it becomes possible to advocate for policies that foster nurturing environments and that can reduce the prevalence of multiple problems. What would happen if behavioral and biological scientists began to link their work to the need for more nurturing environments? Imagine a series of Surgeon General Reports, IOM documents, and NIH monographs that marshaled the evidence for the harmful effects of toxic environments, the need for richly reinforcing environments, and the public policies that favor or stand in the way of such environments.

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Policies regarding family poverty are especially needed. Poverty is a well-­ established risk factor that results in toxic environments and undermines families’ reinforcement of the development of self-regulated, prosocial behavior (National Research Council & Institute of Medicine 2009). A substantial body of evidence supports policies that could reduce family poverty (e.g., National Research Council & Institute of Medicine 2009). Organizing and creatively communicating the evidence about how poverty makes family (and school) environments less nurturing could lend considerable additional support to efforts to enact effective anti-poverty policies. Other policies that would increase the prevalence of nurturing environments include (a) reducing the use of punishment in schools and the criminal justice system and identifying appropriate and effective alternatives; (b) increasing the availability of evidence-based family support interventions; (c) providing drug abuse treatment (which would reduce coercive processes in families); (d) increasing the availability of early childhood education; and (e) adopting innovative evidencebased educational practices for both academic achievement and social behavior. Tobacco control advocates often communicate the extent of harm done by cigarettes by asking, “How many people are killed by cigarettes in the US each year? It is as if two Boeing 747s were crashing every day of the year, killing everyone on board.” The epidemiological facts about poverty and other aspects of non-nurturing environments must be translated into such compelling pictures. For example, how many children fail in school this year because they are living in poverty? How many families break apart due to untreated drug abuse? How many children never learn to read because evidence-based teaching was not in place? Scientists possess a strong and understandable tendency to focus on a confined area and extend knowledge by elaborating our understanding of phenomena in that area. However, we all need to look up from our work to the larger pattern of findings that – when seen as a coherent picture – tells us how to translate our research into significant improvements in human wellbeing. This volume attests to the fact that a coherent picture is emerging. Each investigator, clinician, teacher, community member, and politician can contribute to that picture, eventually affecting the kind of society that we design and live in. We can accomplish this goal by linking the work that we do in particular areas to what that work can do for human wellbeing and to create more effective and humane public policies.

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Grant, K.E., O’Koon, J.H., Davis, T.H., Roache, N.A., Poindexter, L.M., Armstrong, M.L., Minden, J.A., & McIntosh, J.M. (2000). Protective factors affecting low-income urban African American youth exposed to stress. Journal of Early Adolescence, 20:388 – 417. Gunnar MR, Donzella B. (2002). Social regulation of the cortisol levels in early human development. Psychoneuroendocrinology, 27(1-2):199–220. Hardy, D.F., Power, T.G., & Jaedicke, S. (1993). Examining the relation of parenting to children’s coping with everyday stress. Child Dev, 64(6):1829– 41. Hoffman, J.P., Cerbonne, F.G., & Su, S.S. (2000). A growth curve analysis of stress and adolescent drug use. Subst Use Misuse, 35(5):687–716. Hussong, A.M., & Chassin, L. (1997). Substance use initiation among adolescent children of alcoholics: testing protective factors. J Stud Alcohol, 58(3):272–9. Institute of Medicine (2009). Preventing Mental, Emotional, and Behavioral Disorders Among Young People: Progress and Possibilities. National Academy of Sciences: Washington, DC. Johnson, H.L., Glassman, M.B., Fiks, K.B., & Rosen, T.S. (December 1990). Resilient children: Individual differences in developmental outcome of children born to drug abusers. J Genet Psychol, 151(4):523–39. Johnson, V., & Pandina, R.J. (1991). Effects of the family environment on adolescent substance use, delinquency, and coping styles. American Journal of Drug and Alcohol Abuse, 17(1): 71–88. Kliewer, W., Sandler, I.N., & Wolchik, S.A. (1994). Family socialization of threat appraisal and coping: Coaching, modeling, and family context. In K. Hurrelman & F. Nestmann (Eds.), Social networks and social support in childhood and adolescence. Prevention and intervention in childhood and adolescence (pp. 271–291). Oxford, England: Walter De Gruyter. Kumpfer, K.L. (1998). Select prevention interventions: The strengthening families programs. In R.S. Ashery, E.B. Robertson, & K.L. Kumpfer (Eds.), Drug abuse prevention through family interventions (pp. 160–207). Rockwille, MD: National Institute on Drug Abuse. Levin, H.S., Culhane, K.A., Hartmann, J., Evankovich, J., Mattson, A.J., Harward, H., Ringholz, G., Ewing-Cobbs, L., & Fletcher, J.M. (1991). Developmental changes in performance on tests of purported frontal lobe functioning. Developmental Neuropsychology, 7:377–95. Luna, B., Thulborn, K.R., Munoz, D.P., Merriam, E.P., Garver, K.E., Minshew, N.J., Keshavan, M.S., Genovese, C.R., Eddy, W.F., & Sweeney, J.A. (2001). Maturation of widely distributed brain function subserves cognitive development. Neuroimage, 13:786–93. Maccoby, E.E., & Martin, J.A. (1983). Socialization in the context of the family: Parent–child interaction. In P.H. Mussen (Ed.) & E.M. Hetherington (Vol. Ed.), Handbook of child psychology: Vol. 4. Socialization, personality, and social development (4th ed., pp. 1–101). New York: Wiley. Masten, A.S. (2004). Regulatory processes, risks, and resilience in adolescent development. In Adolescent brain development: Vulnerabilities and opportunities (ed. R. Dahl and L. Spear). Annals of the New York Academy of Sciences, Vol. 1021. New York, New York. Mounts, N.S., & Steinberg, L. (1995). An ecological analysis of peer influence on adolescent grade point average and drug use. Developmental Psychology, 31(6):915–22. National Cancer Policy Board & Institute of Medicine and Commission on Life Sciences, National Research Council. (1998). Taking action to reduce tobacco use. Washington, DC: National Academy Press. National Research Council & Institute of Medicine (2009). Preventing mental, emotional, and behavioral disorders among young people: progress and possibilities. Committee on Prevention of Mental Disorders and Substance Abuse among Children, Youth, and Young Adults: Research Advances and Promising Interventions. Washington, DC: The National Academies Press. Patterson, G.R. (1982). Coercive family process. Eugene, OR: Castalia Publishing. Patterson, G.R., Reid, J.B., & Dishion, T.J. (1992). Antisocial boys: A social interactional approach. (vols. 4) Eugene: Castalia Publishing Company. Piko, B. (2000). Perceived social support from parents and peers: Which is the stronger predictor of adolescent substance use? Substance Use and Misuse, 35:617–30. Resnick, M.D., Bearman, P.S., Blum, R.W., Bauman, K.E., Harris, K.M., Jones, J., Tabor, J., Beuhring, T., Sieving, R.E., Shew, M., Ireland, M., Bearinger, L.H., & Udry, J.R. (1997).

