Science of awakening, Volume 93 (International Review of Neurobiology.)

SERIES EDITORS RONALD J. BRADLEY Departmentof Psychiatry, College of Medicine

The University of Tennessee Health Science Center

Memphis,Tennessee, USA

R. ADRON HARRIS

Waggoner Center for Alcohol and Drug Addiction Research

The University of Texas at Austin

Austin,Texas, USA

PETER JENNER Division of Pharmacology and Therapeutics

GKTSchool of Biomedical Sciences

King’s College, London, UK

EDITORIAL BOARD ERIC AAMODT PHILIPPE ASCHER DONARD S. DWYER MARTIN GIURFA PAUL GREENGARD NOBU HATTORI DARCY KELLEY BEAU LOTTO MICAELA MORELLI JUDITH PRATT EVAN SNYDER JOHN WADDINGTON

HUDA AKIL MATTHEW J. DURING DAVID FINK BARRY HALLIWELL JON KAAS LEAH KRUBITZER KEVIN MCNAUGHT JOS�E A. OBESO CATHY J. PRICE SOLOMON H. SNYDER STEPHEN G. WAXMAN

Science of Awakening

EDITED BY

ANGELA CLOW

and

LISA THORN

Department of Psychology

University of Westminster

London

UK

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CONTRIBUTORS

Numbers in parentheses indicate the pages on which the authors contributions begin. Ruud M. Buijs (91), Hypothalamic Integration Mechanisms, Department of Physiology, Instituto de Investigaciones Biomedicas, UNAM, 04510 Mexico, Mexico Christian Cajochen (57), Center for Chronobiology, Psychiatric Hospital of the University of Basel, CH-4012 Basel, Switzerland Sarah Chellappa (57), Centre for Chronobiology, Psychiatric Hospital of the University of Basel, CH-4025 Basel, Switzerland Angela Clow (153), Department of Psychology, University of Westminster, London W1B 2UW, UK Eric Fliers (91), Department of Endocrinology and Metabolism, Academic Medical Center (AMC), University of Amsterdam, 1105 AZ Amsterdam, The Netherlands Beth Goodlin-Jones (177), University of California, Davis, M.I.N.D. Institute, Sacramento, CA 95819, USA Irma Gvilia (1), Ilia State University, Tbilisi 0162, Georgia; Research Service, Veterans Affairs Greater Los Angeles Healthcare System, North Hills, CA 91343, USA; Department of Medicine, University of California, Los Angeles, CA 90024, USA Mitsuo Hayashi (109), Department of Behavioral Sciences, Graduate School of Integrated Arts and Sciences, Hiroshima University, Higashi-Hiroshima City 739-5821, Japan Frank Hucklebridge (153), Department of Human and Health Sciences, University of Westminster, London, W1W 6UW, UK Hiroki Ikeda (109), Department of Behavioral Sciences, Graduate School of Integrated Arts and Sciences, Hiroshima University, Higashi-Hiroshima City 739-5821, Japan; Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo 102-8472, Japan Andries Kalsbeek (91), Department of Endocrinology and Metabolism, Academic Medical Center (AMC), University of Amsterdam, 1105 AZ

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CONTRIBUTORS

Amsterdam, The Netherlands; Hypothalamic Integration Mechanisms, Netherlands Institute for Neuroscience, 1105 BA Amsterdam, The Netherlands Susanne E. la Fleur (91), Department of Endocrinology and Metabolism, Academic Medical Center (AMC), University of Amsterdam, 1105 AZ Amsterdam, The Netherlands Robert L. Matchock (129), The Pennsylvania State University, Altoona, PA 16601, USA Noriko Matsuura (109), Department of Behavioral Sciences, Graduate School of Integrated Arts and Sciences, Hiroshima University, HigashiHiroshima City 739-5821, Japan; S & A Associates, Inc., Chuo-Ku, Tokyo 103-0007, Japan Douglas E. Moul (193), Sleep Disorders Center, Neurological Institute, Cleveland Clinic, Cleveland OH 44195; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA Seiji Nishino (229), Sleep and Circadian Neurobiology Laboratory, Stanford University School of Medicine, Stanford, CA 94304-5489, USA Yohei Sagawa (229), Sleep and Circadian Neurobiology Laboratory, Stanford University School of Medicine, Stanford, CA 94304-5489, USA Christina Schmidt (57), Centre for Chronobiology, Psychiatric Hospital of the University of Basel, CH-4025 Basel, Switzerland Amy Jo Schwichtenberg (177), University of California, Davis, M.I.N.D. Institute, Sacramento, CA 95819, USA Lisa Thorn (153), Department of Psychology, University of Westminster, London W1B 2UW, UK Ursula Voss (23), Johann Wolfgang Goethe-Universita¨t Frankfurt, 60325 Frankfurt, Germany; Universita¨t Bonn, Abt. Fu¨r Allgemeine Psychologie II Kaiser-Karl-Ring 9, 53111 Bonn, Germany Chun-Xia Yi (91), Department of Endocrinology and Metabolism, Academic Medical Center (AMC), University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; Hypothalamic Integration Mechanisms, Netherlands Institute for Neuroscience, 1105 BA Amsterdam, The Netherlands

PREFACE

What happens when we wake up in the morning? This seems like a simple question—yet the science of awakening is relatively under-investigated and much has yet to be learnt, indeed even the definition of an “awakening” requires clarity. Much emphasis has been placed upon the process of falling asleep and the causes and consequences of sleep disorder. This volume, however, focuses on the process of awakening. Gvilia details the neural mechanisms underlying sleep and wakefulness while Voss goes on to elaborate on the specific behavioral and electrophysiological correlates of awakening. Cajochen et al. explore the role of light, melatonin, and the brain circuitry underlying circadian and homeostatic influences on alertness. Kalsbeek et al. review suprachiasmatic nucleus and autonomic nervous system influences on awakening. The impact of self- versus forced-awakening on pre- and post-awakening processes including sleep inertia is described by Hayashi et al. Matchock follows this with a detailed view of cognitive deficits associated with sleep inertia. Clow et al. explore a major neuroendocrine awakening response in relation to other awakening processes. The volume then goes on to examine developmental and pathological nighttime awakenings. Schwichtenberg and Goodlin-Jones review the correlates of night awakenings in early development such as infant temperament and infant–parent attachment. Moul examines insomnia with particular emphasis on the need for conceptual clarity as to the definition of “awakening.” Finally, Nishino and Sagawa discuss the current understanding of narcolepsy as a disease of awakening. Awakening is a complex process making it difficult to study but deserving of further investigation.

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UNDERLYING BRAIN MECHANISMS THAT REGULATE SLEEP–WAKEFULNESS CYCLES

Irma Gvilia
,†,‡


†

Ilia State University, Tbilisi 0162, Georgia Research Service, Veterans Affairs Greater Los Angeles Healthcare System, North Hills, CA 91343, USA ‡ Department of Medicine, University of California, Los Angeles, CA 90024, USA

I. Wakefulness-Regulating Systems II. Sleep-Regulating Neurons in the Preoptic Hypothalamus III. Homeostatic Regulation of Arousal States and Preoptic Sleep Regulatory Systems: Recent Findings IV. Integration of Sleep-Regulatory Neuronal Activity in the Preoptic Area V. Descending Modulation of Arousal Systems by Sleep-Regulatory Neurons in the Preoptic Area Acknowledgments References

Daily cycles of wakefulness and sleep are regulated by coordinated interac­ tions between wakefulness- and sleep-regulating neural circuitry. Wakefulness is associated with neuronal activity in cholinergic neurons in the brainstem and basal forebrain, monoaminergic neurons in the brainstem and posterior hypotha­ lamus, and hypocretin (orexin) neurons in the lateral hypothalamus that act in a coordinated manner to stimulate cortical activation on the one hand and beha­ vioral arousal on the other hand. Each of these neuronal groups subserves distinct aspects of wakefulness-related functions of the brain. Normal transitions from wakefulness to sleep involve sleep-related inhibition and/or disfacilitation of the multiple arousal systems. The cell groups that shut off the network of arousal systems, at sleep onset, occur with high density in the ventral lateral preoptic area (VLPO) and the median preoptic nucleus (MnPN) of the hypothalamus. Preoptic neurons are activated during sleep and exhibit sleep–wake state-dependent dis­ charge patterns that are reciprocal of that observed in several arousal systems. Neurons in the VLPO contain the inhibitory neuromodulator, galanin, and the inhibitory neurotransmitter, gamma-aminobutyric acid (GABA). The majority of MnPN sleep-active neurons synthesize GABA. VLPO and MnPN neurons are sources of projections to arousal-regulatory systems in the posterior and lateral INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 93 DOI: 10.1016/S0074-7742(10)93001-8

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hypothalamus and the rostral brainstem. Mechanisms of sleep induction by these nuclei are hypothesized to involve GABA-mediated inhibition of multiple arousal systems. Normal cycling between discrete behavioral states is mediated by the combined influence of a sleep need that increases with continued wakefulness and an intrinsic circadian oscillation. This chapter will review anatomical and func­ tional properties of populations of sleep- /wake-regulating neurons, focusing on recent findings supporting functional significance of the VLPO and MnPN in the regulation of sleep–wake homeostasis. Evidence indicating that MnPN and VLPO neurons have different, but complementary sleep regulatory functions will be summarized. Potential mechanisms that function to couple activity in these two sleep-regulatory neurons will be discussed.

I. Wakefulness-Regulating Systems

Wakefulness is generated by multiple neuronal systems extending from the brainstem reticular formation to the thalamus and through the posterior hypothalamus up to the basal forebrain. These neuronal systems, including cholinergic neurons in the brainstem and basal forebrain, monoaminergic neurons in the rostral pons, midbrain and posterior hypothalamus, and hypo­ cretin-(orexin)-containing neurons in the perifornical lateral hypothalamus, impart a tonic background level of activity that is crucial for cortical activation on the one hand and behavioral arousal on the other hand. Each of these neuronal groups subserves distinct aspects of wakefulness-related functions of the brain. In 1935, Bremer (1935) demonstrated that transection of the brainstem at the pontomesencephalic level (but not the spinomedullary junction) produced coma in anesthetized cats. This finding provided evidence of an “ascending arousal system” necessary for forebrain and cortical arousal. More than a decade later, Morruzzi and Magoun (1949) provided additional support for the concept of an ascending arousal system when they showed that electrical stimulation of the rostral pontine reticular formation produced a desynchro­ nized electroencephalogram (EEG) in anesthetized cats. These findings chal­ lenged the prevailing view that wakefulness-related activity of the brain and consciousness were dependent upon sensory stimulation and eventually led to the concept that wakefulness requires critical levels of ascending activation originating in the brainstem reticular formation (Moruzzi and Magoun, 1949; Starzl et al., 1951). Reticular formation, a diffuse system of nerve cell bodies and fibers in the brainstem, extends from the medulla oblongata to the thalamus and sends

UNDERLYING BRAIN MECHANISMS THAT REGULATE SLEEP–WAKEFULNESS CYCLES

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nonspecific impulses throughout the cortex to “awaken” the entire brain. In addition, brainstem reticular neurons project into the hypothalamus and basal forebrain where neurons are located that also project to the cerebral cortex and participate in the maintenance of an “alert” cerebral cortex. Neurons in the medullary and caudal pontine reticular formation are particularly important for maintaining postural muscle tone along with behavioral arousal via their descending projections to the spinal cord. Neurons in the oral pontine and midbrain reticular formation are essential for sustaining cortical activation, characterized by the low-voltage, high-frequency cortical EEG patterns. Large lesions of the rostral brainstem reticular formation result in a loss of cortical activation and a state of coma in animals and humans. Electrical stimulation of the reticular formation elicits fast cortical activity and waking. Neurons of the pontomesencephalic reticular formation discharge at high rates during waking in association with fast cortical activity, and they give rise to ascending projections by which they excite the forebrain and thus comprise what Moruzzi and Magoun called the “ascending reticular activating system.” Studies in the 1970s and 1980s revealed that the origin of the ascending reticular activating system was not a neurochemically homogenous mass of reticular tissue in the brainstem tegmentum, but rather is comprised of a serious of well-defined cell groups with identified neurotransmitters (Saper et al., 2001, 2005). As mentioned above, these systems produce cortical arousal via two pathways: a dorsal route through the thalamus and a ventral route through the hypothalamus and basal forebrain. A key component of the dorsal branch of the ascending arousal system, which provides a major excitatory signal from the upper brainstem to the thalamus, is the cholinergic neurons in the pedunculo­ pontine (PPT) and laterodorsal (LDT) tegmental nuclei utilizing acetylcholine, an excitatory neurotransmitter of the central nervous system (Hallanger et al., 1987; Levey et al., 1987; Rye et al., 1987). These cell groups activate the thalamic relay neurons that are crucial for transmission of information to the cerebral cortex and the reticular nucleus of the thalamus acting as a gating mechanism in providing signal transmission between the thalamus and the cerebral cortex (McCormick, 1989). The neurons in the PPT and LDT fire most rapidly during wakefulness and rapid eye movement (REM) sleep, a state characterized by cortical activation (Strecker et al., 2000). These cells are much less active during nonREM sleep, when cortical activity is slow. In this view, the thalamus is thought to function as a major relay to the cortex for the ascending arousal system with the overall activity of the thalamo-cortical system forming the origin of the cortical EEG. Indeed, thalamic relay neurons fire in patterns that correlate with cortical EEG (Steriade et al., 1993). In turn, the overall activity in the thalamo-cortical system is thought to be regulated by the ascending arousal system.

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Another population of cholinergic neurons is intermixed with noncholinergic (largely GABAergic) neurons in the basal forebrain (including the nucleus basalis and magnocellular preoptic nucleus in the substantia innominata and the medial septal nucleus and nucleus of the diagonal band of Broca) that project to the cortex, hippocampus, and to a lesser extent, the thalamus. The basal forebrain cholinergic neurons are also implicated in behavioral arousal and EEG desyn­ chronization (Berridge and Foote, 1996; Lee et al., 2005; Saper, 1984). The cholinergic basal forebrain neurons discharge at high rates in association with cortical activation during waking and REM sleep (Lee et al., 2004), whereas inhibition of these neurons can slow the EEG. Acetylcholine released in the cortex excites cortical neurons so that they discharge at high frequencies sub­ tending cortical fast EEG activity. Lesions of the basal forebrain result in severe deficits in waking and a state of coma (Buzsaki et al., 1988). Other functionally important arousal regulatory cell group is monoaminergic neurons in the upper brainstem and caudal hypothalamus, including the nora­ drenergic locus coeruleus (LC) (Jones and Yang, 1985; Loughlin et al., 1982), the serotonergic dorsal (DR) and median raphe nuclei (Sobel and Corbett, 1984; Tillet, 1992; Vertes, 1991), the dopaminergic neurons in the ventral periaque­ ductal grey matter, and histaminergic tuberomammillary neurons in the tuber­ omammillary nucleus (TMN) (Lin et al., 1988, 1994; Takeda et al., 1984). Monoaminergic cell groups project to the intralaminar and midline thalamic nuclei and also innervate the lateral hypothalamus, basal forebrain, and cerebral cortex (Fuller et al., 2006; Saper et al., 2005). Studies of single neuronal activity during natural sleep and wakefulness in monoaminergic nuclei reveal populations of neurons that display predominately tonic activation during wakefulness and significant reductions in activity at sleep onset (Aston-Jones and Bloom, 1981; Jacobs and Fornal, 1999; McGinty and Harper, 1976; Steininger et al., 1999; Vanni-Mercier et al., 1984). Discharge of neurons in monoaminergic nuclei are frequently characterized as “REM-off” because discharge rates in REM sleep are as low or lower than discharge rates observed during nonREM sleep. The REMoff designation distinguishes these cells from other brainstem cell types, including subsets of brainstem cholinergic neurons that are activated during both waking and REM sleep compared to nonREM sleep (McCarley, 2007; Pal and Mallick, 2007). The input to the cerebral cortex is augmented by lateral hypothalamic peptidergic neurons containing melanin-concentrating hormone (MCH) or orexin/hypocretin and basal forebrain neurons containing acetylcholine or GABA. The hypocretin (orexin) neurons are a functionally important arousal regulatory cell group having the potential to modulate activity of several key arousal regulatory cell types (Saper, 2006). Hypocretin neurons send ascending projections to midline hypothalamic nuclei, to the lateral preoptic area/basal forebrain, and to the neocortex (Peyron et al., 1998). Hypocretin neurons also project to the LC, the TMN, and DR; to the ventral tegmentum; and to brainstem cholinergic nuclei (Espana et al., 2005; Peyron et al., 1998). Lesions

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in the lateral hypothalamus and rostral midbrain produce the most profound and long-lasting forms of sleepiness or even coma (Gerashchenko et al., 2003; Ranson, 1939). Orexin neurons in the lateral hypothalamus are most active during wakefulness (Estabrooke, 2001; Lee et al., 2005; Mileykovskiy et al., 2005), whereas MCH neurons are active during REM sleep (Verret et al., 2003). Thus, cholinergic neurons, monoaminergic cell groups, and hypocretin (orexin) neurons are of considerable functional importance for both electro­ graphic and behavioral arousal. Neuronal activity in most of these systems rapidly declines at sleep onset. But, what turns off this multiple arousal systems to produce sleep when needed?

II. Sleep-Regulating Neurons in the Preoptic Hypothalamus

Transitions from wakefulness to sleep in normal physiological conditions are mediated by the combined influence of a sleep homeostatic need and an intrinsic circadian oscillation. The former keeps track of recent neural workload history, the later is a predictive signal about the optimal timing of wakefulness and sleep in relation to the physical environment, and the light–dark cycle in particular. It is hypothesized that the escalation of sleep need/pressure during sustained wak­ ing results in progressive activation of sleep-regulating neurons that function to promote transitions from waking to sleep via inhibition and/or disfacilitation of the multiple arousal systems. Sleep-regulating neurons are located in several subregions of the preoptic hypothalamus, occurring with particularly high density in the ventral lateral preoptic area (VLPO) and the median preoptic nucleus (MnPN). Neurons in these nuclei share several features, including elevated dis­ charge rate during both nonREM and REM sleep compared to waking (Suntsova et al., 2002; Szymusiak et al., 1998) and co-localization of sleep-related Fos-protein with glutamic acid decarboxylase (GAD), a marker of GABAergic cells (Gaus et al., 2002; Gong et al., 2000, 2004; Sherin et al., 1996, 1998). Activation of GABAergic neurons in the VLPO and MnPN is a factor in the suppression of monoaminergic, cholinergic, and hypocretinergic arousal-regulatory systems during sleep (Saper et al., 2005; Szymusiak et al., 2007). Evidence indicating that MnPN and VLPO neurons have different, but complementary sleep reg­ ulatory functions will be summarized. Potential mechanisms that function to couple activity in these two sleep-regulatory neurons will be discussed. Among the first modern conceptualizations of the central organization of sleep–wakefulness control was that of von Economo, who postulated the existence of sleep-promoting structures in the rostral hypothalamus that function in opposition to wakefulness-promoting systems in the posterior hypothalamus. This functional-anatomical framework evolved from von Economo’s careful

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correlations between disturbances in sleep and consciousness in patients with viral encephalitis and subsequent localization of inflammatory brain lesions. Postmortem brain examination revealed that patients with viral encephalitis, which slept excessively, had lesions at the junction of the midbrain and posterior hypothalamus, suggesting to von Economo that this area of the brain contained wake-promoting circuitry (Von Economo, 1930). Individuals afflicted with viral encephalitis, which were insomniacs, had lesions involving the basal forebrain and anterior hypothalamus, further suggesting to von Economo that this area of the brain contained sleep-promoting circuitry. This basic organizational plan of hypothalamic sleep- and arousal-regulatory neural systems has been repeatedly confirmed and elaborated by contemporary research in sleep neurobiology. The finding that rostral hypothalamic damage causes chronic reductions in sleep has been confirmed many times, with increasingly selective methods of brain tissue destruction (John and Kuma, 1998; Lu et al., 2000; McGinty and Sterman, 1968; Nauta, 1946; Szymusiak and Satinoff, 1984; Szymusiak et al., 1991). These studies have identified the rostral hypothalamus and adjacent basal forebrain as key sleep-regulatory regions. Results of lesion studies, demonstrating sleep deficits following rostral hypothalamic damage, were complemented by findings that electrical, thermal, or chemical stimulation of preoptic hypothalamus can be sleep-promoting (Benedek and Obal, 1982; Mendelson and Martin, 1992; Ster­ man and Clemente, 1962; Ticho and Radulovacki, 1991). Early neuronal record­ ing studies have revealed populations of putative sleep-regulatory neurons in these areas, on the basis of sleep-related increases in discharge rate (Findlay and Hayward, 1969; Kaitin, 1984). However, results indicated diffuse anatomical distribution of sleep-regulatory neurons. Significant progress in characterizing the neuroanatomy and the neurochem­ istry of hypothalamic sleep-regulatory neurons has been achieved by using immunostaining methods that allow mapping of activated neurons at a larger scale than is possible with single-cell electrophysiology. Expression of c-Fos, an immediate-early gene, has been found to be correlated with increased activity in a variety of neurons (Dragunow and Faull, 1989; Morgan and Curran, 1986). Studies employing immunohistochemical detection of the protein product of the c-Fos gene have localized putative sleep-regulatory neurons to the VLPO and MnPN (Gong et al., 2000; Sherin et al., 1996, 1998). Sherin et al. (1996) first examined expression of Fos in the brain of rats that were allowed spontaneous sleep–waking behavior either during the light/rest or the dark/active periods. The number of c-Fos immunoreactive neurons (IRNs) in the VLPO of animals killed during the light phase was significantly higher than in animals killed during the dark. Fos-IRN counts in these animals were positively correlated with the amount of preceding sleep. To determine the role of circadian factors, the normal sleep–waking behavior and circadian phase were dissociated by depriving animals of sleep for 9 or 12 h periods during the light phase. After sleep deprivation, some

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7

animals were killed immediately, whereas others were killed after a recovery sleep for 45, 90, or 180 min before the sacrifice. Following sleep deprivation, significant numbers of Fos-IRNs in the VLPO were observed only in animals that were permitted a recovery sleep prior to sacrifice and the average numbers of FosIRNs in the VLPO of these animals were positively correlated with the time spent asleep during the 2-h period prior sacrifice. Elevated expression of c-Fos in the light period versus the dark period, positive correlation between the average number of Fos-IRNs and the amount of preceding sleep, and significant increases in Fos-IRNs during recovery sleep following sleep deprivation supported the hypothesis that the VLPO was a critical sleep-promoting site. Rats that were sacrificed at the termination of sleep deprivation and not permitted recovery sleep did not exhibit increased numbers of Fos-IRNs in the VLPO, suggesting that c-Fos activation in this nucleus is dependent upon the occurrence of sleep and is not related to sleepiness or sleep propensity. Gong et al. (2000) confirmed the existence of sleep-active neurons in the VLPO and identified a second group of such neurons in the MnPN. Expression of c-Fos was examined under condi­ tions of spontaneous sleep during the light/rest period and short-term (2 h) sleep restriction, achieved with gentle handling, during the same period. More neurons exhibiting Fos-immunoreactivity were present in the MnPN and the VLPO in rats that were predominately asleep during the 2 h prior to sacrifice, compared to rats that were predominately awake. The number of Fos-IRNs in both MnPN and VLPO was positively correlated with total sleep time recorded during the 2 h prior to sacrifice. A partial understanding of the functional organization of preoptic area sleepregulatory neurons comes from the findings on the neurochemical nature of sleep-active neurons in this area. Combining Fos-immunostaining with in situ hybridization for galanin—an inhibitory neuromodulator, Gaus et al. (2002) showed that about 80% of sleep-active cells in VLPO of rats that had been sleeping an average of 84% of the hour prior to death expressed the neuropeptide galanin; conversely, ~52% of galanin-expressing cells were sleep-active. In a previous study from this group, galanin in VLPO neurons was found to be highly co-localized with GABA. Gong et al. (2004) further examined the neurotransmit­ ter phenotype of MnPN and VLPO sleep-active neurons. To evaluate the hypothesis that MnPN and VLPO sleep-active neurons are GABAergic, the authors combined immunostaining for c-Fos protein with immunostaining for GAD. The number of Fos single-, GAD single-, and FosþGAD double-IRNs was quantified throughout the MnPN and VLPO in rats exhibiting varying amounts of spontaneous sleep during a 2-h recording period beginning 2 h after lights on. The numbers of total Fos-IRNs and FosþGAD IRNs in both the MnPN and the VLPO were positively correlated with the amount of preceding sleep; a majority of MnPN and VLPO neurons that were Fos-positive following sustained sponta­ neous sleep also stained for GAD. The same study examined patterns of

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FosþGAD immunoreactivity in the MnPN and VLPO after 24-h sleep depriva­ tion. FosþGAD immunoreactive cell counts in the MnPN were significantly elevated in rats that were permitted 2 h recovery period following 24 h sleep deprivation compared to both sleep deprivation control and spontaneously sleep­ ing rats. Although the three groups of rats did not exhibit significantly different sleep amounts, there was a group effect on the sleep EEG. EEG delta power in nonREM sleep was significantly higher in the recovery versus the control sleep deprivation and spontaneously sleeping groups. The number of GABAergic neurons expressing Fos-immunoreactivity in the MnPN and the VLPO of sleep-deprived versus relevant control rats was slightly, but significantly, elevated even in the absence of the opportunity for recovery sleep. These findings demon­ strated that sleep deprivation is associated with increased activation of GABAer­ gic neurons in the MnPN and the VLPO, suggesting involvement of these neurons in homeostatic regulation of sleep.

III. Homeostatic Regulation of Arousal States and Preoptic Sleep Regulatory Systems: Recent Findings

The two-process model of sleep regulation proposed by Borbely (1982) postulates that sleep propensity at any given point in time results from interac­ tions between homeostatic and circadian aspects of sleep regulation. Although it is accepted that sleep is a homeostatically regulated instinct behavior, details on the neural substrates that mediate homeostatic sleep regulatory responses to sustained wakefulness are not fully understood. The concept of sleep homeostasis implies that drive to enter sleep increases when sleep is not expressed. Therefore, a powerful tool to investigate the mechanisms of sleep homeostasis is sleep deprivation. Sleep deprivation leads to a progressive accumulation of homeostatic sleep need, defined during the deprivation period by EEG slowing and/or increased number of attempts to initiate sleep and by rebound increases in sleep amount and sleep depth during the post-deprivation recovery period. We have recently evaluated patterns of Fosimmunoreactivity in MnPN and VLPO neurons following acute total sleep deprivation and selective REM sleep restriction in an attempt to clarify relation­ ships of preoptic area neuronal activation to homeostatic sleep pressure versus the actual occurrence of sleep (Gvilia et al., 2006a, 2006b). In one set of experiments, patterns of c-Fos-immunoreactivity were compared among groups of rats exhi­ biting different levels of sleep pressure and different amounts of sleep (Gvilia et al., 2006b). Experiment 1 used groups of rats with inherently strong diurnal rhythms in sleep–waking organization, with the assumption that such rats have compara­ tively high homeostatic sleep pressure during the light/rest period compared with

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the dark/active phase. Groups of rats were assigned to 2 h of spontaneous sleep at ZT1–3 (Zeitgeber Time, hours after lights on), a condition of moderate sleep pressure and high sleep amount; 2 h of spontaneous sleep at ZT13–15 (low sleep pressure, low sleep amount); 2 h of total sleep deprivation at ZT1–3 (high sleep pressure, no sleep); and 1 h of recovery sleep (ZT3–4) following the 2 h sleep deprivation (high sleep pressure, high sleep amount). Experiment 2 used rats with inherently weak diurnal rhythms in the distribution of sleep and waking, with the assumption that homeostatic sleep pressure in such rats is similar during the light and dark periods. These rats were subjected to 2 h sleep deprivation during either in the light period (ZT1–3) or in the dark period (ZT13–15). Across the several conditions studied in Experiments 1 and 2, dissociation of sleep pressure, sleep amount, and time of day were achieved. In Experiment 1, Fos-IR in MnPN GABAergic neurons was lowest during spontaneous sleep in the dark, a condition of low sleep pressure and low sleep amount. However, in a condition of high sleep pressure and minimal sleep (sleep deprivation in the light period), Fos-immunoreactivity in MnPN GABAergic neurons was maximal. In two conditions of high sleep amount, spontaneous sleep and recovery sleep in the light, Fos-immunoreactivity in MnPN GABAer­ gic neurons was higher in the condition with higher sleep pressure (i.e., recovery sleep). Fos-immunoreactivity in VLPO GABAergic neurons was significantly higher during both spontaneous sleep and recovery sleep, compared with sleep deprivation. In Experiment 2, rats with weak diurnal rhythms exhibited similar levels of sleep pressure, defined by the number of attempts to initiate sleep, during sleep deprivation in the light period and sleep deprivation in the dark period. FosþGAD immunoreactive cell counts did not differ in these two conditions. Collectively, these results indicate that MnPN GABAergic neurons are most strongly activated in response to increasing sleep pressure, whereas VLPO GABAergic neurons are most strongly activated in response to increasing sleep amount. A second set of experiments was designed to expose rats to conditions that differentially manipulated levels of REM sleep homeostatic pressure and actual REM sleep amount (Gvilia et al., 2006a). Expression of c-Fos in MnPN and VLPO neurons was examined under conditions of spontaneous sleep with differ­ ing amounts of REM sleep, REM sleep restriction, and REM sleep recovery following REM sleep restriction. Across all conditions, the number of Fos-IRNs in the MnPN was highest in REM sleep-restricted rats displaying the highest levels of REM sleep homeostatic pressure/drive, that is, those rats exhibiting the most frequent attempts to enter REM sleep during the restriction procedure. In VLPO, the number of Fos-IRNs also increased with increasing REM sleep pressure during REM sleep restriction. These finding provides the first evidence that activation of subsets of MnPN and VLPO neurons is more strongly related to REM sleep pressure than to REM sleep amount, since accumulated REM

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sleep time in REM sleep-restricted rats was significantly lower than in all other groups. These experiments indicate that MnPN neurons are strongly responsive to homeostatic need for nonREM and REM sleep, independent of sleep amount. These findings suggest a role for these neurons in promoting sleep onset subse­ quent to episodes of sustained waking and in modulating the activity of brainstem REM sleep-generating mechanisms in response to total sleep and/or selective REM sleep deprivation. By comparison, VLPO neurons are only moderately activated in response to increased homeostatic sleep pressure following total sleep deprivation, but do become strongly activated during recovery sleep. This sug­ gests that these neurons are involved in consolidating sleep and promoting sleep maintenance in response to sustained waking. This hypothesized dichotomy of functional roles for VLPO and MnPN neurons in sleep regulation (Frank, 2010; Szymusiak et al., 2007) is supported by electrophysiological (Suntsova et al., 2002; Szymusiak et al., 1998) and anato­ mical studies (Chou et al., 2002; Uschakov et al., 2006, 2007). A majority of neurons recorded in the VLPO and MnPN exhibit elevated discharge rates during both nonREM and REM sleep compared with waking (Suntsova et al., 2002; Szymusiak et al., 1998). A subset of these sleep-active neurons exhibit maximum discharge during REM sleep, but, in most neurons of this type, discharge rates in REM sleep are only moderately higher than rates during nonREM sleep. Most sleep-active VLPO neurons display increased activity during the immediate transition from waking to sleep and become progressively activated from light to deep nonREM sleep (Szymusiak et al., 1998). Sleep-related discharge rates of VLPO neurons are elevated in rats after 16 h of sleep depriva­ tion compared with non-deprived rats, but waking discharge rates are unchanged (Szymusiak et al., 1998). Most sleep-active MnPN neurons show gradual increases in firing rate well in anticipation of sleep onset (Suntsova et al., 2002). Peak discharge rates of MnPN neurons are observed early in the development of nonREM sleep episodes and rates decline across sustained sleep episodes in the absence of intervening waking (Suntsova et al., 2002). There are no published data on discharge patterns of MnPN and VLPO neurons after REM sleep restriction, but, based on our present findings, we predict that discharge of neurons in these nuclei should become more strongly REM sleep related in response to increasing REM sleep pressure. Recent study (unpublished data from Gvilia et al. 2010) examining different aspects of sleep homeostasis in infant rats suggests that developmental elaboration of preoptic sleep-regulatory neuronal circuits contributes to the maturation of sleep homeostasis in the developing rat brain. The study examined diurnal organization of sleep–wakefulness states and the expression of different aspects of the homeostatic response to sleep deprivation, and quantified activity of preoptic area GABAergic neurons during spontaneous sleep, sleep deprivation,

UNDERLYING BRAIN MECHANISMS THAT REGULATE SLEEP–WAKEFULNESS CYCLES

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and recovery sleep conditions, using immunohistochemistry for c-Fos protein and GAD, in 3- and 4-week-old Sprague Dawley rats. On postnatal day 21 (P21), the percentages of total sleep and wakefulness did not differ across the light and dark phases of the 24 h sleep–wakefulness cycle. However, the 24 h distribution of nonREM and REM sleep was not similar. The 24 h maximum of REM sleep was observed in the dark period, whereas nonREM sleep peaked in the light period. By P29, wakefulness was elevated in the dark phase and both nonREM and REM sleeps were highest in the light phase. On P22 and P30, these same rats exhibited increased % nonREM sleep and increased delta power during recovery sleep, compared to baseline. But, the level of sleep consolidation in recovery sleep versus baseline, defined by the number of awakenings from sleep and the mean duration of nonREM sleep bouts, was increased by P30 only. Fosþ cell counts in rostral part of MnPN were elevated in all sleep-deprived and recovery sleep rats (P22 and P30), compared to relevant controls. Numbers of rostral MnPN FosþGADþcells were also elevated in sleep-deprived versus control and recov­ ery sleep rats. Cell counts in the VLPO of P22 rats did not differ across the experimental conditions, whereas P30 rats expressed elevated numbers of FosþGADþ cells in the condition of recovery sleep, compared to sleep depriva­ tion condition and control sleep. In summary, details on MnPN neuronal activity suggest a critical role in coding homeostatic pressure for sleep; MnPN sleep-regulating neurons become progressively activated in response to escalating homeostatic sleep pressure/need accruing during sustained waking and function to promote transitions from waking to sleep. VLPO sleep-regulating neurons may primarily function to regulate sleep maintenance and sleep depth within a sleep episode, once sleep is achieved. Based on these findings, we hypothesize that homeostatic response to sustained wakefulness in normal physiological conditions, including reduced sleep latency, increased sleep amount, increased sleep depth, and sleep consolidation are dependent upon integrated activation of MnPN and VLPO neurons. What might be the potential mechanisms that function to couple activity in these two sleep-regulatory neurons?

IV. Integration of Sleep-Regulatory Neuronal Activity in the Preoptic Area

A complete understanding of the hypothalamic regulation of sleep–wake homeostasis requires knowledge about which endogenous neurotransmitters/ neuromodulators regulate the excitability of preoptic area neurons. While addressing this aspect of sleep regulation, another critical question about the sleep hypothalamic regulation needs to be discussed. Given that the preoptic

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hypothalamus contains populations of putative sleep-regulatory neurons that may have different functional roles in controlling sleep onset and sleep maintenance, how might activity among these neuronal populations normally be coupled? The physiological basis for the accumulation for sleep need has long been a subject of investigation. The most widely held hypothesis is that there is accu­ mulation of some chemical factor in the brain during wakefulness that drives sleepiness. One of the current candidates for a sleep-accumulating compound is the inhibitory neuromodulator adenosine. Adenosine is a byproduct of brain metabolism and adenosine levels in the brain are elevated as a consequence of sustained waking. In the lateral preoptic area/basal forebrain, extracellular adenosine levels rise during sleep deprivation and decline during recovery sleep (Basheer et al., 2004). Sleep-generating effects of adenosine are mediated, in part, through A1 receptor-mediated inhibition of arousal systems, including basal forebrain cholinergic neurons (Alam et al., 1999; Basheer et al., 2004). Adenosine may also promote sleep via excitatory effects on preoptic area sleep-regulatory neurons through both direct and indirect actions. Bath application of adenosine produces an A1 receptor-mediated suppression of spontaneous inhibitory post­ synaptic potentials in rat VLPO neurons recorded in vitro (Chamberlin et al., 2003). Administration of an adenosine A2a receptor agonist evokes direct exci­ tatory effects on a subset of rat VLPO neurons recorded in vitro (Gallopin et al., 2005). The functional importance of this A2a effect is also demonstrated by the finding that perfusion of A2a agonist into the lateral preoptic area in rats promotes sleep (Methippara et al., 2005). The ability of A2a receptor agonists to excite MnPN GABAergic neurons is unknown, but adenosine-mediated excitation/disinhibition of MnPN sleepregulatory neurons, in the conditions of elevated sleep propensity, might be a potential mechanism that integrates sleep homeostasis regulatory activity in the MnPN and VLPO neurons. This hypothesis is supported by substantial body of evidence. The MnPN is a source of afferents to the VLPO (Chou et al., 2002) and a subset of MnPN-to-VLPO projection neurons exhibit sleep-related c-Fos-immunoreactivity (Uschakov et al., 2006). In vitro recordings of VLPO neurons demonstrate that they are subject to local GABAergic inhibition, and as mentioned earlier, bath application of adenosine can activate VLPO neurons through processes of A1-adenosine receptor-mediated inhibition of local GABAergic interneurons (Chamberlin et al., 2003; Morairty et al., 2004). Activa­ tion of MnPN-to-VLPO GABAergic projection neurons during sustained waking could, in turn, activate VLPO neurons through a process of disinhibition, similar to that described for adenosine. As already discussed, VLPO GABAergic neurons are inhibited by norepinephrine and serotonin, and withdrawal of monoaminergic input around the time of sleep onset can be hypothesized to disinhibit VLPO neurons (Gallopin et al., 2000; Saper et al., 2001). While it is not known if GABAergic neurons in the MnPN are inhibited by monoamines, the MnPN

UNDERLYING BRAIN MECHANISMS THAT REGULATE SLEEP–WAKEFULNESS CYCLES

does receive prominent projections (Morin and Meyer-Bernstein, 1999; drawal of monoaminergic inhibitory integrated activation of MnPN and waking to sleep.

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from brainstem monoaminergic nuclei Saper and Levisoch, 1983). Thus, with­ tone could be a mechanism to promote VLPO neurons during transitions from

V. Descending Modulation of Arousal Systems by Sleep-Regulatory Neurons in the Preoptic Area

As discussed earlier, elevation of homeostatic sleep pressure, occurring as a consequence of sustained waking, leads to enhanced GABA- and/or galanin­ mediated inhibition of monoaminergic, hypocretinergic, and dopaminergic arou­ sal systems via activation of MnPN and VLPO neurons at the transition from wakefulness to sleep. This hypothesis is supported by findings from anterograde and retrograde tracer studies and electrophysiological findings that patterns of neuronal activity across the sleep–wakefulness cycle in the MnPN and VLPO are, for the most part, reciprocal to those observed in the brain regions implicated in the control of arousal. The VLPO heavily innervates wake-promoting histaminergic neurons in the TMN as originally described by Sherin et al. (1996, 1998). The VLPO provides dense projections to the histaminergic cell body regions of the TMN and is a major source of afferents to this nucleus (Sherin et al., 1998; Steininger et al., 2001). Discharge of TMN neurons across the sleep–wakefulness cycle is the reciprocal of that observed in most VLPO neurons, that is, elevated discharge during wakefulness and reduced activity during nonREM and REM sleep (Steininger et al., 1999; Szymusiak et al., 1998; Vanni-Mercier et al., 1984). Extracellular levels of GABA are elevated in the posterior hypothalamus during nonREM sleep compared to wakefulness (Nitz and Siegel, 1996). Electrical stimulation of the VLPO area in a horizontal rat brain slice preparation evokes GABA-mediated inhibitory postsynaptic potentials in histaminergic neurons in the TMN (Yang and Hatton, 1997). The VLPO projects to the locus coeruleus and dorsal raphe nucleus (Sherin et al., 1998; Steininger et al., 1999) and to the ventral periaqueductal gray, an area that contains wake-promoting dopaminergic neurons (Lu et al., 2006). The MnPN also projects to these brainstem monoaminergic nuclei (Uschakov et al., 2007; Zardetto-Smith and Johnson, 1995). Discharge of presumed serotonergic neurons in the DR nucleus and of presumed noradrenergic neurons in the LC also exhibits the “REM-off” discharge pattern that is observed in TMN neurons and is the reciprocal pattern to that observed in most VLPO and MnPN sleepactive neurons (Guzman-Marin et al., 2000). Additional evidence of functional

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descending inhibitory projections from the preoptic area to the DR nucleus comes from the finding that local warming of the preoptic area, a manipulation that activates sleep-active neurons, causes suppression of waking discharge in REM-off, presumed serotonergic neurons in the DR nucleus (Guzman-Marin et al., 2000). Projections from the VLPO and the MnPN to hypocretin neuronal field in the perifornical region of the lateral hypothalamus (PFLH) have been documented (Uschakov et al., 2006, 2007; Yoshida et al., 2006). Projection neurons from both the MnPN and the VLPO to the PFLH express c-Fos protein-immunoreactivity during sleep (Uschakov et al., 2006). A subset of projection neurons from the MnPN to the PFLH immunostain for GAD (Gong et al., 2005). Discharge of hypocretin neurons across the sleep–wakefulness cycle is similar to that described for the monoamines, with maximal activity during waking and minimal firing during nonREM and REM sleep and, local warming of the preoptic area evokes suppression of waking-related neuronal activity in the PFLH (Krilowicz et al., 1994; Methippara et al., 2003). Inhibition of preoptic area neurons by local perfusion of muscimol induces Fosimmunoreactivity in hypocretin neurons (Satoh et al., 2003). Electrical or chemical stimulation of the MnPN evokes suppression of waking discharge in several PFLH cell types, including putative hypocretin neurons with REM-off discharge (Suntsova et al., 2007). Suppression of hypocretin neuronal activity during sleep appears to be a consequence of increased endogenous GABA-mediated inhibition. Local microdialyses perfusion of the GABA-A receptor antagonist bicuculline into the PFHL of sleeping rats results in intense expression of Fos-immunoreactivity in hypocretin neurons ipsilateral to the dialysis probe (Alam et al., 2005). Collectively, findings support the hypothesis that deactivation of functionally important arousal systems occurring at sleep onset and during nonREM and REM sleep is a result of GABA-mediated inhibition originating in the preoptic hypothalamus. One possible mechanism contributing to stabilization of sleepwaking states arises from mutually inhibitory interactions between VLPO and the monoaminergic arousal systems. Anatomical studies demonstrate that the VLPO receives synaptic input from the same monoaminergic systems to which it projects. Identified GABAergic neurons in the VLPO recorded in vitro are inhibited by noradrenalin and serotonin (Chou et al., 2002; Gallopin et al., 2000). This suggests that waking-related monoaminergic activity prevents inappropriate activation of VLPO neurons during an animal’s active phase. During waking to sleep transition, activation of VLPO neurons is reinforced by disinhibition as monoaminergic activity wanes. Mutual inhibitory interactions between sleep- and arousal-regulatory neurons function as a bi-stable switch (or flip-flop) and is hypothesized to promote rapid and stable transitions between waking and sleep (Saper et al., 2001).

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It is well accepted that inhibition of monoaminergic cells is a necessary prerequisite for REM sleep generation. These monoaminergic cells are active during waking, decrease activity during nonREM sleep, and become inactive during REM sleep (Fornal et al., 1985; Heym et al., 1982; Reiner and McGeer, 1987; Sakai, 1986; Gervasoni D et al., 2000; Thakkar MM et al., 1998; Yamuy et al., 1995, 1998). Evidence suggests that the quiescence of these cells during REM sleep is attributable to GABA-mediated inhibition (Levine and Jacobs, 1992; Nitz and Siegel, 1997a, 1997b; Nitz and Siegel, 1996; Gervasoni et al., 2000 and Wang et al., 1992). Because both the MnPN and the VLPO project to the DR nucleus and LC (Lu et al., 2002; Steininger et al., 2001; Zardetto-Smith and Johnson, 1995) and they both contain populations of sleep-active GABAergic neurons, they may be a source of inhibition of monoaminergic systems at REM sleep onset. We recently found that in addition to activation of GABAergic neurons, increasing REM sleep pressure activates a large population of nonGABAergic neurons in the MnPN (Gvilia et al., 2006a). Only 22–26% of FosIRNs in the MnPN of high REM sleep pressure/REM sleep-restricted rats were immunoreactive for GAD. This is in contrast to the VLPO, in which 65% of FosIRNs were also positive for GAD. Furthermore, in the MnPN, the proportion of Fos-IRNs double labeled for GAD actually decreased between the high sponta­ neous REM sleep condition and the high REM-sleep pressure/REM-sleep­ restricted condition (26 vs. 40% in rostral MnPN; 21 vs. 36% in caudal MnPN). This indicates that, in conditions of high REM sleep pressure, but low REM sleep amounts, activation in the MnPN occurs predominately in non-GABAergic neurons. What is the potential functional significance of activation of nonGABAergic MnPN neurons in response to increasing REM sleep homeostatic pressure? We hypothesize that these neurons are glutamatergic and function to promote REM sleep in two ways. First, they exert excitatory effects on VLPO GABAergic neurons (Chou et al., 2002), which help to promote suppression of LC and DR nucleus neurons. Second, they augment GABA-mediated inhibition in the LC and DR nucleus via excitatory effects on local GABAergic interneurons in these areas. Thus, under conditions of elevated REM sleep homeostatic pressure, e.g., during REM sleep restriction and during recovery sleep after REM sleep restriction, activation of GABAergic and non-GABAergic MnPN neurons and of GABAergic/galaninergic neurons in the VLPO may function to suppress activity in brainstem monoaminergic neurons, leading to increased propensity for expres­ sion of REM sleep by brainstem REM sleep-generating circuitry.

Acknowledgments Supported by Georgian National Science Foundation Grants GNSF/ST09-722-6-274 and GNSF/ST07/6-219, the Department of Veterans Affairs and NIH Grant MH63323.

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CHANGES IN EEG PRE AND POST AWAKENING

Ursula Voss Johann Wolfgang Goethe-Universita¨ t Frankfurt, 60325 Frankfurt, Germany Universita¨ t Bonn, Abt. Fu¨ r Allgemeine Psychologie II Kaiser-Karl-Ring 9, 53111 Bonn, Germany

I. Introduction A. Some Critical Remarks B. Defining Arousals and Awakenings II. EEG Changes Preceding an Awakening A. Waking up to External Stimuli B. Arousability: Behavioral Reactivity C. Arousability: Sleep-Stage-Specific Effects D. Frequency-Specific Activity in Sleep and Behavioral Arousal Thresholds E. Behavioral Responsiveness and PGO Waves F. Individual Differences in Arousability G. Arousability: Event-Related Potential Studies on Attention in Sleep III. EEG Changes Following an Awakening A. Sleep Inertia or State-Related Effects on Cognition and Behavior B. Partial Awakenings IV. Summary

References

This chapter is concerned with behavioral and electrophysiologic evidence of awakenings. Awakenings are understood here as a state change from sleeping to waking. We will discuss the methodological issues and the problem of properly defining an awakening. With regard to phenomena preceding an awakening, we will look at arousals and compare background to event-related activity in the electroencephalography (EEG). As arousability varies between and within species, the relevant EEG correlates of this variability are described. Concerning EEG changes following an awakening, the discussion focuses on sleep inertia effects.

INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 93 DOI: 10.1016/S0074-7742(10)93002-X

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Copyright 2010, Elsevier Inc. All rights reserved. 0074-7742/10 $35.00

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Wake EEG

Sleep EEG

FIG. 1. Several animals have unihemispheric sleep in which the brain is only partially asleep, facilitating a quick return to waking function. Waking up for marine mammals, for example, is less difficult than for animals setting both hemispheres to sleep simultaneously. Unihemispheric sleep has also been observed in birds in situations that entail danger (for a review, see Voss, 2004), indicating that it may be an innate option that may be accessed under aversive environmental conditions. In humans, unihemispheric sleep has not been described. This picture of a 6-month-old baby girl suggests that a similar process may be activated in early life. However, this has not been investigated scientifically.

I. Introduction

This chapter is concerned with changes in the EEG that precede and follow an awakening. Before I discuss these changes in detail, however, I want to share some thoughts on the functions of awakenings and stress some very important restrictions that apply to most if not all studies of arousals and awakenings from sleep. Awakenings may be regarded as endpoints of the transition between two states, sleeping and waking. They enable us to respond to environmental demands and challenges. Being able to quickly respond to environmental cues is an important requisite for survival. Restoration of behavioral reactivity and orientation in space and time are thus one function of awakenings. For primates and humans, awakenings are much more than the return of behavioral reactivity, however. Waking up enables us to achieve conscious aware­ ness of our emotions, our motifs, and our thoughts. A recent theory of consciousness and sleep proposes that sleep itself, especially REM sleep, constitutes the necessary brainwork to allow for the evolution of higher order or secondary consciousness (Hobson, 2009). For those animals that possess secondary consciousness, awaken­ ings must be considered a privilege. The return of rational thought and reflective conscious awareness thus constitutes another function of awakenings, probably reserved to those animals that have REM sleep (Hobson and Voss, 2010a, 2010b).

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When we wake up from sleep, the shift we make from the virtual world of sleep to the real world has a subjective quality, a phenomenology that is strongly related to the sleep stage we awaken from. When we arouse from NREM sleep, for example, we usually feel as if we had taken a long break in which nothing much happened. As a result, we usually have little problems orienting ourselves in the wake world. It is much different when we wake up from REM sleep in which case an awakening catapults us out of the virtual reality of a dream into the wake world. Since the dreamer is very much involved in the dream (unless he or she is a lucid dreamer), an awakening out of REM sleep often goes along with disorienta­ tion and confusion (Weigand et al., 2007). Our research on the measurable objective changes that indicate and represent these phenomenological experi­ ences have only just begun. In the remainder of this chapter, I will discuss the progress we have made in identifying the electrophysiologic correlates of awaken­ ings and also point to some of the problems we still have to solve.

A. SOME CRITICAL REMARKS My first critical remark concerns methodology and definition of terms. Awakenings need to be distinguished from arousals. Whereas arousals are a common concomitant of sleep that may lead to an awakening, most arousals go unnoticed and are not remembered upon an awakening. Most likely, arousals have a function, i.e., they allow us to monitor the environment for danger cues. Arousals are followed by an awakening either when the need for sleep has been satisfied or when we anticipate behavioral or cognitive demands that require wakefulness. My second point is concerned with the important but often neglected concept of state changes, as I have already addressed in my introductory remarks. When a human or any other sleeping animal wakes up, he or it leaves behind a state of sleep and enters into a state of waking. The brain is active in both states. However, it is occupied with the processing of mostly external sources of information in wakeful­ ness while our thoughts are turned inwardly during sleep. Through a process known as sensory gating, most of the external world is shut out during sleep. An awakening thus entails the return to the external world and increased processing of environ­ mental sensorial experiences (Akerstedt et al., 2002). When we examine EEG correlates of awakenings such as evoked potential responses to auditory stimuli, we should bear in mind that these phasic events take place against a background of state-related brain physiology. As we will discuss in the context of state-related changes in the EEG, our studies on lucid dreaming demonstrate that the brain can, in exceptional cases, occupy both state spaces, sleeping and dreaming, simulta­ neously. Much more attention should be allocated to these important factors in

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waking up to behavioral responsivity and conscious awareness, and we have only just begun to understand the implications of these state-related brain functions. My third point concerns the related but separate issue of behavioral respon­ siveness. Depending on the sleep stage we are in prior to an awakening, we may experience difficulties in behaving and responding to external cues, a process known as “sleep inertia.” State-shifts normally proceed gradually, not abruptly. It usually takes some time to fall asleep and it also takes time to wake up. Although we usually do not remember our thought processes during sleep, we do experi­ ence the behavioral and cognitive impairments associated with the slow settle­ ment into the wake state. Behaviorally, we experience difficulty in carrying out fast and coordinated body movements. This phenomenon has to do with sleep atonia, a paralysis of most skeletal muscles that is present in slow wave sleep (SWS) but most pronounced in REM sleep. Atonia of skeletal muscles has the welcome effect that it prevents us from acting out our dreams. Regarding cognitive functions, our brain is depriving itself of bottom-up sensorial excitation in sleep. Our attention is turned inwardly, and our thought processes are usually not consciously accessible. Even vivid dreams are generally difficult to remember upon awakening. Awakenings represent shifts in attention. The return of waking cognitive functions, such as cognitive awareness, reflective consciousness, and memory, are neurobiologically modulated and do not have a sudden onset. However, cognitive awakenings precede behavioral awakenings. This temporal decoupling becomes impressively evident in a phenomenon called “sleep paralysis” in which the sleeper feels wide awake and perceptive of the external world but remains unable to move due to a continuation of sleep atonia into the wake state. Behavioral responsiveness in sleep is therefore not a very precise temporal measure of an awakening. We may rightfully conclude that our subject has awoken when he or she behaviorally responds to stimulation. However, the absence of a behavioral response is not a guaranteed indication of sleep. Also, behavioral responses are not a valid marker of specific sleep stages because sleep inertia is strongly influenced by time-of-night effects. A person who only responds to stimulation of high intensity during SWS in the first half of the night may require the same stimulus intensity to be behaviorally awakened from stage 2 NREM sleep in the second half of the night. Although both sleep stages differ in their EEG pattern, the behavioral arousal threshold may be quite similar because behavioral responsiveness is at least as much under circadian as under ultradian influence.

B. DEFINING AROUSALS

AND

AWAKENINGS

In 2007, the American Academy of Sleep Medicine has put down concrete rules for the scoring of arousals. They are to be understood as “an abrupt shift in EEG frequency, which may include theta, alpha and/or frequencies greater than

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16 Hz but not spindles” (p. 37). To be able to arouse from sleep, the subject must have been asleep for at least 10 consecutive seconds, and the frequency shift must be at least 3 s in duration. Arousals from REM sleep must be accompanied by an increase in submental electromyographic (EMG) activity. Concerning awakenings, we are lacking a clear definition. We understand an awakening as a relatively permanent shift to the wake state with increases in EMG activity to waking levels and open eyes. With regard to EEG tracings, my search of the literature has not yielded a specific description of awakenings. In most cases, they are described in behavioral terms. A subject is assumed to be awake when he or she somehow responds to an experimental task. Of course, as we will see when we speak about sleep inertia effects, this assumption is not sufficient. Response laten­ cies may be delayed due to sleep inertia effects, responses may not be carried out in spite of wakefulness because the sleeper is in the transition from sleeping to waking, perhaps switching back and forth between an internal and external focus of attention. What, then, is the proper definition of an awakening? I propose that an awakening is accompanied by behavioral responsiveness but defined by the ability to think and the capacity for rational decision making and reflective awareness. With regard to changes in the EEG, it would have to be accompanied by alpha blocking and a dominance of fast frequencies, i.e., beta and gammaband activity. In addition, awakenings would have to be followed by increased coherences as a measure of wake-like levels of cortico-cortical and cortico-tha­ lamic networking.

II. EEG Changes Preceding an Awakening

A. WAKING

UP TO

EXTERNAL STIMULI

Awakenings can occur either spontaneously or in response to stimulation. In general, sleep is accompanied by a lessened attentiveness to external as well as internal stimuli. However, trivial everyday experience tells us that the disengagement of the external sleep environment must only be partial. Anec­ dotal reports of sleeping mothers awakening to very low-intensity sounds uttered from their infants yet maintaining sleep during high-intensity acoustic stimulation by passing trucks are well known and have even been replicated in the laboratory. Scientific investigation of information processing of acoustic stimuli in sleep has shown that acoustic events are processed with regard to stimulus salience instrumentalized by varying intensity, novelty, probability, and semantic properties of the presented stimuli. What, then, determines the probability of an awakening?

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First of all, event-related potential (ERP) and imaging studies have taught us that sleep processing differs from that of wakefulness in some very basic properties. While in wakefulness, salient stimuli are related to a hightened attentiveness, stimulus salience in sleep is met with an inhibitory response, as evidenced by a negative BOLD signal (Wehrle et al., 2003) and a high-amplitude negative deflec­ tion in the sleep ERP (Campbell et al., 1992; Harsh et al., 1994; Voss and Harsh, 1998). Since highly salient stimuli have been shown to disrupt sleep, it can be assumed that the inhibitory response reflects an effort at attention-inhibition which is aimed at sleep maintenance. K-complexes and sleep spindles, so-called epiphe­ nomena of sleep, are most likely involved in sleep maintenance and/or awaken­ ings (Goff et al., 2010; Kokkinos and Kostopoulos, 2010; Voss and Harsh, 1998). In addition to stimulus salience, sleep processing varies as a function of slow wave background activity (SWA). During NREM sleep, high SWA is accompa­ nied by an increased arousal threshold. Within REM sleep, there is a difference in arousal threshold between phasic and tonic REM periods (Ermis et al., 2010; Sallinen et al., 1996).

B. AROUSABILITY: BEHAVIORAL REACTIVITY In the context of evolutionary biology, we can observe a development of sleep patterns that, especially with regard to the ability to react behaviorally to external signals of danger, has led to a diminished capacity to carry out a defensive reaction while sleeping and, at the same time, to an increase in sleep intensity. Figure 2 illustrates the proportion of phases of rest within the sleep/wake cycle of different kinds of vertebrates. Whereas with fish and amphibians, phases of rest REM

Forms of rest

SWS

100% 80% 60% 40% 20% 0%

SLS-3 SLS-2 SLS-1 W sh

ia

Fi

Am

es

til

ib

ph

R

ep

s

s

al

rd

Bi

m

am

M

FIG. 2. Percentage of rest phases in sleep–wake cycle of selected species of vertebrate. 24 h = 100%. W, Wake state; SLS-1, day rest; SLS-2, nocturnal rest; SLS-3, state of rest accompanied by muscle relaxation; SWS, slow wave sleep; REM, rapid eye movement sleep. The percentages are to be understood as simplified estimates, depicting the phylogenetic development of behavioral phases of rest rather than generalizable statistics on the sleep pattern of specific vertebrates. Cited from Voss (2001); reproduced from Karmanova (1982).

CHANGES IN EEG PRE AND POST AWAKENING

29

that are not accompanied by a muscle relaxation (SLS-1 and SLS 2) predominate during the day as well as at night, reptiles show a higher proportion of phases of rest (SLS-3) during which muscular relaxation reduces the ability to react. Birds and mammals go into SWS and REM sleep during which the behavioral reactivity is maximally reduced. A large variability exists in the length of spe­ cies-specific NREM–REM cycles and in the extent to which muscle atonia inhibits voluntary movements, especially in birds (Amlaner and Ball, 1983; Zepelin and Rechtschaffen, 1974). The length of the cycle seems to be influenced by constitutional variables such as metabolic rate and brain weight of a species (Zepelin and Rechtschaffen, 1984), but also by situational variables and factors pertaining to total endangerment and prey status (for a review, see Voss, 2004). Several studies on human subjects have revealed that the sleeper is able to continue to react to stimulation and not wake up. However, response rates in sleep are strongly reduced. Also, responding usually relies on high sleep pressure and very intense stimulation. In other words, very sleepy subjects may sometimes carry out an automated or highly conditioned response. Granda and Hammack (1961), for example, found that subjects were able to respond reliably throughout all stages except REM sleep (not recorded) when they applied small electric foot shocks which subjects could avoid by closing a microswitch on a 3-s schedule. Evans et al. (1970) demonstrated that subjects are capable of interacting with their environment by performing a motor task during sleep, however, at a very low rate. In addition to the fact that response probability was low (20.4%), it strongly decreased with increasing depth of sleep. Similar declines in performance rate across sleep stages have been reported by Harsh and Badia (1989). What these studies show is that the brain continues to process information even when this information does not lead to an awakening. While sleep is normally characterized by behavioral unresponsiveness to external signals, it is not necessarily so. Although it is difficult and rare, it is possible to continue responding although the EEG background activity signals that the subject is fast asleep. Most of our studies on awakenings have utilized the inverse relationship of behavioral responsiveness and sleep depth and we will continue to do so because in most cases, the two measures are antagonistic. We should keep in mind, though, that simple behaviors can be carried out in sleep, and that the instruc­ tions to respond can be ignored in waking. Nonetheless, we will now take a closer look at sleep-stage-specific thresholds of arousal and behavior.

C. AROUSABILITY: SLEEP-STAGE-SPECIFIC EFFECTS Arousability varies as a function of time of night and stimulus salience, varies from sleep stage to sleep stage, and varies between individuals. Time-of-night

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NREM

SS1

SS2

SS3

REM

SS4

First half of night Deep sleep, Light sleep SWS, δ sleep

Phasic

Tonic

REM sleep

Second half of night FIG. 3. Ultradian rhythm of NREM and REM sleep stage (SS) succession in humans. During the first half of the night, we alternate between stages of light sleep (easy arousability) and deep sleep (difficult arousability). In the second half, light sleep alternates almost exclusively with REM sleep.

effects and ultradian rhythmicity of sleep stage succession are related, of course, as can be seen from Fig. 3. Whereas we alternate almost exclusively between light and deep stages of NREM sleep during the first half of the night, in the second half we constantly switch back and forth between REM sleep and light NREM sleep. Several factors influence the arousability from sleep, for example, the mod­ ality of stimulation (Pisano et al., 1966; Schneider-Helmert, 1987), stimulus intensity (Haynes et al., 1985; Keefe et al., 1971), emotional significance of the stimulus (Oswald et al., 1960; Voss and Harsh, 1998; Wilson and Zung, 1966), and state and condition variables. With regard to sleep stage changes, arousal thresholds have been shown to increase with increasing sleep intensity during NREM sleep, i.e., it is easier to awaken a subject from sleep stage 2 than from sleep stage 3 or 4 (Bonnet et al., 1978; Busby et al., 1994; Philip et al., 1994). With respect to REM sleep, however, results are not as clear. Some studies report higher arousal thresholds for this stage compared to SWS in cats (Grahnstedt and Ursin, 1980) and rats (Dillon and Webb, 1965; van Twyver and Garrett, 1972) as well as humans (Williams et al., 1964), others found arousal thresholds to be indifferent in SWS and REM sleep in human subjects (Roehrs et al., 1994), and still others observed lower thresholds in human subjects during REM sleep compared to SWS (Philip et al., 1994; Rechtschaffen et al., 1966). Although this is clearly indicative of a heterogeneity in stimulus processing during REM sleep, the between-studies variability in arousal thresholds might have come about by the neglect to distinguish between phasic and tonic REM sleep. Indeed, data from Sallinen et al. (1996) and Ermis et al. (2010) strongly suggest

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31

that these two REM stages have distinct arousal thresholds and that it is similarly difficult to awaken a subject from phasic REM sleep as from SWS. Thresholds in tonic REM sleep are similar to those of stage 2 NREM sleep. These findings are in accordance with animal studies in which the scoring of REM episodes was more closely linked to the presence of eye movements than in human studies. In the next paragraphs, we will look more closely at arousal thresholds in NREM and REM stages of sleep.

1. NREM Stages NREM sleep is a state of behavioral and physiological quiescence (Jouvet, 1967), accompanied by a diffuse slowing and synchronization of the EEG. Brain and body temperature, heart rate, respiratory rate, cerebral glucose metabolic rate (Maquet et al., 1990), resting muscle tone, and spontaneous motor activity continuously decrease across Rechtschaffen and Kales (1968) sleep stages 1–4 compared to the waking state. Reflex excitability remains intact. Arousal thresh­ old increases across stages 1–4 (Ermis et al., 2010; Evans, 1993). NREM sleep stage 1 is characterized by a low-amplitude, mixed-frequency EEG that is often accompanied by slow rolling eye movements. The arousal threshold is low, i.e., the sleeper can be easily awakened. Stage 2 is defined by a low-amplitude EEG and the presence of grapho-elements such as sleep spindles and K-complexes. When subjects are awakened from this stage, approximately 50% report thought-like cognitive processes (Kelly, 1991). Stage 3 refers to 30-s epochs in which at least 20% but not more than 50% of the epoch consists of slow- and high-amplitude delta waves. Sleep stage 4 is defined by more than 50% activity in the delta frequency range. The generally restrained Babinski reflex turns positive in stages 3 and 4, suggesting a suppression of the supraspinal inhibition. Upon forced awakening in the laboratory, the sleeper rarely reports of dreams or thought-like cognitive activities. Pertaining to the arousability to external sensory stimuli or sleep intensity, stages 1 and 2 are often referred to as light sleep and stages 3 and 4 as deep sleep.

2. Stage REM Sleep REM sleep is characterized by a relatively low-voltage, mixed-frequency EEG activity and episodic rapid eye movement bursts. Specific regions of the central nervous system (CNS) and adjacent structures are increased and several important peripheral structures are decreased. Increases in CNS neuronal firing rates (Evarts et al., 1962; Steriade and Hobson, 1976) are accompanied by elevations in blood flow (Lenzi et al., 1987; Meyer et al., 1987), metabolism (Abrams et al., 1988; Buchsbaum et al., 1989; Maquet et al., 1990), and

32

VOSS

temperature (Kawamura and Sawyer, 1965; McGinty and Szymusiak, 1990). Peripheral deactivation is mediated by a tonic inhibition of the spinal motor neurons which leads to a complete paralysis of all striatal muscles except eye muscles, respiratory muscles and middle ear ossicles. The tendon reflexes are completely suspended. The active closing of the eyelids ceases due to an increase in resting tone of the rectus muscles of the eyes (Jouvet, 1967). Muscle twitches occasionally occur. Blood pressure and heart rate show increased varia­ bility, respiratory irregularity, and poikilothermy, i.e., the incapacity to regulate the body temperature. Independent of whether the surrounding temperature is high or low, shivering or transpiration does not occur (Kelly, 1991; Nicolau et al., 2000; Parmeggiani, 1992). These physiological correlates of REM sleep show that REM sleep is accom­ panied by several phenomena that increase the vulnerability and endangerment of an organism against external threat. In REM sleep, we lose the ability for temperature control and our gravitational muscles become atonic, which, at least theoretically, lessens our ability to react quickly to danger cues and which puts the organism at a disadvantage in terms of its ability to carry out a fight or flight response (Cannon, 1929). In keeping with this assumption of heightened vulner­ ability in REM sleep (see Voss, 2004), laboratory studies show that unfavorable surrounding temperatures lead to an increase in arousal frequency during REM sleep, a fragmentation of sleep pattern, and a shortening of REM episode duration (Libert et al., 1988; Muzet et al., 1983). Moreover, in contrast to all other phases of sleep, REM phases are more often interrupted or brought to an end by spontaneous awakenings (Schulz et al., 1991; Weitzman et al., 1980). Possibly, these interruptions serve the purpose of limiting the very necessary phases of REM sleep unresponsiveness to a minimum. A possible safety gadget that further restricts long periods of unresponsiveness may be the fluctuation of periods with and without rapid eye movements, i.e., phasic and tonic REM periods (Cantero et al., 2000; Ermis et al., 2010; Kohyama, 1996). Tonic REM sleep refers to the state of widespread, low-voltage, fast electrocortical activity with hippocampal theta, a decrease in neck and chin EMG amplitude, and brain temperature elevation (Baust et al., 1964; Pessah and Roffwarg, 1972; Rechtschaffen, 1978). Phasic REM sleep characterizes those periods in which distinct oculo-motor activity (REMs) as well as middle-ear­ muscle activity, extra-ocular phasic integrated potentials, and cardio-respiratory irregularities occur (McCarley and Hobson, 1975; Sallinen et al., 1996). These two REM states are mediated by separate yet interactive neuroanatomic loci (McCarley and Hobson, 1975). Moreover, phasic REM sleep periods include distinctive oculo-motor activity (REMs) that is associated with ponto-geniculo­ occipital (PGO) waves (Callaway et al., 1987; Datta and Hobson, 1994; Lim et al., 2007). PGO waves are a feature of REM sleep, which are generated or propa­ gated in the pontomesencephalic tegmentum. In the presence of PGO waves,

33

CHANGES IN EEG PRE AND POST AWAKENING

Mean Behavioral Thresholds (s.e.) 65

dB

55 45 35 NREM 2

NREM 3

NREM 4 Sleep stage

REMp

REMt

FIG. 4. Mean behavioral arousal thresholds across sleep stages: sleep stage 2, NREM 3 and 4, phasic REM (REMp) and tonic REM (REMt). N = 10. Partially reproduced from Ermis et al. (2010).

higher cortical processing of external stimuli is inhibited (Lim et al., 2007; Miyauchi et al., 2009; Wehrle et al., 2007). PGO waves immediately precede saccadic and microsaccadic activity in REM sleep (Amzica and Steriade, 1996; Fernandez-Mendoza et al., 2009; Martinez-Conde et al., 2009) and it is quite possible that perceptual inhibition is still active during those phases in which REMs occur. Widespread thalamocortical synchronized activity occurs selec­ tively enhanced during phasic REM sleep when compared with predominantly tonic REM sleep background (Wehrle et al., 2007). Not surprisingly, then, phasic REM sleep is characterized by the highest arousal threshold (Ermis et al., 2010; Sallinen et al., 1996) (see Fig. 4) during which external stimuli will often be integrated into dreams (Hobson, 1990). From an evolutionary point of view, the constant alternation of phasic and tonic REM phases appears beneficial, as phasic REM sleep constitutes an extremely vulnerable state lacking both sensory input and executive control of reactions due to general muscle atonia. Phasic REM sleep activations usually appear in short bursts, avoiding prolonged periods of time in this isolated state, whereas tonic periods may be beneficial to detect potential danger cues.

D. FREQUENCY-SPECIFIC ACTIVITY IN SLEEP THRESHOLDS

AND

BEHAVIORAL AROUSAL

In a recent study on arousal thresholds and EEG correlates (Ermis et al., 2010), we found that surprisingly arousal thresholds cannot be matched with activity in specific frequency bands (see Fig. 5). Moreover, NREM background activity is systematic and varies as a function of sleep depth, i.e., SWA. REM sleep background activity shows little variability between its two stages phasic and tonic REM sleep.

34

dB

VOSS

Arousal thresold

60

50

40

100 75

50

25 0

40

** Delta

Theta

power (%)

20

** ** **

**

0 30

15

Alpha

**

Beta

**

0

10

5

0 0.50

**

Gamma

0.25

**

0.00 S2

S3

S4

REMp

REMt

FIG. 5. Top row: Mean behavioral arousal thresholds across sleep stages: NREM 2, 3, and 4, phasic REM (REMp) and tonic REM (REMt). N = 10.Rows 2–6: Boxplots of mean standardized power in frequency bands d, q, a, b, and g in each sleep stage.

Regarding arousability and background EEG activity, sleep stages with similarly elevated arousal thresholds (stage 4 NREM and phasic REM sleep; stage 2 NREM and tonic REM sleep) are completely unlike in their frequency spectra. While we might assume a relationship between background EEG activity and arousability in NREM sleep (Fig. 5), consisting of an increase in delta activity (0.5–4 Hz) and decreases in theta, alpha, beta, and gamma activity coinciding with an increase in arousal threshold (top row), such pattern is not apparent during REM sleep. What we learn from these data is that behavioral arousal thresholds to external stimuli are not clearly depicted in electrophysiologic back­ ground activity of the brain. With regard to activity in specific frequency bands, the most important ones with regard to information processing are gamma (waking) and delta (sleeping). Highest gamma activity was observed for tonic REM sleep. This could be interpreted as evidence of higher awareness of the external world in tonic

CHANGES IN EEG PRE AND POST AWAKENING

35

REM sleep compared to all other sleep stages, with atonia preventing this awareness to be fully translated into a behavioral response. However, as recent studies suggest, gamma-band activity may be confounded by microsaccades and cortical muscle activity (Trujillo et al., 2005; Whitham et al., 2007; Yuval-Greenberg et al., 2008), both of which were not assessed in the current study or any other published study that we have knowledge of. We consider it likely, given the strong oculomotor activity present in REM sleep, that the increase in gamma-band activity in tonic REM sleep may indeed be related to stronger microsaccadic activity in this sleep stage compared to sleep stage 2 (Wu et al., 1989). The only reliable effect of frequency-specific activation that was evident in all stages of sleep concerned delta activity. The comparison of phasic REM vs. tonic REM sleep and stage 4 vs. stage 2 NREM sleep showed significantly elevated delta band power in stages of elevated arousal thresholds, i.e., stages 4 and phasic REM sleep. This indicates that these two stages represent the deepest of sleep stages, accompanied by the highest inhibitory strength toward external stimulation.

E. BEHAVIORAL RESPONSIVENESS

AND

PGO WAVES

The strong difference between responding in the two substages of REM sleep cannot be sufficiently explained by dissimilarities in their respective frequency spectra. Likewise, the similarity in behavioral responsiveness during state 2 and tonic REM sleep or state 4 and phasic REM sleep is not reflected in the respective frequency spectra. This suggests that other—state-related—factors must modu­ late the behavioral and/or brain response. A possible candidate exhibiting such a confounding effect is stimulus-evoked ocular activity such as microsaccadic ocular activity and PGO waves. Several authors have shown that microsaccades and PGO-like waves are related and that they vary as a function of sleep phase, i.e., NREM and REM sleep (Callaway et al., 1987; Chase and Morales, 1990; Miyauchi et al., 2009; Stuart and Conduit, 2009). It is currently not known, however, whether these ocular events constitute a mere artifact that influences levels of EMG inhibition (Chase and Morales, 1990; Wu et al., 1989) or whether they are related to differential inhibition of higher order information processing (Martinez-Conde et al., 2009; Voss et al., 2009; Wehrle et al., 2007). The lower fast frequency band activity during phasic REM sleep observed in our study suggests that a heightened arousal threshold in phasic REM sleep is related to reduced attentiveness to the external environment. However, the behavioral effect might also be modulated by different levels of EMG inhibition

36

VOSS

in the two REM stages (Chase and Morales, 1990; Wu et al., 1989), preventing a behavioral responding but not information processing. We tried to investigate this possibility indirectly by analyzing alpha arousals in the absence of concurrent EMG changes and by comparing alpha power following tone presentations in the two REM stages. We found no evidence of a selective EMG inhibition during the two REM stages in our data. We, therefore, assume that microsaccadic activity and PGO waves exert their primary inhibitory influence on inhibition of higher order information processing of external sensorial information. This interpreta­ tion is supported by imaging (Wehrle et al., 2007) and evoked potential data (Sallinen et al., 1996) showing a lowered REM-P3 response to auditory stimuli presented in phasic vs. tonic REM sleep. However, the final resolution of this issue awaits the availability of an exact measurement device for microsaccadic activity in human sleep.

F. INDIVIDUAL DIFFERENCES

IN

AROUSABILITY

When I was still a graduate student and a novice in sleep research, I remember being amazed at how different our subjects reacted to stimulation, behaviorally and also electrophysiologically. I also have a very clear memory of testing our professor, John Harsh, who was nice enough to volunteer as a first subject for an oddball task in which we wanted to start stimulation following sleep onset. After what seemed like hours, we were happy to finally see spindles and K-complexes in the EEG, so we started to present our stimuli. However, he very quickly called out to us claiming he had never been asleep and we were too quick to start testing! By contrast, other subjects fell asleep easily in spite of high-intensity tones being presented to them. These observations led me to search the literature for differences in arousability and personality. I found that my observations in our laboratory were shared by many other researchers and that these individual differences in responsiveness were well documented. As many studies show, subjects vary substantially in their reactivity to stimulation during sleep (Bonnet and Moore, 1982; Evans et al., 1970; Harsh and Badia, 1989; Weinberg, 1966; Zung and Wilson et al., 1961). Whereas some subjects are very responsive even during sleep stage 4, others cease responding or respond very slowly in sleep stage 1 (Weitzman and Kremen, 1965; Williams, 1973). With regard to factors accounting for these differences in reactivity, I found that most studies on individual differences and sleep had been conducted in clinical settings on patients with manifest psychological and/or psychiatric disorders such as depression and/or anxiety. It seems that both depression and anxiety are often accompanied by insomnia (DSM-IV, 1994; Idzikowski, 1994),

CHANGES IN EEG PRE AND POST AWAKENING

37

albeit the effect of anxiety on sleep has not yet been firmly established (for a review, see Brown et al., 1994). Although most insomniacs are psychologically inconspicuous, depressed and anxious patients suffering from insomnia often complain about light and easily disrupted sleep. This would certainly constitute one factor determining interindividual variations in arousability. Since most of the subjects participating in our studies were not clinically depressed or anxious and they did not complain of light or easily disrupted sleep, I searched for other psychological variables that may have an influence on arousability such as coping style. The most promising concept with respect to arousal from sleep appeared to be the one of information seeking and avoidance. An information-seeking coping style is related to heightened arousability and responsiveness for normal as well as psychiatric patients. The coping styles I then studied were called Monitoring and Blunting, assessed with the Miller Behavioral Style Scale (MBSS). If placed in an uncontrollable situation that is perceived as dangerous, Monitors tend to seek information about the event and Blunters try to distract themselves from the situation (Miller, 1987, 1990). During wakefulness, Monitoring has been associated with a heightened level of arousal (Miller, 1987) and a predisposition to engage in worrying (Davey, 1993, as cited in Miller, 1992). The underlying motif for Monitors is apparently that they are intolerant against uncertainty, whereas Blunters are motivated to prevent hyperarousal. I suspected that Monitors and Blunters would differ in their ability to cope with being asleep. In sleep, we are in a potentially dangerous situation simply because we perceive less of our environment and because we cannot move purposefully. Those who react fearful to uncertainty might be more watchful during sleep and those who want to prevent hyperarousal may be more inclined to block out all stimuli that may lead to an arousal. In a first study, I conducted an oddball study on Monitors and Blunters, presenting salient and meaningless auditory stimuli (own name, name of insig­ nificant other, tones) throughout sleep onset and during light sleep. Results confirmed my expectations in that Monitors responded longer to stimuli (Fig. 6) than Blunters, regardless of whether they were instructed to pay attention to tones (tone = target) or their own names (own name = target). In the evoked potential waveform, Monitors had an augmented N350 ERP component in sleep and an enhanced P3 component during waking (a description of these components and their assumed functions follows below). Blunters showed the opposite trend, i.e., smaller P3s in waking and larger N350s in sleep, suggesting the two components to be stage specific and perhaps related (see Figs. 7A and B). Of course, since P3 attenuates not only as a function of losing focused attention but also as a function of behavioral response inhibition (Harsh et al., 1994; Hull and Harsh, 2001), an inverse relationship is hard to test. Instead, in a later study, we investigated the relationship between evoked 40-Hz activity as a

100

90

80 70 60 50 40 30 20 10

0

VOSS Tone condition Percent responding

Percent responding

38

*

w

1A

1B Sleep stage

2A

2B

100 90 80 70 60 50 40 30 20 10 0

Own name condition

w

1A

1B Sleep stage

2A

2B

FIG. 6. Mean percentage of targets followed by a finger-lift response for Monitors vs. Blunters throughout all recorded sleep stages. Error bars indicate standard errors. Monitors reacted more often than Blunters when the target was a 1500 Hz tone (left frame) or when the target stimulus was their own name (right frame). Stage 1A: breaking up of the alpha rhythm but with alpha present during 50–80% of the epoch. Stage 1B: traditional stage 1 sleep according to Rechtschaffen and Kales criteria. Stage 2A: the first 5 min of stage 2 sleep. Stage 2B: the first 5 min of stage 2 preceded by 5 continuous minutes of stage 2 sleep. & Monitors, & Blunters.
p £ 0.05. Reproduced from Voss and Harsh (1998).

behavior-independent indicator of information processing and N350 as sleeprelated inhibitory response (Kallai et al., 2003). We found that the evoked 40 Hz response was indeed negatively correlated with N350, showing that the two responses are inversely related (see Fig. 8). With regard to individual differences in reactivity to external stimuli, these studies show that arousability and the propensity for stimulus-evoked awakenings vary as a function of coping style. Although I speculated that arousability and the propensity for awakenings also varied as a function of perceived threat, we could not be sure of this relationship because we had not manipulated stress levels and we had not interviewed our subjects thoroughly. In a next step, then, we carried out a double-blind study on the sleepdisruptive effect of different qualities of sleep disturbances and their relation to coping style (Voss, 2001). We tested a group of Monitors, a group of Blunters, and a control group. Subjects spent four nights in the laboratory during which they were subjected to a variety of potentially threatening situations: first night in a laboratory, uncertainty about procedures, anticipation of rare auditory stimulus presentation, anticipation of a psychological screening, and intelli­ gence testing in the morning (testing was actually not planned or carried out). What we observed was that sleep intensity of all subjects was most strongly affected when series of tones with an increasing degree of loudness were presented at unequal intervals. The most impressive change in sleep architec­ ture was a huge increase in stage shifts (mean across all subjects = 115, SD = 34). Those with a great need for information (Monitors) even went through an average of 154 stage changes. The number of stage shifts also

39

CHANGES IN EEG PRE AND POST AWAKENING Tone condition

A.

Cz

Fz

Own name condition

Pz

Fz

Pz

Cz

Other name

Own name

Tones P3

N350 (Cz) to tone target

B.

N350 (Cz) to own name targets

−20

−20

−15

−15

*

−10

*

µV

µV

−10 −5

** 0

5

Wake

1a

1b

Sleep stage

2a

2b

−5

**

0 Wake 5

1a

1b

2a

2b

Sleep stage

FIG. 7. (A)Evoked-related potentials to auditory stimuli in waking. Monitors (thick line) have a higher P3 amplitude than Blunters (thin line) to both target stimuli and salient non-targets (own name is a salient non-target when subjects are instructed to ignore the names and attend to tones). — Monitors, — Blunters. (B) Evoked N350 amplitude in response to stimuli presented in light sleep. In general, Monitors generate a smaller N350 component to target stimuli. N350 to non-salient targets (tones) is larger than that to salient targets (own name), possibly because non-salient targets are easier to be met with an inhibitory response, whereas salient targets frequently lead to an arousal or an awakening. Reproduced from Voss and Harsh (1998). & Monitors, & Blunters.

increased during the other three nights in which the security of the sleep environment was reduced experimentally. Furthermore, we noticed a post­ ponement of REM sleep. In Monitors, their subjective perception of a danger­ ous situation even led to a complete breakup of the NREM–REM cycle. We later found an increased propensity for the development of primary insomnia in Monitors (Voss et al., 2006).

40

VOSS

(A) EVOKED 40-HZ RESPONSE

40-Hz response

0.8

Awake in chair

Fpz Fz Cz

1.2

Power

(B) N100 and N350

−5

0.4 0 −100

−0.4

0

100

200

ms

−0.8 Fpz Fz Cz

40-Hz response

Awake in bed

0.8

0 −0.4

0

100

200

ms

−0.8

Light sleep

0.8

0

−0.4

0

100

200

ms

−0.8 Power

1.2

Slow wave sleep

0 0

100

200

ms

−0.8 Power

REM sleep

0.8

−0.8

µV−10

−200 −100 0 0

N350

Fpz Fz Cz

100 200 300 400 500ms

µV −10 N350

−200 −100 0 0

Fpz

Fz

Cz

100 200 300 400 500ms

0

100

200

ms

Fpz

Fz

Cz

µV −10 −5

0 −0.4

100 200 300 400 500ms

5 Fpz Fz Cz

1.2

0.4 −100

N350

N100

−5

0.4

−0.4

−200 −100 0 0

5 Fpz Fz Cz

0.8

−100

Fpz

Fz

Cz

−5

0.4 −100

N350

100 200 300 400 500ms

5 Fpz Fz Cz

1.2

Power

N100

µV −10 −5

0.4 −100

−200 −100 0 0

Fpz Fz Cz

5

1.2

Power

µV −10

−200 −100 0 0

N100

N350

100 200 300 400 500ms

5

FIG. 8. Inverse relationship between the evoked 40 Hz response and N350 across different stages of conscious awareness. Whereas the evoked 40 Hz response to auditory stimulation is enhanced in wakefulness, it decreases across sleep stages of increasing sleep depth. The N350 response takes the opposite course. (A) The figure shows averaged synchronous 40-Hz activity at sites Fpz, Fz, and Cz. The y-axis shows the power obtained using the Gabor filter. (B) Averaged ERPs (time constant 1.1 s, low-pass filtering 20.67 Hz) at leads Fpz, Fz, and Cz. The y-axis shows the amplitude in microvolts. Reproduced from Kallai et al. (2003).

41

CHANGES IN EEG PRE AND POST AWAKENING

In summary, this shows that in addition to situational and state-related influences on arousability and awakenings, individual differences also contribute to the observed variability in these measures. These individual differences are reflected in the auditory ERP, especially in the inhibitory N350 component and the excitatory 40 Hz deflection.

G. AROUSABILITY: EVENT-RELATED POTENTIAL STUDIES

ON

ATTENTION

IN

SLEEP

Whereas the presence of attentional processes must be inferred when a beha­ vioral response is elicited, the absence of a behavioral response does not necessarily imply the absence of stimulus processing. The sleeper may passively attend to a specific stimulus but choose not to react to it. Such an interpretation is supported by sleep research employing stimuli of different salience such as tone pips (Harsh et al., 1994; Nielsen-Bohlman et al., 1991), meaningless names (Oswald et al., 1960; Voss and Harsh, 1998), pseudo words (Bastuji et al., 2002), and the own name of a subject (Mendelson et al., 1986; Voss and Harsh, 1998), showing that the sleeper is not only able to awaken preferentially to salient stimuli (own name) but also to differentiate between these stimuli based on their physical and psychological properties. Information processing in the absence of behavioral responses has been studied employing auditory-evoked potentials (AEPs) and combined EEG/functional magnetic resonance imaging (fMRI) designs. Results from human AEP studies have shown that the short- and mid-latency components, which are responsive to changes in the physical stimulus properties, are only minimally affected by sleep onset (reviewed in Campbell and Colrain, 2002). Long-latency components indicative of higher order stimulus processing, on the other hand, are very much affected by sleep onset and sleep. Results from these studies on long-latency AEPs have shown that auditory signals continue to be processed during sleep and that information processing varies across sleep stages (e.g., Atienza et al., 2001; Campbell et al., 1992; Harsh et al., 1994; Kallai et al., 2003). However, it appears that the allocation of attention and the proces­ sing of external sensory information in sleep differ qualitatively from that of wakefulness. Accordingly, the components of the sleep AEP have been found to differ in latency and scalp distribution from those of the wake AEP, although several authors have attempted to link components of the wake AEP to later occurring ones in the sleep AEP on the basis of their similar sensitivity to experimental manipulations (Perrin et al., 1999; Weitzman and Kremen, 1965). Effects of stimulus probability (Hull and Harsh, 2001), task relevance (Atienza et al., 2001), and stimulus salience (Kallai et al., 2003; Voss and Harsh, 1998) can reliably be observed throughout all sleep stages. In accordance with the surveil­ lance hypothesis, attention to external acoustic stimuli attenuates with

42

VOSS

progressing sleep depth. Most notably, attention-related changes during the course of sleep can be inferred from the evoked 40-Hz response, the P3 and the N350 components of the AEP. Of these components, the N350 is the most prominent one in sleep.

1. 40-Hz Response The evoked 40-Hz response has been shown to be indicative of selective (Tiitinen et al., 1993) and sustained attention (May et al., 1994), possibly serving as an attention-modulating response reflecting enhanced attentional resourcing (Tiitinen et al., 1993). This component has been found to be absent during SWS and REM sleep (Kallai et al., 2003; Llinas and Ribary, 1993), and markedly reduced during light sleep (Kallai et al., 2003).

2. P3 The P3 occurs at around 300 ms following stimulus onset and has been functionally related to active attention and the completion of a sensory discrimi­ nation process. It has been found to be diminished or absent in sleep (Campbell and Colrain, 2002; Picton et al., 1974), although positive deflections occurring at either 600 ms (Bastuji et al., 2002) or 800 ms (Weitzman and Kremen, 1965) following stimulus onset have been linked to the wake P3. However, these later positivities have been found to have different determinants from the P3 of wakefulness (Hull and Harsh, 2001). Furthermore, considering that other longlatency components occur at comparable latencies in wake and sleep conditions (Kallai et al., 2003) and that P3 has been reliably detected during light sleep (Campbell and Colrain, 2002), the assumption that the P3 component is singu­ larly affected by a considerable latency shift, seems, at this point, rather unlikely.

3. N350 The N350 has a mean latency of 350 ms and is functionally related to inhibition of stimulus processing or blunting (Campbell and Colrain, 2002; Harsh et al., 1994; Voss, 2001). It has been associated with the occurrence of vertex sharp waves (review in Bastien et al., 2002), although Kallai et al. have observed the N350 during relaxed wakefulness as well as in sleep, suggesting that it is not exclusively tied to vertex sharp waves. The N350 amplitude is inversely related to stimulus salience (Harsh et al., 1994; Voss and Harsh, 1998) and to stimulus probability (Nielsen-Bohlman et al., 1991). It reaches maximal amplitudes during the sleep onset period and remains present even during REM

CHANGES IN EEG PRE AND POST AWAKENING

43

sleep (Campbell et al., 1992; Kallai et al., 2003; Ogilvie et al., 1991; Ornitz et al., 1967). During the transition between waking and sleep, attentional processes are still active, as is suggested by the presence of the attenuated but present P3 compo­ nent (Harsh et al., 1994). The large N350 component which dominates the AEP during light and deep sleep, suggests that a greater effort is necessary to actively blunt stimuli during the wake/sleep transition than during deep and REM sleep. The competition between attentional and inhibitory processes active during sleep can be more closely inferred from the inverse relationship between the N350 and the evoked 40-Hz response (Kallai et al., 2003). The inverse relationship between these two components, showing an augmentation of the N350 concurrent with the attenuation of the 40-Hz response, seems to reflect an increased effort during light sleep to counteract attentional mechanisms in order for sleep to prevail. With regard to the differentiation of information processes during SWS and REM sleep, only few studies have included all sleep stages in their analyses (e.g., Kallai et al., 2003). During SWS, the AEP seems to be dominated by the N350 (Nielsen-Bohlman et al., 1991), whereas the amplitude of all sleep-related long-latency AEP components has been shown to be markedly reduced in REM sleep (for a review, see Niiyama et al., 1997). The results from Atienza et al. (2001) suggest that this suppression of the cortical auditory evoked response is initiated at a very early time in stimulus processing. The authors found a reduced amplitude of the mismatch negativity (MMN) during REM sleep as compared to the awake state and an inverse relation to the length of the Intertrial interval (ITI). The MMN is assumed to reflect the existence of a memory trace of a standard stimulus and the triggering of involuntary attention (Na¨ a¨ ta¨ nen et al., 1982). The results of the data suggest that in REM sleep, memory trace formation is weakened and that it decays rapidly. Studies employing combined EEG/fMRI techniques have not only replicated but also extended previous findings from AEP studies on information processing during sleep, especially during REM sleep. Maquet et al. (1996) have found a selective decrease of activity in parietal and prefrontal association cortices during REM sleep in healthy volunteers that may reflect the altered information proces­ sing mechanism. Wehrle et al. (2002) show that during phasic REM sleep, acoustic stimulation leads to a simultaneous deactivation of cortical and thalamic structures, whereas the activation pattern in tonic REM sleep is less clear, some­ times showing a moderate activation, sometimes a minor deactivation to stimula­ tion. These findings suggest that phasic REM sleep may represent an exceptional, qualitatively distinct sleep state characterized by a maximal gating of external events, enabling REM sleep to be maintained. Similar data in response to visual stimuli have been reported by Born et al. (2002). Restrictively, it has to be pointed out that the authors used non-salient stimuli only and that the negative BOLD

44

VOSS

signal found in the fMRI may disappear when biologically significant stimuli are presented. Also using an EEG/fMRI technique, Portas et al. (2000) have pre­ sented salient stimuli, i.e., subject’s own names vs. other names and tone beeps during wakefulness and NREM sleep. They found that the own name led to a higher activation in the left amygdala and left prefrontal cortex than other name or tones. The authors propose that the prefrontal cortex may serve to determine the consequences of an “alarm effect” (Portas et al. 2000, p. 994) which progresses either to a full awakening and acknowledgment of the input or to a sensory neglect, enabling the sleeper to remain in sleep. Due to insufficient data size, the authors did not differentiate between NREM sleep stages and did not report on REM sleep. Clearly, these data invite further studies. In summary, stimulus processing continues during sleep, albeit it seems as if processing of external events was aimed primarily at reaching a decision about its biological significance for the sleeper. Stimuli judged to be salient or suspicious are often followed by an awakening, and repetitive stimuli of meaningless content are being actively blocked out in order for sleep to be maintained. This process of stimulus gating allows a recognition of danger signals during all sleep stages. However, sleep stage-related differences remain, allowing the sleeper to allocate stronger attentional resources to external cues during light sleep than during deep sleep periods, and only to a minimal extent during REM sleep. It seems that phasic REM sleep represents an especially vulnerable state characterized by an activation pattern in response to non-salient acoustic and visual stimuli that is opposite to that observed in wakefulness. In this state, the sleeper’s vulnerability is not only heightened by the muscular atonia but also by the attenuated ability to detect a danger signal.

III. EEG Changes Following an Awakening

A. SLEEP INERTIA

OR

STATE-RELATED EFFECTS ON COGNITION

AND

BEHAVIOR

As stated in my introductory remarks, awakenings must be regarded as a complex state-shift. As such, awakenings are not to be mistaken with short-term arousals. Evidence for the organismic importance of an awakening comes from studies of sleep inertia and those on spontaneous morning awakenings. A shift from sleeping to waking is preceded by sharp rises in body temperature, blood pressure, and heart-rate frequency (Degaute et al., 1991). These changes persist and are followed up on by increased plasma levels of adrenocorticotropic hor­ mone (ACTH), aldosterone and cortisol (Follenius et al., 1992; Spath et al., 1992), as well as colonic motility (Crowell et al., 1991; Karans and Wienbeck, 1991).

CHANGES IN EEG PRE AND POST AWAKENING

45

We also know that auditory arousal thresholds decrease toward the morning (Empson, 1993), suggesting strongly that an awakening is a slowly occurring process with neurobiological and behavioral consequences. Regarding EEG changes, recordings of the awakening process indicate that the first 10–20 min after awakening are characterized by changes in EEG power consistent with increased sleepiness, or of decreased vigilance, when compared with wakefulness before sleep onset (Bruck and Pisani, 1999; Ferrara et al., 2006; Jewett et al., 1999; Salzarulo et al., 2002). These changes, referred to as “sleep inertia,” further demonstrate that the waking up process is a gradual one, and that the sleeping brain has to adjust in several ways to fully meet the demands of the waking world. Sleep inertia refers to the decrease or impairment of performance that occurs immediately upon awakening from sleep compared with that prior to sleep (Bonnet, 1993; Bonnet and Arand, 1995; Dinges et al., 1981). For 1–20 min following an awakening, the subject may be very sleepy, confused, and/or disorientated, (Torsvall et al., 1989; Bonnet and Arand, 1995; Dinges, 1990; Dinges et al., 1981; Kleitman, 1963; Pivik, 1991; Wilkinson and Stretton, 1971). Sleep inertia is most evident when awakening from sleep is abrupt, regardless of whether sleep occurs at night or during a daytime nap (Dinges, 1990; Dinges et al., 1981), and it occurs even when the subject has fully satisfied his or her sleep need (Folkard and Akerstedt, 1992). When comparing sleep inertia effects to the effects of sleepiness, Balkin and Badia (1988) found no conclusive evidence of qualitative differences between the two phenomena. Sleep inertia may thus reflect the incomplete transition from the state of sleep to the state of waking. With respect to EEG changes following an awakening, Ferrara et al. (2006) showed that the first 10 min after awakening differ profoundly from the corre­ sponding presleep waking period. Postsleep awakenings are accompanied by an increase in EEG power in the low-frequency range (1–9 Hz) and by a decrease of EEG power in the beta range (18–24 Hz). Both the heightened lower frequency activity and the augmented faster frequency activity showed an occipital pre­ valence. The authors suggest that this pattern could be considered as the spectral EEG signature of the sleep inertia phenomenon. The literature is inconclusive with regard to the question whether sleep inertia varies as a function of sleep stage, especially NREM or REM sleep. While several studies suggest that sleep inertia is more severe when subjects awake from NREM sleep, especially SWS, as opposed to REM sleep (Akerstedt et al. 1989; Bonnet, 1993; Dinges, 1990; Dinges et al. 1985; Pivik, 1991), others (Koulack and Schults, 1974) did not find significant differences between perfor­ mance following REM and NREM sleep arousal. Possibly, this disagreement would be resolved if REM sleep was portioned into phasic and tonic phases. Dinges et al. (1981) found that waking from SWS compared with REM sleep in a

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nap study did not appear to differentially effect cognitive functioning as assessed by a descending subtraction task. Studies of sleep inertia have so far primarily relied on performance tasks, the majority of which can be considered to be automatic or attentional tasks, rather than tasks involving complex processing. Although I would expect even stronger detrimental effects on more complex cognitive tasks, such speculation still awaits concrete testing. Also, the interaction between the situational factors and the duration of impaired decision making is still unknown. Does the duration of sleep inertia depend on contextual factors, such as perceived safety or stress level? Decision-making performance after REM arousal showed more variability than after SWS arousal. Subjects reported being significantly sleepier and less clear-headed following both SWS and REM awakenings compared with baseline and this was sustained across the full 30 min. In order to generalize this finding to real-life situations, further research is required on the effects of continuous noise, emotional arousal, and physical activity on the severity and duration of sleep inertia.

B. PARTIAL AWAKENINGS As pictured in Fig. 1, some animals maintain only partial sleep. Marine mammals, for example, only sleep with one hemisphere at a time. Unihemi­ spheric sleep allows these animals to go up for air in regular intervals and it also permits a screening of the environment for potential danger cues. Unihemi­ spheric sleep has also been observed in birds if they sleep at the outer edges of a flock, whereas those sleeping at the center have bihemispheric sleep. Unihemi­ spheric sleep allows for a quick awakening and—in a way—it may be the equivalent of a partial awakening. In humans, it has so far not been observed. Humans are capable of another form of partial awakenings, however, as evident from studies on lucid dreaming. Our own studies (Voss et al., 2009) demonstrate that in lucid dreaming, the brain is in two states at the same time, waking and sleeping. In contrast to unihemispheric sleep, the partial awakening is not beha­ vioral in lucid dreaming, but cognitive. Why is this so? The answer is surely multifactorial but we can speculate that for humans, it may be more important to be able to think clearly than to act quickly. We saw that EEG background activity may be a good predictor of arousa­ bility from NREM sleep but that different rules apply in REM sleep. Apparently, background EEG activity cannot be the sole marker of arousabiilty from sleep. The same is true for behavioral responsiveness to external stimuli. The most valid indicators signaling a state change are probably the ERP components evoked

47

CHANGES IN EEG PRE AND POST AWAKENING

40 Hz (evidence of wake-like processing of stimuli) and N350 (sleep maintenance and blocking of higher cortical sensory processing). However, ERP components require external stimuli and do not signal a spontaneous state change. In this last paragraph, I will introduce our latest studies and show that at least the evoked 40 Hz activity is also able to signal an awakening in the absence of external stimulation. What I refer to here is our study on lucid dreaming (Voss et al., 2009) which has provided us with the electrophysiologic correlates of a brain that is both awake and asleep at the same time, but in different parts of the brain (see Fig. 9). In lucid dreaming, the sleeper has insight into the hallucinatory nature of the dream, yet he or she remains in the dream, sometimes partially able to influence the dream plot. Regarding EEG changes in lucid dreaming compared to REM sleep, lucid dreaming is accompanied by an enhanced activity in the 40 Hz frequency band, especially in frontal regions of the brain (see Fig. 10). Furthermore, we found increased coherences in all frequency bands assessed (d, q, a, b, g), also with a frontal dominance (see Fig. 11), suggesting that lucid dreaming has a higher degree of synchronicity than REM sleep. These findings are interesting in itself but also validate the potential of the 40 Hz frequency band as reliable marker of an awakening.

WEC Lucid 10 Power (%)

REM

1

10−1 0

8

16 24 32 Frequency (Hz)

40

48

FIG. 9. Frequency-specific activity (standardized FFT power) in wakefulness (solid line, WEC, waking eyes closed), lucid dreaming (dashed line), and REM sleep (dotted line). The high peak in the alpha frequency band is absent in lucid dreaming and REM sleep, indicating that lucid dreaming is indeed a state of sleep. REM sleep and lucid dreaming are indistinguishable in lower frequencies but start to diverge at around 40 Hz. Fast frequency activity (>32 Hz) is indicative of wake-like thought processes linked to conscious awareness.

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40-Hz power WEC

1.50

0.50

Lucid

1.50

0.50

REM

1.50

0.50

FIG. 10. 40 Hz activity in waking (top), lucid dreaming (middle), and REM sleep (bottom). The increase in 40 Hz activity compared to REM sleep is strongest in frontal regions.

IV. Summary

In this chapter, we have looked at behavioral and electrophysiologic evidence of awakenings in the sense of a return to the wake state. We have discussed methodological issues mostly concerned with the problems of properly defining

CHANGES IN EEG PRE AND POST AWAKENING

Short-range

49

Long-range

Wake

Coh = 0.38

Coh = 0.49

Lucid

Coh = 0.24

Coh = 0.27

REM

Coh = 0.07

Coh = 0.05

FIG. 11. Coherences in waking (top row), lucid dreaming (middle row), and REM sleep (bottom row). Short-range coherences refer to synchronicity between neighboring electrode sites (3–10 cm distance), and long-range coherences describe synchronous activity between distant sites (>15 cm).

an awakening. Arousability is most often used in studies that aim at investigating awakenings to external stimuli. Behavioral responsiveness serves as validation that the individual has made the state change into waking. Besides individual differences in arousability as a trait characteristic, behavioral responsiveness is strongly reduced in sleep but not always absent. Especially highly automated responses have a (low) probability of being carried out even in sleep and in the

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absence of an awakening. Sleep inertia effects further add to the problem of assessing when a subject is really awake. The relationship between responsiveness (arousal thresholds) and background EEG activity is not systematic for NREM and REM stages of sleep, suggesting that we better find a behaviorally indepen­ dent measure of awakenings. Regarding ERPs, especially two components may be valid markers of either waking or sleeping, the evoked 40 Hz component as an indicator of conscious awareness and the N350 as inhibitory response to external stimuli. The 40-Hz activity as marker of an awakening is supported by studies on lucid dreaming in which the subject is partially awake and able to think almost rationally and partially asleep and experiencing bizarre dreams. In lucid dream­ ing, the EEG background activity shows an increase in frontal 40 Hz activity and an increase in coherence across all frequency bands.

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WHAT KEEPS US AWAKE?—THE ROLE OF CLOCKS AND HOURGLASSES, LIGHT, AND MELATONIN

Christian Cajochen
, Sarah Chellappa
, and Christina Schmidt



Center for Chronobiology, Psychiatric Hospital of the University of Basel, CH-4012 Basel, Switzerland

I. Introduction II. Circadian and Homeostatic Impetus for Wakefulness A. Timing and Consolidation of the Human Sleep–Wake Cycle: from Basic Arousal States to Controlled Cognitive Behavior B. Brain Circuitry Underlying Circadian and Homeostatic Influences on Human Cognition: A Possible Scenario III. Effects of Light on Human Wakefulness A. Light Switches on the Clock and the Hourglass B. Alerting Effects of Light C. Dose- and Wavelength Response Relationship of Light Exposure on Alertness D. Neuroanatomical Underpinnings of the Effect of Light on Alertness and Cognitive Performance E. Non-Clinical Applications of Light IV. Effects of Melatonin on Human Sleep and Wakefulness A. Endogenous Melatonin and the Human Circadian Sleep–Wake Cycle B. Effects of Exogenous Melatonin on Human Sleep and Wakefulness C. Implications for the Treatment of Insomnia and Circadian Rhythm Disorders References

What is it that keeps us awake? Our assumption is that we consciously control our daily activities including sleep–wake behavior, as indicated by our need to make use of an alarm clock to wake up in the morning in order to be at work on time. However, when we travel across multiple time zones or do shift work, we realize that our intentionally planned timings to rest and to remain active can interfere with an intrinsic regulation of sleep/wake cycles. This regulation is driven by a small region in the anterior hypothalamus of the brain, termed as the “circadian clock”. This clock spontaneously synchronizes with the environ­ mental light–dark cycle, thus enabling all organisms to adapt to and anticipate environmental changes. As a result, the circadian clock actively gates sleep and wakefulness to occur in synchrony with the light–dark cycles. Indeed, our internal clock is our best morning alarm clock, since it shuts off melatonin production and INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 93 DOI: 10.1016/S0074-7742(10)93003-1

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Copyright 2010, Elsevier Inc. All rights reserved. 0074-7742/10 $35.00

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boosts cortisol secretion and heart rate 2–3 h prior awakening from Morpheus arms. The main reason most of us still use artificial alarm clocks is that we habitually carry on a sleep depth and/or the sleep–wake timing is not ideally matched with our social/work schedule. This in turn can lead hourglass pro­ cesses, as indexed by accumulated homeostatic sleep need over time, to strongly oppose the clock. To add to the complexity of our sleep and wakefulness behavior, light levels as well as exogenous melatonin can impinge on the clock, by means of their so-called zeitgeber (synchronizer) role or by acutely promoting sleep or wakefulness. Here we attempt to bring a holistic view on how light, melatonin, and the brain circuitry underlying circadian and homeostatic pro­ cesses can modulate sleep and in particular alertness, by actively promoting awakening/arousal and sleep at certain times during the 24-h day.

I. Introduction

Despite the fact that humans have invented technologies such as artificial light and online services that allow us to do a certain activity at obviously any time, only a fraction of the humankind is involuntarily awake at night and sleeps during the light phase of the 24-h cycle. This natural synchrony in behavioral states among humans is also surprising because we think that we consciously plan our individual daily activities and thus our bed and wake-up times. There are certainly considerable interindividual and intercultural differences in the timing of sleep and wakefulness (e.g., chronotypes), but as to our knowledge there are no night-active human ethnic groups or cultures. This obviously points to a clear biological basis and an evolutionary adaptive behavior favoring a day-active human species. The neuroanatomical basis of the biological underpinnings of the daily (circadian) regulation of sleep–wake rhythms has been unraveled in the past century, but their physiological functions and implications on our health are still being intensely explored. Thus, how daily rhythms of behavioral states are controlled is an active area of current research. Given its relevance to human health, well-being, and cognitive performance, this is an important challenge to solve, particularly based on the fact that more and more people are forced to be awake at inappropriate or at biologically non-optimal times during shiftwork. In order to assess the effect of any stimuli either from the environment (e.g., light) or from the body itself (e.g., endogenous melatonin) on the regulation of awakening, a good insight of factors, which regulate sleep and wakefulness, is needed. Sleep and wakefulness are controlled by two primary factors: the

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circadian clock and the intrinsic need for sleep reflected in the homeostatic properties of sleep and determined by recent sleep–wake history. In Section II, we describe the neural mechanisms by which the circadian clock influences the sleep–wake system. In particular, we attempt at providing a better grasp of the physiological functions of the circadian clock and their relation to correlates of sleep intensity and its role of actively gating awakening/arousal and sleep at certain times during the 24-h day. We have new evidence from recent electro­ physiological and functional magnetic resonance imaging (fMRI) data, to propose a potential brain circuitry underlying circadian and homeostatic influences on human alertness and cognition. Environmental conditions (e.g., light, sound, temperature, social stimuli) play an important role in the control of sleep and wakefulness as well as their intensity and quality (i.e., spectral composition) respectively. Light is certainly the most regularly occurring stimulus in the environment. The challenge of a daily change of the light–dark (LD) cycle has profound impact on a wide range of biological functions and behavior. Thus, light exerts powerful non-visual effects. In humans, light is intuitively linked with an alert or wakeful state. On the other hand, closing the eyelids or dimming or turning off the lights has a very powerful soporific (i.e., sleep inducing) effect, particularly in children, sleep deprived adults and older people. Compared to the effects of light on human circadian rhythms, little attention has been paid to its acute alerting action. In Section III, we summarize studies from the past two decades, which have defined and quantified the dose (illuminance levels), exposure duration, timing and wavelength of light needed to evoke circadian and/or alerting responses in humans, as well as their temporal relationship to light-induced changes in endocrinological and electrophysiological sequelae of alertness. Furthermore, neuroanatomical and neurophysiological findings from animal and human studies elucidating a potential role of light in the regulation of sleep/wake states and its repercussion on cognitive performance are discussed. A brief outlook of promising non-clinical applications of lights’ alerting properties will be given, and its involvement in the design of more effective lighting at home and in the workplace will be considered. The pinealhormone melatonin is probably the most light-sensitive hormone in humans and also in other organisms, so that measuring the 24-h profile of endogenous melatonin levels provides accurate information about the prior light history of an individual. The phase, amplitude, and duration of the active phase of melatonin secretion are all important measures to assess whether somebody has delayed or advanced circadian rhythms or whether somebody lives in dim or brightly lit environments. Thus, there is an intimate transduction of the LD cycle reflecting external time to the endogenous “melatonin cycle” reflecting internal time. Humans are more light sensitive in terms of melatonin suppression than previously thought. Light intensities as low as 40 lux are sufficient to attenuate the

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evening increase of melatonin secretion when the light source yields predomi­ nance in the blue range of its spectral composition. Interestingly, there is a tight and significant correlation between light’s melatonin suppressing effect and its alerting response, leading some researchers to the speculation that melatonin could act as an internal sleep facilitator. Thus, possible roles of endogenous melatonin in the regulation of sleep and wakefulness are being discussed in Section IV. Furthermore, the use of exogenous melatonin and newly available melatonin agonists to treat sleep disorders such as sleep onset insomnia or premature awakening from sleep are also dealt with in Section IV.