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Protecting adolescents from harm. Findings from the National Longitudinal Study on Adolescent Health. Journal of the American Medical Association, 278(10):823–32. Rubia, K., Overmeyer, S., Taylor, E., Brammer, M., Williams, S.C., Simmons, A., Andrew, C., & Bullmore, E.T. (2000). Functional frontalism with age: mapping neurodevelopmental trajectories with fMRI. Neurosci Biobehav Rev, 24(1):13–9. Rutter, M. (1991). Protective factors: Independent or interactive? J Am Acad Child Adolesc Psychiatry, 30(1):151–2. Rutter, M., & Sroufe, L.A. (2000). Developmental psychopathology: Concepts and challenges. Development and Psychopathology, 12:265–96. Shek, D.T.L. (1998). A longitudinal study of the relations of family factors to adolescent psychological symptoms, coping resources, school behavior and substance abuse. International Journal of Adolescent Medicine and Health, 10:155–84. Steinberg, L., Fletcher, A., & Darling, N. (1994). Parental monitoring and peer influences on adolescent substance use. Pediatrics, 93:1060 – 64. Thatcher, R.W. (1991). Maturation of the frontal lobes: Physiological evidence for aging. Developmental Neuropsychology, 7:397– 419. Thatcher, R.W. (1992). Cyclic cortical reorganization during early childhood. Brain Cogn, 20(1):24 –50. U.S. v. Philip Morris et al. (2006) Final Opinion, 449 F. Supp.2d 1 (D.D.C. 2006; Kessler). U.S. Department of Health and Human Services (1980). The health consequences of smoking for women. A report of the Surgeon General. Washington, DC: U.S. Department of Health and Human Services, Public Health Service, Office of the Assistant Secretary for Health, Office on Smoking and Health. U.S. Department of Health and Human Services (1981). The health consequences of smoking: The changing cigarette. A report of the Surgeon General (Rep. No. DHHS(PHS) 81-50156). Washington, DC: U.S. Department of Health and Human Services, Public Health Service, Office on Smoking and Health. U.S. Department of Health and Human Services (1982). The health consequences of smoking: Cancer: Report of the Surgeon General Washington, DC: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. U.S. Department of Health and Human Services (1983). The health consequences of smoking: cardiovascular disease. A report of the Surgeon General. Rockville, MD: U.S. Department of Health and Human Services, Public Health Service, Office on Smoking and Health. DHHS Publication No. (PHS) 84-50204. U.S. Department of Health and Human Services (1984). The health consequences of smoking: Chronic obstructive lung disease. A report of the Surgeon General. Washington, DC: U.S. Department of Health and Human Services, Public Health Service, Office on Smoking and Health. DHHS Publication No. (PHS) 84 –50205. U.S. Department of Health and Human Services (1986a). The health consequences of using smokeless tobacco. A report of the advisory committee of the Surgeon General. Washington, DC: U.S. Department of Health and Human Services, Public Health Service, National Institutes of Health. NIH publication No. 86 –2874. U.S. Department of Health and Human Services (1986b). The health consequences of involuntary smoking. A report of the Surgeon General. (vols. 1) Rockville, MD: U.S. Department of Health & Human Services, Public Health Service, Centers for Disease Control, Center for Health Promotion & Education, Office on Smoking & Health. CDC 87–8398. U.S. Department of Health and Human Services (1988). The health consequences of smoking: Nicotine addiction. A report of the Surgeon General. Washington, DC: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, Center for Health Promotion and Education, Office on Smoking and Health. DHHS Publication No. (CDC) 88-8406. Government Printing Office. U.S. Department of Health and Human Services (1989). Reducing the health consequences of smoking: 25 years of progress. A report of the Surgeon General. Washington, DC: U.S.

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Chapter 17

Future Directions for Research on Inhibitory Control and Drug Abuse Prevention Michael T. Bardo, Richard Milich, and Diana H. Fishbein

Abstract  Drug abuse is characterized not simply by abnormalities in reward seeking and compulsive use, but also a failure in inhibitory control. The mechanisms of reward seeking and behavioral inhibition are dissociable at the levels of genetic, neurobiological, and personality measurements. Social influences also play a key role in moderating the relationship between inhibitory processes and drug use. This concluding chapter extracts some of the key developments in this research area highlighted in each of the foregoing chapters and points out future challenges in harnessing this knowledge to find utility in the design and implementation of effective anti-drug prevention intervention strategies. General principles may also be derived for application to other health risk-related behaviors during childhood, adolescence, and young adulthood. In tackling the subject matter of this book, the editors posed the same questions as those facing a journalist when investigating a story: What, who, when, why, and how. To date, no other production has addressed or translated the research on each of these issues given that many of these answers require familiarity with a new generation of research in neurobiology and genetics as they apply to prevention science. Thus, each of the chapters chosen for this volume was selected to answer one or more of the basic and more complex questions concerning the relations among inhibition, substance use, and prevention. In order to comprehensively cover this topic, the authors introduced their topic with a description of what is meant by the term inhibition. Who is at risk is discussed primarily in the second section and when individuals are at risk is addressed in the third section. Why are they at risk, i.e., what are the underlying mechanisms, are dealt with in the first and second ­sections. And how do we translate this information into effective prevention ­programs is the focal point for the third and fourth sections. In what follows, we address each of these issues, summarizing some of the most relevant points from M.T. Bardo (*) Center for Drug Abuse Research Translation, University of Kentucky, 741 S.Limestone, Lexington, KY 40536-0509, USA e-mail: [email protected] M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8_17, © Springer Science+Business Media, LLC 2011

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all of the chapters. We then finish up with perhaps the most important issue in the fifth section, where do we go from here? We recognize that there are many factors that determine who is at risk for substance use/abuse beyond merely behavioral inhibitory processes, including those from the micro-molecular level to the macro-societal level. However, the focus of this book was to highlight specifically deficits in inhibitory control as a critical determinant of substance use. Given the disparate levels of analysis of this individual risk factor, we felt it was appropriate to begin integrating these areas of research into a more comprehensive understanding of this risk-related trait.