II. Circadian and Homeostatic Impetus for Wakefulness

“There is no animal which is always awake or always asleep, such that all sleep is susceptible of awakening and all wake time beyond the natural time limit is susceptible to sleep” (Aristotle, On Sleep and Sleeplessness, 350 BCE). Living organisms are permanently exposed to internal and external changes and the combined action of these dynamics may determine the transition between conscious-con­ trolled to unconscious-automated behavioral states (Tononi and Edelman, 1998). Behavioral or perceptual states continuously vary between the extremes, with on the one hand resting sleep during which consciousness is strongly attenuated and on the other hand a state of wakefulness when we actively interact with the environment, and during which we engage in many cognitive and other activities (Dijk and Archer, 2009). It is nowadays largely accepted that in human beings, homeostatic and circadian sleep–wake regulatory processes are continuously work­ ing in harmony or in opposition to each other to allow maintenance of behavioral states such as sleep and wakefulness at appropriate time points within the 24-h LD cycle. However, these states per se seem far from being unitary concepts since their consolidation is achieved by the mutual interaction of multiple brain processes. Even though the interplay between regulatory processes aspires to stability within a given state, there exist fine grained fluctuations in the way we perceive our environment over the waking state. Such slight differences may be exag­ gerated by inter-individual differences in the orchestration of the underlying processes. A good example of such fluctuations is the discovery by Bodenhausen (1990), who observed that human subjects exhibit stereotypic biases in their judgments to a much greater extent when these were rendered at a time of day reflecting reduced arousal levels for them. Judgments were significantly more affected by stereotypic beliefs in the morning hours for evening types and in the evening hours for morning types. Thus, the quality of judgment fluctuated within the state of wakefulness, which itself showed a differential temporal pattern across the 24-h day in morning and evening types.

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A. TIMING AND CONSOLIDATION OF THE HUMAN SLEEP–WAKE CYCLE: FROM BASIC AROUSAL STATES TO CONTROLLED COGNITIVE BEHAVIOR As mentioned above, sleep and wakefulness are periodically occurring at specific times of the 24-h LD cycle. Their consolidation is achieved by the interplay between circadian and homeostatic oscillators, initially conceptualized in the two­ process model of sleep and wake regulation (Borbely, 1982; Daan et al., 1984). The homeostatic process represents an hourglass process steadily building up with increasing time awake and exponentially declining during sleep. The circadian process reflects an endogenous, nearly 24 h variation in the propensity for sleep and wakefulness and was originally assumed to be independent of the homeostatic process (i.e., the amount of elapsed time awake) (Borbely, 1982; Daan et al., 1984). This process originates in the suprachiasmatic nuclei (SCNs) of the anterior hypothalamus, an anatomical structure supporting numerous periodic biological functions and considered as the circadian master clock in most living organisms. Findings acquired under a variety of experimental conditions (e.g., internal desyn­ chronization of the sleep–wake cycle, forced desynchrony paradigms, fragmented sleep–wake cycles, sleep deprivation, sleep displacement) point in a remarkably consistent way to the existence of a powerful and active drive for wakefulness at the end of the habitual waking day in humans (Lavie, 2001). Thus, the circadian master clock is tuned such that peak arousal levels in humans are generated in the early evening hours, just before opening the gate for sleep. Accordingly, this time window is characterized by maximal circadian wake promotion and has been called the wake maintenance zone by Strogatz and colleagues (1987). While the endogenous scheduling of the wake maintenance zone to the end of the habitual waking day seems paradoxical at first sight, it takes all sense when one considers it in combination to the temporal evolution of the homeostatic process throughout the habitual 24-h sleep–wake cycle. For instance, it is the very high circadian-based propensity for wakefulness that prevents us falling asleep early in the evening hours when homeostatic sleep pressure is at its highest level and maximally promotes sleep. Thus, during the latter part of the normal waking day, circadian and homeostatic systems work in opposition to ideally ensure a consolidated period of wakefulness. Edgar et al. (1993) have first conceptualized this opponent action based on the framework of the two-process model and data acquired in diurnal squirrel monkeys. SCN-lesioned squirrel monkeys significantly increased total sleep time, which was associated with a 15-fold reduction in the length of wake bouts during the subjective day and no changes in the length of the wake bouts during the subjective night, leading the investigators to suggest that the circadian clock is actively involved in the promotion of wakefulness, by opposing the homeostatic accumulated drive for sleep. Results from human forced desynchrony studies have confirmed the above-mentioned model (Dijk and Czeisler, 1994, 1995) by showing the paradoxical positioning of the circadian alertness peak just before habitual sleep

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time, as indexed by longest sleep latencies and highest amounts of wakefulness within scheduled sleep episodes at this time of the day. Likewise, the SCN also promotes sleep (i.e., circadian increase in sleep tendency) as the biological night progresses (Dijk and Czeisler, 1994, 1995) counteracting the decrease in sleep propensity associated with accumulated sleep, thus allowing us to maintain a consolidated 8-h sleep episode. Besides sleep and wakefulness, neurobehavioral efficiency seems to be affected by the same paradoxical interplay of circadian and homeostatic sleep–wake promotion over the 24-h cycle such that the wake-dependent dete­ rioration is minimal during the wake-maintenance zone. Data gathered in a constant routine paradigm, which challenged homeostatic sleep pressure condi­ tions by either sleep depriving or sleep satiating study volunteers by regular nap opportunities throughout the circadian cycle, indicate a clear circadian modula­ tion of cognitive performance and subjective sleepiness even in the absence of prominent homeostatic sleep pressure (Fig. 1). This circadian modulation is temporally organized such that neurobehavioral performance (alertness scores and performance lapses) is maximally boosted in the late evening hours. Under sleep deprivation conditions (>16 h of enforced wakefulness), a steep decline on neurobehavioral performance can be observed when the testing is extended into the biological night, i.e., just after the circadian arousal signal has turned off. However, as illustrated in Fig. 1, neurobehavioral performance does not decline linearly with increasing time awake throughout 40 h of sustained wakefulness, but shows a strong improvement coinciding with the biological day, when circadian arousal promotion kicks in again (see also Cajochen et al., 1999b, 2004; Graw et al., 2004; Horowitz et al., 2003). Importantly, compelling data from forced desynchrony studies indicate that circadian and homeostatic processes do not simply add up to characterize daily alertness and performance modulations. It has been observed that the amplitude of the observed circadian modulation in performance depends on homeostatic sleep pressure, such that increasing homeostatic sleep pressure attenuates circa­ dian wake promotion during the subjective evening hours (Dijk and Archer, 2009). Hence, minor changes in the specific interplay between both processes lead to significantly disrupted stability patterns in cognitive states even through­ out a normal waking day. This may explain why a series of studies found significant performance fluctuations in cognitive behavior throughout a normal waking day in morning and evening chronotypes differing in circadian and homeostatic sleep–wake regulatory processes throughout the course of a normal waking day (see Schmidt et al., 2007 for a review). Such interindividual differences have recently been used as a tool in order to investigate the functional neuroa­ natomy subtending modulatory effects of sleep–wake regulation on higher order human behaviors. We will briefly describe these observations within the context of the brain circuitry involved in the circadian control for states of sleep and wakefulness.

Core body temperature (°C)

PVT lapses (number of lapses)

Subjective sleepiness (KSS)

WHAT KEEPS US AWAKE?

63

7 6 5 4 3

6

4

2

37.0 36.8 36.6 36.4 8 12 16 20 24 4 8 12 16 20 24

Relative clock time (h) FIG. 1. Dynamics of subjective sleepiness on the Karolinska Sleepiness Scale (KSS), objective vigilance on the Psychomotor Vigilance Task (PVT), and core body temperature (CBT) across a 40 h SD (high sleep pressure; filled circles) and NAP protocol (low sleep pressure; open circles). The upper two panels indicate the timing of the naps (black bars) and scheduled episodes of wakefulness (white bars) respectively for the SD and NAP protocol. Data are plotted against the midpoint of the time intervals. Relative clock time represents the average clock time at which the time intervals occurred. Modified from Cajochen et al. (2001).

B. BRAIN CIRCUITRY UNDERLYING CIRCADIAN AND HOMEOSTATIC INFLUENCES ON HUMAN COGNITION: A POSSIBLE SCENARIO How circadian oscillations in the SCN as well as circuits controlling for states of sleep and wakefulness interact at the cerebral level in order to regulate arousal and cognitive behavior is still an open question. Output of the SCN indirectly reaches target areas implicated in the regulation of sleep and wakefulness (ventro­ lateral-preoptic area (VLPO), tuberomammillary nucleus (TMN), lateral hypothalamus (LH), thalamus, and brainstem nuclei via its connections to the

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dorsal medial hypothalamus (DMH)) (Mistlberger, 2005). Concomitantly, diffuse monoaminergic activating systems are under circadian control and impinge on many thalamo-cortical areas, suggesting that the interaction with sleep home­ ostasis could take place at many different levels (Dijk and Archer, 2009). Work by Aston-Jones and colleagues (Aston-Jones, 2005; Aston-Jones et al., 2001) has shown that the noradrenergic locus coeruleus (LC) system plays an important role in the circadian regulation of alertness. Within the framework of their model, the SCN indirectly communicates with the LC via projections to the dorsomedial hypothalamic nucleus (DMH). Evidence for that comes from neurophysiological experiments, which revealed circadian variations in LC impulse activity and showed that lesions of the DMH eliminated these circadian changes in LC activity, suggesting a functional significance of the SCN–DMH–LC circuit (Gompf and Aston-Jones, 2008). Through LC activity with its widespread tha­ lamic and cortical connections, this pathway may control a variety of central nervous system functions related to noradrenergic innervations, including alert­ ness and vigilance, and also higherorder cognitive processes. We have recently collected indirect evidence that the circadian arousal signal generated by this circuitry is modulated by homeostatic sleep pressure (Schmidt et al., 2009). In this study, the interaction between these processes at the cerebral level was investi­ gated in chronotypes differing in circadian and homeostatic sleep–wake regula­ tory processes under normally entrained day–night conditions (Baehr et al., 2000; Bailey and Heitkemper, 2001; Kerkhof, 1991; Kerkhof and Van Dongen, 1996; Mongrain et al., 2004, 2006a, 2006b). Extreme morning and evening chronotypes were examined at different time points within a normal waking day, while performing a sustained attention task in an fMRI environment. The main results of this study are summarized in Fig. 2. In agreement with previous studies (Kerkhof, 1991; Mongrain et al., 2006a, 2006b; Taillard et al., 2003), we observed that even when the timing of the scheduled testing session was adapted to the specific sleep–wake schedule of the volunteers, morning-type individuals presented higher increases in homeostatic sleep pressure at the end of a normal waking day, as indexed by slow-wave activity (SWA) at the beginning of the night. This effect was paralleled by higher subjective sleepiness and lower objective vigilance levels in the morning than evening types during the evening hours. Interestingly, the fMRI results revealed that maintenance of optimal sustained attention performance in the subjective evening hours was associated with higher cerebral activity in evening than morning chronotypes in a brainstem region compatible with the LC and in an anterior hypothalamic region putatively encom­ passing the suprachiasmatic area (SCA). Thus, in agreement with the brain circuitry proposed by Aston-Jones and colleagues, our data suggest that activity in these regions contributes to circadian wake promotion in the subjective evening hours. Importantly, we further observed that activity in the SCA decreased with increasing homeostatic sleep pressure, suggesting a direct influence of homeostatic and

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B.

C.

300 250 200 150

>

100 50 100

200

300

400

LC

SCA

>

SCA BOLD response (a.u.)

SWA (percentage of all night)

A.

3 2 1 0 –1 –2 –3 100

150

200

250

300

SWA during first sleep cycle

Time since sleep onset (min)

Morning types Evening types FIG. 2. (A) Exponential decay function adjusted on relative SWA in sleep cycles (NREM sleep) measured from the central frontal derivation for all-night EEG of the night preceding the evening scan acquisition (red line: morning types; blue line: evening types. (B) Increased task-related response in the dorsal pontine tegmentum and the anterior hypothalamus, compatible with the LC and SCA, respectively, in evening as compared to morning chronotypes during the subjective evening for optimal sustained attention during the performance of a Psychomotor Vigilance Task. Functional results are displayed at p < 0.001, uncorrected threshold, over the mean normalized structural MRI of the population. Corresponding parameter estimates (arbitrary units) are displayed for event indicators of fast (
circadian interactions on the neural activity underpinning diurnal variations in human behavior. Our results corroborate findings in the rat, which showed suppres­ sion of SCN activity by SWA throughout various vigilance states (Deboer et al., 2003, 2007), and globally speak in favor of the initial assumption that an increase in homeostatic sleep pressure impacts on the circadian wake-promoting signal during the subjective evening hours. Another study used the differential vulnerability to sleep loss according to a polymorphism in the human PER3 clock gene (Viola et al., 2007) to evidence nonlinear interaction patterns between the two basic processes at the cortical level throughout a normal waking day and after a night of sleep deprivation (Vandewalle et al., 2009; see Dijk and Archer, 2009 for a review). In this study, the temporal profile of cortical activity underlying successful performance on an executive task (n-back paradigm) could be tracked by the dynamics predicted by the interplay between circadian and homeostatic processes according to each subject’s specific genotype. However, the underlying mechanisms by which homeostatic sleep pressure modifies the circadian arousal signal in the evening hours are still unknown. It has been suggested that adenosine is a homeostatic regulator of sleep need (Benington et al., 1995; Landolt et al., 1995; Porkka-Heiskanen et al., 1997; Strecker et al., 2000). During prolonged wakefulness, the energy-producing systems in the brain

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run down: brain glycogen reserves are exhausted and ATP levels are depleted. During prolonged wakefulness, as ATP is degraded to ADP, AMP, and eventually adenosine, extracellular adenosine levels rise in some parts of the brain, including the basal forebrain (see Landolt, 2008 for a review). It has been hypothesized that, once adenosine reaches sufficient concentrations after prolonged wakefulness, it has an inhibitory action on the wake-promoting neural circuitry of the basal forebrain and probably activates VLPO neurons by reducing inhibitory Gamma-aminobutyric acid (GABA)ergic inputs Accordingly, after sleep depriva­ tion, VLPO neurons fire about twice as fast as they do during normal sleep, implying that they are under the influence of homeostatic factors that reflect sleep need (Lu et al., 2002; Saper et al., 2005a; Sherin et al., 1996; Szymusiak et al., 1998). In humans, there is evidence that adenosinergic neurotransmission plays a role in NREM sleep homeostasis. Indeed, a polymorphism in an adenosine-metabolizing enzyme contributes to high interindividual variability in deep SWS duration and intensity (Retey et al., 2005). Furthermore, the adenosine receptor antagonist caffeine has the ability to attenuate electroencephalographic (EEG) markers of NREM sleep homeostasis (Landolt et al., 1995). Accordingly, caffeine administra­ tion is effective in counteracting the detrimental performance effects of extended wakefulness (Retey et al., 2006; Wyatt et al., 2004). To sum up, sleep and wakefulness are determined by the multiple interplay between circadian and homeostatic oscillators. Active circadian wake promotion during the subjective evening hours attempts the achievement of stability of cognitive states throughout a normal waking day, by opposing the increasing homeostatic sleep pressure at this time of the day. Likewise, circadian sleep promotion takes place in the early subjective morning hours to oppose the decreasing homeostatic sleep pressure allowing a consolidated bout of sleep. However, fine-grained interindividual differences in the complex interplay between these processes may result in significant modulations in cognitive beha­ vior even throughout a normal waking day. A couple of studies recently took advantage of such interindividual differences for the investigation of the cerebral correlates underlying circadian and homeostatic influences on human cognition (Schmidt et al., 2007; Vandewalle et al., 2009). Together with data gathered in the animal domain, their results point into the direction that the circadian arousal signal and accumulated homeostatic sleep pressure directly interact at the cere­ bral level in order to control cognitive behavior throughout wakefulness. In one possible scenario (Fig. 3), the efficacy of the circadian arousal signal, generated by the indirect communication of the circadian master clock to the brainstem LC and thereby to widespread cortical areas, may be modified through adenosine, a putative mediator of sleep homeostasis. Importantly, these assumptions should now be investigated in the framework of protocols more systematically manip­ ulating the interaction between both processes and allowing tracking their inter­ action throughout the entire 24-h cycle.

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Wakefulness

Sleep Wakefulness

Sleep

Homeostatic sleep pressure

8 Cortical networks regulating cognition

+ -

13 18 23 4 9 14 19 24 Time of day Circadian wake promotion SCN

-

DMH

LC

5

thalamus/cortex

Adenosine (diffuse system)

FIG. 3. A possible simplified scenario of the circadian and homeostatic interplay to regulate alertness and cognitive performance over the 24 h cycle. With increasing time awake, homeostatic sleep pressures accumulates throughout the day and may affect cortical activation by mechanisms including synaptic potentiation in several circuits. This mechanism is tied to homeostatic regulation of sleep slow-wave activity during the night. Information about the amount of accumulated homeostatic sleep pressure is transferred to hypothalamic structures including the circadian master clock which in turn feeds back in a “time-of-day” specific manner by sending signals to wake-promoting brainstem as well as thalamic structures. From there on, the information is transferred to cortical areas in order to allow the maintenance of an adequate cognitive state (Aston-Jones, 2005).

III. Effects of Light on Human Wakefulness

To be of functional significance, circadian rhythms must be entrained to the 24-h LD cycle. Thus, it is not surprising that light plays a powerful role on behavior and physiology. In fact, a change in the timing of the external LD cycle leads to a shift in endogenous phase of circadian rhythms (Brainard et al., 1997). Besides these long-term effects on circadian phase, many acute effects of light have been consistently shown for a wide range of physiological processes, such as hormonal secretion, heart rate, sleep propensity, alertness, body temperature, pupillary constriction, and gene expression (Aalto and Hilakivi, 1986; Badia et al., 1991; Berson, 2003; Cajochen et al., 1992, 1996, 2005, 2006; Lavoie et al., 2003; Mun˜oz et al., 2005). Both long-term and acute effects of light are usually referred to as non­ visual (or non-image forming, NIF) effects, since they drift apart from the classical involvement of rod and cone photopigments in the visual responses to light. These NIF responses were firstly demonstrated in mice devoid of classic photoreceptors, since light still had the capacity to elicit circadian phase-shifting responses (Freedman et al., 1999) and melatonin suppression (Lucas et al., 1999). In humans, the fact that visually blind people still exhibit light-induced melatonin suppression

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(Czeisler et al., 1995) and that the spectral sensitivity of NIF responses differed from visual responses (Brainard et al., 2001; Thapan et al., 2001) challenged the classical involvement of rod and cone photopigments in responses to light. Furthermore, since Berson and coworkers (2002) detected intrinsic photosensitive retinal gang­ lion cell (ipRGC) in the retina of mammals, it began to emerge that the eye performs a dual role in detecting light for a range of behavioral and physiological responses distinct from the classical visual responses. Melanopsin-containing ipRGCs have a specialized non-visual retino-hypothalamic tract which provides direct neuronal connection to the SCN, as well as direct and indirect (via SCN) projections to brain areas implicated in the regulation of arousal (Gooley et al., 2003). Furthermore, the SCN has connections to the pineal gland, which is responsible for the regulation of melatonin, as well as to many areas that share an input from the visual photoreceptor system, such as the lateral geniculate nucleus, pretectum and superior colliculus (Lockley and Gooley, 2006). The brain areas implicated in the non-visual effects of light beyond these ipRGC projections are still unknown. Nevertheless, if one considers the number of brain areas that are just one synapse away from ipRGCs, and the numerous projections of just one key target of ipRGCs, the SCN, it becomes evident that non-visual responses to light could affect many brain functions, including cognitive functions. In this section, we will address the following points: (1) how light (timing, dose, and wavelength) impinges on human wakefulness; (2) how light modulates cogni­ tion, in particular in tasks associated with sustained attention, and (3) the impor­ tance of lights effect in non-clinical settings.

A. LIGHT SWITCHES

ON THE

CLOCK

AND THE

HOURGLASS

Even in the absence of an LD cycle, the rest-activity rhythm persists with a periodicity of approximately 24 h, instead of redistributing across the 24-h day. The synchronization to LD cycles is obtained through variation in the response of the circadian pacemaker in the SCN to light pulses, whereby light exposure late in the biological day delays sleep onset in humans, while exposure early in the biological day (dawn) advances activity onset (Czeisler and Gooley, 2007). Thus, light acts as a synchronizer (Zeitgeber) by transmitting the information about external time (LD cycle) to the organisms’ internal timing system and as consequence marginally influences the switch between behavioral states such as sleep and wakefulness. Wakefulness requires a certain alertness level to actively interact with the environment. Thus, alertness is a construct associated with high levels of environ­ mental awareness, which can be operationalized through many converging measurements, including subjective responses, behavior, and brain activity (Buysse et al., 2003). Alertness is associated with self-reported high levels of wakefulness and low

WHAT KEEPS US AWAKE?

69

levels of fatigue, short response times, fast and more accurate cognitive perfor­ mance, and lowerlevels of theta activity (4.75–7.75 Hz) in the electroencephalo­ gram (EEG), particularly in the frontal cortex (Badia et al., 1991; Cajochen et al., 1995, 1999a; Daurat et al., 2000). Subjective perception of alertness heavily depends on time-of-day, to the extent that the circadian modulation of alertness has a strikingly similar temporal association with the circadian rhythm of core body temperature (CBT) with its maximum in the evening and nadir in the early morning (see also Section III; Kleitman, 1987). Considering the temporal dynamics of these processes on alertness, one can hypothesize that light exerts its alerting effects most strongly when the circadian drive for sleep is at its maximum (i.e., in the early morning at the CBT minimum) and under high homeostatic sleep pressure conditions (i.e. after more than 16 h of wakefulness). However, besides the temporal occurrence of a light pulse relative to the circadian and homeostatic system, factors such as the intensity of light, light stimulus duration, and its wavelength play a crucial role in determining the impact on alertness and cognitive performance.

B. ALERTING EFFECTS

OF

LIGHT

The vast majority of light studies have been conducted at night (Badia et al., 1991; Cajochen et al., 2000; Campbell and Dawson, 1990; Foret et al., 1996; Lockley et al., 2006) during a time when one would expect most pronounced alerting effects in humans. Indeed light at night significantly enhances subjec­ tive alertness and reduces objective markers of sleepiness, such as EEG theta activity and the incidence of slow-eye movements as assessed by the electro­ oculogram (EOG). However, also during the biological day, when melatonin is at minimal level, light does impact alertness. In an “in-lab” study, individuals who were exposed to polychromatic white light with levels >7000 lux for 20 min during daytime exhibited an enhancement in cortical activity during an oddball task and subjective alertness improved in a dynamic manner, such that these alerting effects declined within minutes after the end of the light stimulus, following various region-specific time courses, such as enhanced responses in the posterior thalamus, including the pulvinar nucleus, which has been impli­ cated in visual attention and alertness regulation (Vandewalle et al., 2006). This suggests that light may modulate activity of subcortical structures involved in alertness, thereby promoting cortical activity in networks involved in ongoing non-visual cognitive processes. Further evidence in support of time indepen­ dency of alertness builds up from a study in which participants were exposed to either bright light (5000 lux) or dim light (<10 lux) (control condition) either between 12:00 and 16:00 h or between 00:00 and 04:00 h. Bright light had a

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time-dependent effect on heart rate and CBT, such that bright light exposure at night, but not during daytime, increased heart rate and CBT (Ru¨ger et al., 2006). On the other hand, nighttime and daytime bright light reduced sleepi­ ness and fatigue significantly and similarly and thus was independent of its timing (Ru¨ger et al., 2006). The aforementioned studies used polychromatic bright light above 1000 lux. It could very well be that light with this high intensity does not exhibit timedependent alerting responses.

C. DOSE- AND WAVELENGTH RESPONSE RELATIONSHIP ALERTNESS

OF

LIGHT EXPOSURE

ON

Although it is clearly recognized that bright light (  1000 lux) is an effective Zeitgeber and alerting factor in humans (Badia et al., 1991; Daurat et al., 2000; Foret et al., 1996; Myers and Badia, 1993), one could assume that the human circadian pacemaker is insensitive to lower levels of light illumination (<100 lux). However, it has been shown that the relationship between the resetting effect of light and its intensity follows a compressive nonlinear function, such that expo­ sure to lower illuminances still exerts a robust effect (Boivin et al., 1996). For instance, the dose–response function to a single episode of light in the phase delay region (light prior to temperature nadir) can be characterized by a logistic function with a high sensitivity, such that half of the maximal resetting and melatonin suppression achieved in response to bright light (9100 lux) can be obtained with 1% of this light (dim room light of ~100 lux) (Cajochen et al., 2000; Zeitzer et al., 2000b) (Fig. 4). Interestingly, the illuminance response function for alertness is similar to that of the dose–response function reported for the magni­ tude of suppression of plasma melatonin concentrations as a function of light intensity, as well as the dose–response function reported for the circadian phase resetting effects of light (Cajochen et al., 2000). This suggests that nighttime exposure to typical room light (90–180 lux) can exert an alerting effect in humans, regardless of whether alertness is quantified by subjective ratings or by analysis of the EOG (i.e., incidence of slow-eye movements) and the EEG (activity in the theta and alpha range). Surprisingly, humans were able to main­ tain stable circadian entrainment to a 24-h cycle in which ambient room light was about 1.5 lux, suggesting that even candlelight can induce small shifts of the human circadian system (Duffy and Wright, 2005). Taken together, this suggests a saturation point for light’s impact on alertness, and that this relatively high sensitivity may explain why in some previous studies a direct effect of light was not observed as the effects of “bright light” were compared to “dim light”

71

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Incidence of slow-eye movements

Subjective alertness

15

More alert

5

10 10 15 5 20 50% of max. alerting response

25

10

100

1000 10000

Illuminance (lux)

0 10

100

1000 10000

Illuminance (lux)

FIG. 4. Dose–response relationship between illuminance and subjective alertness, and the incidence of slow-eye movements. Data points represent the sum of alertness ratings and the number of 30-s epochs containing at least one slow-eye movement during the last 90 min of the light exposure episode for a single individual. The line represents a logistic regression model fit to the individual data points. Modified from Cajochen et al. (2000).

conditions of sufficient intensity to elicit near maximal effects (Dollins et al., 1993; Myers and Badia, 1993). In contrast to the intensity dose-response relationships of light and the circadian system and alertness, very little is known about the duration dependence of the circadian resetting responses to light. However, in analyses of the human phase-response curve (“response to light”), maximum phase shifts to 1 h of bright white light (~10,000 lux) were about 40% as effective as phase shifts measured in response to 6.5 h of white light (~10,000 lux), despite representing only 15% of the stimulus strength (1 h/6.5 h) (Khalsa et al., 2003). Exposure to intermittent light also seems to be highly effective at resetting the human circadian system. The phase-resetting effect of 6.5 h of continuous bright white light (~10,000 lux) is comparable to a 6.5-h intermittent exposure consist­ ing of six cycles of 15 min of bright light (~10,000 lux) and 60 min of dim light (<3 lux) (Rimmer et al., 2000). Despite representing only 23% of continuous bright-light exposure conditions, the intermittentlight regimen elicited compar­ able phase shifts. Thus, a single sequence of intermittent bright-light pulses has a greater resetting efficacy on a per-minute basis than does continuous light exposure. In a subsequent study, exposure to two 45-min pulses of bright light in the early subjective evening entrained the circadian system to a non-24-h day,

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indicating that intermittent pulses are highly efficient at resetting human circa­ dian rhythms (Gronfier et al., 2007), and can significantly contribute to efficient wakefulness. The relationship between the wavelength of light and its alerting response yielded clear superiority of short wavelength light (470 nm and lower) in comparison to other wavelengths (Cajochen et al., 2005; Lockley et al., 2006; Mu¨nch et al., 2006; Revell et al., 2006a). For instance, exposure to 460-nm monochromatic light for 6.5 h during the biological night attenuated subjective sleepiness (Fig. 5) and waking EEG power density in the delta–theta frequency range, with concomitant increase in the high-frequency alpha range, in com­ parison to light exposure to an equal photon density of 555-nm monochro­ matic light (Lockley et al., 2006). Given that greater responses were elicited 0 lux

Light

2 lux

Melatonin (pg/ml)

15 12 9 6

* * *

* Light at 460 nm

3

Light at 550 nm Dim Light Control Condition

7

*

6 More alert

Subjective sleepiness

8

*

5

p < 0.05; 460 nm vs. dark p < 0.05; 460 nm vs. 550 nm p < 0.05; 550 nm vs. dark

* *

*

4 20

21

22

23

24

1

Time of day (h) FIG. 5. Effects of a 2-h light exposure at 460 nm (Dark gray circles), 550 nm (Light gray circles), and no light (Black squares) in the evening under constant posture conditions (i.e., supine in bed) on salivary melatonin levels and subjective sleepiness (mean values (n = 9) and SEM). For clarity, the SEM values for the 550-nm light condition were not plotted. Significant post hoc comparisons (p < 0.05; Duncan’s multiple range test corrected for multiple comparisons) are indicated by the following symbols:
, 460-nm light vs. no light; r, 550-nm light vs. no light; and *, 460-nm light vs. 550-nm light. The pre-light exposure episode represents a 2-h dark adaptation episode under 0 lux, whereas the light level in the 1.5-h post-light exposure was 2 lux. Taken from Cajochen et al. (2006) with permission.

73

WHAT KEEPS US AWAKE?

following exposure to an equal number of photons of 460-nm light, as com­ pared to 555-nm light, it is very likely that photoreceptors mediating the acute effects of light on subjective and objective correlates of alertness are blue shifted relative to the visual photopic system. This blue-shift response was similarly observed in a study that compared a 2-h evening exposure to mono­ chromatic light of two different wavelengths (460 and 550 nm) at very low intensities, whereby subjects were more alert during the 460-nm than the 550-nm light (Cajochen et al., 2005). These findings corroborate to a wide range of non-visual light responses in humans, such as melatonin sup­ pression (Lewy et al., 1980; Zeitzer et al., 2000b), circadian phase shifting (Czeisler et al., 1986), nocturnal decline in EEG SWA (Cajochen et al., 1992; Mu¨nch et al., 2006), and circadian gene expression (PER2) in oral mucosa (Cajochen et al., 2006). Common to these responses is that they are all more sensitive to short wavelength light. However, very recent findings suggest that cone photoreceptors contribute substantially to non-visual responses at the beginning of a light exposure and at low irradiances, whereas melanopsin appears to be the primary circadian photopigment in response to long-dura­ tion light exposure and at high irradiances (Gooley et al., 2010).

D. NEUROANATOMICAL UNDERPINNINGS AND COGNITIVE PERFORMANCE

OF THE

EFFECT

OF

LIGHT

ON

ALERTNESS

The neuroanatomical structures and the concomitant neurophysiology that mediate the capacity of light to enhance alertness and cognitive performance are currently under intensive investigation. It is known that ipRGCs project to a range of targets, including the SCN, subparaventricular zone, and pretectal area that are implicated in mediating NIF responses (Hattar et al., 2002). Furthermore, these cells also project directly to the VLPO that also receives secondary afferents from the SCN, subparaventricular zone, and DMH (Hattar et al., 2002). The VLPO innervates all of the major nuclei of the ascending monoaminergic and in particular the histaminergic pathways, which are thought to play a key role in wakefulness and EEG arousal (Aston-Jones et al., 1999; Lin et al., 1996; Saper et al., 2005b). Direct photic input to this nucleus may therefore alter VLPO activity and waking arousal levels. The LC is also involved in the regulation of the sleep–wake cycle (Saper et al., 2005b), regulating the amplitude of the sleep–wake circadian rhythm set by the SCN by increasing wakefulness during the active period (see also Section III, Gonzalez and Aston-Jones, 2006). Light impacts on cognitive performance through its synchronizing/phase­ shifting effects on the circadian clock or acutely via its alerting effects, as

74

CAJOCHEN ET AL.

performance (in tasks such as digit recall, serial addition–subtraction and simple reaction time tasks) can immediately improve after the onset of light exposure at night (Badia et al., 1991; Campbell and Dawson, 1990; Foret et al., 1996; Lockley et al., 2006) and also during the day (Phipps-Nelson et al., 2003; Ruger et al., 2006). EEG and ocular correlates of alertness can vary with cognitive perfor­ mance, such that EEG alpha (8–12 Hz) and beta (12–20 Hz) activities show a pronounced circadian rhythm with a peak in the second half of the biological day (Cajochen et al., 2002). Light exposure reduces alpha, theta, and low frequency EEG activity, and also the incidence of slow-eye movements, which are correlates of sleepiness, and thus good indicators of inattention that increase as a result of extended wakefulness particularly during the biological night. Lights’ perfor­ mance enhancement, however, does not occur in a similar manner for all subcortical and cortical regions. Light-induced modulations of cortical activity during auditory cognitive tasks occur for alertness-related subcortical structures, such as the brainstem (LC—compatible region) (Vandewalle et al., 2007b); the hypothalamus, in a location encompassing the SCN (Perrin et al., 2004), and dorsal and posterior parts of thalamus (Vandewalle et al., 2006, 2007a), in longterm memory and emotion-related areas, such as the hippocampus (Vandewalle et al., 2006) and amygdala (Vandewalle et al., 2007b). Taken together, these responses indicate that wide-range subcortical and cortical areas are activated by non-visual effects of light, during specific cognitive tasks. Since cognitive performance can exhibit a circadian modulation, the next logical question is whether these cortical responses are wavelength dependent. Blue light (460 nm) appears to be more effective in sustaining performance in a simple vigilance reaction time task compared to green light (550 nm) (Lockley et al., 2006). fMRI­ assessed brain responses undergo a wavelength dependency for higher executive task (2-back task), such that blue light enhances modulations of higher executive tasks in the brainstem (in an LC-compatible location), in the thalamus and insula, in relation to green (550 nm) and violet exposures (430 nm). In this case, the effect of blue light occurs before 1 min after the start of the exposure (Vandewalle et al., 2007b) and last for nearly 20 min (Vandewalle et al., 2007a). However, the magnitude, time course, and regional brain distribution of non-visual effects of light heavily depend on the dose, duration, and intensity of the light exposure. Indeed, longer durations and higher intensities can elicit long-lasting and wide­ spread task-related responses (Perrin et al., 2004). While subcortical regions are activated faster and show short-lasting responses to light, cortical activity requires stronger and longer stimulations, as indicated in a study (Vandewalle et al., 2006), in which 20 min of bright white light induced both thalamic and cortical mod­ ulations that steadily declined after light exposure, albeit its rather lasting effects (responses were observed several minutes after the end of the light exposure). Moreover, when the duration of light exposure was reduced to less than a minute, the effects were mostly restricted to subcortical structures such as the dorso­

WHAT KEEPS US AWAKE?

75

posterior thalamus and the brainstem (LC-compatible area), and cortical mod­ ulations were sharply reduced (Vandewalle et al., 2007b). The importance of LC areas in this case is due to the fact that this region projects to numerous cortical sites and is, therefore, well placed to mediate lightinduced changes in alertness and cognition (Gonzalez and Aston-Jones, 2006). The thalamus, in particular its dorsal and posterior nuclei (i.e., pulvinar), is a key structure involved in the interaction between alertness and cognition (Portas et al., 1998). Thus, light-induced changes in thalamic activity can be directly implicated in enhanced alertness during light exposure. Given that the thalamus plays a critical role in the relay of information to the cortex, it can regulate information flow in the brain, and an effect of light on the thalamus may thus lead to widespread cortical effects.

E. NON-CLINICAL APPLICATIONS

OF

LIGHT

The application of light in non-clinical settings, such as intercontinental travel (jet-lag), shift work, and even non-shift working environments, is under intense scrutiny. The main assumption for the first two cases is the misalignment between the internal circadian pacemaker and the external environment. As a consequence, this circadian deregulation may contribute to health problems in the long term such as sleep disorders, cardiovascular disease, and diabetes (Rajaratnam and Arendt, 2001). Previous strategies to reduce jet-lag have focused on shaping the perceived LD cycle after arrival, in order to facilitate a phase shift in the appro­ priate direction. In one study, phase advancements of habitual sleep–wake sche­ dules and light exposure in the morning were investigated in order to test the idea that if travelers could phase-advance their circadian rhythms prior to eastward flight, they would arrive with their circadian rhythms already partially re-entrained to local time. For this three treatments were used, in which habitual sleep schedule was advanced by 1 h/day for 3 days, together with morning light exposure for the first 3.5 h after waking on each of the 3 days. This exposure was either continuous bright light (>3000 lux), or intermittent bright light (>3000 lux, 0.5 h on, 0.5 off, etc.), or ordinary dim indoor light (<60 lux). Dim light melatonin onset (DLMO) phase advance was higher in the continuous light exposure (nearly 2 h), although it did not drastically differ from the intermittent light exposure. Importantly, in both cases, alertness was significantly higher under light exposure (Burgess et al., 2003). With respect to shift work, it is unambiguous that the circadian misalignment between the endogenous circadian signal and the imposed rest-activity cycle is one of the main sources of sleep, performance, and health troubles in night-shift work­ ers (Lamond et al., 2003). Timed bright light exposure during night work can

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CAJOCHEN ET AL.

reduce circadian misalignment in night workers. As an illustration, shift workers under bright light exposure (7000–12,000 lux) during the night (and darkness during the day) had a temperature nadir shifted after 4 days of treatment to a significantly later, mid-afternoon hour (compared to the previous 03:00 h), indicat­ ing a successful circadian adaptation to daytime sleep and nighttime work. Simi­ larly, there were concomitant shifts in subjective assessment of alertness and cognitive performance, both of which improved substantially under this light exposure (Czeisler et al., 1990). However, despite the fact that scheduled bright light and darkness can phase shift the circadian clocks of night workers for complete adaptation to a night work with day sleep schedule, few night workers would rather be out of phase with the diurnal world on their days off. Similarly in other situations, such as rapidly rotating shifts and the normal office environment, it is more appealing to time light exposures toward improving alertness without phase shifting (Horowitz and Tanigawa, 2002). However, given that there is no dead zone for phase shifting the circadian system in humans (Khalsa et al., 2003), it is not conceivable to enhance alertness with light without affecting circadian phase. Thus, a “compromise” circadian phase position for permanent night-shift work in which the sleepiest circadian time is delayed out of the night work period and into the first half of the day sleep episode would seem a feasible alternative (Smith et al., 2009). In a recent study, the target compromise phase position was a DLMO of 3:00 h, which puts the sleepiest circadian time at approximately 10:00 h. This was predicted to improve night-shift alertness and performance while permitting suffi­ cient daytime sleep after work as well as late-night sleep on days off. For such, intermittent four 15 min of bright light pulses were conducted during each nightshift, together with recommendations such as use of dark sunglasses during the day, sleep in dark bedrooms at scheduled times, and outdoor afternoon light exposure, all of which to keep rhythms from delaying too far. Interestingly, subjects who phase delayed close to the target phase (3:00 h) performed better and were more alert during night shifts. This suggests that light application in night shift workers is both a feasible and promising intervention (Smith et al., 2009). Controlled light and dark exposure during the daytime also has a significant impact on circadian phase and could be an easier alternative to implement in real-life situations. In a recent field study (Viola et al., 2008), the effects of exposure to blue-enriched white light (17,000 K) were investigated in comparison to another white light (4000 K) during daytime work hours in an office setting. Blue-enriched white light substantially improved subjective measures of alertness, mood, performance, evening fatigue, concentration, and dramatically reduced daytime sleepiness (Fig. 6). This suggests that blue-enriched white light can enhance self-reported measures of alertness, performance, and fatigue after day­ time exposure in a “real-life” setting for people who work normal office hours without any abnormal sleep–wake schedule being imposed, which makes it an appealing alternative to enhance alertness.

77

Mean change from baseline score

WHAT KEEPS US AWAKE?

2.0

Alertness

Self-rated performance

Evening fatigue

Sleep quality (PSQI)

1.5 1.0 0.5 0.0 –0.5 –1.0 –1.5

FIG. 6. Exposure to blue-enriched white light at 17000 k (Dark gray bars) during daytime work hours improves subjective alertness, performance, evening fatigue, and sleep quality, in comparison to white light at 4000 K (White bars). Modified from Viola et al. (2008).

To sum up light exerts powerful non-visual effects on a wide range of physiological, behavioral, and subjective parameters, ranging from alertness to complex behavioral processes like cognition. However, in order to achieve optimal alerting response to light, several factors including dose, duration, timing, and wavelength should definitely be taken into account. Novel evidence points to a potential role of the non-image forming system in the regulation of alertness. This opens an exciting area of investigations that may unravel how the retinal and suprachiasmatic networks are involved in the regulation of circadian rhythms and sleep–wake homeostasis.

IV. Effects of Melatonin on Human Sleep and Wakefulness

Pineal melatonin is primarily a neuroendocrine transducer of external time (LD cycle) promoting an increased propensity for “dark appropriate” beha­ vior. The most unequivocal characteristic of endogenous melatonin is its utility to be used alone or in combination with CBT as a phase marker of the endogenous circadian pacemaker located in the SCN. However, there are three major reasons, which imply that melatonin could also play an important role in the regulation human sleep–wake behavior:

78 1. 2.

3.

CAJOCHEN ET AL.

The endogenous melatonin rhythm exhibits a close temporal association with the endogenous circadian component of the sleep propensity rhythm. There is evidence that exogenous melatonin (oral intake) is able to induce sleep when the homeostatic drive to sleep is insufficient, to inhibit the drive for wakefulness emanating from the circadian pacemaker and to induce phase shifts in the circadian clock such that the circadian phase of increased sleep propensity occurs at a new desired time. Light’s acute alerting response depends on its capacity to suppress endogenous melatonin levels during the biological night.

A. ENDOGENOUS MELATONIN

AND THE

HUMAN CIRCADIAN SLEEP–WAKE CYCLE

Melatonin (N-acetyl-5-methoxytryptamine) is a major secretory product of the pineal gland, and its production is under circadian control by the SCN. Because it is produced exclusively at night, it has been referred to as “a chemical code of darkness” (Arendt, 2006). The relationship between external LD cycles and melatonin production can be explained via a multisynaptic pathway begin­ ning with photic transduction of light at the level of the retina; transmission of this LD information via the retinohypothalamic tract (RHT) to the SCN; a descend­ ing pathway from the SCN through the superior cervical ganglion in the spinal cord; and, finally, an ascending pathway to the level of the pineal (Vollrath, 1984). Data in tetraplegic patients, whose melatonin production was absent, support the hypothesis that the human pineal must be stimulated by the sympa­ thetic nervous system to produce melatonin (Zeitzer et al., 2000a). At a functional level, bright light acts through this pathway to acutely suppress melatonin production in the pineal (Lewy et al., 1980). The increase in melatonin secretion in the evening correlates with an increase in sleep propensity (Cajochen et al., 1999b; Tzischinsky et al., 1993). This latter phenomenon has been referred to as “the opening of the sleep gate” (Lavie, 1997) and is most likely related to an inhibitory effect of melatonin on SCN activity (Liu et al., 1997). In parallel, the entire thermoregulatory cascade (i.e., decrease in heat production and increase in heat loss leading to decrease in CBT) starts with the rise in endogenous melatonin levels in the evening (Kra¨ uchi et al., 2000). As a conse­ quence, alertness levels start to decline, sleepiness kicks in, and sleep is eventually commenced. The association of sleep with the melatonin rhythm has been con­ firmed in blind people in whom the circadian pacemaker is not entrained (Lockley et al., 1997; Nakagawa et al., 1992) and in sighted subjects with non-24-h sleep–wake cycle syndrome (Uchiyama et al. 2000). Results obtained from studies using the forced desynchrony protocol to separate out circadian- and wake-dependent com­ ponents of behavior clearly show that the circadian increase in melatonin secretion

79

WHAT KEEPS US AWAKE?

coincides with a decrease in wake episodes during scheduled sleep episodes (Dijk and Cajochen, 1997). Sleep consolidation gradually deteriorates during that phase of the circadian cycle with low melatonin production, and EEG activation during wakefulness is also timed at a specific phase relative to the circadian melatonin rhythm (Cajochen et al., 2002). Despite the close temporal association between endogenous melatonin levels and sleep–wake rhythms, it is still a matter of debate whether endogenous melatonin is causally implicated in the regulation of sleep and wakefulness (van den Heuvel et al., 2005; Zhdanova, 2005), since the ability to sleep is still possible in the absence of detectable endogenous melatonin during the day, or in tetraplegic patients (Scheer et al., 2005), and only a moderate incidence of sleep disturbance has been reported in pinealectomized patients (Macchi and Bruce, 2004). Furthermore, absolute melato­ nin production (which varies enormously between individuals) does not correlate with sleep quality in the elderly (Youngstedt et al., 1998) or elderly sleep-maintenance insomniacs (Hughes et al., 1998). However, several lines of evidence suggest that endogenous melatonin levels may still play a role in consolidated sleep and/or wakefulness. Acute suppression of the nighttime melatonin surge—either by light or beta-blockers —compromises sleep quality, which can be reversed by melatonin supplementation (Cajochen et al., 1998; Van Den Heuvel et al., 1997). In a survey of 13 adult pineal surgery patients, over half the patients (54%) reported nighttime wake periods lasting 1 h or longer, 31% reported total nighttime sleep durations of less than 6 h, and 38% complained of experiencing poor or disturbed sleep every night (Macchi et al., 2002). The extent to which these disturbances are directly attributable to pineal dysfunction rather than to a general effect of brain surgery per se is not entirely clear, but points to compromised sleep under chronic absence of nighttime melatonin secretion. Similarly, in the study of Scheer et al. (2005), all subjects with a complete cervical spinal cord injury, which interrupts the neural pathway required, had chronically impaired sleep efficiency and quality (Scheer et al., 2005). Furthermore, in a study investigating the effect of bright light and melatonin on neurocognitive function and sleep in elderly residents, long-term bright light (5 years) significantly increased endogenous melatonin levels at night concomitant with an improvement in subjective and objective sleep quality (Riemersma-van der Lek et al., 2008).

B. EFFECTS

OF

EXOGENOUS MELATONIN

ON

HUMAN SLEEP

AND

WAKEFULNESS

The first evidence that exogenous melatonin affects wakefulness was provided by the work of Aaron Lerner, who discovered melatonin in 1958 (Lerner et al., 1958). When he started to treat patients suffering from vitiligo, a human

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CAJOCHEN ET AL.

pigmentation disease, he noted that many of his patients became sleepy and fell asleep. Since then numerous laboratory studies under stringent conditions clearly demonstrated that administration of melatonin acutely affects sleep and wakeful­ ness in humans. Exogenous melatonin elicits all the physiological effects which occur in the evening during endogenous melatonin secretion (for a review, see Cajochen et al., 2003). Indeed, exogenous melatonin is most effective when endogenous levels are low during the biological day. It elicits time-dependent soporific effects, which have been corroborated with electrophysiological mea­ sures of sleepiness such as (EEG) theta activity during wakefulness (Cajochen et al., 1997b) and with brain correlates of sleepiness in an fMRI study, which highlighted the role of melatonin in priming sleep-associated brain activation patterns in anticipation of sleep (Gorfine et al., 2006). In an experiment where we blocked the natural evening increase in heat loss, subjective sleepiness, and melatonin secretion by light exposure, we could show that melatonin replace­ ment (5 mg) acutely recovered the evening increase in heat loss, subjective sleepiness, and also theta activity in the waking EEG (Cajochen et al., 1998; Kra¨ uchi et al., 1997). Nighttime melatonin administration does not affect sleep consolidation or sleep efficiency (Cajochen et al., 1997a), whereas, during day­ time, an improvement in sleep efficiency could be found (Dijk et al., 1995). More recent data from a forced desynchrony protocol, where melatonin was given to healthy young adults across a full range of circadian phases, confirm that exogenous melatonin can only increase sleep efficiency outside the time window of its normal production (Fig. 7; Wyatt et al., 2006). Similar findings come from an extended sleep protocol. Chronic administration of melatonin in a slow-release formulation during a 16-h sleep opportunity begin­ ning at 16:00 h resulted in a redistribution of sleep so that sleep efficiency during the first half of the sleep opportunity was substantially higher during melatonin treat­ ment compared to placebo (Rajaratnam et al., 2004). These two studies provide strong support for the hypothesis that exogenous melatonin attenuates the wakepromoting signal of the endogenous circadian pacemaker, allowing for increased sleep efficiency at circadian phases corresponding to the habitual wake episode.

C. IMPLICATIONS DISORDERS

FOR THE

TREATMENT

OF INSOMNIA AND

CIRCADIAN RHYTHM

Melatonin’s soporific and chronobiotic properties make it an optimal candi­ date for treating sleep, in addition to circadian rhythm disorders. In our view, the most successful attempt to treat insomnia and changes in circadian phase position by melatonin has been carried out in free-running blind people. Optimal

81

WHAT KEEPS US AWAKE?

Time of day (h)

1300

2100

0500

1300

2100

0500

300

150

85

Plasma melatonin

Sleep efficiency (%)

0500 100

0

70 0

120

240

0

120

240

0

Circadian phase Placebo Melatonin 0.3 mg FIG. 7. Mean sleep efficiency levels during a forced-desynchrony protocol, folded at the intrinsic circadian period derived from core body temperature. The figure shows the circadian rhythm of endogenous sleep propensity (percentage of sleep of recording time, placebo group in black, melatonin 0.3 mg group in red), as well as the endogenous melatonin levels in the placebo condition (gray area). Modified from Wyatt et al. (2006).

melatonin treatment in those people should utilize not only its soporific effects by administration close to the desired bedtime, but also its chronobiotic properties, in order to entrain sleep–wake behavior (Lockley et al., 2007). Another promising patient group are elderly patients with insomnia. The results of melatonin treat­ ment administered before bedtime in elderly insomniacs were not consistent (for a review, see Olde Rikkert and Rigaud, 2001). However, Olde Rikkert and Rigaud concluded that melatonin is most effective in elderly insomniacs who chronically use benzodiazepines and/or with documented low melatonin levels during sleep. Abnormal timing of sleep with respect to circadian phase occurs in the delayed sleep phase syndrome (DSPS), in which sleep occurs at a delayed clock time relative to the LD cycle, social, work, and family demands. In the first use of melatonin in patients with DSPS, it was found that when administered 5 h before sleep onset for a period of 4 weeks, melatonin (5 mg) advanced sleep onset and wake times compared with placebo (Dahlitz et al., 1991), which was later confirmed by Nagte­ gaal et al. (1998) and Mundey et al. (2005), and is most effective in DSPS patients with shorter habitual sleep time and later clinical onset (Kamei et al., 2000). The first application of melatonin using chronobiological principles was to alleviate the perceived effects of jet-lag. There have been many placebo­

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controlled and placebo-uncontrolled studies that have been summarized in a Cochrane (Herxheimer and Petrie, 2002). This stringent analysis concludes that 9 of 10 trials of melatonin, taken close to the target bedtime at destination, decreased jet-lag symptoms arising after flights crossing five or more time zones. One difficulty in using melatonin for jet-lag is that its use requires admin­ istration at times when it will have undesired soporific properties. There is also a great interest in whether melatonin can facilitate phase-shifting in night-shift workers; however, few studies have measured such phase shifts. In two laboratory studies, circadian rhythms were measured before and after a large shift in the sleep–wake schedule (Dawson et al., 1995; Samel et al., 1991). Melatonin (5 mg) was administered during the phase-advance portion of the phase response curve (PRC) and produced larger circadian phase shifts than placebo (Samel et al., 1991). In the other study, subjects took a 4 mg melatonin (or placebo) before and during their daytime sleep (Dawson et al., 1995) and melatonin did not produce a larger phase delay than placebo. In a night-shift field study, melatonin produced larger circadian phase shifts than placebo in only 7 of the 24 subjects studied (Sack and Lewy, 1997). Overall, these studies do not provide strong evidence that melatonin can help phase shift the circadian rhythms of night-shift workers, in particular, when comparing its action as being less strong than exposure to light. One problem has been the lack of control over time of melatonin administration and of the subjects’ sleep schedules. In a recent study where the timing of melatonin administration, the sleep–wake schedule and, to some extent, the LD cycle could be controlled in a field setting, melatonin clearly produced larger phase advances than placebo in the circadian rhythms of melatonin and CBT (Sharkey and Eastman, 2002). Moreover, significantly larger phase advances with 0.5 and 3.0 mg melatonin compared with placebo have been reported in a study to determine if phase advances induced by morning light could be increased with afternoon melatonin (Revell et al., 2006b). Additional caution is required in this setting to avoid the soporific effects of mela­ tonin during work requiring vigilance, or driving home after the shift. In an attempt to take advantage of the therapeutic opportunities of melato­ nin, several melatonin agonists with improved properties in comparison to melatonin have been developed. Some of these agents are selective for specific melatonin receptors (MT1, MT2). Results from animal studies suggest that MT1 and MT2 receptors have distinct functional roles in the SCN, albeit with some overlapping function (for a review see Turek and Gillette, 2004). These distinct roles provide great potential for receptor-specific pharmacological agents to affect specific aspects of the sleep–wake cycle and/or circadian rhythmicity. It may be possible to develop specific agents that promote sleep without phase-shifting the circadian clock, or the converse. The three more prominent examples of mela­ tonin receptor agonists that are the furthest along in clinical development are Agomelatine, Ramelteon, and Tasimelteon. All of them appear to be efficacious in the treatment of circadian rhythm sleep disorders and some types of insomnia

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(for a review see Ferguson et al., 2010). An important point for the effects of melatonin analogues is to understand that they are not hypnotic drugs that resemble benzodiazepines and their derivatives. Melatonin-like compounds amplify day–night differences in alertness and sleep quality.

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SUPRACHIASMATIC NUCLEUS AND AUTONOMIC NERVOUS

SYSTEM INFLUENCES ON AWAKENING FROM SLEEP

Andries Kalsbeek�,† Chun-Xia Yi,�,† Susanne E. la Fleur,� Ruud M. Buijs,‡ and Eric Fliers� �

Department of Endocrinology and Metabolism, Academic Medical Center (AMC), University of

Amsterdam, 1105 AZ Amsterdam, The Netherlands

† Hypothalamic Integration Mechanisms, Netherlands Institute for Neuroscience, 1105 BA

Amsterdam, The Netherlands

‡ Hypothalamic Integration Mechanisms, Department of Physiology, Instituto de Investigaciones Biomedicas, UNAM, 04510 Mexico, Mexico

I. II. III. IV. V. VI.

Introduction SCN Output Rhythms The Cortisol/Corticosterone Awakening Rise The Dawn Phenomenon The Awakening of the Cardiovascular System Conclusion

Acknowledgments

References

Awakening from sleep is a clear example of an event for which (biological) clocks are of great importance. We will review some major pathways the mamma­ lian biological clock uses to ensure an efficient and coordinated wake-up process. First we show how this clock enforces daily rhythmicity onto the hypothalamo­ pituitary-adrenal (HPA) axis, via projections to neuroendocrine neurons within the hypothalamus. Next we demonstrate how this brain clock controls plasma glucose concentrations, via projections to sympathetic and parasympathetic pre-autonomic neurons within the hypothalamus. Orexin neurons in the lateral hypothalamus appear to be an important hub in this awakening control network.

I. Introduction

In countries where activities tend to be ruled by the clock rather than by natural light, waking-up can be one of the most challenging events of any day. The physiological challenge of waking is caused by a major change in the INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 93 DOI: 10.1016/S0074-7742(10)93004-3

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homeostatic set point—from an inactive, resting, and horizontal position to an upright, alert, and active condition. This involves a range of changes in muscle tone, blood pressure, energy metabolism, and hormone release. In all species, waking-up is preceded by a considerable surge in the release of steroid hormones from the adrenal cortex. Adrenal steroids, such as the glucocorticoids cortisol and corticosterone and the mineralocorticoid aldosterone, are considered major stress hormones as they boost energy production and increase blood pressure, respectively. Together, these hormones gear the body up for the impending activity phase. This daily change in behavioral status is usually coupled to the environmental change from dark to light or light to dark. As this change has always been a feature of this planet, evolution has equipped almost all organisms with an elaborate intrinsic timing system, the so-called biological clock, to anticipate these recurring changes. In this chapter we review some of the major pathways the mammalian biological clock uses to ensure an efficient and coordi­ nated wake-up process. In view of the presence of such a perfectly equipped endogenous timing system, it may seem surprising that waking-up is still such an effort for many people. As we will see, it is especially difficult when we need an alarm clock to wake us at a time that our body clock has not had the chance to adapt to or anticipate. The earliest experiments into the properties of the biological clock have made it very clear that the environmental light/dark cycle is its major entraining factor. Tracing experiments of retinal efferents based on this light sensitivity eventually led to the discovery that the suprachiasmatic nuclei (SCN) in the anterior hypothalamus are the seat of the mammalian biological clock (Hendrickson et al., 1972; Moore and Lenn, 1972). Subsequent lesion and transplantation studies confirmed its function as the master clock (Guo et al., 2006; Moore and Eichler, 1972; Ralph et al., 1990; Stephan and Zucker, 1972; Sujino et al., 2003). As the intrinsic period of the master brain oscillator in the hypothalamic SCN is close to, but not exactly 24 h, resetting of the clock mechanism via the retinalhypothalamic tract on a regular basis ensures that the organism and its internal homeostasis do not drift out of phase with the (exact) 24-h rhythm of the environment. More recently, it has become clear that the intrinsic period of the SCN of approximately 24 h (i.e., circadian) is generated and maintained at the molecular level by transcription/translational feedback loops of the so-called clock genes (Hastings, 1995; Takahashi, 1992). In order for an organism to benefit from its biological clock, the timing signal must be communicated to the rest of the body. Therefore, the products (i.e., proteins) of the clock genes in the SCN neurons are not only involved in the maintenance of their own 24-h transcription/translation feedback loops, but also drive the day/night rhythm in neuronal firing of the SCN neurons (Takahashi et al., 2008), as well as the expression of so-called clock-controlled output genes such as vasopressin ( Jin et al., 1999) and vasoactive intestinal polypeptide (VIP)

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(Hahm and Eiden, 1998), two well-known peptidergic neurotransmitters of the SCN. Indeed, daily rhythms in the mRNA and peptide expression within SCN neurons, as well as release of these peptidergic neurotransmitters (i.e., neuropep­ tides), have been described several times. In principle, the SCN has two ways to convey its rhythmic message to the rest of the brain and subsequently to the rest of the organism: by a humoral and/or by a neural pathway. Transplantation and parabiosis experiments as well as “temporal chimeras” have provided support for the humoral mechanism (Guo et al., 2006; Ralph et al., 1990; Vogelbaum and Menaker, 1992), suggesting that the SCN drives circadian rhythms of (e.g., locomotor) behavior by the rhythmic secretion of paracrine factors in its immediate surroundings and in the third ventricle. By now a number of peptides have been proposed to serve as a humoral SCN output, the most notable examples being vasopressin, transforming growth factor-a (TGF-a), prokineti­ cin-2, and cardiotrophin-like cytockine (Cheng et al., 2002; Kalsbeek et al., 2010; Kramer et al., 2001; Kraves and Weitz, 2006), and there may be more (Hatcher et al., 2008). On the other hand, the same transplantation experiments also provided evidence for the existence of a neural transmission pathway due to the absence of hormonal rhythms in the animals in which (encapsulated) graphs had reinstated behavioral rhythms (Lehman et al., 1987; Meyer-Bernstein et al., 1999; Silver et al., 1996). But the clearest evidence for the existence of hardwired neural connections was provided by elegant experiments by de la Iglesia et al. (2000, 2003). First, these authors demonstrated, in hamsters showing “splitting” of their daily behavioral and endocrine rhythms (i.e., 1 bout of activity and sleep or 1 daily surge of cortisol every 12-h instead of every 24 h), that the daily activity rhythms of the left and right side of their bilaterally paired SCN are exactly 12-h out of phase. Next they demonstrated that in female hamsters showing a splitting of the daily surge of luteinizing hormone (LH), each 12-h surge of LH was coupled to the preferential activation of gonadotropin-releasing hormone (GnRH) neurons on either the left side or the right side of the brain, in concert with the activity of the SCN. Clearly the alternating left- and right-sided activa­ tion of GnRH neurons is more likely to be explained by point-to-point axonal projections from the SCN than by diffusible factors.

II. SCN Output Rhythms

Given the pivotal role of the hypothalamus in homeostatic regulation, the discovery in 1972 (Weaver, 1998) that the master circadian clock also resides in this region did not come as a surprise. Since then, numerous neuroanatomical tracing studies have shown that the projection fibers from the SCN are

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surprisingly limited and by and large restricted to a few hypothalamic nuclei, with the paraventricular nucleus (PVN) of the hypothalamus, the medial preoptic area (MPOA), and the dorsomedial nucleus of the hypothalamus (DMH) as their prime targets (Buijs et al., 1993, 1994, 1995; Teclemariam-Mesbah et al., 1999; Vrang et al., 1995a, 1995b, 1997; Watts and Swanson, 1987; Watts et al., 1987). These studies also indicated that propagation of the timing signal from the SCN mainly proceeds through its contacts with the neuroendocrine and pre-autonomic motor neurons of the hypothalamus (Buijs and Kalsbeek, 2001; Kalsbeek and Buijs, 2002). In the following paragraphs we will first show how the SCN enforces its circadian rhythmicity onto the hypothalamo-pituitary-adrenal (HPA) axis, to generate the awakening rise in glucocorticoids, via its neuronal projections to the neuroendocrine neurons within the hypothalamus. We will then discuss the way in which the SCN controls hepatic glucose production, which ensures sufficient energy availability when waking up, via its projections to the sympathetic and parasympathetic pre-autonomic neurons within the hypothalamus.

III. The Cortisol/Corticosterone Awakening Rise

Under baseline conditions, plasma concentrations of the glucocorticoid hormones released from the cortex of the adrenal gland vary predictably across the day/night cycle. In both nocturnal and diurnal species the plasma corticosterone (cortisol in hamsters and humans) concentrations are highest around the time of arousal (i.e., morning for humans and evening for most rodents). Adrenal gluco­ corticoid hormones have highly integrated effects on both energy metabolism and behavior (Dallman et al., 1993). It is thought that the increased levels of gluco­ corticoids at awakening act to enable foraging behavior by increasing the amount of available energy. Corticotropin-releasing hormone (CRH) is the principal neuroendocrine signal controlling the release of corticosterone from the adrenal gland via its stimulatory action on the adrenocorticotrophic hormone (ACTH)­ producing cells in the anterior pituitary. This neuroendocrine pathway is known as the HPA axis. CRH is synthesized in neuroendocrine neurons in the medial parvocellular part of the PVN, one of the target areas of the SCN. In view of the presumed importance of vasopressin for the output from the SCN (Kalsbeek et al., 2010), we started microinfusions with vasopressin and its antagonist in different SCN target areas. These first experiments demonstrated that vasopressin released from SCN terminals has a strong inhibitory control over basal plasma corticosterone concentrations (Kalsbeek et al., 1992). Further studies on the relation between the circadian release of vasopressin and the control of the daily rhythm in the activity of the HPA axis revealed that vasopressin release in

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Increased release

the rat DMH is important to ensure low circulating levels of corticosterone during the first half of the light period (Kalsbeek et al., 1996b). In addition, the halt of vasopressin release from these SCN terminals in the DMH during the second half of the light period is a prerequisite for the daily surge in plasma corticosterone before the onset of the main activity period of the nocturnal rat, i.e., the dark period (Kalsbeek et al., 1996a). This series of experiments also clearly showed that vasopressin is not the only SCN signal involved in the control of the daily rhythm in HPA activity. Apparently, the general principle of SCN control over daily (hormone) rhythms also holds for corticosterone rhythms and seems to involve a push-and-pull, or ying-yang mechanism, based upon an alternating activity of stimulatory and inhibitory SCN inputs to the appropriate target neurons (Fig. 1). In the case of the HPA axis, the most likely target neurons for the inhibitory effect of arginine vasopressin (AVP) seemed to be the CRH-containing neurons in the PVN. However, several pieces of evidence did not seem to tally with such a primary role for the CRH neuron. First, a direct effect of vasopressin on the CRH neuron would imply a clear daily rhythm in plasma ACTH concentrations. However, in many cases this is not observed (Kalsbeek et al., 1996b). Second, the observed inhibitory effect of vasopressin we observed was not in line with the usual excitatory effect of vasopressin on its target neurons (Joels and Urban, 1982;

0

6

12

18

24

Circadian time (h) Inhibitory SCN signal Stimulatory SCN signal Corticosterone FIG. 1. Schematic representation of the diurnal release pattern of SCN transmitters involved in the circadian control of corticosterone release.

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Arvicanthis

Rat

DMH

DMH PVN

PVN

Sub PVN

Sub PVN

SCN

SCN AVP neuron CRH neuron GABA neuron GLU neuron

FIG. 2. Detailed anatomical scheme of demonstrated and putative connections of the suprachiasmatic nucleus (SCN) in the nocturnal rat and the diurnal Arvicanthis ansorgei brain to explain the opposite effects of arginine vasopressin (AVP) on the HPA axis in these two species. VP is released during the light period, in both the nocturnal rat and the diurnal A. ansorgei. In rats AVP release during the light period will inhibit the corticotropin-releasing hormone (CRH)-containing neurons in the paraventricular nucleus (PVN) of the hypothalamus by contacting gamma-aminobutyric acid (GABA) ergic interneurons in the subPVN and dorsomedial nucleus of the hypothalamus (DMH). On the other hand, in the A. ansorgei, AVP release during the light period will stimulate CRH-containing neurons because it acts on the glutamatergic, instead of GABAergic, interneurons in the subPVN and DMH.

Kow and Pfaff, 1986). Third, contrary to the expected abundant contacts between SCN-derived vasopressin fibers and CRH neurons, only a limited number of such appositions were found (Buijs and Van Eden, 2000; Vrang et al., 1995). A detailed anatomical scheme explaining our current view on the SCN control of the daily rhythm in HPA activity is shown in Fig. 2. The proposed intermediate role of the gamma-aminobutyric acid (GABA)ergic neurons in the subPVN and DMH in rats is supported by electrophysiological in vitro experiments using hypothalamic slices (Hermes et al., 2000). As shown in the image in the right panel of Fig. 2, the proposed important role for intermediate areas such as the subPVN and DMH also provides a good explanation for the mechanism behind the 12-h reversal of the corticosterone rhythm between nocturnal and diurnal species (Kalsbeek et al., 2008), while apparently the phase of SCN activity (including vasopressin release) is similar for nocturnal and diurnal species (Cuesta et al., 2009; Dardente et al., 2004). The above-mentioned mismatch between plasma ACTH and plasma corti­ costerone concentrations made us realize that, in addition to the HPA axis, other (i.e., ACTH-independent) mechanisms of adrenal regulation might be involved

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in the cortisol/corticosterone awakening rise. In view of the close connection of the adrenal gland with the sympathetic branch of the autonomic nervous system (ANS) (the adrenal gland may be considered a modified sympathetic ganglion), and of the essential role of the ANS in the control of the circadian melatonin rhythm (Moore, 1996; Perreau-Lenz et al., 2004), we hypothesized that the ANS is important for setting the sensitivity of the adrenal cortex to ACTH. Transneuronal virus tracing from the adrenal indeed revealed second-order labeling in PVN neurons and third-order labeling in SCN neurons (Buijs et al., 1999). The functional importance of this multisynaptic connection between the SCN and the adrenal cortex for the daily rhythm in adrenal corticosterone release had earlier been proven by an elegant combination of adrenal microdialysis and denervation experiments (Jasper and Engeland, 1994). Thus the SCN apparently uses a dual mechanism to control the daily rise in plasma glucocorticoids: on the one hand it acts on the neuroendocrine motor neurons to influence the release of hypotha­ lamic releasing factors, while on the other hand it also acts—through the ANS—on the adrenal gland to influence the sensitivity of the adrenal cortex to the incoming hormonal ACTH message. In a nice series of experiments, Born et al. (1999) showed how the circadian control of the early morning cortisol rise is intertwined with the expected time of awakening. An expected earlier wake-up time results in a significantly increased release of ACTH, but not cortisol, just before the expected wake-up time. An unexpected earlier wake-up, however, results in an increased release of ACTH only upon awakening. The dissociation of ACTH and cortisol again points to a two-stage control mechanism for the awakening rise of plasma cortisol. More recently, the same group showed that a suppression of the morning cortisol rise results in an impaired memory retrieval (Rimmele et al., 2010).

IV. The Dawn Phenomenon

For the body’s normal physiology the maintenance of a constant blood glucose level is essential, and this is particularly true for the central nervous system (CNS), as it can neither synthesize nor store the amount of glucose required for its normal cellular function. A pronounced daily rhythm in plasma glucose concentrations has been described in experimental animals as well as humans (Bellinger et al., 1975; Bolli et al., 1984; Jolin and Montes, 1973; La Fleur et al., 1999; Shea et al., 2005; Van Cauter et al., 1997). Although the peak time of plasma glucose levels shows a 12-h difference between nocturnal and diurnal species (Cuesta et al., 2009), in both species peak plasma glucose concentrations are attained every day shortly before awakening at the start of the main activity period. Plasma glucose

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concentrations are the resultant of a glucose influx from the gut and liver, and of a glucose efflux by its uptake in, e.g., brain, muscle, and adipose tissue. The liver plays a pivotal role in maintaining optimal glucose levels by balancing glucose entry into and its removal from the circulation. From a hypothalamic and chronobiological view, glucose production by the liver is especially interesting because of the clear involvement of both the sympathetic and parasympathetic inputs to the liver in glucose metabolism (Nonogaki, 2000; Puschel, 2004; Shimazu, 1987) and the earlier demonstrated strong circadian control of glucose metabolism in the liver (Akhtar et al., 2002; Kita et al., 2002; Oishi et al., 2003). In order to maintain glucose homeostasis, a complex glucose sensing and regulatory system has developed within the CNS, especially involving hypothala­ mic brain areas such as the arcuate nucleus (ARC), the ventromedial hypothala­ mic (VMH) nucleus, and the lateral hypothalamus (LH). The major part of the neurochemical makeup of this hypothalamic network is still largely unknown, although recently it has been shown that the neuropeptide-Y (NPY)-containing neurons in the ARC are an important hypothalamic link to effectuate the inhibi­ tory effect of insulin on hepatic glucose production (Van Den Hoek et al., 2008). Using local administration of GABA and glutamate receptor (ant)agonists in hypothalamic target areas of the SCN, we probed the contribution of changes in SCN activity to the daily control of plasma concentrations of glucose and insulin. The daily rhythm in plasma glucose concentrations turned out to be controlled predominantly via the activity of the sympathetic liver innervation (Cailotto et al., 2005, 2008, 2009; Kalsbeek et al., 2004, 2008). The activity of the hypothalamic pre-autonomic neurons in charge of the sympathetic innervation to the liver was controlled according to a mechanism very much similar to the mechanism described previously for the SCN control of the daily rhythm in melatonin release (Perreau-Lenz et al., 2004). Briefly, the mechanism involves a combination of rhythmic GABAergic inputs and continuous glutamatergic stimulation to the pre-autonomic neurons. In case of the liver-dedicated pre-autonomic neurons, the acrophase of this inhibitory input is somewhere around ZT2 (Fig. 3). To investigate in more detail how the just described SCN output signals increase plasma glucose concentrations at awakening, i.e., by stimulating glucose production or by inhibiting glucose uptake, we combined, we combined our hypothalamic administration studies with the systemic infusion of glucose labeled with a stable isotope (D2), i.e., D2-glucose. The use of the stable glucose isotope enabled us to distinguish between changes in glucose production and glucose uptake. These experiments showed that the most pronounced increase in hepatic glucose production was caused by the disinhibition of neurons in the perifornical area lateral to the DMH, and that orexin (but not melanin-concentrating hormone (MCH)) containing neurons were strongly activated by the local administration of the GABA antagonist bicuculline (Mul et al., 2010; Yi et al., 2009). Further studies revealed that the hyperglycemic effect of bicuculline could be blocked by the

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PVN

Pineal

Liver

GABA GLU

SCN

FIG. 3. Schematic presentation of the daily activity pattern of suprachiasmatic GABAergic (i.e., inhibitory) and glutamatergic (i.e., stimulatory) neurons implicated in the autonomic control of the daily rhythms in pineal melatonin release and hepatic glucose production. During the early light period the liver-dedicated sympathetic pre-autonomic neurons in the PVN are inhibited by GABAergic neurons. Although during the light period a glutamatergic input to the PVN neurons is also active, this does not result in an increased activity of the pre-autonomic neurons due to the overwhelming inhibitory GABA input. At the onset of the dark period, the GABAergic neurons become silent, thus enabling the excitatory glutamatergic input to become effective in stimulating the pre-autonomic PVN neurons and subsequently glucose production by the liver. A similiar mechanism, but with a different phasing along the L/D-cycle is operative for the control of the daily melatonin rhythm.

concomitant intracerebroventricular (ICV) administration of an orexin antagonist (Yi et al., 2009) and that orexin fibers impinge upon sympathetic preganglionic neurons in the intramediolateral column (IML) of the spinal cord that project to the liver (Van Den Top et al., 2003). Previously, we had demonstrated that the hyperglycemic effect of a local blockade of GABAergic transmission was very much time dependent (Kalsbeek et al., 2008), indicating SCN control. Using an approach very much similar to ours, Alam et al. (2005) had already demonstrated that perifornical orexin neurons are subject to an increased endogenous GABAer­ gic inhibition during sleep. Together these results indicate that by a disinhibition of the orexin system at the end of the light period the SCN not only promotes arousal, but at the same time also causes an increase of endogenous glucose production to ensure adequate concentrations of plasma glucose when the organism awakes

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PF

SCN GABA GLU Orexin MCH

IML

Glucose, 6-meal

Liver

FIG. 4. Midsagittal view of the rat brain with a schematic representation of the hypothalamic connections involved in the autonomic control of the daily rhythm in hepatic glucose production. The orexin-containing neurons in the perifornical area are innervated by both glutamatergic and GABAergic projections from the SCN. During the main part of the light period, activation of the orexin neurons by the excitatory glutamatergic inputs is prevented by release of the inhibitory neurotransmitter GABA. The circadian withdrawal of the GABAergic input allows the orexin neurons to become active at the onset of darkness. Subsequently, the excitatory effect of orexin on the preganglionic neurons in the spinal cord will activate the sympathetic input to the liver and result in an increased hepatic glucose production.

(Fig. 4). In a recent study, Shiuchi et al. (2009) showed that orexin, via its release in the VMH and subsequent mediation by the sympathetic nervous system, may also stimulate glucose uptake in skeletal muscle. Although it is not yet clear how the message is transported from the VMH to the autonomic nervous system, it is tempting to speculate that the SCN–orexin connection might also be responsible for the daily rhythm in glucose tolerance (La Fleur et al., 2001).

V. The Awakening of the Cardiovascular System

In the cardiovascular system, many components, including heart rate, blood pressure, plasma aldosterone, platelet aggregation, and fibrinolytic activity, exhibit a daily rhythm (Takeda and Memura, 2010). For instance, in the absence of disease, blood pressure undergoes a nighttime dip and a morning rise. These cycles are inverted in nocturnal species such as mice and rats. One of the bestknown examples of the impact of awakening stress in humans is the increased early morning occurrence of cardiovascular incidents such as myocardial infarc­ tions, strokes, pulmonary embolisms, and cardiac arrhythmias (Durgan and

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Young, 2010). In addition, weekly and yearly rhythms have also been reported, with Monday and autumn associated with the highest risk of acute myocardial infarction (Evans et al., 2000; Kloner et al., 1999). This clustering of cardiovas­ cular incidents is usually explained by extracardiac factors such as the sudden change from a horizontal to a vertical position and the accompanying change in blood pressure, as well as additional factors such as an increased platelet aggrega­ tion and fibrinolytic activity at this time of day. However, in essence it is a clear demonstration of the failure of the biological clock to adequately muster all the physiological processes necessary to prepare the individual for the new activity period. In favor of this “failure-of-anticipation” theory is the increased incidence of acute myocardial infarctions on the first 3 weekdays after the transition to daylight saving time in spring, which necessitates a 1-h phase advance of the endogenous clock (Janszky and Ljung, 2008). Vice versa, after the transition from daylight saving time in the autumn (i.e., a 1-h free-run for the endogenous clock), the incidence ratio is decreased for the first weekday. Further corroboration of the importance of a well-functioning biological clock for a proper control of blood pressure is the pronounced decline of the immunocytochemical staining for three prominent SCN neuropeptides (includ­ ing vasopressin) in subjects with a history of essential hypertension (Goncharuk et al., 2001). This observation is all the more interesting because, in the same patients, a clear-cut increase was found in the amount of CRH immunostaining and CRH mRNA expression in the PVN (Goncharuk et al., 2002). The increased activity of CRH neurons in the PVN of hypertensive patients is probably responsible not only for an increased activity of the HPA axis, but also for an increased activity of the sympathetic branch of the ANS, which has been impli­ cated in the pathogenesis of hypertension. The inverse relation between vaso­ pressin and CRH as reflected by the decreased vasopressin staining in the SCN and the increased CRH activity in the PVN resembles the inhibitory effect of SCN-derived vasopressin on the HPA axis found in the animal experiments. In other words, it is tempting to speculate that one of the mechanisms underlying the increased CRH activity in hypertensive patients is a diminished inhibitory input from the SCN (Kalsbeek et al., 2010). The main question to be answered is whether these SCN and PVN changes are a cause or a consequence of hypertension. One way to approach this question is to investigate whether a strengthening of the SCN signal can alleviate the increased blood pressure. Accordingly, we conducted a randomized, double-blind, placebocontrolled, crossover study in which 16 men with untreated essential hypertension were treated with oral melatonin (2.5 mg daily; 1 h before sleep) for 3 weeks. Repeated melatonin administration reduced ambulatory systolic and diastolic blood pressures by 6 and 4 mmHg, respectively (Scheer et al., 2004). Interestingly, spontaneously hypertensive rats also show a change in SCN activity, although in this case it is an increased activity of the VIP neurons (Avidor et al., 1989; Peters et al.,

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1994). This reversal in the direction of changes in SCN activity could be a result of the reversed relation between blood pressure and SCN neuronal activity in diurnal and nocturnal species; i.e., in humans an increasing blood pressure during the light period goes hand in hand with an increased neuronal activity of the SCN, whereas in nocturnal species the increasing SCN activity during the light period results in a decreased blood pressure (Cuesta et al., 2009).

VI. Conclusion

Knowing the time of day presents a selective advantage at multiple biological tiers. Key to this unique selective advantage is anticipation. Awakening from sleep is a clear example of an event for which anticipation is of great importance. Our studies on the morning surges in plasma corticosterone and glucose clearly revealed the neural pathways used by the central clock to integrate its anticipatory message within hypothalamic networks as well as the important role of the autonomic nervous system to convey its message to the periphery. An important hub in this hypothalamic control network are the perifornical orexin neurons, as they affect different aspects of the awakening process, such as increased hepatic glucose production, increased sympathetic activity, and increased body temperature in conjunction with increased alertness (Tsujino and Sakurai, 2009; Yi et al., 2009).

Acknowledgments We thank Dr. Mariette T. Ackermans at the Academic Medical Center in Amsterdam for her help with the stable isotope measurements, Henk Stoffels for preparation of the images, and Wilma Verweij for correction of the manuscript. Special thanks are dedicated to Ewout Foppen for his superb technical assistance in most of the research described above and to Jilles Timmer for animal husbandry. Parts of the research presented were financially supported by the Dutch Diabetes Research Foundation (2004.00.027).

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Van den Top, M., Nolan, M. F., Lee, K., Richardson, P. J., Buijs, R. M., Davies, C., and Spanswick, D. (2003). Orexins induce increased excitability and synchronisation of rat sympathetic preganglionic neurones. J. Physiol. 548, 809–821. Vogelbaum, M. A., and Menaker, M. (1992). Temporal chimeras produced by hypothalamic trans­ plants. J. Neurosci. 12, 3619–3627. Vrang, N., Larsen, P. J., MÆller, M., and Mikkelsen, J. D. (1995a). Topographical organization of the rat suprachiasmatic-paraventricular projection. J. Comp. Neurol. 353, 585–603. Vrang, N., Larsen, P. J., and Mikkelsen, J. D. (1995b). Direct projection from the suprachiasmatic nucleus to hypophysiotrophic corticotropin-releasing factor immunoreactive cells in the paraven­ tricular nucleus of the hypothalamus demonstrated by means of Phaseolus vulgaris-leucoagglutinin tract tracing. Brain Res. 684, 61–69. Vrang, N., Mikkelsen, J. D., and Larsen, P. J. (1997). Direct link from the suprachiasmatic nucleus to hypothalamic neurons projecting to the spinal cord: A combined tracing study using cholera toxin subunit B and Phaseolus vulgaris-leucoagglutinin. Brain Res. Bull. 44, 671–680. Watts, A. G., and Swanson, L. W. (1987). Efferent projections of the suprachiasmatic nucleus: II. Studies using retrograde transport of fluorescent dyes and simultaneous peptide immunohisto­ chemistry in the rat. J. Comp. Neurol. 258, 230–252. Watts, A. G., Swanson, L. W., and Sanchez-Watts, G. (1987). Efferent projections of the suprachiasmatic nucleus: I. Studies using anterograde transport of Phaseolus vulgaris leucoagglutinin in the rat. J. Comp. Neurol. 258, 204–229. Weaver, D. R. (1998). The suprachiasmatic nucleus: A 25-year retrospective. J. Biol. Rhythm. 13, 100–112. Yi, C. X., Serlie, M. J., Ackermans, M. T., Foppen, E., Buijs, R. M., Sauerwein, H. P., Fliers, E., and Kalsbeek, A. (2009). A major role for perifornical orexin neurons in the control of glucose metabolism in rats. Diabetes 58, 1998–2005.

PREPARATION FOR AWAKENING: SELF-AWAKENING VS.

FORCED AWAKENING: PREPARATORY CHANGES IN

THE PRE-AWAKENING PERIOD

Mitsuo Hayashi�, Noriko Matsuura�,†, and Hiroki Ikeda�,‡ �

Department of Behavioral Sciences, Graduate School of Integrated Arts and Sciences,

Hiroshima University, Higashi-Hiroshima City 739-5821, Japan

† S & A Associates, Inc., Chuo-Ku, Tokyo 103-0007, Japan

‡ Japan Society for the Promotion of Science, Chiyoda-ku,

Tokyo 102-8472, Japan

I. Introduction II. Definitions III. Effects of Attempt to Self-Awaken on Sleep A. Changes of Sleep B. Anticipatory Changes of Endocrine C. Anticipatory Changes of Autonomic Nervous System IV. Self-Awakening and Daytime Functions A. Sleep Inertia B. Daytime Sleepiness C. Daytime Naps V. Habit and Ability of Self-Awakening A. Habit of Self-Awakening B. Sleep Habit and Morningness C. Ability to Self-Awaken VI. Factors of Successful Self-Awakening A. Success Rate of Self-Awakening B. Psychological Stress C. Motivation and Self-Efficacy D. Environmental Factors E. Circadian Rhythm and Homeostatic Process F. Time Perception VII. Schematic Model of Self-Awakening VIII. Conclusion References

Self-awakening (SA) is awakening by oneself at predetermined time without external means. Attempting to SA produces various kinds of preparatory changes over the course of a sleep period, including contributing to a decline in the waking threshold immediately before awakening. As a result, one can easily awake from sleep, and sleep inertia immediately after awakening from sleep

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reduces. In addition, daytime arousal level is higher for those who have the habit of SA. Surveys revealed that not a few people have the habit of SA and that they tend to be morningness chronotypes. Many factors are involved in the successful SA, such as psychological stress, motivation, personality trait, ambient environ­ ment, circadian and homeostatic process, and time perception.

I. Introduction

In modern society, many people use alarm clocks to awaken each morning. However, it has also been reported that a number of people can wake-up at a set time each morning without the aid of an alarm. This is called “self-awakening.” This chapter deals with self-awakening and its effects on nocturnal sleep and daytime functions. In addition, we discuss factors that contribute to self-awaken­ ing, its effectiveness, individual differences, and present a schematic model of selfawakening.

II. Definitions

Awakening by external stimuli such as an alarm, noise, or another person is known as “forced awakening” (FA), whereas “spontaneous awakening” is defined as awakening without external stimulation. The latter includes “self­ awakening” (SA), which is awakening by oneself at predetermined time without external means; by contrast, “natural awakening” (NA) is defined as awakening naturally at any time. Moorcroft et al. (1997) referred to people who have the ability to SA as those “who were able to regularly awaken at predetermined time without an alarm or were able to always awaken before the alarm” (p. 41). In practice, it may not be easy to distinguish SA from NA. Minimally, SA is a construct which implies an attempt to try to awaken at a predetermined time, whereas this is not necessarily the case for NA. Therefore, SA and NA can be classified into different categories based simply on the necessity to awake at specific time. For example, we do not always awake during the weekend at the same time as on weekdays. In fact, it has been reported the duration of sleeping is prolonged on weekends, as compared to weekdays; in addition, alarm clock use is reduced from 68.3% usage reported by the

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population for weekdays to only 42.5% on weekends (Monk et al., 2000). This implies that the number of people who naturally awaken should increase on weekends. Sleep extension studies have also revealed that we can easily sleep 1–2 h longer than our usual sleep time (Harrison and Horne, 1996; Kamdar et al., 2004). That is, we terminate our sleep on weekdays shortening the real period of time we are capable of sleeping. Therefore, it is plausible that most spontaneous awakenings at usual waking times on week­ days could be judged as SA. Finally, it is useful to distinguish between two SA subcategories wherein SA is based, respectively, upon habit versus ability. The former includes self-awakening habitually at a certain time daily. The latter category includes self-awakening at any time which is different from the usual awakening time, generally before the usual time.

III. Effects of Attempt to Self-Awaken on Sleep

A. CHANGES OF SLEEP Nocturnal sleep deteriorates when attempting SA. This has been reported in a number of studies where negative changes appear in sleep. Among these changes are prolonged sleep latency (Ikeda and Hayashi, 2010; Lavie et al., 1979), waking after sleep onset (WASO) increments (Bell, 1980; Hono et al., 1991; Matsuura et al., 2002b), prolonged sleep stage 1 (Matsuura et al., 2002b; Matsuura and Hayashi, 2009), and shortened duration of sleep due to earlier awakening (Hawkins, 1989; Matsuura et al., 2002b; Zepelin, 1986). Subjective evaluation of sleep also worsened when attempting SA (Hawkins and Shaw, 1990). These various phenomena can be accounted for by psychological stress, caused by the motivation to successfully awaken at a stipulated time (Hawkins, 1989), or by anxiety prior to going to bed (Fuller et al., 1997; Matsuura and Hayashi, 2009) due to “fear over consequence of failure to wake up on time” (Moorcroft et al., 1997, p. 44). Recently, Matsuura and Hayashi (2009) observed that both electroencepha­ logram (EEG) arousals (American Sleep Disorders Association, 1992) and sleep stage 1 increased 1 h before awakening when participants attempted SA. Sleep efficiency (total sleep time/total time in bed) also decreased in that WASO increased during this time. These results suggest that the arousal threshold declined and sleep lightened as a target awakening time approached. SA asso­ ciated with naps is also relevant. In a short daytime nap of 15–20 min, Kaida et al. (2003b) found that EEG activities remained a relatively high arousal levels

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throughout the nap when the participants tried to SA; by contrast, arousal level was lower as awakening time approached when the participants were forced awaked by the experimenter. Rapid eye movement (REM) sleep has been cited as a positive factor in realizing SA by both Lavie et al. (1979) and Zepelin (1986, 1993). Lavie et al. (1979) found that participants awakened from REM sleep for 8 of 12 nights (67%) when they attempted to SA. Zepelin (1986) also reported that 7 of 11 SAs were related to REM sleep. The fact that awakening is apt to occur after REM sleep has also been confirmed after normal nocturnal sleep, under the conditions of desynchronization without external time cues, forced desyn­ ˚ kerstedt et al., 2002). Murphy et al. chrony, or 60 h of bed-rest (reviewed by A (2000) claimed that REM sleep may provide a “gate” to wakefulness, thus facilitating a smooth transition to wakefulness. The relationship between SA and REM sleep has not been uniformly con­ firmed, however. Other studies report conflicting results. For example, when Zung and Wilson (1971) randomly set a target waking time from 2 to 5 a.m., they found that sleep stages at awakening were not concentrated in a specific sleep stage. Other studies that have compared SA and FA also observed that participants awoke at a range of different sleep stages. Matsuura and Hayashi (2009) studied 11 partici­ pants who had self-reported as habitual SA individuals. When required to self­ awaken at their usual rising time, only three of these participants awoke from REM sleep; the remaining eight fell into two groups of four each: one group woke from sleep stage 1 and the other from stage 2. In FA nights, the number of participants who awakened from REM sleep (3) did not differ from the number awakened from REM sleep in SA nights; another seven participants woke from sleep stage 2 and a one participant awoke from slow wave sleep. In addition, the last REM sleep prior to awakening in the SA nights terminated an average of 18 min before awakening; furthermore, there were no differences in the length of REM sleep during entire nights, during the last NREM–REM sleep cycle, and during the last hour before awakening between SA and FA nights. Ikeda and Hayashi (2010) also asked 10 participants, all of whom reported no habit of SA, to attempt to self-awaken at their normal rising time. Sleep variables were not available for two persons: one could not self-awaken and polysomnogram (PSG) of the other was not recorded due to technical error. Consistent with results of Matsuura and Hayashi (2009), the number of participants who awakened from REM sleep in this group was the same for SA and FA nights. In the SA night, two participants awakened from REM sleep, while one woke from sleep stage 1 and the other five from sleep stage 2. In the FA nights, two participants awakened from REM sleep, three from woke from stage 1, and three from stage 2. In addition, when Kaida and colleagues introduced SA into short daytime naps of 15–20 min, they found that only one participant woke from REM sleep; all others awakened from sleep stages 1 and 2 (Kaida et al., 2003a, 2003b, 2005, 2006a). These results suggest that REM sleep may not be always crucial factor in the timing mechanisms of SA.

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Ikeda and Hayashi (2008b) analyzed EEG activities of sleep stage 2 during the last 90 min of sleep. There was no significant difference between SA and FA nights in the EEG delta, theta, and alpha band activities. However, sigma band activities, which reflect the activity of sleep spindles that occur distinctively during sleep stage 2, were found to decrease as the target wakening time grew nearer in the SA night. The latter type of change did not occur in the FA night. Sleep spindle is considered to provide cortical de-arousal, thereby serving in sleep maintenance (Ueda et al., 2001). Thus, this research suggests that attempting SA results in a reduction of the sleep maintenance function immediately prior to a target awaking time. In summary, when attempting SA, several preparatory changes occur in the latter half of the night which implies preparatory activity for a forthcoming time of awakening. These changes include an increase of EEG arousal, WASO, and sleep stage 1, as well as a decline in sleep spindle activities.

B. ANTICIPATORY CHANGES

OF

ENDOCRINE

Endocrine activity is another marker of preparatory activity for awakening. Typically plasma concentration of adrenocorticotropin (ACTH) and cortisol fluctuates over a circadian period. These secretions are usually minimal before retiring at night, and increase during nocturnal sleep reaching a maximal state immediately at morning awakening (Spa¨ th-Schwalbe et al., 1992). However, these releases intensify when an individual is exposed by stress (Deinzer et al., 1997). Born et al. (1999) have found that ACTH increased sharply in a preparatory fashion 60 min prior to a predetermined time at which participants were instructed to awaken. These researchers considered this anticipatory ACTH increase functioned to facilitate spontaneous awakening. Although the partici­ pants did not attempt to SA, this experimental procedure is the same as an SA paradigm in that experimental participants had to direct attention to the pre­ determined awakening time. Therefore, similar mechanisms could be considered to be involved as when attempting SA.

C. ANTICIPATORY CHANGES OF AUTONOMIC NERVOUS SYSTEM Matsuura and Hayashi (2009) measured heart rate (HR) and heart rate variability during nocturnal sleep for those who have habitual SA. They calcu­ lated the total power of the high-frequency components, HF (HF: 0.15–0.4 Hz)

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and low frequency ones, LF (LF: 0.04–0.15 Hz) that contribute to heart rate variability. The HF component and the ratio of LF to HF (LF/HF ratio) are considered indicators of cardiac parasympathetic activity and cardiac sympatho­ vagal balance, respectively (Montano et al., 1994; Stepanski et al., 2005). Although the HF component did not differ significantly as a function of SA and FA nights, the HR and LF/HF ratio increased in NREM sleep during the last hour of sleep in the SA nights. These results suggest that an anticipatory response occurs in the sympathetic nervous system when attempting SA. Preparatory activation of the sympathetic nervous system is also confirmed in short daytime naps. Kaida and colleagues (2003a) found that in young adults the HR began to increase at 3 min before waking time when attempting SA. On the other hand, in elderly adults (65–80 years), the diastolic blood pressure began to increase at 2 min before target waking time (Kaida et al., 2005) when attempting SA.

IV. Self-Awakening and Daytime Functions

A. SLEEP INERTIA Sleep inertia is a transitional state of lowered arousal that immediately follows awakening. Typically, it is manifest by an increase of sleepiness, confusion, disorientation of behavior, and deterioration of work performance (Ferrara and De Gennaro, 2000; Ikeda and Hayashi, 2008a; Tassi and Muzet, 2000). On the basis of findings that ACTH reveals an anticipatory response in the sympathetic nervous system from 1 h prior to a predetermined awakening time, it is quite conceivable that arousal level immediately before awakening would be higher, hence sleep inertia reduced, when attempting SA. A survey study confirmed that individuals who reported habitual SA awakened more comfortably than those who awake using external means such as alarm clocks (Matsuura et al., 2002a). In an experimental study, Matsuura and Hayashi (2009) examined the effects of SA on nocturnal sleep and sleepiness at awakening for those with habitual SA. Compared to an FA condition, in which the participants were awakened by the experimenter, subjective sleepiness immediately after awakening was indeed reduced in the SA condition; this difference was maintained for 10 min after awakening. For the people without habitual SA, Ikeda and Hayashi (2010) observed that performance in a simple auditory reaction time task deteriorated and these participants felt more uncomfortable after awakening by FA. However, such deterioration of performance and subjective feelings were not observed after awakening by SA. These results suggest that SA reduces sleep inertia that can occur immediately following awakening.

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B. DAYTIME SLEEPINESS It has also been reported that people who report habitual SA less likely to doze off during the daytime, in comparison with participants who awaken daily using external means (Matsuura et al., 2002a). Over a 2-week period, Ikeda (2009) measured wrist activity level of 11 university students who had no habit of SA. The participants awoke by using alarm clocks in the morning during the first week (FA week), and subsequently attempted to self-awaken during the second week (SA week). In the FA week, wrist activity level declined during mid-after­ noon, indicating that a “post-lunch dip” occurred. However, such a dip was not observed in the SA week. In addition, afternoon sleepiness decreased in the SA week compared to FA week. These results suggest that habitual SA should reduce afternoon sleepiness and enhance daytime function.

C. DAYTIME NAPS A daytime short nap of 10–15 min is an effective countermeasure to afternoon sleepiness (Hayashi et al., 2003a, 2003b, 2004, 2005). However, sleep inertia sometimes occurs immediately after awakening from such a short nap (Hayashi et al., 2003a). As previously mentioned, when attempting SA during a short daytime nap, participants’ average arousal level is typically heightened from 10 min before awakening (Kaida et al., 2003b); as well, an anticipatory response of sympathetic nervous system occurred 2–3 min before awakening from the nap (Kaida et al., 2003a, 2005). These results suggest that SA can reduce sleep inertia from the nap. In fact, after awakening by SA from the nap, subjective sleepiness was reduced (Kaida et al., 2003a, 2003b), participants felt more comfortable (Kaida et al., 2006a), and P3 amplitude of event-related potentials (ERPs) were enhanced (Kaida et al., 2003b, 2006a). P3 amplitude is considered to reflect attention allocation and memory updating, whereas attenuation of P3 appears to accompany a reduction of alertness (Polich and Kok, 1995). These results indicate that SA enhances the effectiveness of a short daytime nap.

V. Habit and Ability of Self-Awakening

A. HABIT

OF

SELF-AWAKENING

Studies that have surveyed various measures of awakening methods indicate that a portion of the population self-awakens on a daily basis. Crabb (2003) surveyed

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for 417 university students and reported that 64.3% of the participants used alarm clocks every day, 13.7% were awakened daily by a person in the household, and 8.2% self-awakened every day. Monk et al. (2000) observed from sleep logs of 2 weeks for 266 healthy adults (aged 20–50 years) that 54.1% of the participants had been awakened by an alarm clock or another person for at least 4 days during the working week, 23.7% never awoke without an alarm on weekdays, and 6.4% never used an alarm. Moorcroft et al. (1997) interviewed 269 adults by phone (140 female and 129 male, 21–84 years.) and classified these people into four groups by the method of awakening as follows: “(1) never use an alarm or external source, (2) use an alarm but awaken before the alarm goes off, (3) use an alarm but sometimes awaken before the alarm goes off, and (4) use an alarm and do not awaken before the alarm goes off” (p. 41). The respective proportions of respondents falling into each group were 23, 29, 24, and 24%. The group who did not use an alarm comprised older persons and was more consistent in the duration of night sleep. Matsuura et al. (2002a, 2010) regarded Moorcroft’s first and second groups as ones in which all participants showed an SA habit (habitual SA); they surveyed the sleep/wake habits of persons with habitual SA in various age groups in Japan. The proportion of people with habitual SA was 32.8% of 778 primary school children in fourth to sixth grade (9–12 years), 16.6% of 728 students of the National College of Technology (15–23 years), 10.3% of 643 university students (18–24 years, mean 19.1 year), and 18.9% of 297 persons employed at a regular daytime job (20–59 years) (Fig. 1). In spite of almost the members belonging to the same age groups, the proportion of habitual SA was higher in college students than university students. The starting time of classes was fixed every morning for 40 30 20

University students

College students

0

Primary school children

10

20 s

30 s

40 s

50 s

Day workers

FIG. 1. Proportion of habitual self-awakening with different age groups (Matsuura, 2010).

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these college students, whereas this was not the case for university students, suggesting that social factors such as necessity to awaken at certain time daily influences the habit of SA. In addition, similar to the results of Moorcroft et al., older workers showed higher proportions of habitual SA than younger ones. The proportion is the lowest in the twenties (7.4%) and the highest in the fifties (37.0%). It is medial in the thirties (18.0%) and forties (26.9%).

B. SLEEP HABIT

AND

MORNINGNESS

50 45 40 35 30 25 20 15 10 5 0

100 90 80 70 60 50 40 30 20 10 0

Ratio of SA

Number of participants

It has been reported that people with habitual SA are more consistent in their amount of sleep per night (Moorcroft et al., 1997) and that they tend to be morningness chronotypes (Crabb, 2003; Matsuura et al., 2002a). Figure 2 shows the distribution of morningness score for 612 university students and the ratio of people who have the habit of SA (Matsuura, 2010, p. 23). Because mean score of a population is 50, this distribution is biased toward “eveningness.” If this score is below 42 or above 58, then he or she is classified into “evening type” or ¨ stberg, 1976). Matsuura (2010) reported “morning type,” respectively (Horne and O that evening type occupied 20.0% of 66 students who have the habit of SA, while it did 34.8% of 546 students who have no SA habit. Morning type occupied 17.1 and 4.4% of habitual and non-habitual SA students, respectively. In addition, as can be seen in this figure, the higher the score was, the higher the ratio of SA was. Table I shows the sleep–wake habits of university students with or without the habit of SA (Matsuura et al., 2002a). The members of the habitual SA group go to bed and wake up approximately 20 min earlier than non-SA group. They awakened more comfortably in the morning and also dozed-off less during the day, suggesting that their arousal level tended to be higher in the daytime.

20 25 30 35 40 45 50 55 60 65 70 75 80 85 Morningness score FIG. 2. Morningness score and ratio of self-awakening. Bars are number of participants. Circles are ratio of those who have the habit of self-awakening. (Matsuura, 2010, p. 23).

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SLEEP–WAKE HABIT

OF

TABLE I

UNIVERSITY STUDENTS WITH OR

Sleep–wake habit Bedtime—weekday ––weekend Wake time—weekday ––weekend Duration of sleep—weekday ––weekend Subjective sleep estimation Sleep latency (min) Number of awakenings (per night) Mood after awakeningb Number of daytime symptoms (per week) Sleepiness Dozed-off Nap Morningness score

WITHOUT A

HABIT

OF

SA (MEAN + SD)

SA

Non-SA

p
00:26 (1:07) 01:16 (1:54) 7:28 (1:18) 9:46 (2:01) 405.9 (84.4) 527.2 (90.9)

00:53 (1:02) 01:36 (1:38) 7:51 (1:09) 10:14 (1:45) 398.4 (77.1) 537.0 (107.9)

.01 n.s. .05 n.s. n.s. n.s.

22.4 (30.7) 0.5 (1.0) 3.2 (1.2)

18.6 (17.6) 0.4 (0.8) 2.7 (1.1)

n.s. n.s. .01

4.4 (2.9) 3.1 (2.3) 0.7 (1.0) 47.9 (8.8)

4.7 (2.4) 3.9 (2.0) 0.9 (1.4) 43.7 (8.1)

n.s. .01 n.s. .001

From Matsuura et al. (2002). a Results of analysis of variance (ANOVA). “n.s.” means “not significant” b 5: very comfortable–1: very uncomfortable

C. ABILITY

TO

SELF-AWAKEN

As previously noted, the ability to SA means that one is able to awaken at any time during a sleeping episode even when the target time differs from their usual awakening time. However, persons who reported to have the SA ability do not always self-awaken regularly in their daily lives. Matsuura (2010) revealed that 34.0% of 333 university students surveyed reported that they had SA ability, but only 8.7% of these reported having habitual SA. Students who had the SA ability regardless of SA habit had a higher tendency for morningness and they woke up earlier than those who had no such ability. However, of those who had the SA ability, students with a habit of SA awakened more comfortably and showed less daytime sleepiness, compared to those without the habit. This suggests that the higher daytime functioning which accompanies individuals who habitually selfawaken could be caused by the SA habit lifestyle, but not the personality characteristics of the SA ability. In a pattern that is similar to that characteristic of habitual SA, as people in the population age, they are increasingly more likely to acquire SA ability. Kaida et al. (2006b) reported that 75.7% of 410 elderly persons surveyed (66–89 years, mean 74.3 years) answered they have the ability to SA from nocturnal sleep.

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VI. Factors of Successful Self-Awakening

A. SUCCESS RATE

OF

SELF-AWAKENING

The success rate shown in studies done on SA depends on many variables, such as selection of participants, ability or habit of SA, target time of awakening, trial number of each participants, criterion of success, reward to success, and so on. Therefore, the values reported in previous studies cannot be subject to a simple comparison. Nevertheless, the main findings can be summarized as follows: (1) people who report habitual SA ability can awake at target times more precisely than those without this ability; (2) some persons who report having no such ability can awake at a time close to the target time; (3) success rate increases when attempting SA at home; (4) success rate is high for those who have the SA habit when target waking time is set to their usual waking time. In order to exclude the possibility of NA, many studies set the target waking time earlier than the usual the waking time. Lavie et al. (1979) recorded PSG of four consecutive nights (one night was for adaptation, one night was for baseline, and the other two nights were SA nights). They instructed seven young adults (21–30 years), who reported having SA ability, to self-awaken at 3:30 or 5:30 a.m., all times earlier than their usual waking times. Over 14 nights, they found that these participants awakened within 10 min of the stipulated target during 5 nights (36%) and awa­ kened within 30 min of the target during 9 nights (64%). Zepelin (1986) also recorded PSG and asked for 15 persons (15–32 years) who reported to have SA ability (four persons) or to have willing to try SA (7 persons) to self-awaken at 3:00 or 4:00 a.m. Eleven of the participants (73%) were able to awaken successfully. When SA ability is not involved, the success rate of awakening at a given time decreases. Zung and Wilson (1971) recorded PSG for four consecutive nights (two nights were adaptation and other two nights were SA nights) and asked for 22 persons (20–45 years) who were not selected for concerning about SA ability to self-awaken at randomly from 2 to 5 a.m. Their participants could awaken within 10 min of 14 of 44 nights (31.8%). At home, about half of the participants in a study, regardless of their SA ability, were shown to be able to succeed in SA at a target waking time that was earlier than their usual waking time. Bell (1980) reported that 53% of 38 participants, who were not selected about SA ability, could awaken within 15 min of a target time set to be more than 45 min earlier than their usual waking time. Hawkins and Shaw (1990) asked 146 undergraduate students to log 8 non­ consecutive nights of sleep. For four of these nights, participants were told to selfawaken 60 min after they extinguished the lights and went to bed; for the other four nights they did 60 min before they really wanted to get up. The successful rates for the early and late trials were 50 and 60%, respectively.

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When the target waking time was set to usual waking time, the success rate in meeting this target time is enhanced regardless of whether or not people report SA ability. Hawkins (1989) reported that 74% of 84 university students could awaken within 30 min of their usual waking time when at home. Ikeda (2009) asked 11 university students, who had no habit and no SA ability, self-awaken at their usual waking time every morning for 1 week at home. Seven participants (64%) awakened within 30 min of their usual waking time on the first night, and 9 (82%) did on the last night. The average difference between the waking time and the target time on the successful nights was 16.9 and 13.1 min on the first and the last nights, respectively. Moorcroft et al. (1997) asked 15 persons (19–62 years) who had habitual SA to self-awaken at home and at their usual waking time for three consecutive nights. Over 44 nights except for one night of recording failure, they could awaken within 15 and 30 min from the target time in the 28 (63.6%) and 35 (79.5%) nights, respectively. In half of the nights, the difference between the waking and target time was within 7 min, and the mean time of difference was 3 min and 27 s. In the sleep laboratory, where environmental factors such as light or noise were controlled, the success rate in meeting a target waking time decreased even if the target was set to a usual waking time. Matsuura and Hayashi (2009) recorded PSG of 17 university students who had the SA habit for more than four consecutive nights in a sleep laboratory. The first two nights were for adaptation and the third and forth nights were for either SA or FA nights. If the participants could not awaken in the FA nights within 30 min of the target time, then the participants tried SA again in the additional nights. Their participants could self-awaken within 30 min in 11 of 27 nights (40.7%). Ikeda and Hayashi (2010) also recorded PSG of 10 university students who reported having no SA ability or SA habit for five consecutive nights in a sleep laboratory. The first night was for adaptation, the second night was for FA, and the last three nights were for SA. The participants could self-awaken within 30 min of usual waking time in 9 of 30 SA nights (30%). In a study of daytime naps, Kaida and colleagues reported that the success rate of SA was higher for those who have SA ability than for those who do not. They asked for the university students to awaken 15 min after extinguishing the lights, and for the elderly (65–80 years) to awake at 20 min after lights out. Nine of 11 (81.8%; Kaida et al., 2003a) and 10 of 14 university students (71%; Kaida et al., 2003b), and 9 of 10 elderly persons (90%; Kaida et al., 2005) could self-awaken within 5 min of a target time.

B. PSYCHOLOGICAL STRESS Psychological stress is increased by attempting SA as previously mentioned. This stress is caused by a motivation to awake successfully or by anxiety about the

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failure to wake up on time. In either case, to attempt SA degrades the content of nocturnal sleep. Matsuura and Hayashi (2009) reported that state anxiety was higher before bedtime, when the participants were told to try SA in the morning (SA condition), than when they were told that they would be awakened by the experimenter (FA condition). After awakening from a night’s sleep, state anxiety did not differ between SA and FA conditions. In addition, EEG arousals and sleep stage 1 increased during 1 h before awakening in the SA condition. Sleep efficiency also decreased, that is WASO increased, during that time. These results suggest that attempting SA increases arousal or the likelihood of waking during the latter half of sleep. Bell (1980) has claimed that waking during sleep is a crucial factor for success in SA. He considered two abilities; the one is “an ability to induce greater potential for awakening” during sleep, and the other is “the ability to use accurately the information gathered at these check points” during sleep (p. 507). Although the latter ability has not been confirmed, it is reported that preparatory changes occur in the SA night toward the end of sleep. These changes include, for example, increase in EEG arousal, WASO and sleep stage 1 (Matsuura and Hayashi, 2009), decline in sleep spindle activities (Ikeda and Hayashi, 2008b), increase of ACTH (Born et al., 1999), and sympathetic nervous system activities (Matsuura and Hayashi, 2009), as mentioned previously.

C. MOTIVATION

AND

SELF-EFFICACY

It has been pointed out that motivation is one of the crucial factors of successful SA (Hawkins, 1989; Lavie et al., 1979). In the several experimental studies, participants received rewards to increase their motivation and accuracy in meeting waking time targets (Lavie et al., 1979; Zepelin, 1986; Zung and Wilson, 1971). However, participants with habitual SA have been shown to be naturally highly motivated (Matsuura, 2010). Matsuura et al. (2002b) found that achievement motivation of university students with habitual SA was higher than the average level of achievement measured in university students. It has also been reported that self-efficacy was higher for those who reported SA ability for both young adults (Crabb, 2003; Matsuura, 2010) and the elderly (Kaida et al., 2006a). Self-efficacy is “the belief in one’s capabilities to organize and execute the courses of action required to manage prospective situations” (Bandura, 1995, p. 2). Crabb (2003) stated that “awakening to be on time is a social skill insofar as it coordinates individuals’ sleep wake cycle with the schedule of families, school, work, transportation, and other social systems” (p. 344). He also thought that those who self-awakened daily have high self-adjustment skills,

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thus these people may also have high self-efficacy. Results of a survey of 417 university students appear to confirm this; self-efficacy was positively correlated with confidence in being able to self-awaken regularly (r = .392), while it was negatively correlated with willingness to use an alarm clock (r = –.412) or to be awakened by another person (r = –.207).