What Is Inhibition? The first issue that needs to be addressed is one of definition; what do we mean when we talk about impulsivity or deficits in inhibitory control. In terms of definitional issues, there is good agreement across the chapters that disinhibition is a multifaceted construct. However, there is less agreement about the specific factors that make up this construct. For example, one can define disinhibition at a strictly behavioral level of analysis. An advantage of this definition is that it can be translated across animal and human models, which allows for addressing issues (e.g., neurotransmitter release in specific brain regions) that cannot be undertaken with human participants. As illustrated in the chapter by Richards et al. (Chap. 2, this book), disinhibition may be categorized into at least three behavioral dimensions, namely, (1) impulsive choice due to an insensitivity to delayed consequences, (2) impulsive action due to poor response inhibition, and (3) lapses of attention (de Wit and Richards 2004). Discrete behavioral procedures have been developed to map onto these three different dimensions using either laboratory animals or human subjects. Although these three dimensions are attractive based on their ease of measurement and their cross-species generality, there is not full agreement about whether these three dimensions represent all elements of the construct of disinhibition. For example, the widely used BART test, as described in the chapter by MacPherson et al. (Chap. 10, this book), is assumed to be a measure of disinhibition, but does not readily fit within the three dimensional system proposed by de Wit and Richards (2004). In the BART test, more pumps of the balloon are assumed to reflect reward seeking at the expense of inhibitory control; more pumps also may indicate a relative insensitivity to punishment. In either case, this indicates how a well-validated behavior task may not fit precisely within the three dimensions described by de Wit and Richards (2004). Another level of analysis for defining the construct of disinhibition is strictly from a personality perspective. All major theories of personality have at least one factor reflecting the construct of disinhibition. As described in the chapter by Lynam (Chap. 8, this book), we can identify five separate and valid facets of this construct, namely, urgency (negative or positive), lack of perseverance, lack of persistence, and sensation seeking. These dimensions have been validated as

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p­ redictors of substance use and other risk-related behaviors (Verdejo-Garcia et al. 2007; Whiteside and Lynam 2003). Although all of the facets are thought to be associated with risk for substance use in some fashion, recent research suggests that different facets may predict different aspects of substance use. For example, sensation seeking seems to predict substance use, whereas negative urgency predicts substance use problems (Cyders et  al. 2009). Future research can examine the nuances of the role of impulsive behaviors or traits in predicting substance use/ abuse. A major advantage of the personality perspective is that its assessment involves paper-and-pencil measures, which lends itself to large-scale studies in which subtle distinctions between use and abuse trajectories can be studied (see chapters by Gullo et al., Chap. 9, this book; Ivanov et al., Chap. 7, this book). One of the more problematic issues confronting research in this field has been the inconsistent and sometimes negligible associations between measurements at the behavioral and personality levels. Each of these levels of analyses seems to offer valid insights into the causes or correlates of substance use/abuse. In the case of behavioral measures, the chapter by Fillmore and Weafer (Chap. 5, this book) documents that behavioral tasks of disinhibition both predict and are affected by substance use (also see chapters by Gullo et  al., Chap. 9, this book; Ivanov et  al., Chap. 7, this book; MacPherson et al., Chap. 10, this book). In contrast, personality measures are noted to be excellent predictors of current and future substance use, but are relatively insensitive to short-term fluctuations in substance use as occurs in alcohol challenge studies. Rather than argue about which approach is more valid, it may be more productive to recognize the different strengths and weaknesses of both behavioral and personality approaches to understanding problems in disinhibition (see chapters by Gullo et  al., Chap. 9, this book; Ivanov et  al., Chap. 7, this book). Thus, the personality approach can offer us insights into who exhibits risks for deficits in inhibition and how this risk may predict occurrence of unhealthy behaviors. As a compliment, behavioral approaches offer insights into the underlying mechanisms that shed light on how the impulsive traits translate into at-risk behavior. For example, the chapter by Fillmore and Weafer (Chap. 5, this book) discusses how the inability to inhibit attention toward alcohol-related stimuli leads to increased drinking. Such information can be important in designing prevention intervention strategies.

Who Is at Risk? The second main section of the book is devoted primarily to identify characteristics that define individuals at greatest risk for substance use. A number of at-risk populations have been identified, including children with ADHD (see chapter by Ivanov et al., Chap. 7, this book) and conduct disorder (see chapter by Lynam, Chap. 8, this book). This is not surprising given that both of these groups of children demonstrate high levels of disinhibition, and are consistently found to be at risk for substance use disorders in adulthood (Barkley et al. 2008). A number of treatment interventions have been developed to treat these clinical disorders, including parent ­training,

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behavior modification in the home and school, problem solving training, and ­stimulant medication (Pelham and Fabiano 2008). Unfortunately, as effective as these treatments have been in the short term, these interventions tend to lack longterm efficacy (e.g., MTA or Multimodal Treatment of ADHD; Molina et al. 2009), and the jury is still out on whether we can intervene early to prevent these problems (e.g., Fast Track; Slough et al. 2008). In addition to utilizing clinical diagnostic disorders to characterize risk, it is known that early developmental experiences also can pose risk for later substance use problems. Preclinical work has shown that maternal deprivation has profound and long lasting changes in brain and behavior related to substance use (Kaffman and Meaney 2007). Similar processes seem to be at work in humans. For example, the chapter by Fisher et al. (Chap. 12, this book) illustrates that young children who are raised in adverse environments (i.e., foster care) also may be at risk for problems in disinhibition and subsequent substance use disorders. Specifically, these children show alterations in the stress axis and inhibitory control. Additional studies show abnormalities in brain development, structure, and function in children who have experienced severe adversity (Carrion et al. 2001; De Bellis et al. 2000). While these neurobehavioral changes suggest an enhanced vulnerability to substance use later in life, future longitudinal work will be required to answer this question fully. Further, researchers are beginning to recognize that, regardless of any clinical diagnoses or identifiable early-life environmental stressors, children and adolescents who fall at the extreme ends of relevant personality dimensions also may display altered risk. This represents a rather dramatic change in our thinking about children, as earlier work has tended to discount a role of personality in pre-pubertal development. Attempts to measure personality in childhood have been hindered by the difficulty in obtaining reliable and valid information from self-reports at an early age. In addition, the dramatic developmental changes that children go through seemed to preclude thinking about stable personalities until at least young adulthood. However, more recent formulations in the field recognize that there are a number of stable personality factors in childhood, including early temperament, problems in emotional regulation, and high levels of sensation seeking. Each of these can be conceptualized as a problem in inhibitory control, and each may be a risk factor for early substance use.

When Is the Risk Period? Adolescence is recognized widely as a critical period of risk for the initiation of substance use (Doremus-Fitzwater et al. 2010). When the onset of drug use is in early adolescence (ages 11–13), the risk for abuse and eventual addiction is even greater. Accordingly, many substance use prevention programs are designed to target this age group. Although a number of explanations have been offered as to why this is such an important risk period, most attention has been given to developmental and contextual factors that appear to play a role in initiating drug use

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among this age group. Perhaps the most important developmental and contextual components include the following: adolescents begin to require less from parents, thus promoting separation; adolescents are rewarded for striving to be independent and autonomous; peers begin to exert a greater influence over the adolescents’ attitudes and behavior; and there are just simply more opportunities and access to engage in drug-taking during this period. Some important conceptual models have been offered to explain these effects, including Dishion’s work on deviancy training (Dishion et al. 1999; and see chapter by Dishion et al., Chap. 15, this book) and Moffitt’s (1993) theory of adolescent limited conduct disorder. Dishion’s model documents the important influence peers play in encouraging, shaping, and reinforcing drug using attitudes and behavior, while Moffitt’s model focuses on early maturing adolescents who are ready to engage in adult-consistent behaviors, but who in modern times are still considered children under the influence of their parents. From this perspective, Moffitt argues that behaviors, such as drug use, may be considered normative for adolescents (see also Shedler and Block 1990; and chapter by Churchwell and Yurgelun-Todd, Chap. 6, this book). While these developmental and contextual theories have good empirical support, they do not provide full explanations for adolescent substance initiation. Several of the chapters in the present volume highlight the important role biological maturation factors play in early substance use. Specifically, it is now widely recognized that brain development does not proceed uniformly across all domains, and that the reward system seems to mature at a faster rate than the cognitive control system until a relative balance is achieved sometime after 20 years of age (see chapters by Albert and Steinberg, Chap. 11, this book; Churchwell and Yurgelun-Todd, Chap. 6, this book). The implications of this work are profound. Just as adolescents are showing physical maturation, along with the concomitant increase in exploratory behavior, adolescents also are experiencing heightened sensitivity to rewards without a concomitant increase in their ability to regulate these urges. Further, risky behavior may serve an important evolutionary function by encouraging adolescents to “leave the nest” and engage in exploratory and risky behaviors that can lead to new mates (see chapters by Churchwell and Yurgelun-Todd, Chap. 6, this book; MacPherson et al., Chap. 10, this book). Thus, understanding this maturational gap between the reward and inhibitory control systems in adolescence is crucial in developing effective prevention and treatment strategies. The chapter by Albert and Steinberg (Chap. 11, this book) takes this observation one step further by linking adolescents’ increased sensitivity to reward with the increase in peer influence. Specifically, they review a series of studies showing that the mere presence of peers can lead to emotional arousal and activate the adolescents’ reward system, thus leading to increased risky behavior. In the absence of peers, however, adolescents appear more likely to recruit effective cognitive controls in decision making. Thus, the deviancy training by peers that Dishion et al. (1999) and others refer to does not have to be active or overt since the mere presence of peers may lead to disinhibited behavior. Obviously, however, if the mere presence of peers has this effect, then this sets the stage for adolescents to be receptive to overt modeling of risky and disinhibited behavior. Understanding the

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i­nteraction between the presence of peers and delayed maturation of the biological control system (e.g., prefrontal cortex) may help identify potential new preventive interventions strategies. For example, based on the work in the chapter by Albert and Steinberg (Chap. 11, this book), in some states it has been successfully argued that graduated driving licenses be implemented that prohibit adolescents from driving with peers in the car. And in other states, juveniles are no longer being waived to adult court for serious criminal offenses based on studies showing an incapacity to form mature, full gauged decisions. As discussed in the chapter by Biglan and Fishbein (Chap. 16, this book), other implications for social policy may emanate from this knowledge. A more complex prevention strategy suggested in the chapter by Churchwell and Yurgelun-Todd (Chap. 6, this book) involves training adolescents in specific procedures that would accelerate the maturation of their cognitive control system. This training may focus on helping adolescents recognize the value of long-term consequences over more immediate and impulsive rewards, as well as implementing more elaborate self-control training that targets different executive control functions as described in the chapter by Riggs et al. (Chap. 13, this book).

Why Are They at Risk? As a number of the chapters indicate, there are a host of neurobiological, psychological, and social mechanisms that explain why adolescents and young adults are at increased risk for substance use. From an evolutionary perspective, as mentioned previously, loss of inhibitory control has the advantage of moving individuals from a family-focused to a peer-focused social network. Because this is a general process that occurs across mammals, different species may be used to study the process, including non-human primates, mice and rats as described in the chapters by Richards et al. (Chap. 2, this book) and Jentsch et al. (Chap. 3, this book). Scaling down the construct of impulsivity to laboratory animals has afforded research that utilizes the most state-of-the art neuroscience techniques to determine the genetic, cellular, neurochemical, and anatomical mechanisms involved. Similar to humans, non-human animals show a peak of impulsive hyperactivity during the adolescence period, and individual differences in impulsivity predicts the propensity to selfadminister various drugs of abuse (Perry and Carroll 2008). It has been known for a long time that substance use disorders are, at least in part, heritable. There seems to be little doubt that the mode of heritability is polygenetic and considerable effort has been expended on identifying the expression of specific neuronal proteins that may be associated with inhibitory control. Not surprising, given the ability of medications used to treat ADHD to alter dopamine, polymorphisms of the dopamine receptors and transporters have been investigated, which is reviewed in the chapter by Jacobs et al. (Chap. 4, this book). However, as emphasized in the chapter by Jentsch et al. (Chap. 3, this book), impulsivity is not simply due to one neurotransmitter, but rather is due to multiple neurotransmitter systems that interact within a complex neurocircuitry that has yet to be mapped fully.

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One of the guiding principles in the field is that inhibitory control, and executive cognitive functioning more generally, depends on the integrity of the prefrontal cortex and its circuitry. As discussed in the chapters by Churchwell and YurgelunTodd (Chap. 6, this book) and Albert and Steinberg (Chap. 11, this book), this is envisioned to be a top–down system that controls limbic structures involved in appetitive and reward processing. Albert and Steinberg (Chap. 11, this book) make a strong case that the prefrontal system also blunts the emotional systems activated by peer social influences. Some important prefrontal regions implicated as nodes in the inhibitory neurocircuitry include the medial prefrontal, orbitofrontal, dorsolateral prefrontal and anterior cingulate cortices. Adolescents are prone to be impulsive (adaptive for forming peer bonds) because there is a lag in maturation of these inhibitory prefrontal regions compared to the emotional/social reward system. Using drugs during the adolescent period may delay further the maturation of prefrontal inhibitory systems, thus compounding the problem of drug use into young adulthood and beyond. Regardless of the biological mechanisms involved, individual differences in risk exist across all phases of the lifespan. Within the general population, personality measures of impulsivity, as well as behavioral tests measuring the propensity to delay immediate rewards, have been linked to substance use. Unfortunately, much of this evidence is based on cross-sectional data rather than longitudinal data. Thus, while drug abusing individuals are known to be impulsive (de Wit 2009), it is not clear if this reflects a preexisting condition or a consequence of the drug exposure. It is likely that impulsivity is, at least in part, an antecedent condition because ADHD and conduct disordered children who are characterized by impulsivity exhibit increased vulnerability to abuse later in life (Lambert and Hartsough 1998). For some individuals, increased vulnerability to drug abuse may reflect a tendency toward self-medication, while for others there may be a general propensity to engage in a variety of high-risk behaviors with deviant peers.