D. ENVIRONMENTAL FACTORS The success rate of SA is relatively high when participants attempted SA at home, compared to in the laboratory (as noted earlier). For example, if target waking time was set to the usual waking time, accuracy rate ranged from 64% (Ikeda, 2009; Moorcroft et al., 1997) to 74% (Hawkins, 1989) at the participants’ home, whereas it dropped to between 30% (Ikeda and Hayashi, 2010) and 40.7% (Matsuura and Hayashi, 2009) in the sleep laboratory. Ambient light, noise, or temperature is not available in the laboratory, suggesting that such environmental factors contribute to successful SA.

E. CIRCADIAN RHYTHM

AND

HOMEOSTATIC PROCESS

Moorcroft et al. (1997) queried participants with habitual SA how they awakened without an alarm or prior to the alarm time. The most common response was “an internal clock” (26%), and second was “habit or daily routine” (16%). Some researchers also postulated that SA is caused by an internal alarm clock (Bell, 1980; Zepelin, 1993; Zung and Wilson, 1971). Because biological ˚ kerstedt et al. (2002) pointed out that rhythms can function as an internal clock, A the circadian rhythm of body temperature and ultradian rhythm of NREM–REM sleep cycles are involved in the propensity to awaken from sleep. Circadian rhythm induces additional transition to wakefulness and creates a greater chance for the termination of sleep in the rising phase of the body temperature. The NREM–REM cycle connects with the high probability of awakening from REM sleep, which has been confirmed after normal nocturnal sleep, under the conditions of desynchronization without external time cues, forced desynchrony, or 60 h of bed-rest. As previously noted, however, the relationships between REM sleep and SA are not clear. The two-process model of sleep–wake regulation postulates roles for two different processes: a circadian rhythm mechanism and a homeostatic process

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for determining the timing and structure of sleep (Borbey and Achermann, 1999). In this account, the EEG slow wave activity, which is representative of the homeostatic process, declines with the passage of time during sleep. This means that sleep pressure or sleep maintenance function declines with elapsed time from sleep onset. Thus circadian and homeostatic processes are involved in awakening from sleep, while the relationship between these processes and SA has not still been examined.

F. TIME PERCEPTION Bell (1980) has postulated internal, biological processes that check the elapsed time or duration of sleep. Although Zepelin (1986) denied the possibility of “time judgment” during sleep, it has been reported that we have the ability to estimate time during sleep. Aritake et al. (2009) reported that time estimation ability during sleep is related to elapsed time from sleep onset and that this is positively correlated with the amount of slow wave sleep (sleep stages 3 and 4). This was not related with acrophase of circadian body temperature rhythm, suggesting that time estimation during sleep is not regulated by the circadian system. It has also been reported that time estimation during sleep becomes more accurate when attempting SA. Ikeda et al. (2006) forced participants to awaken during sleep for two nights and asked them to estimate the clock time. These participants were previously instructed to try to self-awaken during one night (SA condition) and to awaken naturally in the morning during the other night (NA condition). To control the sleep stage at awakening, participants were awakened 5 min following REM sleep continued in each NREM–REM cycle. Error time between the real time and the estimated time was reduced in the SA condition, compared to the NA condition. This result supports the notion that more precise judgment of time near the target waking time is a factor contribut­ ing to successful SA. However, the mechanisms that are involved which help make the time perception precise are still unclear.

VII. Schematic Model of Self-Awakening

Figure 3 presents schematic outline of model of self-awakening. Attempts at SA are proposed to increase psychological stress, and this, in turn, affects cognitive and physiological functions during sleep. Time perception during

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Before sleep

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Tendency to awaken

Attempt to SA

Psychological stress

Cognitive function

Circadian factor Rise in body temperature

Accuracy of time perception Trigger of promoting arousal During sleep

Homeostatic factor Decline in sleep maintenance function

Environmental factors Light, noise, temperature, etc.

Increase of ACTH Enhancement of SNS Decline in waking Threshold immediately before awakening Decline in sleep spindle activities Increase of EEG arousal and WASO Lightening of sleep

Factors of habitual SA Personality Ability to SA Self-efficacy Achievement motivation Age Regular sleepwake habit morningness etc. Social factors Necessity to awake at specific time

After sleep

Self-awakening

Improvement of waking function Decline in sleep inertia Enhancement of daytime activity level Decrease of daytime sleepiness

FIG. 3. Schema of self-awakening.

sleep becomes more accurate when SA is attempted; in addition, several events, such as an increase of ACTH and sympathetic nervous system activities, promote arousal during the latter half of sleep. These phenomena trigger a decline in waking threshold immediately before awakening; that is, they increase EEG arousal and WASO, lighten sleep, or decrease sleep spindle activities. As results described earlier have shown, this decline permits a smooth self-awakening from

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sleep, and it reduces chances of sleep inertia immediately after awakening. Thus, the waking function is improved in daytime. Decline in waking threshold during sleep is also promoted by homeostatic, circadian, and environmental factors. In the latter half of a sleep period, the sleep maintenance function declines due to dissipation of homeostatic process during sleep, and arousal increases due to a rise in body temperature. In addition, environment factors, such as ambient light, noise, and temperature, enhance arousal during sleep. Both personality and social factors affect habitual SA. The ability to SA, high self-efficacy and achievement motivation, consistency of daily sleep length, and morningness chronotype are characteristics of those who have the habit of SA. In addition, the number of people who self-awaken daily increases with age. Finally, the necessity to awake at specific time is the most important social factor for SA, which differentiates SA from NA.

VIII. Conclusion

Many previous studies confirm that a number of people can successfully awaken at a desired time and they do so habitually by themselves without the aid of external means such as alarm clocks or other people. In addition, the attempt to self-awaken produces various kinds of preparatory changes over the course of a sleep period, including contributing to a decline in the waking threshold immediately before awakening. However, it remains unclear why and how such changes occur near the target waking time, why time perception becomes precise when attempting to self-awaken, or what type of internal clock affects SA apart from biological rhythms such as circadian rhythm and ultradian rhythm of NREM–REM cycle, among other issues. Further research in SA is required to clarify these clock mechanisms.

References

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CIRCADIAN AND SLEEP EPISODE DURATION INFLUENCES ON

COGNITIVE PERFORMANCE FOLLOWING THE PROCESS OF

AWAKENING

Robert L. Matchock The Pennsylvania State University, Altoona, PA 16601-3794, USA

I. II. III. IV. V.

Introduction Time-of-Day and Cognition Time-of-Day Effects and Waking Up Length of Sleep Episode and SI Different Measures of Cognitive Functioning

References

The process of waking up from an episode of sleep can produce temporary deficits in cognitive functioning and low levels of alertness and vigilance, a process referred to as sleep inertia. Cognitive ability varies as a function of time-of-day; cognitive ability associated with sleep inertia also shows circadian influences with deleterious effects most pronounced when awakened from biological night, possibly paralleling the core body temperature minimum. The length of the sleep episode may contribute to the severity of sleep inertia. Short sleep episodes (<20 min) produce little cognitive impairment, probably because of a lack of slow-wave sleep in the sleep episode. With longer sleep episodes, aspects of sleep depth such as percentage of slow-wave sleep or total length of the sleep episode may be important. Finally, myriad tasks have been used to measure sleep inertia effects, and cognitive deficits associated with waking up have been demonstrated on both simple and complex tasks for both speed and accuracy. More research is needed on how the type of task may interact with sleep inertia. Tests that measure known specific aspects of cognition and that can be mapped to brain systems and neurotransmitters (e.g., the Attentional Network Test: ANT) are recommended to further under­ stand how information processing during the process of awakening is distinct from other aspects of awareness.

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I. Introduction

The time period shortly after awakening from an episode of sleep can, paradoxically, result in various deficits in cognitive and motor performance, confusion, disorientation, and hypovigilance, relative to the pre-sleep period (Ferrara and De Gennaro, 2000; Tassi and Muzet, 2000). Although sleep is usually considered to be restorative, task performance immediately after sleeping can be worse than it was prior to the sleep bout when sleep deprivation was greater (Wertz et al., 2006). This deleterious effect, experienced by almost all humans at least once per day, has been termed “sleep drunkenness” (Broughton, 1968) or, more commonly, sleep inertia (SI) (Lubin et al., 1976). Compared to falling asleep, relatively little is known about the process of waking up. This paucity of information is unfortunate because the beneficial effects of napping must be evaluated against any performance deficits that are associated with SI. In models of sleep/wake regulation, SI is referred to as “process W” and is con­ trasted with homeostatic mechanisms that negatively affect performance as a function of prior waking time (“process S”) and an endogenous sleep-independent circadian component (“process C”) (Borbely, 1982; Folkard and Akerstedt, 1992). See Fig. 1 for a representation of homeostatic, circadian, and sleep inertia processes on task performance. Myriad factors appear to modulate the severity of sleep inertia such as sleep stage prior to awakening (Bonnet, 1983), time-of-day (process C) (Dinges et al., 1985), prior sleep deprivation (process S) (Ferrara et al., 2000a), total length of the sleep bout (Matchock and Mordkoff, 2007), ultradian phase (Lavie and Weler, 1989), whether awakenings are forced or self-imposed (Ikeda and Hayashi, 2010; Kaida et al., 2006), as well as the type of task that is administered to participants (Matchock and Mordkoff, 2007; Tassi and Muzet, 2000). The current chapter will examine circadian and length of sleep bout factors on SI and the types of tasks that are commonly used to document any putative deficits in functioning, with the caveat that these aforementioned etiological factors are inexorably linked and may interact in complex not-well-understood ways.

II. Time-of-Day and Cognition

As an illustration, the simple relation between cognitive processing and timeof-day, without consideration of any SI influences, is not so simple (for reviews see Carrier and Monk, 2000; Schmidt et al., 2007). Early studies have indicated that circadian fluctuation in alertness and cognitive performance on tasks that had small cognitive loads paralleled the circadian rhythm of core body temperature

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SI affected by:

Task performance

Only Process S

Only Process C

• • • • • • •

Both Processes S and C (observed)

Circadian factors Sleep deprivation Sleep stage upon awakening Percentage of SWS in bout Sleep bout length Type of task Thermoregulatory processes

7 h sleep

Process W sleep inertia (SI): lasting approximately 20 min to 2 h

Awakening

24 h

48 h Time

FIG. 1. Diagrammatic representation of homeostatic, circadian, and sleep inertia processes on task performance. Homeostatic pressure due to sleep deprivation increases linearly as a function of time spent awake (approximately 48 h), which occurs against a backdrop of underlying circadian rhythmicity (process C). Process W is the brief period after an episode of sleep characterized by cognitive impairments and functioning that can be worse than performance prior to the sleep episode when homeostatic (and perhaps circadian) processes were greater.

(CBT) (Colquhoun, 1971; Kleitman, 1963). Later research has shown that this relationship may be more complex. One report found that self-rated alertness, using a visual analogue scale (VAS), was phase advanced (acrophase at 1200 h) relative to CBT (acrophase at 1900 h) (Monk et al., 1983). Ultradian 90 min rhythmicities in alertness and performance have been widely reported as well (Broughton, 1975; Conte et al., 1995; Kleitman, 1963; Lavie and Zomer, 1984), including ultradian differences between the two hemispheres (Iskra-Golec and Smith, 2006). Moreover, although self-reported alertness is likely to parallel simple perceptual speed (Colquhoun, 1971; Kleitman, 1963), it may not follow as closely more complex cognitive measures such as performance on a memorysearch task (Owens et al., 1998). According to some research (e.g., Kraemer et al.,

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2000; Owens et al., 2000) it is becoming increasingly clear that different aspects of performance and attention, although still typically circadian in nature, follow different time-of-day patterns. Other research seems to suggest that the nature of the cognitive task is instru­ mental in determining optimal performance and that not all tasks are closely correlated with temperature (Carrier and Monk, 2000). For example, immediate memory (Folkard and Monk, 1980), acoustic processing (Folkard, 1979), and dichotic processing of digits (Morton and Kershner, 1991) appear to peak in the morning hours when body temperature is lower. Delayed memory (Folkard and Monk, 1980), semantic processing (Folkard, 1979), and tests of logical reasoning (Folkard, 1975) may be better if information is encoded later in the day when temperature is higher. Tasks that require simple processing (i.e., card sorting or letter cancellation) also peak late in the day and are more closely associated with body temperature (Carrier and Monk, 2000; Colquhoun, 1971). Folkard et al. (1983) suggested that performance measures might be under multi-oscillatory control, affected by a sleep/wake oscillator, a temperature oscillator, and an oscillator for working memory. It is plausible that the curvilinear relationship between arousal and task performance (i.e., Yerkes and Dodson, 1908) would provide an explanation for some of these results. Performance on complex tasks should peak earlier in the day when arousal is lower or at an intermediate level, while performance on simple tasks should peak later when arousal is higher. Indeed, Folkard et al. (1983) found that performance on a 1-letter cancellation task paralleled the body temperature rhythm, but not a 5-letter cancellation task. Kahneman (1973) argues that the mobilization of effort in a task is also controlled by the demands of the task itself, with difficult tasks increasing arousal more than easier tasks. Infrequent sampling, small sample sizes, exogenous factors impinging on subjects’ daily routines when not constantly restricted to the laboratory, and inadequate research designs may limit confidence in some of these results. Cognitive performance may decrease over time because of time spent awake, perhaps because of adenosine accumulation inhibiting arousal neurons of the basal forebrain (Porkka-Heiskanen, 1999) (process S), because circadian pace­ makers are currently producing a state of low arousal (or high) that is not optimal for performing the task, or because of both (either in an additive or in an interactive fashion). “Forced desynchronization (FD) protocols” allow researchers to disentangle homeostatic and circadian influences. Normally, endogenously generated rhythms from the suprachiasmatic nucleus (SCN) of the anterior hypothalamus are entrained to Zeitgebers (time givers) such as the light–dark cycle or photoperiod. Most humans fall asleep when CBT, which is circadian in nature (Scales et al., 1988), starts to decline. However, when subjects are main­ tained under long (e.g., 28 h) or short (e.g., 20 h) days in a laboratory setting with no time cues, entrainment is not possible and a desynchronization occurs between the endogenous circadian cycle and the sleep wake cycle, resulting in

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sleep episodes that will occur at different circadian phases. This free-running condition allows researchers to control for circadian phase and sleep deprivation. Constant routine (CR) protocols, although not ideal for isolating circadian and homeostatic factors, are characterized by bed rest or inactivity, sleep deprivation, and equally spaced isocaloric snacks in an attempt to separate the effects of endogenous rhythms from exogenous factors. Results from FD designs suggest a much closer coupling of task performance with core body temperature. Johnson et al. (1992) found that subjective alertness and calculation performance were closely linked to CBT. Similarly, Wright et al. (2002) found that performance on various measures of cognitive performance such as the Digit Symbol Substitution Test (DSST), addition tasks, subjective alertness, and a Psychomotor Vigilance Task, all positively correlated with body tempera­ ture. Another recent FD study (e.g., Lee et al., 2009) also found that performance on an addition task and a Psychomotor Vigilance Task was poorest at the circadian nadir of CBT. Taken together, research strongly suggests that human cognition and performance are not uniform across the 24 h day, but vary in a rather systematic way that seems to roughly parallel, or be phase-locked to, endogenously generated rhythms of alertness and temperature (process C). This rhythmicity suggests that the processes associated with waking up from a sleep episode may also differ as a function of time-of-day.

III. Time-of-Day Effects and Waking Up

Immediate and important decisions upon awakening are common occurrences for people in many occupations (e.g., truck drivers, shift workers, emergency workers and military personnel on call, airline pilots, oil rig workers, college students taking naps before examinations). Moreover, these abrupt awakenings that require immediate attentional resources can potentially occur at any point of the 24 h day. As such, it would be extremely valuable to understand how, if at all, circadian regulatory mechanisms affect SI. Napping studies afford the opportunity to examine how the process of awakening can vary according to time-of-day. Most napping studies, though, are interested in the residual restorative properties of the nap and some studies test participants (or report data) after the period of SI has dissipated (e.g., Dinges et al., 1987; Hayashi et al., 1999; Taub et al, 1977). With this caveat in mind, what follows is a representative summary of studies that have manipulated the timing of naps. Naitoh (1981) found that a 2 h nap from 0400 to 0600 h near the circadian nadir (low CBT) had little recuperative value when performance was measured shortly after the nap compared to a 2 h nap from 1200 to 1400 h. Other studies

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(e.g., Naitoh et al., 1982) have similarly found that short naps near the circadian nadir seem to have attenuated restorative properties immediately after the nap, presumably because of SI. Awakenings from short afternoon naps seem to produce fewer cognitive impairments than nighttime naps, although there may be a subjectively experienced increase in effort that is required to perform tasks which is not manifested in any detectable behavioral impairments (Asaoka et al., 2010). Bonnet and Arand (1995) found that psychomotor performance after four 1 h naps in the middle of the night was more impaired than after a 4 h nap in the middle of the afternoon. Despite more SI associated with the nighttime naps during the circadian nadir, there was a delayed improvement in performance the following evening that was not observed with the afternoon nap. That is, naps during the circadian nadir during biological night may be more restorative in the long-term than daytime naps, perhaps because of more slow-wave sleep (SWS) (Takeyama et al., 2004), but may initially show more SI. In patients with narco­ lepsy, awakening from short 30 min daytime naps can show much SI, as measured by subtraction tasks and a four-choice reaction time (RT) task, probably because of SWS arousals (Mullington and Broughton, 1994). Much evidence suggests a drop in alertness and an increase in sleep propen­ sity during the afternoon, the so-called post-lunch dip (Bes et al., 2009; Busby and Broughton, 1983). Lavie and Weler (1989) allowed subjects a mid-afternoon, “sleep gate” nap (1500–1700 h) and an early evening, “forbidden zone” nap (1900–2100 h). Even though the mid-afternoon nap was characterized by more SWS, subjects had less SI as measured by mood scores and sleepiness data; performance data were not reported. Hayashi and colleagues found that 20 min naps during the late afternoon (post-lunch dip) and before the postlunch (early afternoon) dip both decreased sleepiness, but only the late afternoon nap resulted in increases in performance (Hayashi and Hori, 1998; Hayashi et al., 1999). Late afternoon naps may be restorative and may also be characterized by waking up processes that do not greatly interfere with task performance. These findings also suggest that ultradian cycles may be another potential variable to consider for researchers who study the awakening process. When comparing daytime and nighttime naps, it is important to note that when people are awakened during biological night, they are likely to have elevated melatonin. Melatonin secretion by the pineal gland occurs during biological night and has soporific (Wirz-Justice and Armstrong, 1996) and hypothermic properties (Hughes and Badia, 1997). Exogenously administered melatonin during the day has produced decrements on a Psychomotor Vigilance Task (Graw et al., 2001) and a two-choice visual RT task (Rogers et al., 1998), although not on a letter cancellation task (Graw et al., 2001). The ability of exogenous melatonin to reduce CBT and increase sleepiness may be functionally related to its ability to promote vasodilation of distal skin surfaces (Cagnacci et al., 1997).

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Indeed, some have postulated that the dissipation of SI is related to distal vasoconstriction, especially the distal-to-proximal skin temperature gradient (DPG; Kra¨ uchi et al., 2004). Kra¨ uchi et al. (2004) monitored subjective sleepiness, CBT, and proximal and distal skin temperatures in subjects before and after an 8 h sleep bout (2300–0700 h) and a 2 h nap (1600–1800 h). CBT dropped during the 8 h sleep bout but not during the nap. However, for both the longer sleep bout and the nap, distal temperature increased upon falling asleep and decreased shortly after awakening. The dissipation of subjective sleepiness (performance measures were not taken) correlated with the rate at which the extremities cooled (i.e., distal vasoconstriction). Sleep stage upon awakening was not associated with subjective sleepiness or the extent of vasoconstriction. The authors hypothesized that a 2 h relaxation period characterized by no sleep but with distal vasodilation would induce SI. Using a hybrid CR/FD procedure, Kra¨ uchi and colleagues (2006) found that although sleep deprivation (process S/homeostasis) increased sleep propensity and SWS rebound, homeostatic mechanisms had no effect on the thermoregulatory system (i.e., core, distal, and proximal temperature). In this study, the dissipation of SI also correlated well with the extent of distal vasoconstriction. Circadian aspects of sleepiness were highly correlated with CBT, and distal temperature changes were phase advanced (occurred earlier). That is, hypnagogic distal vasodilation and hypnopompic distal vasoconstriction (distal warming and cooling, respectively) may regulate both CBT and SI, at least as measured by subjective sleepiness. If further studies confirm these findings (e.g., cold water applications to the extremities in order to negate SI), this would lend support to the hypothesis that circadian factors can influence SI, even though the relation between CBT and distal temperature is inversely correlated (Gradisar and Lack, 2004; Kra¨uchi et al., 2006), and influences such as an afternoon nap may affect distal temperature but not CBT (Kra¨ uchi et al., 2004). As well, it would be informative for future investigations to examine how well distal thermo­ regulatory changes correlate with cognitive functions and task performance, in addition to self-reported sleepiness. At least one study (e.g., Werken et al., 2010) found a parallel between decreases in distal skin temperature, DPG, and decreases in subjective sleepiness and simple RT and improvements on an addition task. Artificial dawn simulation for the 30 min prior to awakening at 0700 h also improved all of these measures. Other studies have examined the process of awakening from a sleep episode primarily within the period of biological night. In perhaps the earliest attempt to examine this, Wilkinson and Stretton (1971) awakened naval service men either at 0030, 0130, 0330, or 0530 h and administered an addition task, a simple RT task, and a coordination task. All awakenings show impaired performance rela­ tive to an afternoon control condition. Although sleep bout length and circadian time were confounded, simple RT was impaired more early in the night and steadily improved across the sessions. Performance on the addition and coordi­ nation tasks were more impaired later in the night, although there was a sharp

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improvement in performance in the 0530 h condition for the addition task. As partially suggested by the authors, simple RT appeared to be more of a function of depth of sleep (length of sleep bout or percentage of SWS), while the addition task appeared to be more circadian in nature (Fig. 1;Wilkinson and Stretton, 1971). That is, simple RT appeared to be influenced by process S and perfor­ mance on the adding task by process C (but see Matchock and Mordkoff, 2007 for an opposite pattern). Balkin and Badia (1988) had subjects go to sleep at 2400 h and were awakened at 0040, 0140, 0240, 0340, 0440, and 0540 h for a 20 min testing session for four consecutive days. Latency to fall asleep and performance on an addition test decreased across the night, and sleepiness ratings increased. This pattern of results is suggestive of a circadian influence. However, protocols characterized by frequent awakenings typically result in more degraded performance (Downey and Bonnet, 1987), and sleep deprivation can increase the percentage of SWS in a nap (Dinges et al., 1985; Matsumoto, 1981) and even rapid eye movement (REM) sleep (Matsumoto, 1981). Brief arousals that produce a transient burst of alpha activity on the electroencephalography (EEG) but do not fully awaken subjects nor reduce the time spent sleeping are characterized by diminished recuperative value (Levine et al., 1987). Tassi et al. (1992), using a spatial memory task, found increased RTs in a 1 h nap during the first part of the night (0100 h) compared to later in the night (0400 h), presumably because subjects were more likely to be awakened in SWS in the early-night nap. However, exposure to arousal-increasing noise abolished this SI in the 0100 h nap but not in the 0400 h nap. Gil and colleagues have also found more impaired performance upon awakening from a nap during the first part of the night (Gil et al., 1994, 1995). Dinges et al. (1985) awakened subjects after 2 h naps that were either in the peak or in the trough of circadian body temperature after 6, 18, 30, 42, or 54 h of sleep deprivation. Prior sleep deprivation increased the amount of SWS in naps. For simple RT (i.e., how long it took subjects to answer a phone), sleep stage upon awakening was associated with increased RTs. For a higher cognitive functioning using the descending subtraction task (DST), the total amount of SWS accumulated in the nap was a better predictor of performance. Finally, naps during the circadian trough of core body temperature (when it is easier to fall asleep) were associated with more severe cognitive decrements than naps during peak core body temperature with more sleep deprivation, suggesting that circa­ dian influences can counteract sleep deprivation. Other studies have produced results on the contrary. Naitoh et al. (1993) reasoned that since there is a best time to fall asleep quickly, there should also be an optimal time to wake up quickly. Experimental subjects were awake for 64 h and took a 20 min nap every 6 h, while control subjects were awake for 64 h without any napping. Performance on Baddleley’s logical reasoning task indi­ cated an SI effect with the experimental subjects performing worse than control subjects for number of problems attempted and accuracy. However, there were

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no circadian time effects of SI, relative to the controls, despite performance on the reasoning task showing a circadian rhythm. This study, though, had a small sample size and only sampled logical reasoning every 6 h. These above results may be equivocal due to different dependent measures that are used to measure SI and the confounding effects of prior sleep deprivation and sleep stage upon awakening. In perhaps the best controlled study to date, Scheer et al. (2008) employed an FD protocol that desynchronized the sleep/wake cycle from the circadian cycle by using a 28 h “day.” The data clearly indicated an endogenous circadian rhythm of SI (as measured with a 2-digit serial addition task) with degraded performance during subjects’ biological night than the biological day, especially just prior to the CBT minimum. Neither sleep stage upon awakening nor the cumulative proportion of various sleep stages in an epoch were significant predictors of SI. In another study of SI that employed an FD protocol, Silva and Duffy (2008) found that older adults had impaired performance on the DSST when they were awakened during their biological night. Awakenings from non-REM sleep were also associated with worse performance than from REM sleep. Collec­ tively, these two well-controlled studies provide convincing evidence that circadian factors have an effect on SI, such that if participants are awakened at a time when their bodies indicate that they should be sleeping, cognitive impairments are greater. It may be somewhat premature to completely discount the effect of sleep stage upon awakening (see Silva and Duffy, 2008). The subtle effects may be missed when overall sleep depth (i.e., actual awakening while in SWS or percentage of SWS in bout) is not controlled. For example, awakenings from stage 2 or REM sleep (considered to be the same “depth” of sleep) at the same time of the night result in left hemisphere task enhancements (verbal memory) and right hemisphere task enhancements (spatial task), respectively (Lavie et al., 1984). Stickgold et al. (1999) used a semantic priming task to sample cognitions upon awakenings from REM and stage 2 NREM sleep. Priming was better for strongly associated word pairs (e.g., long–short) in NREM sleep and for weakly associated word pairs (e.g., thief–wrong) in REM, suggesting that the associative connections in a dream state may be different than in NREM or the waking state. Interestingly, this weak priming upon awakening from REM decreased in strength as the night progressed when the duration of REM episodes are known to increase. This attenuation may reflect the masking of stage-specific effects by more robust circadian influences.

IV. Length of Sleep Episode and SI

Researchers have devoted less attention to sleep parameters such as the total amount of REM, SWS, or the overall length of the sleep bout. Sleep episodes as short as 30 min (Brooks and Lack, 2006; Tietzel and Lack, 2001) to as long as 8 h

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with no prior sleep deprivation (Jewett et al., 1999) can be followed by an SI period that can last up to 2 h (Jewett et al., 1999). The nature of the relationship between sleep bout length and SI upon awakening is not well understood and difficult to experimentally isolate. One way to partially address this issue is to examine the recuperative effects (or lack thereof) of naps of different durations. Afternoon naps as brief as 10 min are associated with improvements in subjective alertness and cognitive performance (Brooks and Lack, 2006; Tietzel and Lack, 2001), while longer 30 and 50 min naps are associated with impaired alertness and performance, followed by improvements after the SI period has dissipated (Brooks and Lack, 2006; Sallinen et al., 1998; Tietzel and Lack, 2001). This pattern of results seems to suggest that either longer total sleep periods, longer periods of time consisting of SWS, or awakening in SWS contributes to SI. Circadian factors are controlled for since these short naps occur at the same relative time (e.g., between 1400 and 1504 h in Brooks and Lack, 2006). Ultrabrief naps of 30 and 90 s appear not to have beneficial effects which downplay the role of stage 1 sleep onset as a mediator of the recuperative effect of naps (Tietzel and Lack, 2002), and to be restorative, the sleep episode should consist of at least 10 uninterrupted minutes (Downey and Bonnet, 1987). Stampi et al. (1990) allowed subjects 4 h of nocturnal sleep followed by either 20, 50, or 80 min daytime naps. The 80 min nap was followed by greater deficits on a DST, while the 50 min nap showed greater deficits on a Memory and Search Test; overall, SI was marginally more pronounced for the 50 min nap than the 80 min nap. The 20 min nap only produced mild deficits on both of these measures. It has been suggested (i.e., Tassi and Muzet, 2000) that perfor­ mance was worse following the 50 min nap because most awakenings were in SWS, and no awakenings from the 20 min nap were in SWS; the 80 min nap had a few REM awakenings. It could also be argued that percentage of SWS in the sleep episode explains the data equally well, especially when considering perfor­ mance on the DST (Stampi et al., 1990). In support of this, an analysis of short naps between 60 and 120 min in duration taken during a 16 h night shift revealed a positive linear correlation between nap length and self-reported fatigue (Takahashi et al., 1999). Although EEG measures were not obtained, the longer naps in this study, which had more self-reported fatigue, should have been less likely to have an SWS awakening. Ferrara et al. (2000b) suppressed SWS in subjects for two nights and administered a DST after sleeping 2, 5, and 7.5 h. Without the presence of SWS, there was a linear decrease in SI across the night (i.e., less SI in the early morning awakening/7.5 h sleep bout), especially for performance speed. During the recovery night with the SWS rebound effect, the early morning awakening had more severe SI, especially for performance accuracy. These results hint at the possibility that without the depth of sleep (total percentage of SWS) present, longer sleep episodes are associated with fewer cognitive impairments shortly after the sleep bout. When depth of sleep was

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introduced again during the recovery night, longer sleep episodes (which have more total SWS) show greater cognitive impairments. In a way, these results mirror the findings of Matchock and Mordkoff (2007) who administered a flankers task with two levels of target-distractor spacing (0.75 and 1.50�) and three trial types (compatible, incompatible, and neutral) to participants after a 1 h sleep bout (2300 h), a 4 h sleep bout (0300 h), and a 7 h sleep bout (0600 h) in a repeated-measures design with length of sleep bout counterbalanced across participants. Specifically, for three consecutive nights, participants went to sleep at 2300 h and were awakened at either 2400, 0300, or 0600 h; thus, all participants obtained approximately 7.5 h of sleep each night and did not have any prior sleep deprivation or major sleep deprivation differ­ ences between the testing nights. Simple RT from neutral-flanker trials was slowest at 0300 h, appearing to parallel circadian body temperature; the trend was the same for near and far spacing trials (Fig. 2). RTs at 2400 h were not significantly different from the pre-sleep condition at 2100 h. It is plausible that process S at 2100 h (continuous waking for 14 h) and process W (SI) at 2400 h were similar in their magnitude. In contrast, the flanker effect, which is a measure of selective attention that is similar to Posner’s executive attention or conflict resolution (Posner and Peterson, 1990), increased linearly as a function of the length of the sleep bout; that is, longer sleep bouts were associated with larger flanker effects, for both the near and the far spacing conditions (Fig. 3).

Mean neutral RT (ms)

510

490

Near spacing Far spacing

470

450

430

410 2100

2400

0300

0600

Time FIG. 2. Mean RT and SEM (vertical bars) for neutral flankers at near and far flanker spacing at 2100, 2400, 0300, and 0600 h (from Matchock and Mordkoff, 2007).

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50

Mean flanker effect (ms)

Near spacing Far spacing 40

30

20

10

0 2100

2400

0300

0600

Time FIG. 3. Mean flanker effect scores for far and near spacing conditions at 2100, 2400, 0300, and 0600 h (from Matchock and Mordkoff, 2007).

The finding of a flanker effect demonstrates that subjects are not able to selectively process only the target, even when the location of the target is known in advance and when the flankers are at such wide eccentricities so as to not normally affect performance in a high arousal awake state (Broadbent et al., 1989). Similar results have been reported in an earlier study which had a broader focus (see Matchock and Mordkoff, 2005), suggesting that this pattern may not be an anomaly. Taking into consideration the limitations of this study, depth of sleep (as defined as total percentage of SWS in a sleep episode) seemed to have a more deleterious effect on selective attention, while circadian factors modulated simple RT. Of theoretical interest is that exogenously administered melatonin, which when secreted naturally by the pineal gland closely follows body temperature (Cagnacci et al., 1992), has produced decrements on a psychomotor vigilance task (Graw et al., 2001) and a two-choice visual RT task (Rogers et al., 1998), which is similar to the Matchock and Mordkoff’s (2007) analysis of neutral-trial flankers. Exogenously administered melatonin did not affect performance on a letter cancellation task (Graw et al., 2001), which is better measure of attention rather than simple RT. Tassi et al. (2006) also manipulated length of sleep bout and measured attention, albeit with different results. Participants were tested with the Stroop test for 1 h upon awakening at 0700 h. However, one group of participants was not sleep deprived, going to sleep at 2300 h (8 h sleep bout) and the other group

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was sleep deprived, going to sleep at 0500 h (2 h sleep bout). The participants in the 8 h sleep bout experienced no detrimental SI effect, and the 2 h sleep bout participants had increased RT for the first half of the testing session (but a return to normal during the second half hour) and an increase in error rate during the second half of the testing session. Task differences (Stroop vs. Flankers task) and design differences (between-subjects vs. within-subjects) could explain differences in the Tassi et al. (2006) and Matchock and Mordkoff (2007) studies, respectively. In the Tassi et al. (2006) study, sleep bout length and amount of prior sleep deprivation are confounded, while circadian time is better controlled. In the Matchock and Mordkoff (2007) study, sleep bout length and circadian time are confounded, while prior sleep deprivation is better controlled. Moreover, although both studies measured attention, the Tassi et al. (2006) study appears to report overall RT from the Stroop, rather than subtract RT scores on congruent trials from non-congruent trials which is a measure of interference that more closely approximates the flankers task. Many contemporary cognitive psychologists argue that button-press Stroop tests are not really Stroop tests, but a flankers task (Mordkoff, personal communication, June 12, 2010). Stroop tasks are those where the irrelevant information overlaps with both the relevant information (e.g., written word and ink color) and the irrelevant information also overlaps with the response to be made (e.g., written word and spoken name). If the procedures only have only the first type of overlap, then it is a flankers task. If the procedures have only the second type of overlap, then it is a Simon task. The rationale for these distinctions is that the underlying mechanism for the effects of the two types of overlap is thought to be different, so it is thought to be important to keep track of which type or types of overlap one has in a given experiment (Kornblum, 1994; Kornblum et al., 1999; Mordkoff, personal com­ munication, June 12, 2010). Finally, in the well-controlled FD protocol of Scheer et al. (2008), participants slept for approximately 8 h under free-running conditions. Participants were awa­ kened at three different equally spaced times during these 8 h episodes and tested. For the first two 20 min SI testings 1/3 and 2/3 into the sleep episode, participants went back to sleep after the testing; upon the last testing, participants stayed awake and continued with their normal day. As mentioned previously in this chapter, there was a strong circadian effect of SI, but an analysis of the tertiaries or length of time into the sleep episode was not significant. In this respect, each SI testing would be akin to an increased sleep bout length, albeit fragmented. However, as pointed out by the authors (Scheer et al., 2008), an increase in the cumulative prior sleep duration during the later testings (which could impair cognitive performance) coupled with the decrease in homeostatic sleep pressure (which could enhance cognitive performance) could offset each other. Taken together, depth of sleep, especially SWS (either at the time of awaken­ ing or at the total percentage) seems to play a role in how humans process

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information after awakening from a sleep bout. It may be that sleep­ stage-at-awakening effects are more robust after shorter sleep episodes and when not masked by more powerful circadian influences. Another component of depth of sleep may be total percentage of SWS in a sleep episode. This component may be more salient when examining longer sleep bouts and could operate in a manner similar to process S (time spent awake), except that this factor is time spent sleeping, process S0 . Process S0 may not be as linear as process S and could be curvilinear or level off at a horizontal asymptotic level when participants are completely sated after extremely long sleep episodes (>8 h) in comfortable environments, a condition not frequently encountered in SI studies. More research is needed in order to better clarify the role that sleep episode length has on SI.

V. Different Measures of Cognitive Functioning

In the published literature, myriad tasks have been administered to assess cognitive impairments following an episode of sleep. This variability is likely to capture different aspects of cognition and attention, but it may also contribute to unclear and inconsistent findings concerning task performance upon awakening. Researchers most familiar with the validity and other psychometric properties of cognitive tasks (e.g., cognitive psychologists) are traditionally not the same researchers who study sleep and awakening (e.g., biologists or physiological psychologists). Furthermore, whether a task is subject-paced or experimenterpaced may affect results. In order to study cognitive impairments upon awakening, researchers have used (emphasizing specific tasks below rather than the number of studies that used each task) the Stanford Sleepiness Scale (Hofer-Tinguely et al., 2005), other measures of self-reported sleepiness or fatigue (Bonnet and Arand, 1995; Ferrara et al., 2000a; Jewett et al., 1999), Baddeley’s Logical Reasoning Task (Bonnet and Arand, 1995), auditory simple RT (Hofer-Tinguely et al., 2005), visual simple RT (Miccoli et al., 2008), finger tapping (Ferrara and De Gennaro, 2000), addition tasks (HoferTinguely et al., 2005; Wertz et al., 2006), subtraction tasks (Dinges et al., 1985), digit symbol substitution tasks (Bonnet and Arand, 1995; Silva and Duffy, 2008); tests of spatial memory (Tassi et al., 1992), Flanker’s tasks (Matchock and Mordkoff, 2007), Psychomotor Vigilance Task or other measures of self-reported alertness (Achermann et al., 1995; Van Dongen et al., 2001), letter cancellation tasks (Tietzel and Lack, 2001), P3 amplitude/latency (Kaida et al., 2006), Stroop tests (Tassi et al., 2006), grip strength (Jeannaret and Webb, 1963), time estimation of sleep intervals (Carlson et al., 1978), return to sleep latencies (Balkin and Badia, 1988), oculomotor performance (Ferrara et al., 2000a), semantic priming (Stickgold et al., 1999), event-related potentials (Asaoka et al.,

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2010; Ferrara et al., 2001), “decision-making” tasks (Bruck and Pisani, 1999), and the Arrow-orientation task (Asaoka et al., 2010). Williams et al. (1959) suggested that in sleep-deprived subjects, speed should be most affected in subject-paced tasks, but accuracy most affected in experi­ menter-paced tasks. Some researchers have theorized (Tassi and Muzet, 2000), and research has supported the idea (Bruck and Pisani, 1999; Tassi et al., 2006), that performance deficits because of SI are distinct from deficits due to sleep deprivation (sleepiness) and that SI may primarily affect processing speed. Others (e.g., Balkin and Badia, 1988) have also suggested that SI and sleepiness are distinct but that SI alone should affect accuracy more so than speed, which has also been documented in the literature (Tassi et al., 2003). However, sleep deprivation and SI are often confounded as increasing sleep deprivation (process S or sleepi­ ness) also exacerbates later SI effects. To better elucidate the nature of this distinction, Miccoli et al. (2008) compared an uninterrupted sleep group (which measures only SI), a total sleep deprivation group (which measures sleepiness only), and a partial sleep reduction group (which measures both sleepiness and SI) for mean response time and number of lapses on a visual simple RT task. Although error rates were not directly accessed, RTs increased in all groups. The number of lapses (i.e., microsleeps) only increased in the sleep deprivation (sleepiness) group, thus still suggesting a distinction between sleepiness and SI. Apparently, the process of awakening (progressing toward wakefulness) is differ­ ent than sleepiness (progressing toward sleep). Finally, the time course for recov­ ery of performance over the initial hour of awakening from sleep has been shown to vary depending on the task (Hofer-Tinguely et al., 2005; Tassi et al., 2006). Taken together, how well people perform shortly after awakening may depend, in part, on what they are asked to do. Complex tasks purport to measure higher cognitive processes, but these processes are not always well understood or salient. On the one hand, to measure a complex task, such as the executive functions component of attention, may require more infrequently administered tasks that are novel and require goal-directed behavior by the prefrontal cortex (Blatter et al., 2005; Schmidt et al., 2007). On the other hand, it has been suggested that the slower paced delivery of some complex tasks (compared to tasks in which stimuli are frequently presented to subjects) may not be as sensitive to SI or sleepiness-induced impairments (Dinges and Kribbs, 1991). Frequently delivered simple RT trials have been recom­ mended as optimal for detecting SI or sleepiness deficits, such as cognitive lapses (Miccoli et al., 2008). However, real-life tasks that people have to perform shortly after awakening may differ markedly from tasks that only measure simple RT. For example, a truck driver, pilot, or air traffic controller may have to scan many aspects of the visual scene searching for relevant information (e.g., a passing car in the rear view mirror, airspeed indicators and altimeters, or a novel blip on a radar screen) and then suddenly narrowly focus on this aspect of the visual scene

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while simultaneously ignoring other irrelevant information, followed by a wellinformed logical decision. This former process can best be described as visual selective attention. Numerous methods for measuring selective attention can hinder attempts to quantify process C or W-associated changes in this variable. Nonetheless, research has indicated that there are time-of-day fluctuations in selective atten­ tion. Responding to a specific aspect of a color shape (Zuber and Ekehammar, 1988), negative priming of word pairs in older subjects (Intons-Peterson et al., 1998) and pencil and paper letter cancellation tasks (Casagrande et al., 1997) have shown increases in performance near the circadian peak in CBT. Results can vary, though, depending upon cognitive load (i.e., the number of target letters to cancel) and sleep deprivation (Babkoff et al., 1988; Mikulincer et al., 1989). Using a cued reaction time task (CRTT), Casagrande et al. (2005) found that although RT increased in sleep-deprived subjects, performance was not differentially affected for valid, invalid, and neutral trials. This pattern of results indicates a decrease in vigilance or alertness, but not in attention-orienting mechanisms. In contrast, Versace et al. (2006) found that RTs significantly increased on invalidly cued trials but not on validly cued trials, suggesting deficits in selective orienting. Few studies have attempted to measure SI with more fine-grained analyses that allow delineation of different stages of information processing and different components of attention. For example, identification of underlying informationprocessing stages can be gleaned by using Donder’s (1868/1969) subtraction method. Donders postulated that by measuring Simple RT (e.g., responding to any stimulus), Go/no-go RT (i.e., responding to one stimulus but not to another), and Choice RT (e.g., responding with one hand in response to one stimulus but the other hand in response to a different stimulus) that the duration of various underlying cognitive stages could be inferred. Presumably, each task is comprised of different hypothetical stages (e.g., Simple: stimulus detection and motor execution; Go/no-go: stimulus detection, stimulus discrimination, and motor execution; and Choice: stimulus detection, stimulus discrimination, response selection, and motor execution). Thus, Go/no-go RT—Simple RT should be a measure of stimulus discrimination, while Choice RT—Go/no-go RT should be a measure of response selection. The subtraction method has been criticized, in part, because of its dependence upon certain assumptions (e.g., Luce, 1986; Sternberg, 1969). First, it is assumed that the underlying stages occur sequentially; second, that the stages are functionally distinct from each other with only one stage being activated at a time; and third, the assumption that stages can be added or deleted without affecting the other stages (i.e., “pure insertion”). The first two assumptions have received fairly strong support (McClelland, 1979; Miller, 1988), although pure insertion has been the most criticized (Sternberg, 1969). Despite this criticism, recent research has emerged that appears to support

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pure insertion as well (Gottsdanker and Shragg, 1985; Miller and Low, 2001; Ulrich et al., 1999). Moreover, the basic logic of subtracting one RT task from another RT task to identify underlying mechanisms is employed in widely used measures of attention such as the Attentional Network Test (ANT: Fan et al., 2002), and subtraction, in general, is at the crux of functional magnetic resonance imaging studies. A recommendation is that future studies on the process of awakening and SI use tasks like the flankers task or tests like the Attentional Network Test (ANT) (Fan et al., 2002). The ANT combines spatial cuing with a modified version of the flankers task (Eriksen and Eriksen, 1974). Tests such as the ANT can be fre­ quently delivered to ensure that they are sensitive to SI and sleepiness deficits such as attentional lapses. Moreover, simple RT can be measured, as well as other higher cognitive functions (e.g., executive control, orienting, and alerting) that have been fairly well mapped to known brain areas and neurotransmitter sys­ tems. The executive function component of attention involves the anterior cingulate cortex (ACC) and lateral prefrontal cortex (Bush et al., 2000; Fan et al., 2003, 2005; MacDonald et al., 2000; Tanji and Hoshi, 2008), and the alerting response has been associated with the frontal and parietal regions of the right hemisphere (Posner and Peterson, 1990) and the reticular formation (Sturm and Willmes, 2001). The orienting response is associated with the superior colliculus, frontal eye fields, and superior and temporal parietal areas (Fan et al., 2005). Neurochemically, two brain regions associated with executive func­ tion are both input areas from the tegmental dopaminergic system, implicating dopamine (see, e.g., Benes, 2000). The cholinergic system, originating in the basal forebrain area, is implicated in orienting (Beane and Marrocco, 2004), and norepinephrine in the locus coeruleus is implicated in the alerting component (Foote et al., 1991; Witte and Marrocco, 1997). Limited information from neuroimaging studies is available on the underlying brain changes that are associated with the process of awakening. One study found that blood flow first increased in the brain stem and thalamus within 5 min after awakening from a sleep episode, followed by gradual increases in cerebral blood flow to the prefrontal cortical regions 20 min later (Balkin et al., 2002). Of interest is that flanker effects activate areas of the brain associated with executive control such as the dorsolateral frontal cortex and ACC (Botvinick et al., 1999, 2004; Bunge et al., 2002; Casey et al., 2000; Fan et al., 2003; Hazeltine et al., 2003). Reduced prefrontal cortex activity may underlie the large SI-induced flanker effects observed in the Matchock and Mordkoff (2007) study. As yet, no neuroi­ maging study has examined specific characteristics of the sleep episode such as length or timing. Process W has been an under-investigated area in the field of sleep/wake research. Given the many unanswered questions in this area, the process of awakening should warrant, and is ripe for, future investigations of contributing factors.

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THE CORTISOL AWAKENING RESPONSE IN CONTEXT

Angela Clow
, Frank Hucklebridge†, and Lisa Thorn



†

Department of Psychology, University of Westminster, London W1B 2UW, UK Department of Human and Health Sciences, University of Westminster, London, W1W 6UW, UK

I. II. III. IV. V. VI. VII. VIII. IX.

Introduction History of the Investigation of the CAR Distinct Regulation of the CAR and Relationship with the SCN The CAR as an Awakening Process CAR and Cognitive Awakening CAR and Immunological Awakening CAR and Behavioral Awakening Measurement of the CAR Conclusions References

The cortisol awakening response (CAR) is a crucial point of reference within the healthy cortisol circadian rhythm, with cortisol secretion typically peaking between 30 and 45 min post awakening. This chapter reviews the history of investigation into the CAR and highlights evidence that its regulation is relatively distinct from cortisol secretion across the rest of the day. It is initiated by awakening, under the influence of the hypothalamic suprachiasmatic nucleus, and “fine tuned” by a direct neural input to the adrenal cortex by the sympathetic nervous system. This chapter also examples the CAR in relation to other awakening-induced processes, such as restoration of consciousness, attainment of full alertness, changes in other hor­ mones, changes in the balance of the immune system, and mobilization of the motor system, and speculates that there is a role for the CAR in these processes.

I. Introduction

The cortisol awakening response (CAR) is a period of increased cortisol secretory activity initiated by morning awakening and typically peaking between 30 and 45 min post awakening (Pruessner et al., 1997; Wilhelm INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 93 DOI: 10.1016/S0074-7742(10)93007-9

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et al., 2007). The CAR is recognized as a crucial point of reference within the healthy cortisol circadian rhythm but is generally studied in isolation from other awakening-induced processes (e.g., restoration of consciousness, attain­ ment of full alertness, changes in other hormones, changes in the balance of the immune system, and mobilization of the motor system). In psychobiolo­ gical research the CAR has frequently been used as a biomarker of hypotha­ lamic pituitary adrenal axis (HPA) status. However, evidence indicates that regulation of the CAR is relatively distinct from cortisol secretion across the rest of the day. The CAR is not a straightforward measure of HPA respon­ sivity (such as the Trier Social Stress Test), it is initiated by awakening, under the influence of the hypothalamic suprachiasmatic nucleus (SCN) and “fine tuned” by a direct neural input to the adrenal cortex by the sympa­ thetic nervous system (Buijs et al., 2003; Clow et al., 2010). Thus although different patterns of the CAR have been associated with different psycho­ pathologies, it is not entirely clear what these different patterns tell us about the underlying biological basis of the condition being studied. Furthermore as there is currently no clear understanding about the role of the CAR it is not clear what the downstream consequences of aberrant patterns of the CAR may be. It is becoming increasingly understood that circadian coordination via the central body clock is crucial for physical and mental flourishing and that disruption of circadian function is linked with multiple downstream negative physiological, psychological, and clinical consequences (Eismann et al., 2010). One of the main ways the SCN communicates with peripheral target tissues is via the neuroendocrine system and secretion of the hormones melatonin (at night) and cortisol (most prominent during the day). In this way the SCN coordinates peripheral cellular rhythms important for health. The dual SCN-mediated awakening-induced regulatory input to the CAR (i.e., via the HPA axis and the sympathetic nervous system) may make it a more accurate marker of the function of the central biological clock than examina­ tion of the HPA axis alone and account for its well-documented sensitivity to psychosocial and health variables. This chapter discusses the history and the use of the CAR as a biological marker of psychosocial status and health. Further, we aim to contextualize the CAR within the process of normal healthy awakening by exploring it in relation to other processes of awaken­ ing. The CAR is not an isolated response to awakening, rather it is part of a well-orchestrated physiology closely tuned to circadian cycles and essential for healthy functioning. Unhealthy states are often associated with poor circadian coordination and these can potentially be detected by exam­ ination of the CAR. Using this approach we hope to advance understanding of the CAR as well as potentially enlighten its physiological meaning and roles.

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II. History of the Investigation of the CAR

Glucocorticoids (cortisol in humans) are secreted in response to stress, affect multiple organ systems, and have a wide range of physiological and behavioral effects (Evanson et al., 2010; Sapolsky et al., 2000). Chronic stress and aging are associated with changes in the HPA axis and other glucocorticoid sensitive brain regions (e.g., the hippocampus) with consequent changes in the basal circadian pattern of cortisol secretion (Hsiao et al., 2010; Lightman, 2008). Aberrant basal patterns of cortisol secretion have been implicated in a range of psychological and somatic disease (Eismann et al., 2010; Minton et al., 2009; Sephton et al., 2000; Yehuda, 2001). Hence, there is a need for a thorough understanding of the components of the circadian pattern of cortisol secretion in order to develop meaningful biomarkers able to advance clinical and research studies involving this neuroendocrine system. A healthy basal pattern of cortisol secretion is characterized by a distinct circadian rhythm, largely controlled by the hypothalamic SCN, which influences adrenocortical activity via input to the paraventricular nuclei (PVN) of the hypothalamus (Buijs et al., 2003; Dickmeis, 2009; Kalsbeek et al., 2006; Krout et al., 2002). Under the influence of the SCN HPA axis activity gradually increases toward the end of nighttime sleep and gradually falls from a postawakening acrophase to a 24 h nadir in the early hours of sleep. This cyclical pattern of cortisol secretion can generate 14- to 15-fold changes in salivary free cortisol concentration across the day (e.g., Evans et al., 2007). Obviously the dynamic nature of the pattern of cortisol secretion makes it difficult to capture basal cortisol status accurately. Measures derived from a single blood sample taken in the early morning have been used frequently in assessment of adreno­ cortical status, particularly in clinical situations. However, these single point measures have low intra-individual stability (Schulz and Knabe, 1994), and hence limited utility. Twenty-four hour urinary measures of cortisol excretion provide a more reliable clinical index of overall cortisol secretion. However, this measure lacks subtlety in terms of the insight provided (different patterns of secretion could give identical results) as well as collection methodology (unplea­ sant and demanding on the participants). The adoption of saliva as the medium of choice for repeated measurement of cortisol across the day provided both a participant-friendly sample collection regime and the opportunity to look at dynamic change in cortisol secretion over the entire day and over short periods of time within the day (Kirschbaum and Hellhammer, 1994). As this approach was developed observations from multiple sleep research studies suggested that reported variability in cortisol levels stemmed from a stimulatory effect of awakening on HPA activity (Linkowski et al., 1993; Spathsch­ walbe et al., 1991, 1992; Vancauter et al., 1994; Weitzman et al., 1974). For

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example, Spath-Schwalbe et al. (1992) obtained polysomnographic recordings and 15 min blood sampling via forearm catheter from participants in a sleep laboratory. These authors revealed that the transition from sleep to wakefulness in the morning provoked brief elevations in plasma cortisol, a phenomenon later to be called the CAR (until recently also sometimes called the awakening cortisol response: ACR or even the cortisol awakening rise). However, it was Pruessner and colleagues (1995), then working at the Uni­ versity of Trier in Germany, who first brought the CAR into widespread notice. They reported that the concentration of salivary free cortisol showed a 50–100% increase within 30 min following awakening in healthy participants on five con­ secutive days. A more comprehensive account of the CAR was published by the same group two years later (Pruessner et al., 1997). This was the first paper to report intra-individual stability of the CAR over consecutive days and weeks in children, young, and older adults. In all three age groups the increase in salivary cortisol levels peaked at 30 min post awakening and the increase was relatively consistent, exhibiting good intra-individual stability. It was concluded that the CAR pro­ vided a reliable estimation of adrenocortical activity (Pruessner et al., 1997). Further evidence for intra-individual consistency, both in the overall levels of post-awakening cortisol secretion and the dynamic of the CAR, followed (Edwards et al., 2001a; Wuest et al., 2000b). Subsequent studies demonstrated that the CAR was not associated with postural change, sleep duration, or mode of awakening (see Clow et al., 2004). However, one noteworthy feature of the CAR to emerge from this early literature was that although overall cortisol secretion during the first 45 min follow­ ing awakening was representative of (i.e., correlated with) underlying diurnal cortisol secretory activity measured over the rest of the day the dynamic of the CAR did not, implying that they were in some way independent measures (Edwards et al., 2001a; Schmidt-Reinwald et al., 1999). Perhaps this was the first evidence that the CAR is a complex phenomenon, fine tuned by HPA-independent mechanisms, and therefore is not a simple index of HPA activity. This early evidence was supported by reports that the CAR was more closely associated with genetic variables than cortisol secretion across the rest of the day (Wuest et al., 2000a). This complex phenomenon has continued to be used as a simple biomarker of HPA axis activity in relation to health and psychosocial variables. Since the first papers concerning the CAR were published some 13 years ago, interest in this specific aspect of salivary cortisol secretion in humans has grown steadily, with a total of 280 outputs published up until the present, i.e., July 2010 (see Fig. 1). However, perhaps the full impact of work on the CAR can be best described from an analysis of the number of times each paper has been cited. There are a total of 4720 citations of the currently published 280 papers. This represents an impressive average citation count of 16.86 for each CAR paper. This means that

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0 19 9 19 7 9 19 8 99 20 0 20 0 0 20 1 0 20 2 0 20 3 0 20 4 0 20 5 0 20 6 0 20 7 0 20 8 09

Number of peer-reviewed publications

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Year FIG. 1. Peer-reviewed publications about the CAR in humans per year.

if all the CAR papers were published in the same journal it would have an impact factor of 16.86, which is in excess of Nature Neuroscience (which has an impact factor of just 14.345)! The conclusion from this analysis is that the findings from the relatively small set of CAR outputs are of interest to a wide range of people outside the area. The year by year increase in citations of the currently published CAR papers is shown in Fig. 2. Studies have examined the CAR in relation to a very diverse range of individual differences in psychosocial variables and health and there have been a multitude of interesting findings. However, the literature is by no means straightforward: there are inconsistent results about associations with different

Number of times cited

1400 1200 1000 800 600 400 200

19

9 19 7 9 19 8 9 20 9 0 20 0 01 20 0 20 2 0 20 3 0 20 4 0 20 5 0 20 6 0 20 7 0 20 8 09

0

Year FIG. 2. Average citations per year for papers published on the CAR.