How Do We Translate to Prevention? In the assembly of these book chapters, the invited authors were charged with speculating about how basic research findings in their subspecialities might translate into prevention applications. In some cases, the charge was tall because basic and applied views often work either in isolation (at worst) or in parallel (at best). Rarely do basic and applied researchers work in an integrated or truly transdisciplinary fashion. The challenge ahead is to forge more reciprocal connections among researchers who are conducting basic research and applied field-based efficacy trials. Take the example of the genetics researcher who has identified a polymorphism of the dopamine transporter that appears associated with ADHD (see the chapter by Jacobs et al., Chap. 4, this book). Can this bit of basic science information be put to any use in the real world? The optimistic answer must be “yes,” although a number of steps would be needed along the way to bridge the gap. One way to do so would

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be to use the dopamine transporter polymorphism as a targeting variable for the prevention intervention. If one allele is associated with deficits in inhibitory control and greater risk for substance use, then identification of such a biomarker could inform us about which children should receive the highest intervention dosage. Genetic biomarkers are sometimes viewed skeptically by the general public and by ethicists because they may be subverted into uses which may produce social stigma and invasion of privacy. This is particularly concerning when the subject of such inquiry is a child who has yet to manifest a real problem. Genetic manipulations are routinely conducted in basic research using laboratory animals, including manipulations such as selective breeding, genetic knockouts, and gene silencing. However, this work sometimes raises an unfounded fear that these techniques could be used as tools to implement a policy directed at engineering the human genome to prevent drug abuse. Scientists need to engage the community in bidirectional dialog on this topic to address this fear. Contrary to the naïve view that our genome destines some individuals to become substance abusers, there are many environmental pressures that can move the trajectory toward or away from this disorder. At the biological level, epigenetics refers to the study of phenotypic changes that occur without any change in the underlying DNA sequence. These changes show transmissibility for multiple generations, and it is through these nongenetic factors that prevention efforts may be reasonably expected to overcome any genetic predisposition. One example might be when a parenting program has influences not only on the direct recipients, but also subsequent generations. There is speculation that such transmission may be measurable in alterations of neurodevelopment and neurochemistry. One of the most salient examples of a gene × environment interaction relevant to prevention is illustrated in the work of Caspi et al. (2002). In that study, it was found that individual differences in functional polymorphisms of monoamine oxidase A (MAOA) interacted with childhood maltreatment to predict antisocial behavior. Maltreated children were at greater risk, but only when they expressed low levels of MAOA; maltreated children with high MAOA levels were protected. These findings indicate that genes alone do not determine risk, but rather they set the conditions for environmentally induced outcomes. As argued by others (Goldman et  al. 2005), however, the relatively high rate of drug abuse and the economics of the problem justify using public health strategies aimed at preventing drug abuse across the general population, regardless of genetic vulnerability. In conjunction with this universalistic strategy, it is also important to know the efficacy of prevention interventions on individuals with heritable differences in vulnerability. This knowledge can be subsequently used to target those with the greatest risk. Separate from the issue of individual genotyping, there are numerous examples in which environmental factors influence neurobehavioral mechanisms related to impulsivity and drug abuse. In rats, exposure to enriched environments during the adolescent period reduces both impulsive choice in a delay discounting task and amphetamine self-administration in young adulthood (Stairs and Bardo 2009). In humans, the chapter by Fisher et  al. (Chap. 12, this book) provides an excellent example of how neurobiological and psychosocial variables can be utilized to inform

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prevention interventions tailored to at-risk populations. In that program of research, children raised in foster care were found to display a deficit in salivary cortisol levels measured in the early morning and this dysfunction in the stress axis has been associated with antisocial personality disorder and other risk-related traits indicative of poor self-regulation (Gunnar and Fisher 2006). It is possible that normalization of the blunted cortisol response may thus serve as an end-point marker to validate the effectiveness of prevention intervention strategies that target stress-related high-risk behaviors. As described in that chapter, Fisher et  al. (Chap. 12, this book) are conducting randomized clinical trials of preventive interventions with the dual goal of normalizing the psychosocial outcomes and underlying neural systems.

Where Do We Go from Here? In order to conclude, it is helpful to remember the original charge laid out in the first chapter by Ginexi and Robertson (Chap. 1, this book). That chapter emphasized several key gaps and emerging trends that set the stage for the development of effective prevention intervention approaches. As the chapters in this book document, impulsivity or disinhibition is a multifaceted construct that plays a major role in contributing to substance use at varying degrees at different developmental phases. Therefore, it stands to reason that our prevention approaches need to reflect the complexity of this construct. At a minimum, there are at least three broad ways that future prevention strategies can incorporate the findings presented in this book. First, inhibitory deficits can be used as a targeting variable to identify those at greatest risk, which would allow for a more focused and intense intervention. As discussed in the chapter by Zimmerman et al. (Chap. 14, this book), high impulsives are often targeted for anti-drug public service announcements. A major benefit of a targeted approach is that problems in disinhibition are manifest at an earlier age than are many of the traditional risk factors for substance use (e.g., alcohol expectancies, personality traits such as sensation seeking, peer influences). Second, investigators can use the information presented in this book to design novel prevention interventions that enhance behavioral inhibitory processes, especially when an individual is faced with a choice between drug and nondrug alternative experiences. Since problems in disinhibition are evident at several different levels of analysis, including neurochemical, psychological, and social/community, research in this area should reflect these different levels of analysis. Third, since there is often an interaction between traits and the environment that best explains substance use problems, it is important to identify not only the most effective interventions, but also the specific environments in which these interventions should be delivered. As an illustration of this last point, Lynam et al. (2000) demonstrated that the predictive relation between childhood impulsivity and antisocial behaviors is markedly increased in neighborhoods characterized by low parental monitoring; in contrast, either risk factor alone had only a weak relation with these disordered behaviors. This type of research opens the door for developing personality-targeted ­interventions, as has been used