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patterns of the CAR. The confusion in the literature may stem from causes such as participant non-adherence to protocol; different experimental designs; differ­ ences in group demographics such as gender, age, and genotype; subtle difference in the psychosocial and health measures. Furthermore a full interpretation of the findings is not yet possible as the role or roles for the CAR has not yet been clarified. Indeed it is as if the role of the CAR is being deduced from these crosssectional studies (e.g., if the CAR is attenuated in condition X then it must be related to causes of condition X). This is a rather precarious approach and a more systematic analysis of the direct physiological correlates of the CAR, preferably in healthy participants in the first instance, would be helpful and inform cross-sectional studies more accurately. It is not the purpose if this chapter to fully review the disparate findings of studies examining between-subject differences in the CAR (reviewed in Chida and Steptoe, 2009; Clow et al., 2004; Fries et al., 2009). However, it is noteworthy that increasing age (Kudielka and Kirschbaum, 2003) as well as a range of conditions, e.g., cardiovascular disease, autoimmune conditions, slow wound healing, clinical depression, mild cognitive impairment, Alzheimer’s disease, and attachment anxiety, are associated with a high first waking sample and an attenuated dynamic increase following awakening (e.g., Arsenault-Lapierre et al., 2010; Buske-Kirschbaum et al., 2007; Ebrecht et al., 2004; Huber et al., 2006; Kudielka and Kirschbaum, 2003; Quirin et al., 2008). A notable and consistent exception to this pattern is post-traumatic stress disorder which is characteristically associated with an attenuated CAR with a low first waking sample (Fries et al., 2009). What is clear from the literature is that, despite early reports of individual day-to-day consistency, the CAR is not a simple trait measure as it is also prone to significant state influences (Hellhammer et al., 2007). It seems that healthy individuals can unknowingly modify their CAR in response to previous day’s experiences and in anticipation of the forthcoming day ahead (Adam et al., 2006; Dahlgren et al., 2009; Doane and Adam, 2010; Stalder et al., 2009). Indeed, anticipation of the time of awakening is known to impact upon the neuroendo­ crine system (Born et al., 1999) so it may not be surprising that anticipation of the day ahead can have similar effects. This corresponds to the reported weekday/ weekend differences in the CAR, where the CAR is typically reported to be attenuated at the weekend, when it is assumed that most people have fewer obligations (Kunz-Ebrecht et al., 2004; Schlotz et al., 2004). However, it has recently been reported that a better measure of positive psychosocial status is not the size of the CAR but rather greater day-to-day variation (Mikolajczak et al., 2010). In other words it is suggested that healthy functioning is associated with efficient anticipatory physiological responding that is flexible. It is also clear that the CAR is sensitive to non-psychological factors such as gender (Wright and Steptoe, 2005), time of awakening (Edwards et al., 2001b;

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Kudielka and Kirschbaum, 2003; Stalder et al., 2009), light (Scheer and Buijs, 1999; Thorn et al., 2004), hippocampal volume (e.g., Buchanan et al., 2004; Wolf et al., 2005), glucocorticoid receptor feedback (Pruessner et al., 1999), and genotype (van Leeuwen et al., 2010). These multiple factors again testify to the complexity of the CAR and the difficulty drawing meaningful conclusions about its role from cross-sectional studies in humans.

III. Distinct Regulation of the CAR and Relationship with the SCN

In a recent review the authors have argued that the CAR is subject to a complex range of physiological influences that facilitate the rapid increase in cortisol secretion initiated by awakening in healthy people (see Clow et al., 2010). In addition to awakening-induced SCN activation of the HPA axis (Wilhelm et al., 2007) direct sympathetic innervation from the SCN to the adrenal gland by the splanchnic nerve (Edwards and Jones, 1993; EhrhartBornstein et al., 1998; Engeland and Arnhold, 2005; Sage et al., 2002; UlrichLai et al., 2006) is implicated in the fine tuning of the CAR. In the immediate pre-awakening period there is evidence that this pathway induces reduced adrenal sensitivity to rising levels of adrenocorticotropic hormone (ACTH) (Bornstein et al., 2008; Buijs et al., 2003). The process of awakening is associated with “flip-flop” switching of regional brain activation (Braun et al., 1997; Lu et al., 2006; Saper et al., 2001; Sil’kis, 2009) which, it has been argued, initiates activation of the HPA axis. At the same time the SCN orchestrates a reversal of pre-awakening reduced adrenal sensitivity to ACTH (Bornstein et al., 2008; Buijs et al., 1997, 2003; Fehm et al., 1984). Indeed in the immediate post-awakening period adrenal sensitivity to ACTH is increased in response to light, a function again mediated by a SCN extrapituitary pathway (Buijs et al., 2003). Thus the SCN plays a pivotal role in the determination of the CAR by a combination of pre- and post-awakening influences operationalized via a dual control system: the HPA axis and the direct neural input to the adrenal cortex (see Fig. 3 for a diagrammatic representation of these pathways). One of the most consistent findings from the literature is that the hippocampus appears to play a permissive role in the regulation of the CAR (see Fries et al., 2009). This conclusion is derived from studies of clinical populations in which the hippocampus is impaired and the CAR attenuated (e.g., Buchanan et al., 2004; Wolf et al., 2005). In addition brain imaging studies have demonstrated positive associations between hippocam­ pal volume and the CAR (Bruehl et al., 2009; Pruessner et al., 2007). This

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Hippocampus Retina Light

PVN Negative feedback by cortisol

SCN

CRH Anterior pituitary

Dual SCN-mediated regulatory input to the CAR

ACTH Cortisol secretion

Adrenal cortex

FIG. 3. Simplified diagrammatic representation of some proposed regulatory inputs to the CAR. The hypothalamic suprachiasmatic nucleus (SCN) influences the secretion of cortisol via input to the paraventricular nucleus (PVN) and the HPA axis cascade (CRH and ACTH). In addition the SCN has a direct neural input to the adrenal cortex via the splanchnic nerve of the sympathetic nervous system; a pathway that may also be modulated by activity of the hippocampus (see text). Upon awakening the SCN enhances cortisol secretion in response to light.

evidence, although not extensive, suggests a causal linkage between func­ tional integrity of the hippocampus and the CAR. This is a feasible hypoth­ esis as there are anatomical and functional pathways linking the hippocampus to the SCN (Krout et al., 2002; Pace-Schott and Hobson, 2002; Stranahan et al., 2008). However, the hippocampus is known to have inhibitory effects on HPA axis activity (Herman and Cullinan, 1997; Herman et al., 2005). Thus the ambiguity as to why the hippocampus is permissive for the CAR has yet to be adequately explained. It has been argued (see Clow et al., 2010) that the role of the hippocampus in the regulation of the CAR occurs prior to awakening. This possibility is consistent with the fact that rapid eye movement (REM) sleep (typically dominant in the later stages of sleep and immediately pre-awakening) is associated with marked hippocampal activation which provides inhibitory tone on cortisol secretion, whereas awakening is associated with switching off of hippocampal activation (Balkin et al., 2002; Braun et al., 1997). It is speculated that pre-awakening activation of the hippocampus restrains pre-awakening cortisol secretion. Again it is possible that this regulation may be related to the SCN-mediated extrapituitary fine tuning of adrenal sensitivity to ACTH in the pre-awakening period, as described above. Although speculative there is sufficient circum­ stantial evidence to merit further investigation of these relationships in their role in the determination of the CAR.

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IV. The CAR as an Awakening Process

As its name suggests the CAR is a response to awakening. Although awakening corresponds to the transition between sleep and wakefulness, i.e., a clear disconti­ nuity of an ongoing sleep episode (Salzarulo et al., 2002), the physiological data available clearly show that the sleep-to-wake transition is not a rapid shift from one state of consciousness to another, but a complex process that takes some time to be completed. Awakening initiates the CAR, but the CAR may play a role in this transition from sleep to full alertness, awakening both the mind and the body in preparation for daytime activity. Cortisol is one of the most potent hormones of human physiology; virtually all of the body’s cells are potential targets for cortisol. It provides one of the means by which the circadian message from the SCN is transmitted to peripheral tissues. The peak of cortisol following awakening may play a particular part in synchronizing the body to both the sleep–wake and light–dark cycles via a range of nongenomic actions (Evanson et al., 2010). Further, it is becoming increasingly understood that circadian rhythms, particularly that of cortisol, transcribe the time of day message to the immune system. Circadian coordination is crucial for healthy physical and mental flourishing and disruption of circadian function is linked with multiple downstream negative physiological, psychological, and clinical consequences (Eismann et al., 2010). As detailed above, however, the CAR has generally been studied as an isolated phenomenon; it has rarely been considered as one of the physiological processes involved in the complex process of awakening. In fact, the CAR literature is characterized by an absence of a discourse on its role in the awaken­ ing process. Therefore, there is a need to re-contextualize the CAR as part of the awakening process. Here we are concerned with spontaneous morning awaken­ ing at the end of nocturnal sleep, leading to long-lasting and consistent awakening representing the termination of a full nocturnal sleep episode and a new beha­ vioral state. Other chapters in this volume (see Moul from Chapter 5 and Voss from Chapter 8) describe the difficulties in defining and identifying awakening. Voss proposes that as well as physiological markers, an awakening is accompa­ nied by behavioral responsiveness and the ability to think and the capacity for rational decision making and reflective awareness. Interestingly, these criteria for awakening are fully met in the measurement of the CAR which requires selfcollection of saliva samples. Participants are usually instructed to take the first sample as soon as they are conscious of being awake, which involves a cognitive component (“I am awake”) and a behavioral component (taking the saliva sample). Indeed difficulty in determining the precise time of awakening and delays in attainment of behavioral responsiveness may contribute to inaccuracies in its measurement and variation in the CAR literature. Below we review the potential role of the CAR in cognitive, immune, and also behavioral awakening.

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V. CAR and Cognitive Awakening

Hormones other than cortisol show a distinct circadian rhythm, most notably melatonin (Benloucif et al., 2005), which along with core body temperature, is a classic circadian rhythm marker. Melatonin has sleep-promoting effects in humans (Pandi-Perumal et al., 2008). The timing of melatonin secretion is closely associated with the timing of sleep propensity and it also coincides with decreases in core body temperature, alertness, and performance. Exogenous melatonin administered during the day has soporific effects; it lowers body temperature, induces fatigue, and generates a brain activation pattern resembling that which occurs during sleep (see Cajochen, this volume). In humans, melatonin secretion increases soon after the onset of darkness, peaks in the middle of the night (between 2 and 4 a.m.), and gradually falls during the second half of the night. In other words it has the opposing rhythm to that of cortisol, with melatonin promoting sleep and cortisol promoting wakefulness. At awakening, when cortisol levels rise, melatonin levels are falling. Although studies have observed that administration of melatonin alters the timing of circadian rhythms including that of cortisol (Arendt and Skene, 2005), no study to date has explored the explicit relationship between the rise in cortisol following awakening and the decline in melatonin. As both these hormones are regulated by the SCN and are controlled by the same underlying mechanism, an inverse relationship could be hypothesized. The attainment of consciousness following sleep constitutes cortical arousal/ activation. Switching of brain circuitry associated with the transition between sleep and consciousness may be associated with initiation of the CAR as such switching is known to be actively initiated by the process of awakening (Spathschwalbe et al., 1992; Vancauter et al., 1994; Wilhelm et al., 2007). Studies of brain activity support the notion that brain activation levels upon awakening largely differ from those characterizing wakefulness, and that awakening is a process. Both EEG and brain imaging studies have revealed that although awakening from sleep comprises rapid reestablishment of consciousness, the reestablishment of alertness is relatively slow. For example, Ferrara et al. (2006) demonstrated that visual evoked potentials (VEP) recorded upon awakening have decreased amplitude and increased latency of 100–300 ms components relative to the pre-sleep waking state. The sleep–wake transition is characterized by an EEG pattern of decreased beta power and of increased power in the delta-theta-lower alpha range for the first 10 min following awakening. Balkin et al. (2002) used positron emission tomography (PET) methodology to examine changes in regional cerebral blood flow during the transition to wakefulness and full alertness revealing that upon awakening reactivation in the brainstem, thalamus, basal ganglia, and anterior cingulate cortex was rapid, for example, the reactivation of the thalamus was complete at 5 min post awakening. Taken together these studies confirm that a

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state of cortical hypoarousal characterizes the early awakening. As detailed else­ where in this volume, this period between regaining consciousness (i.e., awakening) but before attainment of full alertness is described as “sleep inertia”: a transitory period of impaired arousal and behavioral performance lasting between 15 and 60 min (Ferrara et al., 2006; Ikeda and Hayashi, 2008). It seems that the initiation of the CAR is temporally associated with the attainment of consciousness and that the dynamic of the CAR closely parallels that of reactivation of the prefrontal cortex and attainment of full alertness. This temporal association could be considered simply as two parallel processes linked by the same underlying mechanism. However, there is some evidence indicating that the CAR may indeed play a role in the attainment of alertness following awakening. Indirect support is provided by the relatively consistent finding that acute bursts of cortisol have a stimulatory influence on psychological arousal and lead to a reduction of fatigue. This effect has been confirmed using self-report measures (Tops et al., 2006), arousal ratings in response to non-arousing stimuli (Abercrombie et al., 2005), as well as electro­ encephalographic (EEG) indicators of central alertness (Chapotot et al., 1998). Additionally, in sleep-deprived individuals early morning exposure to bright light induced an immediate elevation of cortisol levels, suppressed melatonin secretion, and limited the deterioration of alertness assessed by computerized vigilancesensitive performance tasks (Leproult et al., 2001). Few studies have directly tested the hypothesis that the CAR is associated with state arousal or levels of physiological activation. However, the results available to date have been supportive of a role for the CAR in the regaining of arousal, suggesting a positive association between state arousal at 45 min post awakening and post-awakening cortisol levels (Thorn et al., 2004) as well as the dynamic of the CAR (Thorn et al., 2009). This finding is also in general agree­ ment with results of Adam et al. (2006) showing an association between a larger mean CAR and lower average fatigue levels over a 3-day period. State arousal/ anticipations of a busy day ahead at 45 min post awakening have also been shown to relate positively with the CAR (Stalder et al., 2009). In addition high levels of sleepiness were associated with lower levels of cortisol 15 min after awakening in healthy office workers (Dahlgren et al., 2009). In summary the proposition for causal linkages between the CAR and recovery from sleep inertia, although speculative, certainly deserve further investigation. As well as general effects on alertness a further role for the CAR in awakening cognition can be discerned through its effects on memory retrieval. Rimmele et al. (2010) suppressed the CAR via administration of the cortisol synthesis inhibitor metyrapone. Participants were asked to recall emotional and neutral texts and pictures learned 3 days prior at 30 min following awakening. The metyraponeinduced cortisol suppression significantly impaired free recall in comparison to placebo. This finding corresponds to the view that memory-related processes are

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of importance for the CAR. Wilhelm et al. (2007) speculate that the CAR may play a role in the “booting” of memory representations in the organization of personality, identity, and the self, as well as of representations that have remained preactivated from more acute experiences. This idea is endorsed by research showing that the CAR is attenuated in patients with hippocampal damagerelated memory disorders (Buchanan et al., 2004; Wolf et al., 2005).

VI. CAR and Immunological Awakening

According to Dimitrov et al. (2009) circadian rhythms have been underinvestigated in relation to the processes underlying the regulation of the immune system. Evidence indicates that both enumerative and functional immune mea­ sures exhibit circadian rhythmicity and these rhythms seem to be closely asso­ ciated with the circadian rhythm of cortisol (Kronfol et al., 1997). Hence, disruption of circadian endocrine rhythms has been found to be associated with many disease states, including cancer. In fact evidence points toward circadian disruption as a risk factor for tumor initiation and accelerated progression (Eismann et al., 2010). The relationship between cortisol and the immune system is complex. Corti­ sol and melatonin appear to counter-regulate the Th1/Th2 balance by inhibiting Th1 and promoting Th2 immune responses (Cutolo et al., 2006). There is a bias toward Th1 responses during the night and Th2 responses during the day. The circadian rhythm of cortisol may play an important role in regulating the diurnal rhythmicity of Th1 and Th2 immunity. In particular it has been suggested that a primary role of the increase in free cortisol in response to awakening may be to switch the immune system from nighttime Th1 to daytime Th2 domination (Hucklebridge et al., 1999). In support of this hypothesis there is evidence that the Th1 cytokine profile during nocturnal sleep is switched to a Th2 cytokine profile on awakening (Petrovsky and Harrison, 1997). Furthermore these authors reported that the degree of switch correlated with cortisol levels measured at the time the cytokine switch was detected. This immune-switching hypothesis has yet to be investigated in any systematic way. However, in a more recent study Dimitrov et al. (2009) demonstrated that the regulation of circadian rhythms in T cell populations is tightly controlled by the rhythms of cortisol and catecholamines. Interestingly, epinephrine appears also to exhibit a response to awakening. Dodt et al. (1997) observed that during REM–NONREM sleep both epinephrine and norepinephrine were significantly lower than earlier sleep stages. On morning awakening epinephrine concentrations gradually began to increase, whereas norepinephrine levels were not affected by

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awakening, but were enhanced by change to an upright body position. Dimitrov et al. (2009) report similar findings with both epinephrine and norepinephrine reaching peak levels following awakening in the morning. Furthermore in this study, administration of both cortisol and epinephrine at low doses, purportedly mimicking the endogenous morning increase in these hormones, produced mark­ edly differential effects on the T cell subpopulations. For example cortisol infusion decreased naive T cell counts by approximately 40%, whereas administration of epinephrine produced an increase in circulating effector CD8þ T cells. Further investigation is warranted to explore the relationship between the shift in two major hormones following awakening in the morning and also their effect on immune parameters, particularly in relation to day and nighttime immunity.

VII. CAR and Behavioral Awakening

One of the functions of awakening (planned or otherwise) is to be able to respond to environmental cues by initiation of appropriate behavioral responses. Behavior requires coordinated and efficient motor function. This section pro­ poses a potential role for the CAR on the facilitation of voluntary motor function. Sleep states (especially REM sleep) are associated with inhibition of motor function called “sleep atonia.” This paralysis of most skeletal muscles is essential to ensure physical passivity during the periods of dynamic brain activation associated with dream states. Muscle atonia during sleep results from descending inhibitory projections to the spinal motor neurons from the caudal dorsolateral pontine tegmentum (Jones, 1991). Interestingly, an inability to initiate muscle atonia during REM sleep is increasingly being interpreted as an early sign of a range of neurodegenerative conditions (Boeve et al., 2001). As described earlier the process of awakening, in healthy individuals, involves the rapid switching off of these inhibitory pathways to restore the full waking state including voluntary motor function (Hobson, 2009). It is possible that the CAR may play a supplementary role in the reactivation of motor function post awakening as acute bursts of cortisol administration (similar in time course to the CAR) in humans have been shown to increase the excitability of the motor cortex as well as increase variability in motor cortex excitability (Milani et al., 2010). In their study Milani and colleagues examined motor-evoked potentials (MEPs) in the thumb in response to transcranial magnetic stimulation of the appropriate part of the motor cortex. Participants were assessed before and after either an injection of 20 mg of hydro­ cortisone or saline solution. Mean plasma cortisol levels rose rapidly and peaked around 10 min after hydrocortisone injection, at which time the mean MEP ampli­ tude and mean standard deviation of MEPs were significantly greater than pre­

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injection levels. Thus, although this study does not examine the CAR per se it does demonstrate that an equivalent acute burst in cortisol can have marked effects on the excitability of the motor cortex, effects that would facilitate voluntary movement. Currently the full significance of cortisol-induced increases in the variability of the excitability of the motor cortex is not fully understood. However, it is plausible that this state would facilitate appropriate motor responses to novel patterns of behavior, i.e., the capacity to explore and try out new motor skills. There is some supportive evidence for this theory showing that acute, physiologically relevant, corticosterone administration to rats rapidly increased exploratory locomotor activity when the animals were placed into a new activity cage but that the same dose of corticosterone failed to increase locomotion when administered to rats that had been previously exposed to the activity cage (Sandi et al., 1996). It has been suggested that this increase in locomotor activity may relate to risk-assessment behavior, which is also rapidly increased after treatment with corticosterone, without any change in anxi­ ety-like behavior or general locomotion in rats (Mikics et al., 2005). In contrast to its effects on motor cortical excitability, reported above, it is known that cortisol inhibits neural plasticity in the human motor cortex. Motor plasticity is associated with consolidation of learnt skills and the efficiency of plasticity is lowest in the morning and inhibited by acute bursts of cortisol administration (Sale et al., 2008). It is possible, and speculated here, that explora­ tory behavior (associated with increased motor cortical excitability and variabil­ ity) is facilitated by cortisol secretion in the morning, whereas consolidation of these actions (associated with neural plasticity) is facilitated later in the day when cortisol levels are low and in a steady state. Thus it seems plausible that the CAR may play a part in rapidly inducing specific behavioral adjustments to meet the immediate requirements set by the challenge of awakening. These speculations resonate with the observation that the dynamic of the CAR has been shown to be greater with more anticipated obligations in the day ahead: the CAR may play a role in literally “preparing for action.” Of course a role for the CAR in this type of motor function, although plausible, is speculative. It would be interesting to test this hypothesis by examin­ ing the impact of overnight cortisol synthesis inhibition (which abolishes the CAR) upon post-awakening motor cortical excitability in healthy participants.

VIII. Measurement of the CAR

Within the literature the CAR is most frequently derived from saliva samples taken by the participants themselves, within the domestic setting. This confers ecological validity but also lacks rigor in terms of reassurance that the sampling

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Awakening

regime is strictly adhered to (especially problematic due to the occurrence cogni­ tive deficits immediately post awakening). Instructions are typically given to collect samples on awakening and at a number of subsequent time points, e.g., 15, 30, and 45 (sometimes even 60) min post awakening. Due to the difficulties in accurately capturing this dynamic aspect of cortisol secretion within the domestic setting it is advisable to collect samples from each participant on more than one day as this allows for examination of day-to-day consistency, which should always be reported. Furthermore measures to assess and take account of participant adher­ ence to protocol, a notorious problem with this area of research (Broderick et al., 2004; Kudielka et al., 2003; Kupper et al., 2005) should be employed. The CAR is sometimes used as an umbrella term to describe both overall levels of cortisol secretion as well as the dynamic change in cortisol post awakening; these different elements are illustrated in Fig. 4. The area under the curve with reference to ground: AUCG (sample 2 þ s3 þ (s1 þ s4)/2) gives a good measure of overall cortisol secreted whereas the area under the curve with reference to the first waking sample: AUCI (sample 2 þ s3 – (2 * s1) þ ((s4 – s2)/2)) or the mean increase: MnInc (sample 2 þ s3 þ s4)/3 – s1) provide closely correlated measures of the dynamic change in cortisol following awakening. (The dynamic change in post waking cortisol is also sometimes calculated as levels 30 min post awakening minus the waking value, or the maximum concentration minus the first waking sample.) We would argue that the CAR is by its very nature a “response” to awakening and thus should always be presented as the change in concentration from the first waking sample rather than the overall AUCG. The main reason for this is that identical measures of AUCG can be derived from completely different, indeed even oppo­ site patterns of secretion, e.g., a high first sample and low last sample would equate

AUCI AUCG

S1 AUCB

Time FIG. 4. Graphical representation of the area under the curve with reference to increase (AUCI) and the first sample on awakening (S1). The area under the curve with reference to ground (AUCG) is the sum of the AUCi and the area under the curve with reference to base (AUCB).

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with low fist sample and high last sample. While AUCG may be informative under some circumstances, e.g., overall low as compared to overall high levels of cortisol secretion; it does little to enlighten knowledge of patterns of post-awakening cortisol secretion. We recommend that the most meaningful data to present from studies of the CAR are both the fist waking sample (S1) plus a measure of the change in cortisol secretion following awakening: AUCI or MnInc (the com­ posites from which the AUCG are derived). It is interesting to note that high levels of cortisol in S1 are sometimes associated with an attenuated CAR (Adam et al., 2006; Dahlgren et al., 2009; Stalder et al., 2009; Vreeburg et al., 2009; Wilhelm et al., 2007). However, this inverse association is not always the case (e.g., Evans et al., 2007) implying that the relationship between S1 and the AUCI is not fixed. Indeed it has been argued that S1 (if collected correctly) represents a measure of pre-awakening cortisol secretion, whereas AUCI or MnInc are post-awakening measures of cortisol secretion (Clow et al., 2010). As pre- and post-awakening cortisol secretions are under different types of regulatory control (see earlier) it is possible that dysfunc­ tion in either or both of these regulatory systems could affect the pattern of the CAR. For example a high first sample could implicate hypofunctioning of the hippocampus and/or SCN pathways to the adrenal (e.g., inefficient pre-awaken­ ing inhibition of adrenal sensitivity to ACTH). If S1 is in normative range but the AUCI is attenuated this could implicate a role for post-awakening processes (e.g., a role for light and the SCN). If both the S1 and the AUCI are affected then this might imply a role for the HPA axis more generally (e.g., low availability of ACTH and consequent low cortisol secretion). In order to help determine which of the CAR pathways are implicated in any particular pattern of post-awakening cortisol secretion, it would be helpful to have additional measures of cortisol from across the day. If the CAR is aberrant yet the rest of the diurnal pattern is not (e.g., Evans et al., 2007; Oskis et al., 2010) then this would imply that a CAR-specific mechanism is implicated, rather than HPA axis more generally. If, however, both the CAR and the rest of the diurnal cycle are aberrant then this might implicate a more general HPA axis-related phenomenon. It may also be useful to look at post-awakening patterns of salivary dehydroepiandrosterone (DHEA) (note that DHEA sticks to some types of saliv­ ettes, so care is required in choice of saliva collection methodology). DHEA does not mount an awakening response (Hucklebridge et al., 2005). This has been attributed to the fact that cortisol is synthesized predominantly in the adrenal zona fasciculata, whereas DHEA is synthesized in the zona reticularis only. In contrast to the reticularis, the zona fasciculata is subject to sympathetic innerva­ tion, a pathway that might form the light-sensitive extra-pituitary input to the adrenal cortex that contributes to the CAR (discussed earlier; see Fig. 3). As a consequence levels of post-awakening DHEA are a “cleaner” index of ACTH availability than the more complex CAR.

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More recently it has been suggested that the best way to capture individual differences in the CAR is to assess day-to-day variability, i.e., a measure of the flexibility of the CAR (Mikolajczak, 2010). This may be a promising new approach although work along these lines is in the early stages of verification. If adopting this strategy it is still necessary to ensure accurate measures of S1 and AUCI. It will be interesting to see how different degrees of variability in the CAR are related to different patterns of CAR. We would hypothesize that those with the most advantageous psychosocial profiles would typically present with a moderate S1 followed by a responsive AUCI and would also be capable of the most day-to-day variability, i.e., generate appropriate CARs in response to anticipation of the forthcoming demands of the day.

IX. Conclusions

Awakening from sleep can be a “hazy” phenomenon. Recently, when we asked people to recall and describe their first waking thoughts they said things like: I experienced a flow of disconnected thoughts; A woolly awareness; My thoughts were incoherent and jumpy. Indeed many of those asked to recall their first waking thoughts were unable to do so. This haziness belies the range of dynamic physiological activities that accompany the process of awakening and the restora­ tion of full waking alertness and function. In this chapter we have attempted to review the status of the CAR within the field of psychobiological research, summarize some of its distinctive regulatory characteristics, and contextualize it in relation to other post-awakening changes. There can be little doubt that the CAR holds great promise as a biomarker, but it represents more than an index of HPA axis function. Evidence is presented for dual control links with the hypothalamic SCN nucleus. It is increasingly apparent that physical and psychological flourishing is associated with close coordination of physiological functioning around the 24-h day (Eismann et al., 2010). It is argued that the CAR is part of a SCN-synchronized response to morning awakening in healthy participants. We propose that the CAR may play a part in the restoration of alertness and cognitive function, immune system balance, and voluntary motor function following nighttime sleep. Indeed evi­ dence is presented that the CAR can vary within an individual in response to the anticipated demands of the forthcoming day in order to meet those demands, both physically and mentally. Evidence is presented that individual differences in psychological and physi­ cal status (e.g., chronic stress, aging, and gender) are associated with the pattern of the CAR in distinct ways. These effects could be mediated by any of the

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regulatory pathways that affect the CAR, e.g., ACTH availability via the HPA axis, SCN-related mechanisms, hippocampal function, and provide a window into the brain that is broader than examination of the HPA axis alone. The relative ease by which the CAR can be measured in salivary samples enables large-scale population studies and can provide useful insight into risk factors. However, such work needs to pay attention to the particular issues associated with self-collection of saliva immediately upon awakening, to ensure that data collection is as free from non-adherence to protocol as possible. In addition due to the complex regulation of the CAR it is recommended that all studies should present data on the first waking sample (as a measure of pre-awakening cortisol secretion) and the dynamic of the cortisol rise post awakening. These measures are the two key determinants of the CAR and different states and regulatory pathways may be associated with either or both of these measures. Research on the CAR is making a wide impact upon the psychobiological research community and its significance and use is set to increase. We hope that in the near future greater clarification on the regulation and roles of the CAR in healthy participants will emerge. It is plausible that it plays a part in a range of functions as discussed in this chapter. These hypotheses are yet to be fully tested, but once we have a clearer view of its regulation and roles this biomarker will surely become even more significant. For example, in the future, it is possible that distinct patterns or characteristics of the CAR will be recognized biomarkers for different patterns of functioning associated with distinct brain system and neuroendocrine dysfunction (e.g., SCN-related mechanisms, hippocampal function, as well as of the HPA axis) and also point to downstream consequences in relation to health, cogni­ tion, and function. If this is the case then the measurement of the CAR will prove to be an increasingly valuable tool in the armory of researchers and clinicians alike.

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Spathschwalbe, E., Gofferje, M., Kern, W., Born, J., and Fehm, H. L. (1991). Sleep disruption alters nocturnal ACTH and cortisol secretory patterns. Biol. Psychiatry 29, 575–584. Spathschwalbe, E., Scholler, T., Kern, W., Fehm, H. L., and Born, J. (1992). Nocturnal adrenocorticotropin and cortisol secretion depends on sleep duration and decreases in association with spontaneous awakening in the morning. J. Clin. Endocrinol. Metab. 75, 1431–1435. Stalder, T., Hucklebridge, F., Evans, P., and Clow, A. (2009). Use of a single case study design to examine state variation in the cortisol awakening response: Relationship with time of awakening. Psychoneuroendocrinology 34, 607–614. Stranahan, A. M., Lee, K., and Mattson, M. P. (2008). Contributions of impaired hippocampal plasticity and neurodegeneration to age-related deficits in hormonal pulsatility. Ageing Res. Rev. 7, 164–176. Thorn, L., Hucklebridge, F., Esgate, A., Evans, P., and Clow, A. (2004). The effect of dawn simulation on the cortisol response to awakening in healthy participants. Psychoneuroendocrinology 29, 925–930. Thorn, L., Hucklebridge, F., Evans, P., and Clow, A. (2009). The cortisol awakening response, seasonality, stress and arousal: A study of trait and state influences. Psychoneuroendocrinology 34, 299–306. Tops, M., Van Peer, J. M., Wijers, A. A., and Korf, J. (2006). Acute cortisol administration reduces subjective fatigue in healthy women. Psychophysiology 43, 653–656. Ulrich-Lai, Y. M., Arnhold, M. M., and Engeland, W. C. (2006). Adrenal splanchnic innervation contributes to the diurnal rhythm of plasma corticosterone in rats by modulating adrenal sensitivity to ACTH. Am. J. Physiol. Regul. Integr. Comp. Physiol. 290, R1128–R1135. van Leeuwen, N., Kumsta, R., Entringer, S., de Kloet, E. R., Zitman, F. G., DeRijk, R. H., et al. (2010). Functional mineralocorticoid receptor (MR) gene variation influences the cortisol awa­ kening response after dexamethasone. Psychoneuroendocrinology, 35, 339–349. Vancauter, E., Polonsky, K. S., Blackman, J. D., Roland, D., Sturis, J., Byrne, M. M., and Scheen, A. J. (1994). Abnormal temporal patterns of glucose-tolerance in obesity: Relationship to sleeprelated growth hormone secretion and circadian cortisol rhythmicity. J. Clin. Endocrinol. Metab. 79, 1797–1805. Vreeburg, S. A., Kruijtzer, B. P., van Pelt, J., van Dyck, R., DeRijk, R. H., Hoogendijk, W. J. G., Smit, J. H., Zitman, F. G., and Penninx, B. (2009). Associations between sociodemographic, sampling and health factors and various salivary cortisol indicators in a large sample without psychopathology. Psychoneuroendocrinology 34, 1109–1120. Weitzman, E. D., Nogeire, C., Perlow, M., Fukushim., D., Sassin, J., McGregor, P., Gallaghe, Tf., and Hellman, L. (1974). Effects of a prolonged 3-hour sleep-wake cycle on sleep stages, plasma cortisol, growth hormone and body temperature in man. J. Clin. Endocrinol. Metab. 38, 1018–1030. Wilhelm, I., Born, J., Kudielka, B. M., Schlotz, W., and Wust, S. (2007). Is the cortisol awakening rise a response to awakening? Psychoneuroendocrinology 32, 358–366. Wolf, O. T., Fujiwara, E., Luwinski, G., Kirschbaum, C., and Markowitsch, H. J. (2005). No morning cortisol response in patients with severe global amnesia. Psychoneuroendocrinology 30, 101–105. Wright, C. E., and Steptoe, A. (2005). Subjective socioeconomic position, gender and cortisol responses to waking in an elderly population. Psychoneuroendocrinology 30, 582–590. Wuest, S., Federenko, I. S., Hellhammer, D. H., and Kirschbaum, C. (2000a). Genetic factors, perceived chronic stress, and the free cortisol response to awakening. Psychoneuroendocrinology 25, 707–720. Wuest, S., Wolf, J., Hellhammer, D. H., Federenko, I. S., Schommer, N., and Kirschbaum, C. (2000b). The cortisol response to awakening—normal values and confounds. Noise Health 7, 77–85. Yehuda, R. (2001). Biology of posttraumatic stress disorder. J. Clin. Psychiatry 62, 41–46.

CAUSES AND CORRELATES OF FREQUENT NIGHT AWAKENINGS

IN EARLY CHILDHOOD

Amy Jo Schwichtenberg and Beth Goodlin-Jones University of California, Davis, M.I.N.D. Institute, M.I.N.D. Institute, Sacramento,

CA 95819, USA

I. Parenting Practices A. Bedtime Settling Routines B. Co-sleeping C. Breastfeeding D. Sleep Aid Use E. Culture II. Family Context A. Socioeconomics B. Parental Psychopathology III. Child Characteristics A. Temperament B. Parent–Child Interactions and Attachment C. Developmental Problems and Diagnoses IV. Summary

References

Night awakenings are a normative part of early development. In the first year, night awakenings are associated with birth order, feeding route, sleep aid use, sleep location, infant temperament and development, infant–parent attachment, family socioeconomics, and cultural norms. In the second year, additional factors build on these foundational features, including parenting practices and object attachment. As children grow, contextual factors like preschool entry or changes in family member status may influence the continuation or exacerbation of awakenings. Future research should consider the multitude of factors that influ­ ence not only awakenings but also parental perceptions, family dynamics, and cultural norms. Night awakenings in young children are a normative part of development (Goodlin-Jones et al., 2001; Iglowstein et al., 2003; Sadeh et al., 2009). Children are born with a homeostatic drive for sleep based on their hunger–satiety cycles and gradually entrain to light–dark cycles (i.e., circadian cycles) in the first months of development. Entrainment marks the beginning of a shift from equally

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Copyright 2010, Elsevier Inc. All rights reserved. 0074-7742/10 $35.00

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distributed sleep to gradually more sleep at night and less during the day. Sleep consolidation at night is an evolutionally driven change that is highly amendable to contextual and biological pressures. Gradually from 3 to 12 months children sleep for longer periods of time at night, and as they develop they require less sleep per day–night cycle. By 12 months of age, the duration of an infant’s nighttime sleep stabilizes. After 15–18 months of age, developmental shifts toward less sleep per sleep-wake cycle are primarily seen in the reduction and cessation of naps (Iglowstein et al., 2003). Throughout early childhood middle-of-the-night arousals occur on a nightly basis, for some children these arousals develop into awakenings. Some nighttime awakenings are normative through to 5 years of age (Iglowstein et al., 2003). Night awakenings become problematic with increased frequency and/or duration and when there is daytime impairment due to sleepiness in either the parent or the child (e.g., Snyder et al., 2008). There have been many attempts to define problematic night awakenings with no clear agreement yet developed (Anders et al., 2000; Buckhalt and El-Sheikh, in press; Galland and Mitchell, in press; Goodlin-Jones et al., 2000; Ivanenko and Gururaj, 2009). Regardless of definition issues, most studies indicate that 15–20% of toddlers and preschool children have problematic night awakenings (Goodlin-Jones et al., 2009; Ivanenko and Gururaj, 2009). Within this chapter we will not discuss/define what constitutes proble­ matic nighttime awakenings or the clinical nomenclature (for a discussion of these issues, see Moul, Chapter 5 of this volume). Rather we will review the literature on the causes and correlates of problematic night awakenings in young children. In most research studies, night awakenings are indexed by parent report and they assess awakenings on a continuum with more and longer awakenings generally perceived with more concern (Goodlin-Jones et al., 2001; Santos et al., 2008; Schwichtenberg and Poehlmann, 2009; St James-Roberts and Plewis, 1996). Because parent-report indices are the most common, many studies include the bias/confound of parental perceptions. When sleep schedules are the focus of the study, this bias appears minimal (Sadeh, 2008). However, if sleep quality is the critical feature, the bias and confound of parental report may be significant. Both objective assessments (e.g., actigraphy, polysomnography) and parental report (e.g., questionnaires, diaries) will be included in the discussion that follows. Problematic nighttime awakenings occur for many reasons. For most children night awakenings are associated with learned patterns of behavior and not linked with specific medical conditions (Sadeh et al., 2010). Within this chapter, psycho­ logical, behavioral, and developmental factors are discussed and other causal factors are briefly reviewed (e.g., medical conditions). Medical concerns may involve the presence of reflux, pain due to infection or injury, obstructive sleep apnea, chronic conditions such as asthma or cystic fibrosis, or a neurological disorder such as restless leg syndrome. Each of these medical factors may have numerous impacts that increase night awakenings and require medical attention.

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The recommended treatments may also involve medications that further pre­ cipitate frequent sleep arousals (Givan, 2004). Most often early childhood night awakening problems are linked to behavioral factors and are amendable to behaviorally based treatments. Therefore, this chapter will focus on nighttime awakenings that are not a result of specific medical conditions. Within a devel­ opmental context, the correlates and predictors of night awakenings are reviewed below by parent/contextual and child factors (Fig. 1).

I. Parenting Practices

Parenting practices are the most commonly studied element of infant night awakenings. Parenting practices encompass a wide range of behaviors and may include bedtime settling routines, sleep location (e.g., co-sleeping), feeding route, sleep aid use, and culture. Each of these realms is reviewed below with attention to their role in night awakenings.

A. BEDTIME SETTLING ROUTINES One of the most studied factors in night awakenings is the role parents play in the beginning of the night. Parenting practices at bedtime that require the

Birth

3 Months

6 Months

12 Months 18 Months 24 Months 36 Months 48 Months

Culture (values pertaining to sleep, parenting, childcare, pre-school) Sociodemographics (family income, neighborhood, parent work status, changes in family membership) Maternal Mental Health (Parenting beliefs, depression, anxiety) Birth Status (health, birth order) Feeding Route (breast, bottle) Soothing Object Use (pacifier, thumb) Parent-Child Interactions (parenting practices, bedtime routines) Sleep Location (solitary crib, family bed, inconsistent locations) Temperament (adaptability, rhythmicity) Attachment (secure, insecure, disorganized)

Reactive co-sleeping (parents consistently sleep with child after an awaking)

Object Attachment (blanket, dolly)

FIG. 1. Conceptual figure of the developmental progression of factors that play a role in sleep behaviors.

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presence or assistance of parent(s) for the child’s transition to sleep have been associated with longer and more night awakenings (Mindell and Owens, 2003; Sadeh et al., 2009). These activities, sometimes called negative sleep-onset asso­ ciations, may include feeding, car rides, lying next to a parent or being cuddled, being held by a parent, pacifier use, and sleep aid use (Anuntaseree et al., 2008; Fehlings et al., 2001). It is unclear if the association between parenting practices at bedtime and elevated awakenings is mediated by or interacts with child char­ acteristics (e.g., temperament). However, the relationship between parenting practices and infant awakenings is likely bi-directional and it is not likely causal, although this has also been debated within the field. Additionally, it is unclear if the type of parenting activity used to assist the child’s transition to sleep impacts this relationship or if it is being placed in their nighttime sleep location asleep (DeLeon and Hildebrandt Karraker, 2007). Many studies, including clinical studies, have demonstrated that focusing on parents as a means for change at the beginning of the night can create change in children’s sleep in the middle of the night. The important role for parent behavior continues during middle­ of-the-night awakenings. Parental actions following an awakening that include active settling techniques have been associated with elevated awaken­ ings (Mindell and Owens, 2003). Active settling techniques may include removal from the child’s sleep location, walking, rocking, feeding, or reposi­ tioning. During awakenings, parental latency to respond has also been studied (Burnham et al., 2002a). In this study, maternal response to infant signaling ranged from immediate to 27 min later. Interestingly, parents who responded with slower (longer) latencies at 3 months of age were more likely to have infants self-soothing at 12 months of age (Burnham et al., 2002a). Another factor to consider is paternal involvement, which is less common during middle-of-the-night parenting. For example, in a recent study more parental involvement in general caregiving predicted fewer night awakenings at 6 months of age (Tikotzky et al., 2010).

B. CO-SLEEPING Cultural expectations and socioeconomic conditions often influence early childhood sleeping practices, especially in co-sleeping practices. Numerous studies report elevated night awakenings in families that co-sleep when compared to families that practice solitary infant sleeping (e.g., in their own room alone) (Fukumizu et al., 2005; Mao et al., 2004; St. James-Roberts et al., 2006). However, multiple considerations must be noted when inter­ preting these findings. Most studies that report more night awakenings in

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families who co-sleep assess infant sleep via parent report. Parents who co­ sleep are physically closer to their infant are therefore more likely to notice and report an awakening. Additionally, there are multiple types of co­ sleeping arrangements (McKenna and McDade, 2005; Taylor et al., 2008). Some parents co-sleep by choice (e.g., the family bed, a sleeping room) and others co-sleep to accommodate their child’s frequent bids at night, some­ times called reactionary co-sleeping. Consistent co-sleeping is associated with less maternal depression, longer breastfeeding, and less infant temperamental intensity (Taylor et al., 2008). Whereas co-sleep in response to a reactive or bidding child (part-night co-sleeping) is not linked with these positive corre­ lates. Grouping all co-sleeping arrangements into one group creates a hetero­ geneous group and makes interpretation difficult. A culturally sensitive developmental perspective on co-sleeping may provide a clearer picture of how, when, and why young children co-sleep and its relation to night awakenings. Cultural aspects of co-sleeping and awakenings are discussed in more detail below. Regarding the developmental progression of co­ sleeping and night awakenings, a longitudinal study in Switzerland of 493 families found relativity low levels of co-sleeping early in development (10%) with a gradual increase in co-sleeping through 4 years of age (38%) (Jenni et al., 2005). They reported a similar pattern in night awakenings with gradually more children waking from 6 months to 4 years and a consistent positive relationship between co-sleeping and parent-reported night awaken­ ings. However, the relationship between co-sleeping and parent-reported night awakenings is not consistently found in cultures where co-sleeping is the most popular sleeping agreement (see Section I.E). Studies of co-sleeping that move beyond parent report are sparse. To our knowledge, there is only one research team that has assessed infant and mother sleep in co-sleeping dyads using polysomnography. Within their study, Mosko and colleagues (1997) reported more nighttime arousals in young infants who co-slept. These arousals were more often led by the child and then followed by a parent arousal. A study using actigraphy and diaries confirmed this finding (Mao et al., 2004). More research on physiological correlates of co-sleeping across numerous co-sleeping “types” is needed.

C. BREASTFEEDING Studies assessing infant sleep via maternal report sleep logs or diaries have found more night waking and less nighttime sleep among breastfed infants when compared to bottle-fed infants (DeLeon and Hildebrandt Karraker, 2007; Wolke et al., 1995). In a study of 41 healthy 9-month-old infants, DeLeon and

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Hildebrandt Karraker (2007) reported that breastfed infants spent more time awake at night (i.e., more night waking and less nighttime sleep). Researchers have hypothesized that breastfed infants are awake more at night because of shorter hunger–satiety cycles. Although numerous studies have found links between feeding route and night awakening (Messer and Richards, 1993; Schwichtenberg and Poehlmann, 2009), little support has emerged for an asso­ ciation between breastfeeding and infant sleep problems. In addition, a study by Doan and associates (Doan et al., 2007) found that parents of healthy 3-month-old infants slept more at night than parents of formula-fed or combination fed infants, as indexed by actigraphy.

D. SLEEP AID USE Although many parenting resources (e.g., Brazelton and Sparrow, 2003; Ferber, 1985; Mindell, 1997) recommend sleep aid use (e.g., a blanket, pacifier, baby doll) empirical studies of sleep aid use and night awakenings in young children are limited. In 1998, Jencius and Rotter reported fewer night awaken­ ings among infants who consistently used a sleep aid in their study of 16 families (Jencius and Rotter, 1998). In a larger sample of infants studied in a crosssectional design, a majority of 3- to 15-month-old infants used a sleep aid during the night, yet its use was not related to self-soothing after an awakening (Burnham et al., 2002b). It is unclear if sleep aid use itself is associated with fewer awakenings or if factors common among infant who use sleep aids are contributing factors. For example, Green and colleagues (2004) reported less parent contact at night among infants who consistently used a sleep aid (Green et al., 2004). Similarly, Wolf and Lozoff (1989) presented a robust association between sleep aid use and parent presence at sleep onset. Infants who fell asleep with a parent present were less likely to use a transition object (Wolf and Lozoff, 1989). Differences in family sociodemographic factors have also been associated with sleep aid use (Litt, 1981; Milan et al., 2007). In a study of 285 children, Litt (1981) report substantially higher rates of sleep aid use in upper-middle class Caucasian children when compared to lower-middle class African American children. In a more recent study, Milan et al. (2007) reported similar results with higher rates of sleep aid use in Caucasian children when compared to African American children. Although sleep aid use is associated with fewer night awakenings, an intervention study that introduced a sleep aid as an intervention tool found no significant changes in infant sleep when the novel sleep aid was used (Burnham et al., 2002b). Future research in sleep aid use should work to disentangle the relations between object use, parenting practices, and family sociodemographic factors.

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E. CULTURE A discussion of parenting practices/behaviors should be based within the con­ text of culture. Multiple studies (Latz et al., 1999; Mindell et al., 2010) report that culture moderates the relationships between parenting behaviors and night awaken­ ings. For example, Latz et al. (1999) reported a stronger association between co­ sleeping and night awakenings in the US when compared to families in Japan. Similarly, Mindell and colleagues (2010) reported that within primarily Caucasian cultures co-sleeping is associated with more awakenings; however, this relationship is weaker in primarily Asian cultures where co-sleeping is the most common sleep arrangement. Cultural practice may also dictate common parenting practices that consistently relate to more awakenings. In a study of 174 families, St. James-Roberts and colleagues (2006) reported that “infant led” parenting practices (like those common in Copenhagen and Denmark) were associated with more awakenings when compared to more “Western” parenting practices (i.e., sleep independently in another room) (St James-Roberts et al., 2006).

II. Family Context

As stated above, the primary factors that influence awakenings are negative sleep associations. Negative sleep associations are conditions present at sleep onset that require parental presence (e.g., rocking or swinging). In contrast, positive sleep associations are those conditions that do not require parental presence and can be completed by the individual child (e.g., thumb sucking). The development of negative sleep associations and problematic night awaken­ ings are impacted by several ecological or familial characteristics such as interparental conflict, employment status, education levels of parents, and the general stability of the home environment.

A. SOCIOECONOMICS Socioeconomic conditions influence parental psychological health and parenting behaviors. For example, parental response to nighttime awakenings and the pattern of letting a child “cry it out” was more common in middle class parents while working class parents responded immediately (Scott and Richards, 1990). Mothers who work outside the home during the day reported that their infants woke more at night (Scher et al., 1995; Van Tassel, 1985). More recent results continue to support the negative statistical association between lower socioeconomic status (SES) and elevated

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awakening problems in infants, toddlers, and preschoolers (Brown and Low, 2008; Lozoff et al., 1996; Santos et al., 2008). Crowding, clutter, and lack of daily routines have been found in families of lower SES samples, and these conditions have also been associated with parent-reported sleep problems in 3 year olds (Koulouglioti et al., 2008). However, a recent infant study at 9 months of age suggested no relation of family SES and night waking problems (Bayer et al., 2007).

B. PARENTAL PSYCHOPATHOLOGY Parental psychological features, particularly depression and marital conflict, may be related to socioeconomic status but they have also been studied as independent factors impacting sleep behavior. There is a fairly extensive litera­ ture that links early childhood sleep problems to maternal psychopathology. Multiple studies have suggested that mothers with poor well-being have children with higher levels of parent-reported behavior problems, including night waking sleep problems (Bayer et al., 2007; El-Sheikh et al., 2007; Goodman and Gotlib, 1999; Hoffman et al., 2006; Richman, 1981a; Shang et al., 2006; Zuckerman et al., 1987). Snyder and colleagues (2008) highlight how a constellation of factors in the family context (marital conflict and poor maternal mental health) play a role in the development of sleep night waking problems (Snyder et al., 2008). These patterns of association begin early life, as indicated by research by sleep research­ ers in Australia (Armstrong et al., 1998; Bayer et al., 2007; Hiscock and Wake, 2001). In general, throughout the first year, mothers who endorse high rates of night waking problems reported more difficulties with mental health concerns and negative correlation coefficients are reported between child sleep problems and marital conflict and poor mental health in mothers. Maternal depression is the primary mental health condition studied in rela­ tion to childhood sleep problems (e.g., Armstrong et al., 1998; Richman, 1981b; Sadeh et al., 2010). Mothers who endorse depressive symptoms have young children with higher rates of night waking (Bayer et al., 2007; Morrell and Steele, 2003). The related constructs of maternal separation anxiety and parental sleeprelated cognitions have also been implicated in night waking problems (Scher, 2008; Tikotzky and Sadeh, 2010). Toddlers and older children of affectively ill mothers also had more disrupted sleep (Stoleru et al., 1997). In an observational study with actigraphic-measured infant sleep at 10 months of age, mothers with higher levels of separation anxiety from their infants had infants with more disrupted sleep. This pattern of night waking still remained significantly asso­ ciated with the mother’s own separation anxiety after infant temperamental fussiness was controlled for statistically (Tikotzky and Sadeh, 2010). However, in a relatively large study with 80 infants measured objectively on nighttime

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waking behavior, there were no consistent differences in parental well-being and night waking behavior (Goodlin-Jones et al., 2001). Other researchers have also reported negligible evidence of maternal mood or mental health on young children’s sleep patterns once environmental context is controlled for statistically (Morrell and Steele, 2003; Van Tassel, 1985). Maternal depression or anxiety may alter cognitions and parenting behavior in a dramatic manner. However, frequent night awakenings may clearly impact parental well-being in a bi-directional manner, leading to feelings of incompe­ tence and low self-esteem as a parent. Exhausted parents feel the challenge of parenting more intensely. Hence, the multiple factors underlying sleep problems are clearly bi-directional. Parental beliefs and cognitions about their child’s competence to self-sooth play a role in these interactions. The link of parental cognitions to childhood sleep problems is assumed to be mediated through the way parents behave with their children (Sadeh et al., 2009). Sadeh and colleagues have completed recent studies that describe one developmental route to night waking problems. Specifically, if a parent was upset about a child’s demands or if the parent reported more difficulty in limit setting than parents’ also reported more nighttime parent involvement and more night waking episodes (Sadeh et al., 2007; Tikotzky and Sadeh, 2010).

III. Child Characteristics

Multiple child factors may impact night awakenings. Factors present from birth (e.g., gender, birth order) may influence awakenings as well as factors that develop over time (e.g., temperament, attachment). For example, Scher and Blumberg (1999) found that first born children were more likely than later born children to signal upon waking at 12 months of age (Scher and Blumberg, 1999). A few studies report more night awakenings in male children (Anuntaseree et al., 2008; Goodlin-Jones et al., 2001). For example, Goodlin-Jones et al. (2001) reported that male children were more likely to signal upon waking in a crosssectional sample from 3- to 12-month-old infants; however, they found no gender difference in signaling behavior or vocalizing rate. This indicated that the vocalizations were specific to awakenings. Future research is warrant to address gender differences in night awakenings; the elevated rates of awakenings in male infants are not a consistent finding. Behavioral research on child characteristics and nighttime sleep generally focuses on three areas: temperament, attachment (parent–child relations), and other developmental problems. We address each below with specific attention to night awakenings.

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A. TEMPERAMENT Temperamental characteristics are considered important intrinsic factors of the individual by many developmental researchers. Sleep studies have investigated the contribution of infant temperament with two methods: parent-report indices and observational assessments. Most commonly, studies rely on parental report of infant temperament while a minority of researchers measure infant temperament with direct assessments. Both methods show greater agreement on positive ratings of temperament (e.g., adaptability) and greater divergence on the negative dimen­ sions of temperament (Stifter et al., 2008). However, negative temperament dimen­ sions are more commonly implicated with night waking problems. For example, the temperament characteristic of poor rhythmicity (or poor regularity of behavior) has been associated with more night awakenings in parent-report studies (Atkinson et al., 1995; Jimmerson, 1991) of young children. However, it is possible that a sleep-deprived parent rate their children in a less positive manner. During early infancy, infants rated with more negative mood were reported to waken more often in the middle of the night (Kelmanson and Adulas, 2004; Schaefer, 1990; Scher et al., 1998). Halpern et al. (1994) reported that infants who spent more of the night awake at 3 weeks of age were more irritable at 3 months according to parental report (Halpern et al., 1994). Most researchers do not support a direct link between temperament and sleep problems, however, given the reporter bias involved in parent reports. Indeed, issues of rater bias were described in a study that observed different associations between sleep and temperament depending upon whether the raters were mothers or fathers (Keener et al., 1988).

B. PARENT–CHILD INTERACTIONS

AND

ATTACHMENT

Previous research on infant sleep and infant–mother attachment presents mixed findings (Benoit et al., 1992; McNamara et al., 2003; Morrell and Steele, 2003; Scher and Asher, 2004). Benoit and colleagues (1992) drew attention to infant sleep and infant–mother attachment when they reported that 100% of their clinic sample referred for infant sleep problems were classified as insecurely attached on the Adult Attachment Interview. Concurrently, Anders (1994) high­ lighted the undeniable resemblance between infant–parent bedtime separations and the separations seen in the strange situation. However, later studies do not report robust relationships between sleep behaviors and infant–mother attach­ ment classification in healthy infants. Scher and Asher (2004) reported no significant relationships between actigraph measured infant sleep and concurrent attachment Q-set scores in healthy 12-month-old infants. Conversely, Morrell

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and Steele (2003) reported a modest but significant relationship between parentreported sleep problems and ambivalent attachment. In a longitudinal study, McNamara and colleagues (2003) found more frequent and longer lasting parentreported nighttime awakenings in infants who were classified as insecure resis­ tant, when compared to infants classified as insecure avoidant in the Strange Situation. Although the previous literature in this area is limited, it appears that parent perceptions of infant sleep may be more indicative of later attachment than more objective measures.

C. DEVELOPMENTAL PROBLEMS AND DIAGNOSES Frequent night awakenings often co-occur with other developmental problems or medical conditions. For example, infant feeding problems may impact nighttime sleep as infant hunger–satiety cycles are shorter or irregular (Thunstrom, 1999). Similarly, frequent night awakenings are more common among children with neurodevelopmental conditions, such as Down syndrome. The cranial–facial fea­ tures common in Down syndrome place these children at increased risk for sleep apnea, as many as 55% of children with Down syndrome suffer from frequent apnea-related night awakenings (de Miguel-Diez et al., 2003; Marcus et al., 1991). A recent study of preschoolers with neurodevelopmental disorders studied sleep patterns with actigraphy and observed longer night awakenings in preschoolers with developmental delay, which included Downs syndrome, compared to pre­ schoolers with autism or with typical development (Goodlin-Jones et al., 2009). Other common childhood diagnoses associated with frequent night awakenings include autism (Krakowiak et al., 2008), Smith-Magenis syndrome (Boudreau et al., 2009), intrauterine growth retardation (Leitner et al., 2002), cerebral palsy (Pruitt and Tsai, 2009), Chiari malformations (Gosalakkal, 2008), and many more. Sleep is a universal and fundamental element of development; disrupted or alternative paths in development are often associated with atypical sleep patterns (which commonly include frequent night awakenings). Consistent consolidated sleep requires the coordination of several neurological and biological systems, alterations in one or more system often lead to disturbed sleep.

IV. Summary

Night awakenings in early in life are a normative part of development. However, night awakenings that increase with frequency and duration as chil­ dren develop may negatively impact families and later developmental outcomes.

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As children grow, the number of factors that affect their sleep builds, leading to an increasingly more complex picture (Fig. 1). Researcher, clinicians, and inter­ ventionists need to consider the multitude of factors that could influence not only the child’s sleep behaviors (night awakening) but also parental perceptions, family dynamics, and cultural norms. References

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PATHOLOGIES OF AWAKENINGS: THE CLINICAL PROBLEM OF INSOMNIA CONSIDERED FROM MULTIPLE THEORY LEVELS

Douglas E. Moul Sleep Disorders Center, Neurological Institute, Cleveland Clinic, Cleveland OH 44195; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA

I. Chronic Insomnia: Syndromes of Pathological Awakenings A. Clinical Context B. Classification of “Pure” Insomnias C. Limitation of Explanatory Ambitions for the Defined Syndromes of Chronic Insomnia II. Background Conceptual Features of Analysis of Realities About Sleep A. Ambiguity of “Awakening” and “Sleep” in Ordinary English B. Mereological Ambiguities of the Term “Sleep State” C. Process S and Process C as Empirically Necessary but Contradictory Explanatory Principles for Understanding Sleep Regulation D. Necessary Inter-Level Theoretical Vaguenesses and Incommensurate Temporalizations E. Other Temporalizations Relevant to Understanding Chronic Insomnia Patients F. The Mnemonic and Integrative Duties of Sleep III. The Spielman three-factor High-Level Model of Insomnia and Mid-Level Therapeutic The­ ories of Insomnia Therapies A. The Spielman Model: Implications for Cognitive Behavioral Therapists B. Nonignorable Psychologically Based Mid-Level Theories IV. Cautions About Conceptual Transitions to the Theory Level of Neuronal Processes V. An Aristotelian Method of Review A. Correlation with Spielman Factors B. Problems Emerging from Relating Theories from Different Conceptual Levels C. Substantial Causes (i.e., Substances) D. Formal Causes (i.e., Structures) E. Efficient Causes (i.e., Processes, Contemporarily Understood “Causes”) F. Telic Causes (Telos, Final Causes, Limit Cycles, Outcomes, Goals, and Needs) VI. Conclusion References

Limit cycle-based mid-level theories that rationalize effective clinical treat­ ments for chronic insomnia have empirical support from whole-organism studies of sleep physiology, but their relation to network-level and cellular neurobiologies remains obscure. The neurobiology of pharmacological treatments for insomnia has been increasingly understood, but has not been fully integrated with INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 93 DOI: 10.1016/S0074-7742(10)93009-2

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Copyright 2010, Elsevier Inc. All rights reserved. 0074-7742/10 $35.00

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psychological theories or electroencephalographic descriptions of sleep. Better clinical diagnostic and treatment frameworks will require both greater conceptual clarity as to what an “awakening” is descriptively and detailed investigations to relate fundamental neuroscience to clinical technologies can be both accessible and diagnostically useful to clinicians. Out, damned spot! Out, I say! One - two - why then ‘tis time to do’t. Hell is murky. ––Lady Macbeth The clinical topic of awakenings is indeed murky. For many chronic insomnia patients, awakenings are hellish. Awakening can give rise to an impulse to take some actions to address being awake. When such actions fail, their failure may occasion self-loathing, amplifying the awakening further. Stress about awakening may cause further despair and further wakefulness. Actions to end wakefulness yield then only more wakefulness.

I. Chronic Insomnia: Syndromes of Pathological Awakenings

A. CLINICAL CONTEXT Chronic insomnia is quite common (Ohayon, 2002). If without another clinically identifiable cause, it is called primary insomnia. Primary insomnia is thought to affect 6% of the general population. Women harbor chronic insomnia twice as much as men and have major depression and anxiety disorders twice as frequently as well (Balter and Uhlenhuth, 1992; Robins and Regier, 1991; Taylor et al., 2005). Comorbid forms of insomnia, as associated with affective, anxiety, and other disorders, affect an additional 10–20% of the general population. Often insomnia persists for long periods of time (Buysse et al., 2008a). Some evidence (Breslau et al., 1996; Chang et al., 1997; Ford and Kamerow, 1989) suggests that chronic insomnia is a longer-term risk factor for major depression. Chronic insomnia is associated with older age, female gender, low socioeconomic status, and medical comorbidity. Insomnia is a potential co-occurring symptom in many mental and somatic disorders. This fact places a premium on understanding the pathophysiology of primary insomnia, an “only insomnia” disorder, since in all other disorders, the potential exists that insomnia has syndrome-specific features that could confound one’s understanding of awakenings or arousals.

PATHOLOGY OF AWAKENINGS

B. CLASSIFICATION

OF

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“PURE” INSOMNIAS

From the electroencephalographic (EEG) perspective, awakenings considered as such are not pathological by themselves. From an evolutionary perspective, nocturnal awakenings may have been functionally important for insuring that the sleeper will have sufficient awareness during the night to monitor for nocturnal threats (e.g., predators), or simply to avoid going into coma (Hal
asz et al., 2004). Insomnia researchers who consider awakenings from an EEG perspective might try to generalize awakenings as difficulties either with getting to sleep (initially, or if awakened) or with waking up too much. Yet awakenings are common in noncom­ plaining sleepers. Some are culturally or seasonally normative. So what is the “damn spot” here? Are awakenings pathological even if people may not know they are having them? Are only certain kinds of awakenings pathological, or are awakenings just false groundings for other heterogeneous psychological complaints? Insomnias have sometimes been subclassified by whether the patient suffers from a difficulty falling asleep (DFA), difficulty maintaining sleep (DMA), or early morning arousal (EMA), with some notion that DFA may suggest an anxiety diathesis, whereas EMA an endogenous depressed one. However, evidence (Hohagen et al., 1994) has indicated that insomnias do not breed true over time: A person may switch between DFA, DMA, and EMA over time or in various combinations. Diagnostic conventions take awakenings seriously (and not merely as epiphe­ nomena of other underlying conditions) in the construction of scientific and nosological approaches to studying insomnia and awakenings, by considering time-based metrics of sleep and of awakenings in diagnostic definitions despite DFA, DMA, and EMA being unstable subtypes. The DSM-IV generic definition of chronic insomnias (American Psychiatric Association. Task Force on DSM-IV, 2000) includes (1) “The predominant com­ plaint is difficulty initiating or maintaining sleep, or non-restorative sleep, for at least 1 month,” and (2) “The sleep disturbance (or associated daytime fatigue) causes clinically significant distress or impairment in social, occupational, or other important areas of functioning,” with the proviso that the complaint is not best described by another sleep disorder. Note that in this definition sleep variations and poor sleep quality considered unto themselves are necessary, but not sufficient, criteria for claims of sleep pathologies or of pathological awakenings. To meet this and related defini­ tions of chronic insomnia, daytime pathological consequences are also required. But even daytime effects are not enough either: The patient must actually complain. So there is an implied speech act (Searle, 1969) requirement in the definition. Indeed, there is no reason to suppose that there are not persons who awaken at night and have daytime consequences, but who do not complain. In other words, the present psychiatric definition of a pure “disorder of awakenings” is based upon a person’s recall about a sleep period’s characteristics as related to a causal interpretation he/she may have upon an assessment of his/her daytime functioning and additional ability

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or willingness to make these assessments known to a clinician. When comorbid conditions (e.g., depression, stress) are present, one’s ability to relate any possible awakenings directly back to neurobiological explanations becomes murkier. The International Classification of Sleep Disorders (American Academy of Sleep Medicine, 2005) defines insomnia similarly to the DSM-IV; however, its definition also specifies that there be adequate opportunity and circumstances for sleep (to distinguish it from sleep restriction), and there be at least one of the daytime consequences: fatigue or malaise; attention, concentration, or memory impairment; social or vocational dysfunction or poor school performance; mood disturbance or irritability, daytime sleepiness (taken to be generally more subjectively experienced than objectively demonstrated); motivation, energy, or initiative reduction; proneness for errors or accidents at work or while driving; tension, headaches, or gastrointestinal symptoms in response to sleep loss; and concerns or worries about sleep. The ICD-10 (World Health Organization, 2007) definition of nonorganic insomnia stipulates clinical features of (1) DFA, maintaining sleep, or poor sleep quality; (2) sleep disturbance at least 3 times per week for at least 1 month; (3) patient’s preoccupation with sleeplessness and excessive concern over its conse­ quences at night and during the day; and (4) the unsatisfactory quantity and/or quality of sleep either causes marked distress or interferes with ordinary activities in daily living. Insomnia may or may not occur with comorbid conditions.

C. LIMITATION OF EXPLANATORY AMBITIONS FOR CHRONIC INSOMNIA

THE

DEFINED SYNDROMES OF

From the perspective of polysomnogram (PSG) recordings in good sleepers, the insomnia complaint can be framed in relation to sleep architectures of initial and middle insomnia, in which the inability to go to sleep can be observed on the PSG. The traditional (Rechtschaffen and Kales, 1968) and recently updated (Iber et al., 2007) approaches to analyzing sleep both utilize fixed-length epochs in scoring sleep. This places awakenings under the hegemony of the fixed-epoch conception as a “Truth” to which self-reports can be compared. Such approaches have led to doubts about the validity of self-reported accounts about sleeping. However, extolling such validity problems begs the question about the validity of the fixed-epoch approach to measurement, particularly if awakening is considered to be a process. When is an awakening considered as a process true or false? It is not clear. The nosological measurement problem concerning the validity of awakenings can be illustrated by attempts to measure the sleep onset process (SOP) in small epochs. In the experimental approach, the SOP is investigated from a starting point of research subjects’ being definitely awake. Using a fixed, small epoch approach for classifying small sleep epochs during the SOP, Hori and colleagues

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(1994; Tanaka et al., 1996) constructed a labor-intensive method for classifying intermediate epoch types during the SOP. However, it is not clear how such an intensive system could be easily implemented in clinical research studies, or be at all practicable for everyday clinical work. A similar-spirited attempt at classifying small epochs was put forward by Moul and colleagues (2007a), but while this is a simpler approach, it nonetheless faces similar criticisms. By contrast, as regards the awakening process, added difficulties follow from various sleep stages becom­ ing the process starting points into awakenings of various kinds. It will be difficult for awakening onset process (AOP) researchers to stipulate the temporal location for the start of an awakening process amidst manifold sleep processes. The dimensioning of time is a key experimental conundrum. The limited available literature on the EEG-based SOP studies on primary insomnia has produced a variety of approaches of dealing with time. Perusal of Table I illustrates various attempts. Some investigators have tried to look at the problem as one of shortening the epoch size (Lamarche and Ogilvie, 1997; Merica and Gaillard, 1992; Merica et al., 1998; Moul et al., 2007a; Staner et al., 2003), if only to perform artifact rejection (Marzano et al., 2008). Others have utilized relativized time (Lamarche and Ogilvie, 1997; Staner et al., 2003), in relation to starting points and endpoints, and framing the analysis according to percentage of clock time elapsed. Others (Bonato, 1997; Buysse et al., 2008b; Perlis et al., 2001) have looked at the frequency distributions or wave forms present during an initial sleep period to give some data about relative speed to which sleep becomes deep. The various epoch­ series studies (Bonato, 1997; Freedman, 1986; Jacobs et al., 1993; Lamarche and Ogilvie, 1997; Marzano et al., 2008; Merica and Gaillard, 1992; Merica et al., 1998; Moul et al., 2007a; Staner et al., 2003) point to the SOP differing between insomnia patients and controls, but specific findings differ across studies. The prospects for generalizability in this SOP domain already look dim; those for the AOP domain will be yet more problematic. Review of these various efforts points to an additional discouraging metho­ dological insight. To test subject-group contrasts statistically imposes the con­ straint that only outcome variables (as determinate nominal, ordinal, or continuous values) can be utilized, not processes as processes. Statistical analysis cannot utilize process data streams irrespective of temporal startpoints, cutpoints, or endpoints, if and when time is treated as a latent variable. Time can only be modeled and never be measured as a real intensive phenomena (e.g., like temperature). The nature of the neurological processes about which clinical interventions are constructed will likely elude the methodological requirement for “outcoming” these neuronal processes. This statistical intractability of pro­ cesses will make it difficult to validate statistical generalities about subject-group differences in processes or sleep state transitions. The fixed-epoch-size approach to measurement has statistically afforded making some attempts at group comparisons; however, these comparisons may

Table I

TIME-BASED SLEEP ONSET PROCESS SPECTRAL COMPARISONS Study

Dx

Freedman (1986)

SOI Ctrl 1M, 11F; 31.8 4M, 8F; 27.8

PPI Ctrl Merica and Gaillard (1992) CL Ctrl Merica et al. (1998)

Gender; mean age

Match

Sleep onset definition

Time metric (zero =)

Age group

Stage 1

Sleep stage (GNT)

5M, 7F; 35.911M, 17F; 30.0

Age group

Stage 1

8M; 12F; 30.2 9M; 10F; 25.3

Age group

NREM onset without prior wake Stage 1

Jacobs et al. (1993)

SOI Ctrl 5M, 7F; 37.8 Age group 5M, 9F; 36.9

Lamarche and Ogilvie (1997)

PPI PDS 3M, 3F; 27.8 Age group Ctrl 3M, 3F; 31.5 6M, 9F; 27.8

First 5 min of stage 2

WITH

Data analysis

CENTRAL LEADS

IN

PRIMARY INSOMNIA

Delta onsets in PI versus controls

First nonIncreased 1 Hz artifact power awake minute of with eyes each sleep closed stage Real (SO) Digitally filtered Slower increase 10 s with with later smoothing lower power in NREM Percentile NREM FFT Slower increase of means of four in delta (and NREM 4-s epochs theta) episode (SO) 5-min FFTs: 5-min Not reported wake vs awake/eyes stage 1 shut and (SO) stage 1 Quartiles Mean 14-s FFT No increase in and absolute first 3 min sleep powers quartiles; stages binned by overall slower (GNT) ordinal time increase

Beta onsets in PI versus controls

Other in PI versus controls

Higher beta in wake with eyes open and stage 1

Decreased alpha in awake eyes closed

Hard to show slower decrease

Higher beta/ delta ratio instability

Same initial decrease yet higher in NREM

Lower power in bands slower than beta

Higher presleep waking with eyes closed

Reduction in beta with behavioral treatment Low baseline and no change in alpha at all by quartile

Highest during wake stage; no overall group effect by quartile

(continued )

Bonato (1997)

Staner et al. (2003)

PPI Ctrl

6M; 9F; 34.0 Age and sex First Sleep all rightilndividual 15 min stage handed of (GNT) 6M, 9F; stage 2 34.1 PI MDD 11M, 7F; Age and sex First 30-s. Ordinal Ctrl 40.5 11M, group epoch of (GNT); 10F; 46.5 stage 2 real 11M, 10F; (SO) 44.3

Moul et al. PI Ctrl (2007)

Marzano et al. (2008)

PdI Ctrl

5M, 6 F; 45.3 Age and sex Both stage Real (SO) 5M, 6F; individual 1 and 44.8 stage 2 (1-min epochs) 4M, 6 F; 30.0 Age and sex First 20-s Real (SO) 4M, 6F; epoch of 30.2 stage 2

Stage-wise period amplitude

Less half wave; less full and half wave in stages 1–2

More full wave and first derivative in stage 2

Medians of 2-s FFTs at quantiles by each minute

No delayed increase

No beta (13–21.5 Hz) change from low baseline with ordinal time Delayed power decrease

4-s epoch visual Delayed power score and increase in FFT relative power modeling 4-s epoch FFT No differences; 5 min prepost both had SO anterior-toposterior progress

More alpha full wave in stage 1 and half wave overall Alpha decrease no different

Other power bands not informative

Higher beta in Lower sigma in PdI, also in Cz anterior sites on second night

CI, Chronic insomnia; Ctrl, control; Def’n, definition; Dx, diagnoses; FFT, fast Fourier transform; GNT, good night time (lights out); MDD, major depressive disorder; PdI, paradoxical insomnia; PDS, psychiatric disorders; PI, primary insomnia; PPI, psychophysiological insomnia; SO, sleep onset; SOI, sleep onset insomnia

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be temporal distortions of underlying sleep and wakening processes. Utilizing one standard time metric, or even just a fixed time-epoch measurement scheme, is a metaphysical choice, and possibly out of tune with underlying neuronal reality. Researchers of the SOP have yet to decide on the metaphysical “correct” temporal measurement perspective, even when starting the analysis from wake­ fulness. Researchers studying awakening processes face a worse metaphysical quagmire. So group-comparison generalizability showing differences between normal sleepers and poor sleepers is currently only a wish. The absence of plausible supporting neurobiological theory about group-level neuronal process differences (to guide the construction of a plausible statistical approach) makes general conclusions inappropriate. And even if statistically appropriate methods for treating processes could be established, there still remain serious concerns (Buysse et al., 1994) about misclassification biases affecting sleep disorder diag­ noses. With all this considered, proposing specific between-group generalities now would have high liability to making false claims to knowledge. Marzano and colleagues (2008) are correct to insist that drug effects be absent in the research subjects, if the bases for the SOP and awakening abnormalities are to be known in unmedicated subjects. But most patients come with the prior drug exposures. So it will be difficult to know how studies on drug-naı¨ ve patients would be generalizable to most patients.

II. Background Conceptual Features of Analysis of Realities About Sleep

A. AMBIGUITY

OF

“AWAKENING”

AND

“SLEEP”

IN

ORDINARY ENGLISH

Understanding “awakening” as a temporal word is a key issue. Like other temporal aspect words (Vendler, 2005) that allow flexibility in temporal refer­ ence, “awakening” is capable of being interpreted either as a process in which the endstate of being awake is not yet fully attained (e.g., as an activity now-moving toward getting awake), or as the endstate itself (e.g., the “being awake”). The former connotes a process-toward-waking with the outcome left unspecified, or elided. This permits the application of adjectives like “partial” or “failed” to some incomplete awakening event trains like that which occur in sleepwalking, or another parasomnia. Parasomnias are described as “partial awakenings” as a way of describing these ambiguous, in-between, brain “states.” Additionally, there are prepotent awakenings as well, as exemplified by awakening specifically to one’s child’s crying at night, made possible by a selective vigilance maintained during sleep. The K-complex itself is a well-known example of this diathesis. Other modes of intentional agency over the process of awakening also obtain.

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Latent intensional states (e.g., depression, anxiety), servicing as quasi-agencies, are said to harass deep sleep or sleep continuity. Such quasi-agential explanatory treatment of awakenings, as partial or intentional, loosens up possibilities of attributing awakenings’ causation(s) to temporally distant conscious intentions, processes, or states occurring during the daytime, and conversely, with awaken­ ings causing daytime disturbances. On the other hand, “awakening” can refer to the endstate of the arousing process from some state of sleep: One underwent a now-past arousing process, but now has arrived at being awake. In this usage, the endstate is the main conceptual focus, not the antecedent process, nor the antecedent state. The simple fact of an “awakening” means one was previously asleep, but gives no specifics about the previous kind of sleep or previous process(es). Taking “awakening” in this way treats awakening as an accomplishment, like winning a race. A sleep endstate is a complex entity, defined by the presence of several cotem­ poraneous values of intensive variables typical of that modeled state. It is a beha­ vioral state heralded by relative behavioral quiescence, postural recumbency, and reduced responsivity to environmental stimuli. The normative wake state, by con­ trast, is evidenced by a state of discriminative awareness, quick responsivity to environmental stimuli, and fluid, self-monitored enactment of complex behaviors. For many practical life problems, distinguishing between these sleep and wake states so defined has worked very well. But perhaps sleep and wake have been too obvious (few critical cross-cultural linguistic studies of sleep and wakefulness have been done). Perhaps sleep and wake are dogmatic constructs. Pathologically “awakening” could have been legislated free of its “partial” aspects in some discussions, in the service of some doctrinal mind–body (i.e., wake–sleep) dualism. In such a dualism, sleep and wake could then be seen as having stigmatized forms. Patients with such forms of experience would be subject to interpersonal stigmatization or self-stigma­ tization. From focus groups we have learned that this stigmatization is a real risk that patients have faced (Carey et al., 2005). But happily, one finds that “sleep,” “wake,” and other sleep/wake-related words at best refer to obscure accomplish­ ments even in good sleepers.

B. MEREOLOGICAL AMBIGUITIES OF

THE

TERM “SLEEP STATE”

In biological descriptions, “state” language cannot be taken literally. Organisms as alive are outcome-less processes in dynamic equilibria. Live organisms are not outcomed, dead, thermodynamic equilibria. But it is permissible to say organisms are in “dynamic states” if this phrase is understood as a figure of speech. At the neural level, the contradictory term “dynamic state” means that neurons express ongoing discharge patterns during both wake and sleep. These

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processes exist as dynamic equilibria conformable to Onsager equations (Katch­ alsky and Curran, 1965), but not as static chemical equilibria. Accordingly, “behavioral sleep state” refers to molar, “whole-organism” behavioral variables that seem grossly constant; while, in contrast, “neuronal sleep state” refers indirectly to fluctuating EEG waveforms. Awakening and sleeping, then, considered as idealized endstates according to a conventionalized ontology derived from phenomenal rules of classification, are not states when the analysis shifts to ontologies that involve discussions of the voltage behavior of neurons. Stipulating this conceptual antinomy is necessary to relate sleep states to the real-time behavior of dynamic neural networks without the risk of sophomoric contradictions. However, ranging across the ontological domains of gross behavioral descriptions down through genetic determinants of awakenings, such a discussion does unmask metaphysical contrasts. The different levels of causal analysis of awakenings invoke plural ontologies at different levels of description that require mereological vagueness in their interrelating, and also invoke plural temporalities that might arguably be mutually inconsistent. So it is important to recognize “awakenings” not only as having many common-lan­ guage usages, but also that various theories related to awakenings will have different state and process commitments that will be mutually incommensurable. When such incommensurate commitments arise, attempts at forming broader understandings will demand the use of “hand-waving” mereological treatments, so that there can be partially coherently relationships set up between the “parts” of the temporalized process-state coagulum to the “whole” of the explanandum. (Whether to consider light as a wave or as a particle is a familiar example of the part-to-whole problems in theories.) The two-process model of sleep regulation (Borbely, 1982, 2009) is a central example of a framework in which such metaphysical awareness plays a role in one’s ability to comprehend sleep/wake regulation.

C. PROCESS S AND PROCESS C AS EMPIRICALLY NECESSARY BUT CONTRADICTORY EXPLANATORY PRINCIPLES FOR UNDERSTANDING SLEEP REGULATION Process C and Process S have been elaborately verified. Process C refers to the Circadian timing of sleep propensity. Process S refers to the homeostatic regulation of Sleep that builds up from periods of unbroken wakefulness. These processes are described elsewhere in this volume, along with the nuclei and biology that supports them. Process C invokes an image of circular time, while Process S invokes that of a linear, Newtonian time. These two juxtaposed molar processes and their anatomic seats illustrate well the need for metaphysical awareness when teaching patients about their sleep problems. It is hard to

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imagine giving scientifically grounded advice about sleep problems without reference to them. The model invokes these process principles as hegemonic forces, but not as the totalized set of forces governing sleep. Such forces can be modified (Borbely, 2009) or overridden at times. Some investigators propose an additional process force of generalized hyperarousal at work in chronic insomnia patients, partially counteracting Processes C and S. So to relate these processes together in one unitary explanation, vague­ nesses of references to physiological forces are presumed, and artfulness at melding fundamentally incommensurable temporalities is required. An under­ standable metaphysical framework of discussion is needed, so that the clinician and the patient can both form an understanding of why the clinician is prescrib­ ing specific treatments.

D. NECESSARY INTER-LEVEL THEORETICAL VAGUENESSES AND INCOMMENSURATE TEMPORALIZATIONS With available clinico-pathological correlations between nuclei and physio­ logical functions, constructing an integrated understanding of Process S and Process C now requires the relation of two ontologies, one referencing particular neurons and neuronal subparts, to another referencing abstract principles/ forces that can be discussed with patients. However, the part-to-whole relation­ ships between the neurons’ behaviors and the whole-person functions involve vaguenesses, if only because of the need to address other physiological proces­ ses—possibly a disposition to vaguely referenced hyperarousal at this theoretical level—as well as the influence of random situational influences over sleep and wake. Similarly, how time is treated in this explanation requires some tolerance for side-by-side temporalizations. Process S connotes a conception of time as similar to linear clock time, according to the linear buildup of sleep pressure as the continuous time duration awake increases. This evokes a quasi-Newtonian con­ ception of time, as if time were a reified dimension existing without ambiguity across the universe. The Newtonian conception is consistent with the McTaggart B-series (McTaggart, 1993), which involves placing events in linear relations of before and after, from past to future, without repetition. By contrast, Process S connotes time as circular. While some anti-Kantians (Le Poidevin, 2003) abjure the possibility of circular time, circular time is a metaphysically necessary biolo­ gical reality. (Biological process–event identities deflate the presumed necessity of place–time identities of atomic states.) So within the two-process model are two conceptions of time that are mutually incommensurable, yet required, in the understanding of sleep propensity and awakenings.

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E. OTHER TEMPORALIZATIONS RELEVANT TO UNDERSTANDING CHRONIC INSOMNIA PATIENTS Not included in the two-process explanation is the McTaggart A-series of ordered past, present, and future. This is the “tensed” version of temporality. This more psychological kind of time assumes that a healthy person places herself into a fluidly complex, self-referencing temporal context. Despite claims to the unreality of tense (Mellor, 1993), most patients will implicitly think of their symptoms in relation to tense, rather than in a “clocktimed” way as autistic patients are wont to do (Boucher, 2001) Consistent with this notion, item-response theory analysis of the 65-item Pittsburgh Insomnia Rating Scale items has suggested (Moul et al., 2007b) that items querying the clocktimes of nocturnal events are not as important to patients in the grading of insomnia severity as are tensed items that ask about sleep quality and daytime fatigue. As the nosological definitions of chronic insomnia require, patients’ accounts about their awakening symptoms will bring into view their other temporalities that bear on their organization of narratives (Ricœur, 1984), grammars of role structures (ter Meulen, 1995), and resulting disabilities (L
eger et al., 2002). Such accounts will also be subject to various modifications arising from neurobiological constraints on time-referencing memory functions, compromising the literal truth value of any symptom account. So the clinician’s additional task will be to relate patients’ verbal accounts about awakening events, as they are experienced and reported, to a more general, science-based account of sleep and wakefulness. In order to motivate patients to comply with treatments, the clinician needs to be able to invent artful, individualized metaphysical treatments of patients’ complex tensed understandings in order to engage them with underpinning mid-level and neurobiological principles that rationalize clinical interventions. Patients need to be understood and engaged metaphysically if pathological awakenings are to be addressed and treated.

F. THE MNEMONIC

AND INTEGRATIVE

DUTIES

OF

SLEEP

Sleep has a duty to assist memory functions. During sleep, memory traces are strengthened, synapses pruned, and experiences integrated at the neuronal level (Walker, 2009). Sleep serves the hegemonic force of memory consolidation and integration. But unlike the account for whole-brain processes, memory processes during sleep are local and specific to brain areas that were active during a prior wakeful­ ness. At the EEG level, this is noted by local/regional changes in neuronal

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discharges during non-rapid eye movement (NREM) sleep (Hanlon et al., 2009) (yet another form of “partial awakening”?). Memory processing involves EEG dis­ charges in the gamma frequency band, surprisingly observed during NREM sleep (Destexhe et al., 2007; Steriade and McCarley, 2005). Such gamma band discharges are typical of wakefulness and neuronal “activation”, but during NREM sleep can occur on top of a slow oscillation typical of NREM sleep (Le Van Quyen et al., 2010). So the “day” jumps into the “night” during NREM sleep, both figuratively and literally. And since narrative dreams mostly occur during REM sleep, traumatic nightmares that replay past psychological traumas are also evi­ dence for the natural “wake-like” processes of neuronal memory processing during REM sleep. The success of nightmare therapy in treating nightmares (Krakow et al., 2001) is coherent with this general notion. Apparently sleep makes the consolidation and integration of memories more efficient, but locally to particular brain regions. Given each night’s specific local neuronal “activations,” sleep is never abso­ lutely self-identical from night to night, or moment to moment; Mutatis mutandis, neither are awakenings. During each night’s sleep, the presentist and localist neural activations tending toward awakenings in normal memory processing play along the nonlocal, network-wide sleep-propensity waveguides of Processes S and C. And so memory functioning complicates sleep metaphysics yet further. For the purposes of effectively classifying and treating chronic insomnias, a key problem will become that of deciding what rules of relative identity and of boundary conditions should apply to candidate awakening events, despite the gamma-band “micro-awakenings,” and other fast activities, associated with nocturnal memory management.

III. The Spielman three-factor High-Level Model of Insomnia and Mid-Level Therapeutic Theories of Insomnia Therapies

A. THE SPIELMAN MODEL: IMPLICATIONS FOR COGNITIVE BEHAVIORAL THERAPISTS One relatively successful attempt at finding a whole-person metaphysical systematization of causes of insomnia in chronic insomnia patients is Spielman’s 3-factor model (Spielman et al., 1987a). It is a metaphysical framework because these factors are classes of causes, rather than actual causes. Actual causes group together into these classes in this general stress-diathesis framework (Perlis et al., 2005). The three factors are predisposing factors, precipitating factors, and perpetuating factors. Predisposing factors include a person’s genetic and epigenetic makeup, but Spielman also includes general factors such as psychological temperament.

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Temperament would be seen as a general and more remote causal influence over chronic insomnia. Being female might be an example. Other predisposing causes might be longer-term processes shaping probabilities for insomnia over many years, but be elided in some explanations. Their effects might be considered only as developmental, socialization, or allostatic backgrounds. Preexisting psychiatric disorders such as previous posttraumatic stress disorder (PTSD), mood or anxiety disorders, or psychotic conditions are clearly predisposing. A person’s lifetime allostatic load of past nonpsychiatric stressors (Bliwise, 2005) might also be included among the predisposing factors, along with situations of emotional strain (e.g., being a caregiver, having a troubled work life, being in adverse socio­ economic circumstances). Whether a cause is predisposing or precipitating may amount to the choice of time period thought to be relevant for the particular explanation. Precipitating factors include the proximal causes of insomnia getting started. They include concretely experienced life events (e.g., significant losses, antici­ pated threats, excitements, pain, etc.). Most precipitating factors cannot be addressed clinically because by the time a clinician is consulted, they have already had their effects. So they are comparatively irrelevant as factors to be addressed or treated. Perpetuating factors are the sleep clinician’s focus. The wise clinician does not try to stamp out all isolated nights of poor sleep and awakenings, but rather, to treat those causes that serve to keep insomnia going from night to night to night (Spielman et al., 1987a). Perpetuating factors in essence invoke a temporal circularity of causes, i.e., limit cycles, which serve to keep the patient in a selfsustaining do-loop of insomnia and reactive counterresponding. The limit cycle has a telic attractor of chronic insomnia directed at “its” own self-perpetuation. Lady Macbeth’s limit cycle of worry and self-accusation is a related literary example. The clinical goal is to break up the perpetuating limit cycle, to allow other, healthier forces (i.e., Processes S and C) to be the major influences over sleep and wakefulness.

B. NONIGNORABLE PSYCHOLOGICALLY BASED MID-LEVEL THEORIES 1. Discriminate Stimuli as Leading to Awakenings Consistent with Spielman’s conceptual framework, mid-level theorists have proposed ways in which chronic insomnia patients get stuck in the chronic insomnia limit cycle. Bootzin (1972; Bootzin and Perlis, 1992) proposed a stimu­ lus control therapy model, based upon principles of conditioning, in which arousal responses are cued by stimuli associated with the bed or the bedroom. In this scenario, the chronic insomnia patient gets stuck trying to sleep in bed

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while awakening due to the somatic and other arousal habits cued by being in the bed. His treatment model of only allowing sleep and sex in bed is one of the most effective for chronic insomnia (Morgenthaler et al., 2006), even though its neu­ robiological mechanisms have not been confirmed experimentally (and may never be, since the stimulus–response linkages may be person specific). Spielman himself proposed sleep restriction therapy (Spielman et al., 1987b), in which a patient’s time in bed (TIB) is reduced even to the point of causing some sleep deprivation (in essence, squeezing out awakenings). By restricting TIB, the patient is placed into a regimen where sleep deprivation may override a variety of mechanisms perpetuating the insomnia, among them stimulus control limit cycling, but also others.

2. Limit-Cycling Cognitions Affecting Sleep Other theorists have focused on 24-h limit cycles. Morin’s systematized cognitive behavioral approach (Morin, 1993; Morin and Espie, 2003) focuses on the way that many insomnia patients’ cognitive distortions about sleeping lead to mental overactivation about poor sleeping that then leads to poor sleep, and further continued insomnia. This effective (Morgenthaler et al., 2006) therapy targets changing the distorted cognitions. Harvey (2002) has extended the cognitive behavioral conception further by a discussion of the psychology of safety behaviors (i.e., actions taken to ensure against the feared consequences) that insomnia patients employ. These behaviors ironically set up limit cycles of insomnia. For example, one “defensive” safety behavior is often to go to bed early, in the hope that giving oneself more TIB would permit sleep. But doing this only runs in the face of the forbidden zone of sleep (Lavie, 1986) that occurs in the early evening. Trying to sleep during this “forbidden” time only causes frustration, mental activation, and further insom­ nia. Her model encourages patients to de-cycle these effects by learning more about sleep, and by disconfirming their own theories for themselves through supervised behavioral experiments.

3. Sleep Behaviors and Cognitions Follow Operant Principles of Reinforcement For mid-level theorists, perpetuating factors are limit cycles, the “goals” of the pathological processes. Operant principles of conditioning are part of the psy­ chological mechanics of these limit cycles, particularly in reference to how extinction bursting “protects” the pathological limit cycle. There is commonly an extinction response burst increasing the to-be-extinguished behavior(s) just after non-reinforcement is imposed on an old habit and before the old frequency of responding decreases (Kearney, 2008; Lerman and Iwata, 1995). It is as if the

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old habit will “fight to survive.” This tendency gives rise to a behavioral inertia to keep an old habit when trying to change it. A key insight is that habits can be not only in behaviors, but also in feelings and thoughts. The limit cycle of habits leading to awakenings are no exception, so that awakenings can get worse before they get better, as one is breaking a habit of awakening. To get better from insomnia, a patient must get past the extinction barrier “put up” by the old habit. The sleep restriction approach nicely enhances the homeostatic drive to sleep (Process S) for a period imposed long enough to get past the habit barrier, overwhelming cognitive or other habit factors that have set up separate persisting limit cycles. But for other cycle-breaking interventions, patience and persistence are required. Alternatively, habit substitution may work. Side effects from extinc­ tion bursting can be fewer if additional treatments are included (Lerman et al., 1999). The method of paradoxical intention has the patient try the habit of staying just barely awake, instead of trying hard to go to sleep, so as to break the paradox (i.e., limit cycle) of having the high effortfulness toward sleep cause general bedtime arousal and awakening. Espie (Espie et al., 2006) has described a pattern in many insomnia patients in which the active intention to sleep is the paradoxical roadblock to sleeping. Intentions can be habits, too. Alas, insomnia is often not about sleep deprivation as such as it is about breaking weird limitcycles, when the insomnia is not due to brain trauma, toxicities, or neurodegen­ erative changes. 4. Mid-Level Theory Dependencies on the Notion of “ Hyperarousal” The forms of limit cycles are often felt to be insufficient by themselves as explanations, as some theorists want there to be a motive force that “turns the cranks” of the limit cycles. This often is “hyperarousal,” a word that provides political cover against the encroachments of reductionism. Some controlled comparisons of insomnia patients against matched controls have supported the conclusion that insomnia patients have more whole-body metabolic activity across 24 h (Bonnet and Arand, 1995) and brain glucose update during NREM sleep (Nofzinger et al., 2004), lending support to the idea of “hyperarousal.” While being cast as a force, this hyperaroused disposition is also thought of as an endstate to be changed so as to improve sleep. Riemann and colleagues (2010) provide a recent review of evidence for the hyperarousal literature. While hyperarousal theory may be now popular, the relationship between this dispositional “hyperaroused” endstate and the individual nighttime awaken­ ings is obscure, both because the ontological status of the one endstate to the other is not clarified experimentally and because the mereological relation between the 24-h circular conception of temporality and the linear temporality of the nighttime awakenings is unaddressed. To address the various proposed

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mid-level theories without vague appeals to hyperarousal, the decomposition of their temporal cycles into their component linearly temporalized parts will be necessary, as will the determinate references to physiological processes. (This has been done in a limited way for the linear processes within the transcription–translation limit cycle of the SCN in relation to the circadian limit cycle—see elsewhere, this volume.) To address the predictions of mid-level theories, experimental methods will be needed to confirm or test how the particular neurobiologies of stimulus–response pairings, semantic functions, fron­ tal executive functions, etc., are connected in one or more temporal linearity(ies) in series to explain the one or more causal–temporal circularities perpetuating a speci­ fically clinical insomnia-related pathology. Till now, mid-level theorists have used “hyperarousal” as a heuristic anchor point to permit hermeneutic spelunking into neurobiological realms. Obtaining more practical and grounded descriptions of chronic insomnia, though, will involve moving the focus of studies to the real-time behavior of neural networks during sleep and wakefulness—further away from the semantic anchor point of “hyperarousal.” By itself, “hyperarousal” is too nonspecific a characterization to be applicable to all chronic insomnia patients. Saying that insomnia patients are “hyperaroused” is well meaning, but not very helpful in practice.

IV. Cautions About Conceptual Transitions to the Theory Level of Neuronal Processes

With a change of focus to neural network-level of explanations, however, one cannot automatically assume that the nouns and verbs used in the therapeutically productive mid-level theories will have mereologically con­ cise relationships to processes and endstates as they can be described for neuronal discharge patterns and synaptic relationships. First, the behavioral endstates of sleep and wakefulness are not static endstates for the neurons themselves, but rather differing regimens of ongoing electrical activity. Sleep is an endstate in the mid-level theory, but a process in the neural network level of explanation. Inattention to exact references can give rise to misapplications of nouns (e.g., “state”) or of predicates (e.g., “aroused”) between theoretical levels, and result in muddled thinking. Similarly, care needs to be taken not to confuse findings across temporal domains (linear versus circular) and scale (sleep macrostructure versus sleep microstructure). It is bad enough that the mereological arrangements between levels of theories will demand vagueness in explanations, but it will be worse if one cannot be precise about what future theory levels are being discussed and how they may be best conceptually linked.

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V. An Aristotelian Method of Review

A. CORRELATION

WITH

SPIELMAN FACTORS

Spielman’s factors are three of the four categories of causes that Aristotle described in the Metaphysics (Aristotle, 1968). Aristotle’s four categories are sub­ stantial, formal, efficient, and final (telos in Greek). The first two of these cate­ gories do not implicate time, while the later two do. (for Aristotle, a cause was a principle that could be cited for why a think exists, or has Being.) Spielman’s predisposing factors are substantial causes, his precipitating factors are the effi­ cient causes, and the perpetuating factors are final causes (the “goal” or telos of the insomnia limit cycle). DFA, middle-of-night awakenings (MNAs), and EMA are formal-structural causes in this rendering. Aristotelian style review enables one to be more conscious of the need to consider main categories in an overall expla­ natory scheme. Honoring Aristotle, I will use his categories to review briefly other theoretical levels as well, particularly about NREM sleep, but with the stipulation that a given entity may be placed into one Aristotelian category at one theory level, but be placed into another category at another level (e.g., “awakening” as endstate-telos versus as process-efficient cause). The mereological coordination of theories requires such flexibility.

B. PROBLEMS EMERGING FROM RELATING THEORIES FROM DIFFERENT CONCEPTUAL LEVELS Several background problems can be anticipated. First, we cannot “abandon” or dismiss useful clinical theories simply because they are at super­ ordinate levels of explanation. All explanatory domains (even the ones I myself do not like) will need to be somehow related to one another if research and clinical practices are to be mutually adapted. Second, many causal categories will be relevant, but be of different types. Third, probabilistic causations should be preferred over presupposing law-like causations. Awakenings should be framed in relation to their probabilities. (Part of the insight into cognitive behavioral therapy is that patients want to suppose that there are laws of sleep that they can master, but these “laws” are just cognitive distortions that interfere with sleeping.) Fourth, use of 1:1 mappings of causes to phenomenal effects is probably unrea­ listic. One should avoid considering EEG signals as rigid 1:1 causal maps of awakenings or sleep. Finally, one can anticipate theories (Perlis et al., 1997) that describe feedback loops between a patient’s cognitions and neurobiological factors, even though they implicate the use of radically different explanatory

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ontologies. Harvey’s observation that the false-causality cognitions driving many insomnia patients’ safety behaviors act at odds with Processes C and S is already a clear example of this at the macrotheoretical level. The remaining parts of this chapter discuss ontologies below mid-level theories.

C. SUBSTANTIAL CAUSES (I.E., SUBSTANCES) For a clinician, a substantial cause of sleep and wakefulness would be chemi­ cals (substances) that affect sleep–wake functioning in some way. The pharma­ cology of awakenings is described elsewhere in this volume.

D. FORMAL CAUSES (I.E., STRUCTURES) 1. Nuclear Structures Other chapters describe more completely key normatively formal relation­ ships between brain nuclei that bear on sleep and awakenings in psychiatric patients. Among these are the general ascending reticular activating system, with its rostrally projecting neurotransmitter pathways; the relationships between the ventro-lateral preoptic nuclei, the hypocretin system, and the brainstem nuclei; the SCN-governed circadian control system and its circuit output channels; and the pontine control system for the NREM/REM cycling. Several internuclear linkages appear to have relations of bi-stable switching (Saper et al., 2001; Siegel, 2005), which in turn help to explain normal limit cycles found in wakefulness and sleep. There are also linkages between the hippocampus and brain cortical regions (O’Neill et al., 2010) that subserve nocturnal memory consolidation and likely have some role in nocturnal awakenings. Damage to or frailty of the formal elements of brain circuitry undoubtedly leads to fragmented sleep/wake states with awakenings, circadian misalignments, or frank sleep/wake arrhythmicity. Ancillary structures are also pertinent to consider in an account about pathological awakenings. When considering the causes of insomnia, one cannot avoid considering the biology of fear responding for its relevance for awakenings. Fear-related awakenings are a frequent problem for patients with PTSD and other psychiatric disorders. Fear responding is partly regulated by the amygdala. Studies support its role in biasing the sleep/wake system toward constant vigi­ lance against threat, even during sleep (Benca et al., 2000). There are mutual innervations between amygdala and brainstem nuclei (Price, 2003), so that the amygdala’s physiological functions have regulatory influence affecting stress responding in the hypothalamus and pons. An overactive amygdala has axonal

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outflow channels to bias the sleep/wake system toward wakefulness and awakening. People also worry. Worrying responses can arise without overt environmental stimuli and may not necessarily be aversive for the worrier. Worrying may at times be ego-syntonic and appetitive. There is a distinction between normal and pathological worry. The neural circuitry involved in worrying will likely involve several cortical regions. Worrying is partly linked to intentional semantic proces­ sing. Imaging studies (Paulesu et al., 2010; Schienle et al., 2009) point to increased general brain activity in the anterior cingulate and orbital prefrontal cortex in pathological worriers, a group who are likely to suffer from chronic insomnia. Brain regions that mediate semantic processing are also relevant for the under­ standing of pathological awakenings, if only by reason of the fact that semanti­ cally based worrying interferes with sleep, and worrying involves memory processing. What neurophysiological observations might support the stimulus control theory of insomnia? As far as acquisition of a stimulus–response association is concerned, it would seem difficult to pin down any one brain region as the culprit, since many brain regions aside from the hippocampus are involved in forming stimulus–response associations. However, candidate areas might be those linked more tightly to threat responding to neural pathways with high bandwidth, or to nuclei that are proximal to the regulation of NREM sleep. However, it may be more important (per Spielman’s rationale, discussed above) to identify the nuclei involved in impaired habit extinction. Mouse and human genetic variants of brain-derived neurotrophic factor point to atypical frontoamygdala activity in these subjects (Soliman et al., 2010); however, other evidence points to involvement of the mediodorsal nucleus of the thalamus, orbital prefrontal cortex, and amygdala, but not the nucleus accumbens (Izquierdo and Murray, 2010). Extinction may depend partly upon factors related to REM sleep (Spoormaker et al., 2010). The amygdala has metacircuit influences from the prefrontal cortex, which allows a person a means to override automatic fear responding. In PTSD patients, some evidence (Shin et al., 2006) points to frailty in this controlling circuit, affecting both the inability to decondi­ tion from fear stimuli and also possibly the inability to sleep without awakenings. While studies are pointing to abnormalities in the prefrontal-to-thalamic circuit that are involved in impairments to extinction, the semantic maps that provide discrimination to the fear responding also need better clarification. Psychothera­ pies often address such cognitive/affective mappings. Since worry involves a kind of self-sustaining habit-like semantic processing, it seems that linkages to and from language processing circuits will be involved with the extinction of sleepimpairing worrying. Also, since patients experience their symptoms in semantic tenses, any pathophysiology involved with abnormalities of the tensing of experiences needs development too. Furthermore, while worry is often habitual,

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semi-intentional, and sometimes appetitive, the role that executive frontal regions play in selecting for the appetitive aspects of worry remains obscure. Some neuroimaging studies (Nofzinger et al., 2004, 2006) suggest that chronic insomnia patients suffer from persistent frontal metabolic activation. It is also possible that cerebellar, labyrinthine, or other brain regions may be important for understanding the persistence of awakenings and insomnia in some patients. This brief review of possible structural–psychological–functional rela­ tionships implicated in chronic insomnia suggests that clinicians cannot assume that only one kind of regional neuropathology will be involved across all patients with chronic insomnia.

2. Some Relevant Network and Neuron Structures Sleep medicine has used the EEG to measure sleep more objectively. To understand the EEG waveforms in NREM sleep, an understanding of the thalamocortical circuit between particular cell types is required. Figure 1 displays the thalamocortical circuit as depicted by Amzica and Steriade (2002). Cells from the reticular nucleus of the thalamus (RTN) provide gamma-amino butyric acid (GABAergic) stimulus to thalamocortical (ThCx) cells. ThCx cells have main glutamatergic output to cortical (Cx) cells, but also back-collaterals to RTN cells. Cx cells in turn have glutamaturgic feedback axons to both ThCx and RTN cells (Steriade and McCarley, 2005). Of special note, RTN cells have dendrodentritic synapses with each other, which greatly enhance their characteristic of coordi­ nated, en masse burst firing during NREM sleep. These connections are relevant for understanding observable EEG sig­ nals. The connections between the thalamus and other areas inaccessible to EEG observation are less researched. Limbic–thalamic connections may be distinct, or nonexistent, in relation to sleep spindles or other NREM EEG waveforms in humans (Nakamura et al., 2003). If so, then a person could be defined as asleep by conventional PSG criteria, but self-report that he/she “did not sleep at all,” and be truthful! Since limbic cortex is involved with emotional functioning, this may be where one “feels” sleep, sleepiness, or fatigue. Or, intriguingly, recently Buysee (personal communication) has a potential finding that suggests it might be in the precuneus nucleus where sleep perception may occur. Wherever it may be located, a hidden sleepperception “nucleus” might explain the biological basis for the diagnosis of paradoxical insomnia (American Sleep Disorders Association, 2005)—a com­ plaint of not having slept despite behavioral and/or PSG evidence for having slept. If hidden biology is the structural cause, then paradoxical insomnia would be just another mundane example of partial awakening rather than a target for stigmatization and clinical puzzlement.

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KC + spindle Cx Cx b-c

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FIG. 1. (Continued )

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Another structural level is worth discussing in more detail. At the neuronal level of explanation, hyperpolarization-induced T-type calcium-channel depolar­ ization in some neuron types plays key roles in sleep physiology. When opened, this ion channel will stay “stuck” open for a microperiod, letting a calcium ion influx to activate calcium-dependent potassium ion channels (Steriade and McCarley, 2005). These in turn set in motion a membrane voltage depolarizing cascade. This membrane voltage behavior occurs more spontaneously in RTN cells than in ThCx or Cx cells. The hyperpolarization gating of the calcium current in RTN cells sets up a kind of membrane voltage cycling, as follows. The gradual depolarization leads to a low-threshold spike that gives rise to a brief burst of repetitive action potentials. This burst spiking is followed by rehyper­ polarization and cycle repetition. This cycling occurs both in RTN cells and, with RTN cell GABAergic stimulus, also in ThCx cells. In the millisecond domain, these series of events are linearly chained processes, and their circular chaining serves as a limit cycle time structure, or cell membrane clock. On the EEG this limit cycle behavior is observed as a sleep spindle. Changes in ion channel behavior change the membrane clock’s behavior. Three are worth mentioning here. First, in RTN and ThCx cell dendrites, benzodiazepine receptor occupancy will cause normal GABA-mediated chlor­ ide-channel openings to be more persistent than otherwise, giving rise to a hyperpolarization of the postsynaptic membrane. The clock’s frequency is increased. This is one reason why benzodiazepine receptor agonists (BZRAs) are used as sleeping pills, in that they encourage the hyperpolarization associated with initiating NREM sleep. These agents cause an increased rate of spindles on the EEG (Bazil, 2002), the EEG sequela of ThCx cell spike-bursting. Second, evidence exists for the nuclear specificity of benzodiazepine receptors. In rats, zolpidem has been shown to predominantly modulate the ThCx cell and not the RTN cell, whereas eszopiclone modulates the RTN but not the ThCx cell (Jia et al., 2009). Differential changes in the membrane clocks of the RTN and ThCx cells may set up conditions for a higher rate of parasomnias with zolpidem than with eszopiclone (Dolder and Nelson, 2008). Third, the Cx-to-ThCx glutama­ turgic circuit is thought to be metabotropic rather than ionotropic (Crunelli and

FIG. 1. Several modes of electrical relationships between thalamocortical circuit elements. The thalamocortical (ThCx) cell, cortical (Cx) cell, and thalamic reticular (RE in figure) nucleus cell form a network involved in forming sleep spindles and other wave forms observed on the EEG channel of a PSG. RE cells have dendrodentritic synapses with each other, whereas ThCx and Cx cells do not. Cx cells have feedback axons to RE and ThCx cells. ThCx cells have feedback axons to RE cells. In panels A, B, and C, different microtemporal connections are in force, as separately depicted. In each case, the discharge patterns can bear a surface resemblance to each other as slow or delta waves, but arise from differing microtemporal conditions of cellular response in the three circuit components (copied with permission from Amzica and Steriade, 2002).

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Hughes, 2010). This is thought to facilitate the relative cross-coordination of spindle expression in relation to K-complexes (see below) insofar as the alteration in ion channel behavior would affect how fast one part of the cell membrane clock would run through its portion of the limit cycle.