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for reducing alcohol use (Conrod et  al. 2006). In a similar vein, since inhibitory d­ eficits are apparent at multiple levels during different phases throughout the lifecourse, developing combination treatments that target each different level and developmental phase may be most effective. Thus, for example, in the treatment of children with ADHD, the most effective treatment may be one that addresses both the underlying neurochemical dysfunction (e.g., stimulant medication), along with environmental contingencies (e.g., behavior modification) and in the context of their developmental age (e.g., early childhood versus adolescence). A general challenge for the future is to find more effective ways to enhance inhibitory control among children and adolescents in situations of high risk. Since disinhibition may interfere with the ability to absorb and act on curriculum materials from even the best preventive interventions for drug abuse, utilizing new strategies to remediate the deficits are warranted. In fact, behavioral inhibition may be a prerequisite for intervention amenability. Thus, it may be important for preventive interventions to initially reinforce the building blocks for effective behavioral inhibition prior to participation in standard programs that generally rely upon the ability to resist impulses to engage in behavior that yields immediate rewards, but longterm negative consequences. Interventions that may be more beneficial to children and adolescents might start by incorporating an evaluation of the level of proficiency in behavioral inhibition skills and using techniques for weighing consequences. There are a number of potential interventions specifically designed to target problems in disinhibition, including self-regulation training (see chapter by Riggs et al., Chap. 13, this book) and mindfulness training (see chapter by Dishion et al., Chap. 15, this book). Such prevention strategies might potentiate the neurological development of brain systems that subserve inhibitory control, and there is some promising evidence that programs which facilitate brain functioning may improve self-regulation and reduce substance abuse (Fishbein 2000). Implementation of developmentally appropriate interventions that facilitate self-control over impulses and shifting behavior based on new information would potentially benefit all children, not just those exhibiting inhibitory deficits. On an individual basis, training in the ability to predict outcomes and develop a future orientation among other related skills may reinforce the ability to withhold impulsive responses and act on an assessment of consequences (Trad 1993). Program strategies to help children “slow down and think” in order to control impulsive reactions may be indicated for individuals most likely to be unresponsive to existing approaches. Cognitive neurorehabilitation approaches may be particularly effective in addressing efficiency of behavioral responding. Remediation would target learning, attention, problem-solving, and visual–spatial skills using two approaches: (1) repeated exposure to a task; and (2) deconstruction of complex tasks into their simpler component parts. For example, components of a complex task, such as scanning or psychomotor speed, may be trained separately and then integrated into performance on the complex target task. The training would also focus on memory and problem-solving, stressing the use of strategies, such as forming images or drawing diagrams. Intervention components directed at strengthening the recognition of social cues, regulation of emotional responses, judgment and decision making based on consequences may further reinforce inhibitory control.

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Finally, it should be emphasized that the accumulating research findings on i­nhibitory control do not demand that new prevention intervention strategies be developed “from scratch.” Instead, enhancements to or targeting of existing universal programs may be indicated by this research. For example, one may incorporate approaches that accentuate neurological development that supports inhibitory behaviors with lasting benefits, such as in-depth cognitive training, behavioral self-management, inhibitory training, and language development. Such strategies also have potential to eventually identify children who may benefit from additional intervention components, combining the universal model with additional supports in a targeted manner (Lopez et  al. 2008), thus informing booster and readiness programs for those who require more intensive support. Examples of more specific, targeted and intensive program components may include small friendship groups or individualized coaching to reinforce these skills, which provides more real world practice directed toward children with disinhibitory and externalizing problems (Bierman 1996). Reinforcement components may include modeling stories, scripted practice to provide structured settings for skill development, process support to enhance self-monitoring and skill refinement, and multiple opportunities for performance feedback, communication, self-expression, and prosocial interactions. Using imagery to focus on cue salience, language expression and language internalization, based on evidence that inhibitory control is an important mediator, might be another effective component. Such training exercises are specifically designed to teach and practice universal skills subserved by prefrontal-limbic circuitry, with the ultimate goal of providing an effective and longlasting reduction in drug use and other health-related negative outcomes.

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Lopez B, Schwartz SJ, Prado G, Campo AE, Pantin H (2008) Adolescent neurological development and its implications for adolescent substance use prevention. J Prim Prev 29:5–35. doi:10.1007/ s10935–007–0119–3 Lynam D (2011) Inhibitory control as predictor for conduct disorders. In: Bardo MT, Fishbein D, Milich R (eds) Inhibitory Control and Drug Abuse Prevention: From Research to Translation. Springer, New York Lynam DR, Caspi A, Moffitt TE, Wikstrom PO, Loeber R, Novak S (2000) The interaction between impulsivity and neighborhood context on offending: the effects of impulsivity are stronger in poorer neighborhoods. J Abnorm Psychol 109:563–74 MacPherson L, Richards J, Anahi Collado C (2011) A Functional Analytic Framework for Understanding Adolescent Risk Taking Behavior. In: Bardo MT, Fishbein D, Milich R (eds) Inhibitory Control and Drug Abuse Prevention: From Research to Translation. Springer, New York Moffitt TE (1993) Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy. Psychol Rev 100:674–701 Molina BS, Hinshaw SP, Swanson JM, Arnold LE, Vitiello B, Jensen PS, Epstein JN, Hoza B, Hechtman L, Abikoff HB, Elliott GR, Greenhill LL, Newcorn JH, Wells KC, Wigal T, Gibbons RD, Hur K, Houck PR (2009) The MTA at 8 years: prospective follow-up of children treated for combined-type ADHD in a multisite study. J Am Acad Child Adolesc Psychiatry 48:484–500. doi:10.1097/CHI.0b013e31819c23d0 Pelham WE, Jr., Fabiano GA (2008) Evidence-based psychosocial treatments for attentiondeficit/hyperactivity disorder. J Clin Child Adolesc Psychol 37:184–214. doi:10.1080/ 15374410701818681 Perry JL, Carroll ME (2008) The role of impulsive behavior in drug abuse. Psychopharmacology (Berl) 200:1–26. doi:10.1007/s00213–008–1173–0 Richards JB, Gancarz A, Hawk Jr L (2011) Animal Models of Behavioral Processes that Underlie the Occurrence of Impulsice Behaviors in Humans. In: Bardo MT, Fishbein D, Milich R (eds) Inhibitory Control and Drug Abuse Prevention: From Research to Translation. Springer, New York Riggs N, Greenberg MT, Rhoades B (2011) Early Risk for Problem Behavior and Substance Use: Targeted interventions for the promotion of inhibitory control. In: Bardo MT, Fishbein D, Milich R (eds) Inhibitory Control and Drug Abuse Prevention: From Research to Translation. Springer, New York Shedler J, Block J (1990) Adolescent drug use and psychological health. A longitudinal inquiry. Am Psychol 45:612–30 Slough NM, McMahon RJ, Bierman KL, Coie JD, Dodge KA, Foster EM, Greenberg MT, Lochman JE, Pinderhughes EE (2008) Preventing Serious Conduct Problems in SchoolAge Youths: The Fast Track Program. Cogn Behav Pract 15:3–17. doi:10.1016/j. cbpra.2007.04.002 Stairs DJ, Bardo MT (2009) Neurobehavioral effects of environmental enrichment and drug abuse vulnerability. Pharmacol Biochem Behav 92:377–82. doi:10.1016/j.pbb.2009.01.016 Trad PV (1993) The ability of adolescents to predict future outcome. Part I: Assessing predictive abilities. Adolescence 28:533–55 Verdejo-Garcia A, Bechara A, Recknor EC, Perez-Garcia M (2007) Negative emotion-driven impulsivity predicts substance dependence problems. Drug Alcohol Depend 91:213–9. doi:10.1016/j.drugalcdep.2007.05.025 Whiteside SP, Lynam DR (2003) Understanding the role of impulsivity and externalizing psychopathology in alcohol abuse: application of the UPPS impulsive behavior scale. Exp Clin Psychopharmacol 11:210–7 Zimmerman R, Donohew R, Palmgreen P, Noar S, Cupp P, Floyd B (2011) Designing media and classroom interventions targeting high sensation seeking or impulsive adolescents to prevent drug abuse and risky sexual behavior. In: Bardo MT, Fishbein D, Milich R (eds) Inhibitory Control and Drug Abuse Prevention: From Research to Translation. Springer, New York

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Index

A Addiction disorders, 66–67 ADHD. See Attention deficit/hyperactivity disorder Adolescence substance use biological underpinnings, 169–170 intervention and prevention, 170–173 personality change, 168–169 social dominance, 168 Adolescent drug use and deviant peer association, 284 Adolescent impulsive action, 110 Adolescent limited conduct disorder, 321 Adolescent risk taking behavior contextual role, 180 covert behavior, 193–195 environmental contexts, 195, 196 functional analysis, 179 learning theory, 178, 179 negative consequences delay discounting, 191–192 neurobiological factors, 192–193 negative reinforcement negative urgency, 190–191 neurobiological factors, 191 peer influences anticipatory, 214 cognitive control system, 213–217, 221 decision-making approaches, 212, 213 incidental, 215 socio-emotional reward system, 213, 216–220 positive consequences delay discounting, 184–185 neurobiological factors, 187–188 personality, 182 positive reinforcement, 181–182 positive urgency, 185–186

risk-taking propensity, 186–187 sensation seeking, 183–184 trait impulsivity, 184 prevention and intervention, 195–198 Adolescents and intervention approaches, 115 Adolescent self-regulation, 286 Adverse outcomes allostatic load, 230 brain activation, 236–238 Go/No Go task, 235, 237–238 grand average waveforms, 241 heterogeneity, foster children, 231–232 hypothalamic-pituitary-adrenal (HPA) axis, 233 neural mechanisms, 235–239 neural plasticity, 239–242 parameterizing early adversity effects, 232 pathways, 230 placement instability, 234–235, 239 Alcohol, inhibitory control abuse, 90–92 acute effects, 87–89 impairment and vulnerability, 89–90 trait impulsivity and alcohol impairment, 93–94 Anticipatory emotions, 214 Attention, 126 Attention deficit/hyperactivity disorder (ADHD) alcohol, inhibitory control, 93–94 substance use, risk, 319–320 Attention network task (ANT), 285 B BAC. See Blood alcohol concentration Behavioral approach system (BAS), 163, 165 Behavioral inhibitory processes, 7

M.T. Bardo et al. (eds.), Inhibitory Control and Drug Abuse Prevention: From Research to Translation, DOI 10.1007/978-1-4419-1268-8, © Springer Science+Business Media, LLC 2011

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332 Behavioral mechanism behavioral control, 86–87 inhibitory control and alcohol abuse, 90–92 alcohol, acute effects, 87–89 alcohol impairment and vulnerability, 89–90 cognitive-behavioral therapies (CBTs), 94–97 trait impulsivity and alcohol impairment, 93–94 Behavioral processes adjusting-amount (AdjAmt) procedure, 17–20, 31 definitions and measures, 14, 15 delay discounting and response inhibition C57 and 129s mice, 23–24 stop task, 21–23 delay discounting functions, 15–18 Diagnostic and Statistical Manual of Mental Disorders IV, 14 humans go reaction time (RT), 21 stop task procedure, 21–22 impulsive behavior, 14–16, 30–31 lapses of attention, 15, 25–27 limitations C57 vs. 129s mouse strains, 33–34 drug abuse prevention implications, 34–35 drug self-administration, animals impulsive, 32–33 human vs. animal tasks measure, 31–32 impulsive behavior, 30–31 Behavioral self-regulation, 5 Blood alcohol concentration (BAC), 87–89 Borderline personality disorder (BPD), 146 Brain development and health behavior, 249–250 Brain mechanisms, self-regulation, 287–290 C Cannabis, 73–74 Catechol-O-methyltransferase (COMT), 72 Cognitive-behavioral therapies (CBTs), 94–97 Cognitive control system, 213, 217, 220–221 Cognitive inhibition tasks, 131–132 Conceptual model, 290–291 Covert behavior, 193–195 D DAT. See Dopamine transporter DCCS. See Dimensional change card sort

Index Decision-making tasks, 132 Delay discounting, 16–18, 184–185 and response inhibition, 15 task, 18–20 DevMod approach, 26 Difficulty awaiting turn, 16 Diffusion tensor imaging (DTI), 104 Dimensional change card sort (DCCS), 254 Dishion’s model, 321 Dopamine D1 receptor, 67–68 Dopamine D2 receptor, 69–70 Dopaminergic systems, monoaminergic regulation attention, 47 behavioral flexibility, 46 cognitive flexibility, 46–47 decision making, 47–48 ventral tegmental area (VTA), 45 working memory, 46 Dopamine transporter (DAT), 70–71 E Early life stressors, 75 Executive attention network, 288 Executive cognitive function (ECF) and behavioral health outcomes, 250–251 brain development and health behavior, 249–250 implications and future directions developmental theory, use of, 254–255 diagnosis and classification, 257–258 health interventions, 258–259 and neural activity, 255–257 potential mediator, prevention trials, 251–254 Exon, 69–70 F Feedback-related negativity (FRN), 240–241 Five Factor Model (FFM), 147, 148 Functional magnetic resonance imaging (fMRI), 101 G Gene × environment (G × E) interactions, 75–76 Genetic and environment factors cannabis, 73–74 catechol-O-methyltransferase, 72 dopamine D1 receptor, 67–68 dopamine D2 receptor, 69–70

Index dopamine transporter, 70–71 “Go” and “NoGo” concepts, 66 inhibitory control, 67 neurobiology of impulsivity, 65–66 prevention, 76–77 Go/No-Go task, 65–66, 103, 104 H Haplotype, 68, 70 Heterogeneity, adversity, 131–132 High sensation seekers (HSS), 265, 269, 270 Hyperbolic function, 17 Hypothalamic-pituitary-adrenal (HPA) axis, 233 Hypothetical hyperbolic discount function, 17 I Improving school learning environments (ISLE), 275–276 Impulsive behavior, 14–16, 30–31, 33–34 Impulsive behavior–positive urgency (PU), 149–150 Impulsive decision-making, 265–266 Impulsivity biopsychosocial definition, 127 and deviance Diagnostic and Statistical Manual for Mental Disorders (DSM-IV), 146 general theory of crime, 146 UPPS model, 147–149, 152–154 and inhibitory control, youth, 128–129 theoretical models behavioral approach system (BAS), 163, 165 extraversion, 163 negative emotionality dimension, 167 negative temperament, 167 neuroticism, 166 psychoticism, 164 rash impulsiveness, 166 sensation seeking, 164–165 Incidental emotion, 215 Inhibit maladaptive behavior, 13, 15, 16, 25 Inhibitory control (IC) deficits adolescent SUD, 133–136 BART test, 318 clinical considerations, 138–140 cognitive inhibition tasks, 131–132 decision-making tasks, 132 and impulsivity definitions, 126–127 disordered inhibitory control, 127

333 laboratory measures, 131–133 neural mechanisms, 235–239 neural plasticity, 239–242 neurobiological basis, 136–137 placement instability, 234–235 self-report measures, 128–131 Interpersonal cognitive problem solving, 252 Intron, 71 ISLE. See Improving school learning environments L Laboratory measures, impulsivity, 131–133 Lack of perseverance (PSV), 146, 148 Lack of planning, 126 Lack of premeditation (PMD), 148, 149, 151 Lapses of attention C57 and 129s mice, 29–30 choice RT task, 27–28 DevMod approach, 26 hypothetical distribution, 25 impulsive behavior, 15 Liveliness, 126 Locus coeruleus (LC), 48–49 Low sensation seekers (LSS), 265, 269, 270 M Maladaptive behavior, 13 Mediational pathways, 274–275 Message sensation value, 269–270 Missense coding change, 69 Modified version of Reducing the Risk (MRTR), 272 Monoamine neurotransmitters dopaminergic influences attention, 47 behavioral flexibility, 46 cognitive flexibility, 46–47 decision making, 47–48 ventral tegmental area (VTA), 45 working memory, 46 noradrenergic influences attention, 50–51 behavioral flexibility, 47 cognitive flexibility, 46–47 decision-making, 51 working memor, 49 prevention interventions, 55–56 serotonergic influences attention, 54 behavioral flexibility, 53–54 cognitive flexibility, 53

334 Monoamine neurotransmitters (cont.) decision-making, 54–55 working memory, 52–53 Monoamine oxidase A (MAOA), 324 Motor activation, 126 Motor inhibition tasks, 131 Multiple domain model (MDM), 274 N Narrow impulsiveness, 126 Negative consequences delay discounting, 191–192 neurobiological factors, 192–193 Negative reinforcement negative urgency, 190–191 neurobiological factors, 191 Negative urgency (NU), 149 Neural activity, ECF, 255–257 Neural and behavioral analysis, 7 Neural plasticity, 239–242 Neurobehavioral inhibition, 132, 135 Neurobiological factors, 187–189 Neurobiological substrates. See Genetic and environment factors Neurobiological systems, 64 Neurocognitive variables, 6 Neurocognitve systems, 4, 6 Neuroimaging adolescents and intervention approaches, 116 adolescent substance, 102 decision making, 107–108 diffusion tensor imaging (DTI), 104 fMRI data, 106 impulsive action vs. choice, 113–114 impulsive choice and delay discounting of loss, 112–113 of rewards, 111–112 response inhibition, 102–106 self-control and impulsivity, 103 Niche finding, 282 Nonplanning, 126 Noradrenergic neurons, monoaminergic regulation noradrenergic influences attention, 50–51 behavioral flexibility, 47 cognitive flexibility, 46–47 decision-making, 51 working memor, 49 Novelty seeking, 128 Nurturing environments inhibitory control, 308 nature of, 311–312

Index O Original UPPS model, 147–148 P PMD. See Lack of premeditation Polymorphism, 69, 70 Positive urgency, 149–151, 185–186 Prefrontal cortex (PFC), 48–49, 52 Project Alliance study, 283–284 Promoting alternative thinking strategies (PATHS), 5, 6, 253 PSV. See Lack of perseverance Psychopathology approach, 3 Public service announcements (PSAs), 263, 269 R Reaction time procedure, 27 Reducing the risk (RTR), 272 Response inhibition, 21–24 Response inhibition and adolescent substance, 102 Risk-taking, 126 Risk-taking propensity, 186–187 Risky sexual behavior activation model of information exposure, 266–267 alcohol, 272–273 extended model, 277 HSV/IDM curriculum, 272–273 improving school learning environments (ISLE), 275–276 impulsive decision-making, 265–266 vs. individual differences, 267–268 mediational pathways, 274–277 message sensation value, 269–270 novelty and sensation, 264–265 sensation seeking, 264–265 two-city time-series study ONDCP marijuana initiative, 274 safer sex campaign, 273 S Self-control loss, 103 Self-regulation, 4 Self-regulation and adolescent-onset drug use attention network task (ANT), 285 brain mechanisms underlying self-­regulation, 287–289 child-centered interventions, 292–293 conceptual model, 290–291 family-centered interventions, 292–293

Index mindfulness, 291–292 susceptibility to peer influence, 283–284 Self-report measures, impulsivity, 128–131. See also Impulsivity Sensation seeking (SS), 146, 148, 149, 183–184, 265 Serotoninergic system, monoaminergic regulation attention, 54 behavioral flexibility, 53–54 cognitive flexibility, 53 decision-making, 54–55 working memory, 52–53 Social and emotional learning (SEL), 4 Socio-emotional reward system, 213, 216–220 SS. See Sensation seeking Stop task procedure, 21–22, 24 Strengthening families program (SFP), 293 Substance use disorder (SUD). See Inhibitory control deficits

335 T Trait impulsivity, 184 Translational prevention research coercive vs. positive reinforcements, 309 evolutionary perspective, 310 inhibitory control developmental phases, 306–309 nurturing environments, 308–309 promote inhibitory control, 310–312 U UPPS model development, 147–149 integrate research findings, 152–154 UPPS-P model, 149–154 V Variable number tandem repeat polymorphism (VNTR), 70–71 Ventral tegmental area (VTA), 45 Ventromedial prefrontal cortex (VMPC), 156, 193