E. EFFICIENT CAUSES (I.E., PROCESSES, CONTEMPORARILY UNDERSTOOD “CAUSES”) In contemporary parlance (contra Aristotle), “causes” are conventionally limited to Aristotle’s efficient (“process”) causes occurring in linear time. It is statistically impossible to study processes as processes (see above), so usually process causes are statistically modeled as dyadic contrasts between antecedent and consequent event “states.” In clinical contexts, the efficient causes of awa­ kenings are taken to be events like obstructive apneas or other measurable events. In this construction, awakenings are taken to be the simple results of the pre­ sumed processes originating from the antecedent events. Clinical arousals and awakenings are conceptualized in relation to small time epochs with prompt antecedent–consequent linkages. However, awakenings are also considered in relation to longer epochs extending from the prior day’s experiences (being unable to sleep well after an exciting day, etc.). Longer time periods during “aroused” sleep have been documented by greater high-frequency EEG power during sleep, even for the “first night effect” in first night of PSG studies. Shortand long-period processes causing arousals thus appear abundant, but their actual process details remain obscure. For the SOP itself, we do know that at sleep onset the hyperpolarizing of the thalamic cells is partly the result of the disfacilitation of wakefulness arising from reduced depolarizing stimulation from the brain stem (Timofeev et al., 2001). Additionally, there is evidence for direct hyperpolarizing stimuli from the ante­ rior hypothalamus (Steriade and McCarley, 2005). When RTN cells reach sufficiently hyperpolarizing voltages, their cell membrane clocks set up coordi­ nated burst-mode firing that delivers inhibitory postsynaptic potentials (IPSPs) to the ThCx cell. The volley of IPSPs further hyperpolarizes the ThCx cell, and leads to its own burst-mode firing, now coordinated with the RTN mass-coordi­ nated firing pattern. On the cortex this is observed as a spindle: The patient is now in stage N2 sleep. As part of the NREM sleep process, the neocortex has its own intrinsic slow (<1 Hz) rhythm (Crunelli and Hughes, 2010) that becomes more apparent during NREM sleep. In this slow rhythm, the Cx cell goes through a DOWN phase, which is a period of action potential silence, followed by an UP phase, a period of comparable depolarization. This is in essence another, but slower, cell membrane clock behavior. The back-connections to RTN and ThCx cells from Cx cells are

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already becoming active as NREM is getting underway, so that the thalamic cells are exposed to a metabotropic stimulus at the frequency of the slow (~0.5 Hz) rhythm. The RTN and ThCx cells are also thought to be capable of slow rhythms, but as dependent upon the cross-coordination provided by metabotro­ pic stimulation from Cx cells. With sufficient momentum into NREM sleep, the thalamocortical network finds modes of temporalized firing resonance, as depicted in Fig. 1 (Amzica and Steriade, 2002), back and forth between the cortex and the thalamus. Depending on the specific timing, a given signaling from one neuronal group to another may give rise to mass cortical discharging observed on the EEG as coordinated slow waves (as in a K-complex), as delta (1–4 Hz) waves, or as spindles. Since the momentary responsiveness of a circuit element is semi-chaotic, the EEG patterns are not rigidly stereotyped, but have semi-regularities. One such semi-regularity is that of a sleep spindle following along the UP phase of a slow wave, riding on a K-complex. Since its discovery, the K-complex was known to be inducible by environ­ mental stimuli. Researchers long puzzled over whether the K-complex is not a micro-awakening. Yet it is one of the desiderata of stage N2 sleep! How can a micro-awakening be a marker of solid sleep? The functional telos of the K-complex has been debated from the time of its first observation (Colrain, 2005). There have been other persisting clinical puzzles. Why is it true that for some patients, BZRAs are arousing, rather than sleep promoting? They increase spindling, but why then do they tend to decrease N3 sleep (i.e., deeper NREM sleep) and increase the level of beta power in the EEG (Bazil, 2002). For mice, low-dose BZRAs are activating, whereas higher doses are sedating (Pellow and File, 1987). Some patients say that BZRAs give some but not especially restora­ tive sleep: Does this mean that BZRAs have hidden awakening properties? As already mentioned, zolpidem, an alpha-1-specific BZRA acting on ThCx cells, does not inhibit deeper stages of sleep as much, but tends to give rise to para­ somnias. These paradoxes about BZRAs as hyperpolarizing agents not having causal determinacy in inducing deep sleep (many patients’ fondest wish!), and indeed sometimes causing arousal-like events, call into question the idea that NREM sleep can be thought of simply as a hyperpolarized “state.” Along this line of skepticism, Terzano and others (2005) advocated that there is actually a pattern of “activation” seen in sleep called the cyclic alternating pattern (CAP). CAPs are widely enough observed that a scoring atlas (Terzano et al., 2002) has been published for them. In a CAP event, an initial series of deltawave bursts occurs in NREM sleep, followed after a few seconds by a quick frequency shift up to beta (16þ Hz) frequencies for a short period of several seconds. A CAP has been proposed as a kind of short awakening, particularly because of the fast activity associated with it. CAP events can be graded for their severity, so that more frequent and longer CAPs are associated with insomnia

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(Terzano et al., 2003), bruxism (Kato et al., 2003; Zucconi et al., 1995), and other clinical problems. These CAPs are thought to occur even in the absence of other external causes of awakenings. The presence of CAPs may not affect scoring judgments about stages of sleep. They may occur during conventionally scored, architecturally “sound” sleep. One can suppose a general interpretive direction in understanding K-complexes and CAPs if one can consider, in reference ideas depicted in Fig. 1, a process-understanding of the EEG waveforms. It has been proposed that for “stimulated” K-complex events, the stimulus “hits” the cortex at the right prepotent moment during its slow rhythm cycle, so as to time-synchronize the thalamocortical network to produce a K-complex and its trailing elements (Amzica and Steriade, 2002; Colrain, 2005). Often spindles can be seen occurring in the rebound-negative portion of the K-complex, implying that the spindle was time-locked by the K-complex. This is thought to occur because of Cx-to-ThCx cell depolarizing stimulation (Crunelli and Hughes, 2010). When considering that the RTN, ThCx, and Cx neurons mutually influence each other’s synaptic responding, one could expect membrane time-linked response coordination between them. But to expect this, one would also expect that NREM sleep is not a literal “clamped” voltage state for neurons, but rather a membrane voltage response process that involves rhythmic membrane-voltage cycling under certain conditions of circuit resonance. Fig. 1 presents some network response scenarios of Amzica and Steriade concerning NREM EEG wave packets. It is remarkable that several different patterns of network connectivity can give rise to nominally equivalent slow and delta waves, as far as conventional EEG scoring is concerned. Considering this multiplicity of connectivities, it is hard to say that there could be a simple identity proposed between a specific waveform sequence (say CAP) and a “conventional awakening.” “Conventional awakening” seems conceptually and linguistically impoverished here, yet apparently some sequences of waveforms like CAPs, if they occur too much, make people feel poorly during the daytime. While possibly pathological, these CAP transients do not conform to sleep or wake state classi­ fication: They are literally neither “state,” and yet both “states,” at the same “time.” Being too attached to “state” language may have obscured our understanding of BZRA actions. Trying to make chronic insomnia patients feel better by giving them BZRAs to drive them into a sleep “state” may have been missing the therapeutic target. The constant, unremitting state-domain presence of a BZRA on its receptor may not be biologically natural for neurons, if the normal biology of neurons during NREM sleep implicates that both (1) the individual neurons are not usually in a substance-induced membrane voltage clamp that prevents them from toggling naturally between relatively hyperpolarized and depolarized membrane voltages and (2) the network of neurons electrically

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interacting with each other is hindered in their mutual reactivity if voltage clamped by BZRAs, so as to prevent certain wave packets from forming across the neuronal circuit. That most BZRAs would increase spindling rate at the expense of deltawave expression would therefore be expected, if Amzica and Steriade’s proposal is correct. Similarly, the prevention of circuit resonance in delta frequencies might appear as increased beta frequency power, or prevent some individuals from getting therapeutically essential resonance patterns of NREM sleep itself. It would also be no surprise that an agent that acts only on one portion of the circuit (e.g., zolpidem) would make possible circuit responses that would be abnormal enough to lead to sleepwalking. Across the night, the pattern of normal responses may well involve moments of “partial awakening” in a time-limited voltage series, but which would then be prone to finding limit cycles of more durable partial awakening, if the right drugs are present. This suggests that too much stable limit cycle resonance, without any developing resonance entropy, would be undesirable (e.g., causing sleepwalking) or dangerous (i.e., causing seizures) during NREM sleep (Pearlmutter and Houghton, 2009). Some “Nicomachean” moderation of circuit resonance alongside circuit disresonance may be vital to healthy sleep, a circumstance which might mereologically impli­ cate some ironic neuroprotective role for “arousals.” These considerations point to the need to conceptualize sleep as involving milli- or micro-temporalities. It might be best to avoid conceptualizing sleep, even microstructurally, as a synchronic state. Rather, it may be better to conceptualize “objective” EEG sleep as an inordinate ensemble of time-limited mass-action neuronal firing processes that appear on EEG channels as epiphenomenal pat­ terns, as classified in conventionalized, fixed-time epochs (e.g., 30 s). Such is a “state” only in a manner of speaking. Exemplifying the diachronic perspective is the CAP phenomena. CAPs can be scored for their severity, into types A1, A2, and A3 (see Fig. 2). These are graded types where the higher grades of CAPs contain more dramatic slow waves and more prolonged “arousal-like” phenomena. CAP researchers have presented data pointing to CAP rate increases occurring in insomnia patients (Terzano et al., 2003), and there is some evidence that sleep bruxism is more associated specifically with CAP type A3 (Kato et al., 2003). By the description of CAPs, part of the process involves “deeper sleep” waves followed by “arousal” wave patterns. The diachronous features of CAPs are used as the data for their classification, and their grades relate to the pathology of the phenomena of complaints, but not necessarily to gross awakening “states.” If one consider the processes described in Fig. 1 in relation to the structural fact that ThCx cells do not have the dendrodentritic connections with each other the way RTN cells do, then it becomes easier to understand how a delta-wave Cx–ThCx resonance in a CAP would be more likely to decompose after several “beats” of a delta-wave rhythm, if the population of ThCx cells came to be

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A1

C4-A1 EOG EMG EKG

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FIG. 2. Grades of cyclic alternating pattern (CAP). A1, A2, and A3 describe grades of increasing severity of CAPs. In a CAP A phase, there is an initial alteration in the EEG signal characteristics toward expression of delta (1–4 Hz) or slow (<1 Hz) waves, followed by some expression of faster activity in the EEG signal. In the progression of severity from A1 to A3, a key diachronic difference is the time length of the faster activity, before the EEG signal returns to a more normal NREM background (copied with permission from Terzano et al., 2002).

mistimed to each other. In such a circumstance, their depolarizing output to cortex would also become disresonant, and potentially give rise to a greater likelihood that Cx cells would migrate into a brief depolarized, or simply dis­ organized, pattern of discharging. This disordered resonance would appear on the EEG as a faster, desynchronized wave pattern appearing to be more like an awakening or arousal. But, owing to NREM background influences, this cortical de-resonance would eventually be overcome by the pressure to return to coordi­ nated firing and return to the usual NREM EEG appearance. This may or may not be akin to an awakening returning to sleep. The irony in this account would be that the initial “stimulus” to get to these disorganized CAP “microarousals” were delta waves in the initial portions of the CAP that one would suppose to be deeper “sleep,” if one were to suppose that delta waves necessarily represent a deeper sleep “state.” However, these “deeper sleep delta-wave packets” may well be driven by autochthonous cortical slow wave processes. That is, it may be wrong to presuppose that Cx cells do not

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normally have the effect of setting in motion electrical events that would be deeper sleep but for the discoordination that might well occur in any large, massively distributed signaling network like the glomerularized thalamocortical network. In such a scenario it would be as erroneous to claim that a CAP represented a teleological cortical “arousal” as it would be to claim that it represented a teleological “intention to sleep more deeply.” At the neural net­ work level, it is simply a process, without reference to the English words “arousal” or “sleep.” The prevalence of various types of CAPs during a night may reflect latent pathological network processes that extend over an entire sleep cycle. This might result in higher power in higher EEG frequency domains across a sleep cycle (Buysse et al., 2008b), but would not allow distinguishing whether its causation would be a more distal “mental activity during sleep” or just a proximal neuronal circuit resonance abnormality. For the consideration of CAP rates, the neuro­ cognitive model (Perlis et al., 1997) might be quite plausible, but, without a developed account of neurocognitive biology, such a model will remain largely speculative, in a similar way to how the stimulus-control theory is plausible, but remains neurobiologically obscure.

F. TELIC CAUSES (TELOS, FINAL CAUSES, LIMIT CYCLES, OUTCOMES, GOALS, AND NEEDS) Final causes are entities taken to be self-evidently justified and finalizing of a complete explanation. A telic cause can be considered as a conceptual organizing principle, binding an explanation together in an intelligible and practical, but only virtual package. Aristotle meant telos was as an entity moving things toward it “from the future,” but such is not what is meant in current usage. In Mayr’s (1988) consideration of telic causes in discussions about biological evolution, he instead used the term “teleonomic” to describe the general telic cause notion, and offered the term “teleomatic” for cases where the teleonomic explanation appeared especially compelling. A example of a teleomatic cause would be that of moths changing their coloration when residing in soot-infested trees: The process involved is that the sooty-appearing moths are less vulnerable to pre­ dators because of their coloration, but the teleomatic “shorthand” construction would have the soot “causing” the moths to change their coloration, yet without the teleological privilege Aristotle might have supposed for the soot’s causal “power.” Teleonomic explanatory arrangements are, therefore, as a practical matter, unavoidable in biologically relevant explanations, but are not to be understood as anything mystical arising from some intention originating in the future (e.g., future fitness of the species).

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Understood in this qualified sense, a telic cause can be considered as a selfevident static endstate of an antecedent efficient causal process, but with such prior process details elided. Or it can be considered as a stable circular-temporal process pattern following a telic attractor of linearly looped efficient causes (e.g., like the loop of transcription/translation events in the SCN, or as a system of mutually balanced but antagonistic energies residing in a chemical equili­ brium). Without something like a telic cause in an explanation, no explanation would be practicable, as there would be no intermediate or final outcomes in which to orient the explanation. Unanchored processes would simply go on and on and on without an end-of-process reference, that is, without an outcome to orient a causal story. From psychiatric and sleep medicine standpoints, the nocturnal awakenings of patients are a central clinical issue for numerous disorders. These awakenings are (telic) outcomes, whether considered as intermediate or as essential targets of treatment interventions. For the primary insomnia patient, DFA, MNAs, and EMAs can be intermediate outcomes giving rise to bitter complaints, affecting impaired mindfulness and motivations (also outcomes) during the daytime, as described in official definitions. In sleep medicine, other kinds of complete or partial outcome awakenings include those for sleep apnea, sleepwalking, and REM behavior disorder, among others. For psychiatric disorders more generally, the impacts of objective and perceived stressors include nighttime awakenings across more complex disorders of major depression disorder, manic phase of bipolar illness, generalized anxiety disorder, PTSD, and other disorders. But these disorders only add to the collection of limit cycles (i.e., outcomes) of daytime and nighttime events locked into patterns of circular telic causation. Not all disorders causing nocturnal outcome awakenings, judging from their symptom pictures and neurobiologies, are the same, so their processes of producing these outcome awakenings cannot be assumed to be the same either. But as foreshadowed in the DSM-IV definition of primary insomnia, there is also a distinction between hard outcomes and soft outcomes (Checkland, 1981). One can measure the objective presence of a frequency change on an EEG channel (i.e., a hard outcome); but in clinical practice what often counts is the appraisal (i.e., a soft outcome) that the patient makes about awakenings, even if measured objectively. The temporal difference is that the hard outcome, whether viewed from past, present, or future, never changes in its limits or description; whereas the appraisal for the same event (e.g., how one thinks about a specific past experience to lead to speech acts about it) may indeed change. To address awakenings as clinical problems involves their conceptualization as both hard and soft outcomes. So the antinomy of outcome types gives rise to chronic problems for validating diagnoses and treatment strategies. Telic awakenings span across a wide range of supervenient clinical syn­ dromes. The efficient causes (process mechanisms) of different syndromes that

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accomplish telic awakenings are surely heterogeneous. Many unexplored neuro­ nal process heterogeneities probably underlie not only awakenings, but also other syndromal outcomes. We now work mostly on just the PSG’s EEG channels. With this limitation, we are faced with Plato’s analogy of the cave, but with two additional complications, regarding diagnostic typing of the awakenings. On the EEG one sees only the shadows of neuron network processes, but the “arousals” one sees might be outside the realms of “sleep” and “wake” as we normally understand them, and what may be most clinically important is not the objective phenomena we can see, but the neurobiology of the patient’s appraisals about the events, however characterized. It seems that for advancements in the understanding of what “awakening” means clinically that there be a effort to develop some clinical nosology, as anchored at least in the microstructural (<5 s epoch) domain, for subjunctively classifying neurotemporal events associated with suspected awakenings. In doing such a nosology, the phasic patterns in the EEG signal stream during NREM sleep will need to be related to putative thalamocortical network electrical event series that have some basis in empirical neuroscience. Such a project may not be practicable now, but continuing to look at EEG waveform patterns as a way of understanding awakenings without doing so will not be any more diagnostic than it has already been (Morgenthaler et al., 2006). Developing this nosology will be a laborious bootstrap process, in which it will undergo many subjunctive respeci­ fications over historical time. Present methods of studying person-level awaken­ ings will be unlikely to shed much new light on understanding or managing clinical conditions in which awakenings, in the common-English sense, are a major teleonomic component. The nosology will need to be also constructed with a view that normal memory consolidation processes may play a role in nocturnal awakenings. While this review has focused on NREM awakenings, similar con­ ceptual concerns arise for the domain of REM sleep awakenings.

VI. Conclusion

Psychotherapies for insomnia reduce awakenings, but the psychology-based mid-level theories underpinning them remain relatively separated from theories of neuronal network functioning. Brain-regional explanations of sleep–wake functioning are helping to move clinical understanding into biological domains of explanation. Theories of neuronal network function are starting to relate basic neuroscience concepts to problems of pathological awakenings, now increasingly clarified with network-level theories. Naturally enough, further research work needs doing on these separate levels, both conceptually and empirically. But as

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new results come in, practicing clinicians will require better meta-theories to relate the different theories to each other, if biological and psychological thera­ pies are to be interdigitated in practice. Developing better practical, integrative rationalities for the discriminative uses of psychotherapies, medications, and other interventions for particular sleep medicine and psychiatric disorders remains a priority. In developing these new understandings, new conceptual frameworks will need to be invented, and old concepts may need to be histor­ icized. Among these historicized concepts is that of the generic awakening itself. Out damn awakening! The future of “awakening” may be just that of an expletive, in which concept specificity is elided in favor of affect display.

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THE NEUROCHEMISTRY OF AWAKENING: FINDINGS FROM SLEEP DISORDER NARCOLEPSY

Seiji Nishino and Yohei Sagawa Sleep and Circadian Neurobiology Laboratory, Stanford University School of Medicine, Stanford, CA 94304-5489, USA

I. II. III. IV. V. VI. VII.

VIII. IX.

X. XI.

Introduction Neurobiology of Wakefulness Narcolepsy and Symptoms of Narcolepsy Discovery of Hypocretin Deficiency and Postnatal Cell Death of Hypocretin Neurons Idiopathic Hypersomnia, Hypocretin Non-deficient Primary Hypersomnia Symptomatic Narcolepsy and Hypersomnia: Hypocretin Involvements How Does Hypocretin Ligand Deficiency Cause the Narcolepsy Phenotype? A. Hypocretin/Orexin System and Sleep Regulation B. Hypocretin/Orexin Deficiency and Narcoleptic Phenotype Considerations for the Pathophysiology of Narcolepsy with Normal Hypocretin Levels Changes in Other Neurotransmitter Systems in Narcolepsy and Idiopathic Hypersomnia A. Narcolepsy in Dogs and Humans B. Idiopathic Hypersomnia Involvements of Histaminergic Neurotransmission in Human Narcolepsy and Other Hypersomnia Conclusion Acknowledgments References

Recent progress in our understanding of the pathophysiology of excessive sleepiness (EDS) is particularly indebted to the 1999 discovery of narcolepsy genes (i.e., hypocretin receptor and peptide genes) in animals and the subse­ quent discovery of hypocretin ligand deficiency in idiopathic cases of human narcolepsy-cataplexy. Hypocretin deficiency is also involved in many cases of symptomatic narcolepsy and EDS. Changes in other neurotransmitter systems (such as monoamines and acetylcholine) previously reported in these conditions are likely to be secondary to the impaired hypocretin neurotransmission; however, these may also mediate the sleep abnormalities seen in hypocretin deficient narcolepsy. The pathophysiology of hypocretin non-deficient narcolepsy is debated. Similarly, the pathophysiology of idiopathic hypersomnia, another defined primary hypersomnia, is largely unknown. INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 93 DOI: 10.1016/S0074-7742(10)93010-9

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This chapter discusses our current understanding of the neurochemistry of EDS, a disease of awakening.

I. Introduction

In this review, we discuss our current understanding of the neurochemistry of excessive sleepiness (EDS) (i.e., disease of awakening). Our recent progress in understanding the pathophysiology of EDS is particularly indebted to the 1999 discovery of narcolepsy genes (i.e., hypocretin receptor and peptide genes) in animals and the subsequent discovery (in 2000) of hypocretin ligand deficiency in idiopathic cases of human narcolepsy-cataplexy. The discovery in human narco­ lepsy lead to (1) the establishment of a new diagnostic test (i.e., low CSF hypocretin-1 levels) and (2) development of hypocretin replacements to be used in the treatment of hypocretin deficient narcolepsy. Further refinement of this therapeutic option is the focus of current, ongoing research (see Nishino et al., 2009a). The prevalence of primary hypersomnia, such as narcolepsy and idiopathic hypersomnia, is not high (0.05 and 0.005%, respectively), but the prevalence of symptomatic (secondary) hypersomnia may be much higher. For example, sev­ eral million subjects in the USA suffer from chronic brain injury. Seventy five percent of these patients have sleep problems and about half complain of sleepi­ ness (Verma et al., 2007). By comparison, the prevalence of symptomatic narco­ lepsy is likely to be much smaller and only about 120 such cases have been reported in the literature in the past 30 years. Nevertheless, meta-analysis of these cases indicates that hypocretin deficiency may also partially explain the neuro­ chemical mechanisms of both symptomatic EDS and EDS associated with symptomatic cases of narcolepsy (Nishino and Kanbayashi, 2005). The recent discovery of hypocretin peptidergic systems in 1998, followed by the discovery of narcolepsy genes only a year later, immediately prompted and illuminated related studies, seeking specific understanding of the roles of hypocretin peptidergic systems in sleep regulation under both normal and pathological conditions. Anatomical and functional studies demonstrate that the hypocretin systems integrate and coordinate multiple wake-promoting systems (such as monoamine and acetylcholine systems) to keep subjects fully alert (Jones, 2005). Histamine is one of these wake-active monoamines, and, notably, low CSF histamine levels are also found in narcolepsy with hypocretin deficiency (Kanbayashi et al., 2009a; Nishino et al., 2009b). Since hypocretin neurons project and excite histamine neurons in the posterior hypothalamus, it is conceivable that impaired histamine neurotransmission may mediate sleep abnormalities in hypocretin deficient

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narcolepsy. However, low CSF histamine levels were also observed in narcolepsy with normal hypocretin levels and in idiopathic hypersomnia, a primary hypersomnia not associated with hypocretin deficiency (nor with rapid eye movement [REM] sleep abnormalities). Thus, decreased histamine neuro­ transmission may be involved in the broader category of EDS rather than in hypocretin deficient narcolepsy (Kanbayashi et al., 2009a). Since CSF histamine levels are normalized in EDS patients treated with wake-promoting compounds, low CSF histamine levels may be a new state marker for primary hypersomnia. The functional significance of this finding requires further study (Kanbayashi et al., 2009a). A large majority of patients with diagnosed EDS are currently treated with pharmacological agents, such as amphetamine-like compounds and modafinil. These treatments are symptomatic cures, and treatments that restore the primary impairments are not yet available. In this regard, further knowledge of the neuro­ chemistry of EDS/awakening will likely lead to the development of new treatments and management strategies for patients with hypersomnia with various etiologies.

II. Neurobiology of Wakefulness

In order to help in the understanding of the neurochemistry of hypersomnia, we will discuss current understandings of the neurobiology of wakefulness. Sleep/wake is a complex physiology regulated by brain activity, and multiple neurotransmitter systems such as monoamines, acetylcholine, excitatory and inhibitory amino acids, peptides, purines, and neuronal and non-neuronal humoral modulators (i.e., cytokines and prostaglandins) ( Jones, 2005) are likely to be involved. Monoamines are perhaps the first neurotransmitters recognized to be involved in wakefulness ( Jouvet, 1972), and the monoaminergic systems have been the most common pharmacological targets for wake-promoting compounds in the past years. On the other hand, most hypnotics target the gamma­ aminobutyric acid (GABA)nergic system, a main inhibitory neurotransmitter system in the brain (Nishino et al., 2004a). Cholinergic neurons also play critical roles in cortical activation during wakefulness (and during REM sleep) ( Jones, 2005). Brainstem cholinergic neu­ rons originating from the laterodorsal and pedunculopontine tegmental nuclei activate thalamocortical signaling, and cortex activation is further reinforced by direct cholinergic projections from the basal forebrain. However, currently no cholinergic compounds are used in sleep medicine, perhaps due to the complex nature of the systems and prominent peripheral side effects. Monoamine neurons, such as norepinephrine (NE) containing locus coeruleus neurons, serotonin (5-HT) containing raphe neurons, and histamine

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containing tuberomammillary neurons are wake-active and act directly on cor­ tical and subcortical regions to promote wakefulness ( Jones, 2005). In contrast to the focus on these wake-active monoaminergic systems, researchers have often underestimated the importance of dopamine (DA) in promoting wakefulness. Most likely, this is because the firing rates of midbrain DA-producing neurons (ventral tegmental area [VTA] and substantia nigra) do not have an obvious variation according to behavioral states (Steinfels et al., 1983). In addition, DA is produced by many different cell groups (Bjo¨ rklund and Lindvall, 1984), and which of these promote wakefulness remains undetermined. Nevertheless, DA release is greatest during wakefulness (Trulson, 1985), and DA neurons increase discharge and tend to fire bursts of action potentials in association with significant sensory stimulation, purposive movement, or behavioral arousal (Ljungberg et al., 1992). Lesions that include the dopaminergic neurons of the VTA reduce behavioral arousal ( Jones et al., 1973). Recent work has also identified a small wake-active population of DA-producing neurons in the ventral periaqueductal grey that project to other arousal regions (Lu et al., 2006). People with DA deficiency from Parkinson’s disease are often sleepy (Moller et al., 2000), and DA antagonists (or small doses of DA autoreceptor (D2/3) agonists) are fre­ quently sedating. These physiological and clinical findings clearly demonstrate that DA also plays a role in wakefulness. Wakefulness (and various physiologies associated with wakefulness) is essential for the survival of creatures and thus is likely to be regulated by multiple systems, each having a distinct role. Some arousal systems may have essential roles for cortical activation, attention, cognition, or neuroplasticity during wakefulness while others may only be active during specific times to promote particular aspects of wakefulness. Some of the examples may be motivated-behavioral wakefulness or wakefulness in emergency states. Wakefulness may thus likely be maintained by many systems with differential roles coordinating in line. Similarly, the wake-promoting mechanism of some drugs may not be able to be explained by a single neurotransmitter system.

III. Narcolepsy and Symptoms of Narcolepsy

As narcolepsy is a prototypical EDS disorder, and since the major pathophy­ siology of narcolepsy (i.e., deficient in hypocretin neurotransmission) has recently been revealed, a discussion of the neurochemical aspects of narcolepsy will also help establish a general understanding of the neurochemistry in EDS. Narcolepsy patients manifest symptoms specifically related to the dysregula­ tion of REM sleep (Nishino and Mignot, 1997). In the structured, cyclic process

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of normal sleep, two distinct states—REM and four stages (S1, S2, S3, S4) of nonREM (NREM) sleep—alternate sequentially every 90 min in a cycle repeating 4–5 times per night (Nishino et al., 2004b). As electroencephalogram signals in humans indicate, NREM sleep, characterized by slow oscillation of thalamocor­ tical neurons (detected as cortical slow waves) and muscle tonus reduction, precedes REM sleep, when complete muscle atonia occurs. Slow wave NREM predominates during the early phase of normal sleep, followed by a predomi­ nance of REM during the later (Nishino et al., 2004b). Notably, sleep and wake are highly fragmented in narcolepsy, and affected subjects cannot maintain long bouts of either state. Normal sleep physiology is currently understood as dependent upon the coordination of the interactions of facilitating sleep centers and inhibiting arousal centers in the brain, such that stable sleep and wake states are maintained for specific durations (Nishino et al., 2004b). An ascending arousal pathway, running from the rostal pons and through the midbrain reticular formation, promotes wakefulness (Nishino et al., 2004b; Saper et al., 2005). This arousal pathway may be composed of neuro­ transmitters (acetylcholine, NE, DA, excitatory amino acids), produced by brain­ stem and hypothalamic neurons (hypocretin/orexin and histamine) and also linked to muscle tonus control during sleep (Nishino et al., 2004b; Saper et al., 2005). Whereas full alertness and cortical activation require coordination of these arousal networks, effective sleep requires suppression of arousal by the hypothalamus (Saper et al., 2005). Narcolepsy patients may experience a major neurolo­ gical malfunction of this control system. Narcoleptics exhibit a phenomenon, termed short REM sleep latency or sleep onset REM period (SOREMP), in which REM sleep is entered more immedi­ ately upon falling asleep than is normal (Nishino and Mignot, 1997). In some cases, NREM sleep is completely bypassed and the transition to REM sleep occurs instantly (Nishino and Mignot, 1997). Moreover, intrusion of REM sleep into wakefulness may explain the cata­ plexy, sleep paralysis, and hypnagogic hallucinations which are cardinal symp­ toms of narcolepsy. Significantly, however, whereas paralysis and hallucinations are manifest in other sleep disorders (sleep apnea syndromes and disturbed sleep patterns in normal population) (Aldrich et al., 1997), cataplexy is pathognomonic for narcolepsy (Nishino and Mignot, 1997). As such, identifying cataplexy’s unique pathophysiological mechanism emerged as potentially pivotal to describ­ ing the pathology underlying narcolepsy overall. More than 90% of patients diagnosed with narcolepsy receive pharmacological treatments. The pharmacological treatments of EDS include amphetamine-like central nervous system (CNS) stimulants and modafinil (and its r-enantiomer), but these are symptomatic treatments and do not cure the disease, and often unsatis­ factory for some patients due to the side effects and incomplete efficacy (see Nishino and Mignot, 2005).

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IV. Discovery of Hypocretin Deficiency and Postnatal Cell Death of Hypocretin Neurons

The significant roles of, first, hypocretin deficiency and, subsequently, post­ natal cell death of hypocretin neurons as the major pathophysiological process underlying narcolepsy with cataplexy emerged from a decade of investigation, employing both animal and human models. In 1998, the simultaneous discovery by two independent research groups of a novel hypothalamic peptide neuro­ transmitter (variously named hypocretin and orexin) proved pivotal (De Lecea et al., 1998; Sakurai et al., 1998) (Fig. 1). These neurotransmitters are produced A.

signal sequence

GKR

GRR

Propro-hypocretin (Prepro-orexin)

Hypocretin-1 (Orexin A)

Hypocretin-2 (Orexin B) HcrtR2 (OX2R)

HcrtR1 (OX1R) Receptors

Gq Gi/Go

Gq

B.

Acetylcholine Noradrenalin Serotonin Dopamine Histamine

Cortex Thalamus (1=2)

BF

Ach

DR

(1>2)

LDT PPT LC (1 only)

(1<2)

VLPO POA

(1=2) Glu

Hcrt LHA

(1<2)

Activation Inhibition

Glu

TMN (2 only)

VTA SN

RF (1=2) (1<2)

Pons

Glu

Spinal cord

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exclusively by thousands of neurons, which are localized in the lateral hypotha­ lamus and project broadly to specific cerebral regions and more densely to others (Peyron et al., 1998) (Fig. 1). Within a year, Stanford researchers, using positional cloning of a naturally occurring familial canine narcolepsy model, identified an autosomal recessive mutation of hypocretin receptor 2 (Hcrtr 2) responsible for canine narcolepsy, characterized by cataplexy, reduced sleep latency, and SOREMPs (Lin et al., 1999). This finding coincided with the simultaneous observation of the narco­ lepsy phenotype, characterized by cataplexic behavior and sleep fragmentation, in hypocretin ligand-deficient mice (prepro-orexin gene knockout mice) (Chemelli et al., 1999). Together, these findings confirmed hypocretins as princi­ pal sleep-modulating neurotransmitters and prompted the investigation of hypocretin system involvement in human narcolepsy. Although screening of patients with cataplexy failed to implicate hypocretin­ related gene mutations as a major cause of human narcolepsy, narcoleptic patients did exhibit low cerebrospinal fluid (CSF) levels of hypocretin-1 (Nishino et al., 2000) (Fig. 2). Postmortem brain tissue of narcoleptic patients, assessed through immunochemistry, radioimmunological peptide assays, and in situ hybridization, revealed hypocretin peptide loss and undetectable levels of hypocretin peptides or pre-hypocretin RNA (Fig. 2). Further, melanin-concentrating hormone neurons, normal to the same brain region (Peyron et al., 2000), were observed intact, thus indicating that damage to hypocretin neurons and produc­ tion is selective in narcolepsy, rather than due to generalized neuronal degeneration. As a result of these findings, a diagnostic test for narcolepsy, based on clinical measurement of CSF hypocretin-1 and detected hypocretin ligand deficiency, is now available (ICSD-2, 2005). Whereas CSF hypocretin-1 concentrations above 200 pg/ml almost always occur in controls and patients with other sleep and neurological disorders, concentrations below 110 pg/ml are 94% predictive of

FIG. 1. (A) Structures of mature hypocretin-1 (orexin-A) and hypocretin-2 (orexin-B) peptides. (B) Schematic representation of the hypocretin (orexin) system. (A) The topology of the two intrachain disulfide bonds in orexin-A is indicated in the above sequence. Amino acid identities are indicated by shaded areas. (B) The actions of hypocretins are mediated via two G protein-coupled receptors named hypocretin receptor 1 (Hcrtr 1) and hypocretin receptor 2 (Hcrtr 2), also known as orexin-1 (OX1R) and orexin-2 (OX2R) receptors, respectively. Hcrtr 1 is selective for hypocretin-1, whereas Hcrtr 2 is nonselective for both hypocretin-1 and hypocretin-2. Hcrtr 1 is coupled exclusively to the Gq subclass of heterotrimeric G proteins, whereas in vitro experiments suggest that Hcrtr 2 couples with Gi/o, and/ or Gq (adapted from Sakurai Sakurai, 2002) VTA, ventral tegmental area; SN, substantia nigra; LC, locus coeruleus; LDT, laterodorsal tegmental nucleus; PPT, pedunculopontine tegmental nucleus; RF, reticular formation; BF, basal forebrain; VLPO, ventrolateral preoptic nucleus; LHA, lateral hypothalamic area; TMN, tuberomamillary nucleus; DR, dorsal raphe; Ach, acetylcholine; Glu, glutamate; GABA, gamma-aminobutyric acid.

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a

CSF Hypocretin 1 Levels (pg/ml)

800

600

b

Familial case

f

f

400 DQB1*0602 (-)

c

d

200 DQB1*0602 (-)

0 Narcolepsy Neurological (n=38) Control (n=19)

Control (n=15)

f

f

FIG. 2. Hypocretin deficiency in narcoleptic subjects. (A) CSF hypocretin-1 levels are undetectably low in most narcoleptic subjects (84.2%). Note that two HLA DqB1*0602-negative and one familial case have normal or high CSF hypocretin levels. (B) Preprohypocretin transcripts are detected in the hypothalamus of control (b) but not in narcoleptic subjects (a). Melaninconcentrating hormone (MCH) transcripts are detected in the same region in both control (d) and narcoleptic (c) sections. f and fx, fornix. Scale bar represents 10 mm (a–d), (adapted from Peyron et al., 2000).

narcolepsy with cataplexy (Mignot et al., 2002). As this represents a more specific assessment than the multiple sleep latency test (MSLT), CFS hypocretin-1 levels below 110 pg/ml are indicated in the ICSD-2 as diagnostic of narcolepsy with cataplexy (ICSD-2, 2005). Moreover, separate coding of “narcolepsy with cataplexy” and “narcolepsy without cataplexy” in the ICSD-2 underscores how the discovery of specific diagnostic criteria now informs our understanding of narcolepsy’s nosology; narcolepsy with cataplexy, as indicated by low CSF hypocretin-1, appears etio­ logically homogeneous and distinct from narcolepsy without cataplexy, exhibited by normal hypocretin levels (Mignot et al., 2002). Further, the potential of hypocretin receptor agonists (or cell transplantation) in narcolepsy treatment is currently being explored, and CFS hypocretin-1 measures may be useful in identifying appropriate patients for a novel therapeutic option, namely hypocre­ tin replacement therapy. Soon after the discovery of human hypocretin deficiency, researchers identified specific substances and genes, such as dynorphin and neuronal activity-regulated pentraxin (NARP) (Crocker et al., 2005) and, most recently, insulin-like growth factor binding protein 3 (IGF BP3) (Honda et al., 2009), which colocalize in neurons containing hypocretin. These findings underscored selective hypocretin cell death

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as the cause of hypocretin deficiency (as opposed to transcription/biosynthesis or hypocretin peptide processing problems) because these substances are also deficient in the postmortem brain lateral hypothalamic area (LHA) of hypocretin deficient narcoleptic patients (Crocker et al., 2005; Honda et al., 2009). Further, these findings, in view of the generally late onsets of sporadic narcolepsy compared with those of familial cases, suggest that postnatal cell death of hypocretin neurons constitutes the major pathophysiological process in human narcolepsy with cataplexy. Narcolepsy is associated with the human leukocyte antigen (HLA)­ DQB1*0602 allele (Mignot et al., 1997). Many have therefore hypothesized that narcolepsy is caused by an autoimmune process that kills the hypocretin/orexin­ producing neurons. This perspective was further reinforced recently by the observation that narcolepsy is also associated with a polymorphism in the T-cell receptor alpha gene (Hallmayer et al., 2009), but still, direct evidence for an autoimmune process has been lacking. Quite interestingly, Cvetkovic-Lopes et al. (2010) found that some patients with narcolepsy have elevated levels of antibodies against a protein known as Tribbles homolog 2 (TRIB2) (Fig. 3). TRIB2 is produced in hypocretin neurons, and TRIB2 is also known to be a potential autoimmune target in some patients with autoimmune uveitis. Anti­ TRIB2 titers seemed higher in the first 2 years after the onset of narcolepsy. The results were immediately replicated with two independent studies (Kawashima et al., 2010; Toyoda et al., 2010), and these exciting researches may be leading toward some of the firmest evidence yet for an autoimmune cause of narcolepsy. The possibility that TRIB2 antibodies are a consequence of hypocretin neuron loss and not a cause should further be evaluated (see Lim and Scammell, 2010). Hypocretin targeted therapy, such as hypocretin replacement therapy, is awaited for the treatments of hypocretin deficient narcolepsy/EDS, but as of yet is not available. Attempts to treat narcolepsy patients at the disease onset with immunotherapy, including plasma changes and IVIG with some positive results, is reported in small case reports (Chen et al., 2005; Dauvilliers et al., 2004; Plazzi et al., 2008). However since no control studies have been done yet, further evaluations are critical.

V. Idiopathic Hypersomnia, Hypocretin Non-deficient Primary Hypersomnia

With the clear definition of narcolepsy (cataplexy and dissociated manifesta­ tions of REM sleep), it became apparent that some patients with hypersomnia suffer from a different disorder. Bedrich Roth was the first in the late 1950s and early 1960s to describe a syndrome characterized by EDS: prolonged sleep and sleep drunkenness with the absence of “sleep attacks,” cataplexy, sleep paralysis,

2.6 P < 3 x10–9

2.4

Relative Trib2-specific antibody titer

2.2

P < 0.0001

2

P < 0.01

1.8

P < 0.001

1.6

P < 0.01

1.4 1.2 1 0.8 0.6 0.4 Narcolepsy Cataplexy

Narcolepsy without cataplexy

Control

Idiopathic hypersomnia

Multiple sclerosis

OIND

FIG. 3. ELISA determination of Trib2-specific antibodies in sera. Each symbol corresponds to the serum of a single subject. Mean + 1 SD of each group is shown next to the individual values. The dotted horizontal line indicates the mean Trib2-specific antibody titer in healthy control subjects plus 2 SD. All values are relative to the optical density of a healthy control subject (which is equal to 1). P values correspond to independent t-tests between indicated groups. OIND, other inflammatory neurological diseases (from Cvetkovic-Lopes et al., 2010).

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and hallucinations. Although the terms “independent sleep drunkenness” and “hypersomnia with sleep drunkenness” were initially suggested (Roth, 1962), this syndrome is now categorized as idiopathic hypersomnia with and without long sleep time (ICSD-2, 2005). Idiopathic hypersomnia should not be considered synonymous with hypersomnia of unknown origin. In the absence of systematic studies, the prevalence of idiopathic hypersomnia is unknown. Nosologic uncertainty causes difficulty in determining the epide­ miology of the disorder. Recent reports from large sleep centers reported a 1:10 ratio of idiopathic hypersomnia to narcolepsy (Bassetti and Aldrich, 1997). The age of onset of symptoms varies, but is frequently between 10 and 30 years. The condition usually develops progressively over several weeks or months. Once established, symptoms are generally stable and long lasting, but spontaneous improvement in EDS may be observed in up to one quarter of patients (Bassetti and Aldrich, 1997). The pathogenesis of idiopathic hypersomnia is unknown. Hypersomnia usually starts insidiously. Occasionally, EDS is first experienced after transient insomnia, abrupt changes in sleep–wake habits, overexertion, general anesthesia, viral illness, or mild head trauma (Bassetti and Aldrich, 1997). Despite reports of an increase in HLA DQ1, 11 DR5, Cw2, and DQ3, and of a decrease in Cw3, no consistent findings have emerged (Bassetti and Aldrich, 1997). The most recent attempts to understand the pathophysiology of idiopathic hypersomnia relate to the potential role of the hypocretins. However, most studies suggest normal CSF levels of hypocretin-1 in idiopathic hypersomnia (Bassetti et al., 2003; Mignot et al., 2002). Thus, it is now confirmed that the pathophysiology of idiopathic hypersomnia is distinct from that of narcolepsy.

VI. Symptomatic Narcolepsy and Hypersomnia: Hypocretin Involvements

Narcolepsy symptoms can also occur during the course of other neurological conditions (i.e., symptomatic narcolepsy), and the discovery of hypocretin ligand deficiency in idiopathic narcolepsy has led to new insights into the pathophysiol­ ogy of symptomatic (or secondary) narcolepsy and EDS. In a recent meta-analysis, 116 symptomatic narcolepsy cases reported in the literature were analyzed (Nishino and Kanbayashi, 2005). As several authors have previously reported, inherited disorder (n = 38), tumors (n = 33), and head trauma (n = 19) are the three most frequent causes for symptomatic narcolepsy. Of the 116 cases, 10 are associated with multiple sclerosis (MS), one with acute disseminated encephalomyelitis, and relatively few with vascular disorders (n = 6), encephalitis (n = 4), degeneration (n = 1), and heterodegenerative disorder (autosomal

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dominant cerebrospinal ataxia w/deafness, three cases in one family). Although it is difficult to rule out the comorbidity of idiopathic narcolepsy in some cases, literature review reveals numerous unquestionable cases of symptomatic narco­ lepsy (Nishino and Kanbayashi, 2005). These include cases with HLA negative and/or late onset and cases where occurrence of narcoleptic symptoms parallels the rise and fall of the causative disease. Notably, the review of these cases (particularly those with brain tumors) clearly illustrates that the hypothalamus is most often involved (Nishino and Kanbayashi, 2005). Also, quite a few EDS cases without cataplexy or REM sleep abnormalities (defined as symptomatic cases) are associated with these neurological conditions (Nishino and Kanbayashi, 2005). While the same review lists about 70 sympto­ matic EDS cases, prevalence of symptomatic EDS is likely much higher. For example, several million USA subjects suffered chronic brain injury, and 75% experienced sleep problems and about 50% reported sleepiness (Verma et al., 2007). Thus, symptomatic EDS may have significant clinical relevance. CSF hypocretin-1 measurement was also conducted in these symptomatic narcolepsy and EDS cases, and reduced CSF hypocretin-1 levels were noted in most with various etiologies (Nishino and Kanbayashi, 2005). EDS in these cases is sometimes reversible with an improvement of the causative neurological dis­ order or hypocretin status, thus suggesting a functional link between hypocretin deficiency and sleep symptoms in these patients. Low CSF hypocretin-1 concentrations were also found in some immunemediated neurological conditions, namely subsets of Guillain–Barre syndrome (Nishino et al., 2003), Ma2-positive paraneoplastic syndrome (Overeem et al., 2001), and MS (Nishino and Kanbayashi, 2005), and EDS is often associated with the patients with low CSF hypocretin-1 levels. Of note, Kanbayashi et al. (2009b) recently experienced seven cases of EDS occurring in the course of MS patients initially diagnosed with symmetrical hypothalamic inflammatory lesions together with hypocretin ligand deficiency that contrasts with the characteristics of classic MS cases (Fig. 4). Symptomatic narcolepsy in MS patients has been reported for several dec­ ades. Since both MS and narcolepsy are associated with the HLA-DR2 positivity, an autoimmune target on the same brain structures has been proposed to be a common etiology for both diseases (Poirier et al., 1987). However, the discovery of the selective loss of hypothalamic hypocretin neurons in narcolepsy rather indi­ cates that narcolepsy coincidently occurs in MS patients when MS plaques appear in the hypothalamic area and secondarily damage the hypocretin/orexin neurons. In favor of this interpretation, the hypocretin systems are not impaired in MS subjects who do not exhibit narcolepsy (Ripley et al., 2001b), although MS patients frequently show other sleep problems such as insomnia, parasomnia, and sleep-related movement disorders (Tachibana et al., 1994). Nevertheless, it is also the case that a subset of MS patients predominantly show EDS and REM sleep

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#1

MRI scan gender/age hcrt-1 level (pg/mL)

#3

FLAIR F/45* < 40

Anti-AQP4

#2

MRI scan gender/age hcrt-1 level (pg/mL) Anti-AQP4

#5

FLAIR F/43** 190 Anti-AQP4 (+)

#4

FLAIR F/21* < 40

#7

T2 F/45* 106

FLAIR M/61 173

Anti-AQP4 (+)

#6

T2 F/45* 91

FLAIR F/54** 184

Anti-AQP4 (+)

FIG. 4. Magnetic resonance imaging findings (FLAIR or T2) of MS/NMO patients with hypocretin deficiency and EDS. A typical horizontal slice including the hypothalamic periventricular area from each case is presented. The gender (male [M] and female [F]), ages (years), as well as CSF hypocretin levels are listed below the MRI image. All cases were initially diagnosed as MS. Cases 3–5 exhibited optic neuritis and/or spinal cord lesions and are seropositive for anti-AQP4 antibody and thus being diagnosed as NMO. *Met with ICSD-2 criteria for narcolepsy because of medical condition, and **met with ICSD-2 criteria for hypersomnia because of medical condition.

abnormalities, and it is likely that specific immune-mediated mechanisms may be involved in these cases. CSF hypocretin measures revealed that marked (110 pg/ml, n = 3) or moderate (110–200 pg/ml, n = 4) hypocretin deficiency was observed in all seven cases (Kanbayashi et al., 2009b). Therefore, four cases met with ICSDII criteria (ICSD-2, 2005) for narcolepsy due to a medical condition, and three cases met with the hypersomnia criteria due to a medical condi­ tion. Interestingly, four of them had either or both optic neuritis and spinal cord lesions, sharing the clinical characteristics of Neuromyelitis optica (NMO). HLA was evaluated in only two cases (case 2 and case 4) and was

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negative for DQB1*0602. Repeated evaluations of the hypocretin status were carried out in six cases, and CSF hypocretin-1 levels returned to the normal levels or significantly increased with marked improvements of EDS and hypothalamic lesions in all six cases. Since four of them exhibited clinical characterization of NMO, anti-AQP4 antibody was evaluated and it was found that three out of seven cases were anti-AQP4 antibody positive, thus being diagnosed as an NMO-related disorder (Kanbayashi et al., 2009b). AQP4, a member of the AQP super-family, is an integral membrane protein that forms pores in the membrane of biological cells (Amiry-Moghaddam and Ottersen, 2003). Aquaporins selectively conduct water molecules in and out of the cell while preventing the passage of ions and other solutes and are known as water channels. AQP4 is expressed throughout the central nervous system, especially in periaqueductal and periventricular regions (Amiry-Moghaddam and Ottersen, 2003; Pittock et al., 2006) and is found in non-neuronal structures such as astrocytes and ependymocytes, but is absent from neurons. Recently, the NMO-IgG, which can be detected in the serum of patients with NMO, has been shown to selectively bind to AQP4 (Lennon et al., 2005). Since AQP4 is enriched in periventricular regions in the hypothalamus where hypocretin-containing neurons are primarily located, symmetrical hypothalamic lesions associated with reduced CSF hypocretin-1 levels in our three NMO cases with anti-AQP4 antibody might be caused by the immuno-attack to the AQP4, and this may secondarily affect the hypocretin neurons. However, another four MS cases with EDS and hypocretin deficiency were anti-AQP4 antibody negative at the time of blood testing. This leaves a possibility that other antibody-mediated mechanisms are additionally respon­ sible for the bilateral symmetric hypothalamic damage causing EDS in the MS/NMO subjects. There is a possibility that the four MS cases whose anti­ AQP4 antibody was negative could be NMO, since anti-AQP4 antibody was tested only once for each subject during the course of the disease and the assay was not standardized among the institutes (Kanbayashi et al., 2009b). It is thus essential to further determine the immunological mechanisms that cause the bilateral hypothalamic lesions with hypocretin deficiency and EDS and their association with NMO and AQP4. This effort may lead to estab­ lishment of a new clinical entity, and the knowledge is essential to prevent and treat EDS associated with MS and its related disorders. It should also be noted that none of these cases exhibited cataplexy, contrary to the 9 out of 10 symptomatic narcoleptic MS cases reported in the past (Nishino and Kanbayashi, 2005). Early therapeutic intervention with steroids and other immunosuppressants may thus prevent irreversible damage of hypocretin neurons and prevent chronic sleep-related symptoms.

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VII. How Does Hypocretin Ligand Deficiency Cause the Narcolepsy Phenotype?

Because hypocretin deficiency is a major pathophysiological mechanism in narcolepsy-cataplexy, some discussion of how hypocretin ligand deficiency may cause the narcolepsy phenotype is warranted.

A. HYPOCRETIN/OREXIN SYSTEM

AND

SLEEP REGULATION

Hypocretins/orexins were discovered by two independent research groups in 1998. One group called the peptides “hypocretin” because of their primary hypotha­ lamic localization and similarities with the hormone “secretin” (De Lecea et al., 1998). The other group called the molecules “orexin” after observing that central administration of these peptides increased appetite in rats (Sakurai et al., 1998). Hypocretins/orexins (hypocretin-1 and hypocretin-2/Orexin-A and Orexin-B) are cleaved from a precursor preprohypocretin (prepro-orexin) peptide (De Lecea et al., 1998; Sakurai, 2002; Sakurai et al., 1998)). Hypocretin-1 with 33 residues contains four cysteine residues forming two disulfide bonds. Hypocretin-2 consists of 28 amino acids and shares similar sequence homology, especially at the C-terminal side, but has no disulfide bonds (a linear peptide) (Sakurai et al., 1998). There are two G-protein-coupled hypocretin receptors—Hcrtr 1 and Hcrtr 2, also called orexin receptor 1 and 2 (OX1R and OX2R). The distinct distribution of these receptors in the brain is known: Hcrtr 1 is abundant in the locus coeruleus (LC) and Hcrtr 2 is found in the tuberomamillary nucleus (TMN) and basal forebrain. Both receptor types are found in the midbrain raphe nuclei and mesopontine reticular formation (Marcus et al., 2001). Hypocretins-1 and -2 are produced exclusively by a well-defined group of neurons localized in the lateral hypothalamus. The neurons project to the olfactory bulb, cerebral cortex, thalamus, hypothalamus, and brainstem, parti­ cularly the LC, raphe nucleus, as well as to the cholinergic nuclei (the laterodorsal tegmental and pedunculopontine tegmental nuclei) and cholinoceptive sites (such as pontine reticular formation), which are thought to be important for sleep regulation (Peyron et al., 1998; Sakurai, 2002). A series of recent studies have now shown that the hypocretin system is a major excitatory system, which affects the activity of monoaminergic (dopamine [DA], norepinephrine [NE], serotonin [5-HT] and histamine) and cholinergic systems, significantly affecting vigilance states (Sakurai, 2002; Willie et al., 2001). Thus, it is likely that a deficiency in hypocretin neurotransmission induces an imbalance between these classic neurotransmitter systems, with primary effects on sleep-state organization and vigilance.

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Many measurable activities (brain and body) and compounds manifest rhyth­ mic fluctuations over a 24-h period. Whether or not hypocretin tone changes with Zeitgeber time was assessed by measuring extracellular hypocretin-1 levels in the rat brain CSF across 24-h periods, using in vivo dialysis (Yoshida et al., 2001). The results demonstrate the involvement of a slow diurnal pattern of hypocretin neurotransmission regulation (as in the homeostatic and/or circadian regulation of sleep). Hypocretin levels increase during the active periods and are highest at the end of the active period, with the levels declining at sleep onset. Furthermore, sleep deprivation increases hypocretin levels (Yoshida et al., 2001). Electrophysiological studies have shown that hypocretin neurons are active during wakefulness and reduce the activity during slow wave (Lee et al., 2005). The neuronal activity during REM sleep is the lowest, but intermittent increases in the activity, associated with body movements or phasic REM activity, are observed (Lee et al., 2005). In addition to this short-term change, the results of microdialysis experiments also suggest that basic hypocretin neurotransmission fluctuates across the 24-h period and slowly builds up toward the end of the active period. Adrener­ gic LC neurons are typical wake-active neurons, involved in vigilance control, and it has been demonstrated that basic firing activity of wake-active LC neurons also significantly fluctuates across various circadian times (Aston-Jones et al., 2001).

B. HYPOCRETIN/OREXIN DEFICIENCY

AND

NARCOLEPTIC PHENOTYPE

Human studies have demonstrated that the occurrence of cataplexy is closely associated with hypocretin deficiency (Mignot et al., 2002). Furthermore, the hypocretin deficiency was already observed at very early stages of the disease ( just after the onset of EDS), even before the occurrences of clear cataplexy. Occurrences of cataplexy are rare in acute symptomatic cases of EDS, associated with a significant hypocretin deficiency (see Nishino and Kanbayashi, 2005); therefore, it appears that a chronic and selective deficit of hypocretin neuro­ transmission may be required for the occurrence of cataplexy. The possibility of an involvement of a secondary neurochemical change, related to the occurrence of cataplexy, cannot be ruled out. If some of these changes are irreversible, hypocretin supplement therapy may only have limited effects on cataplexy. Sleepiness in narcolepsy is most likely due to the patients’ difficulty in main­ taining wakefulness as normal subjects do. The sleep pattern of narcoleptic subjects is also fragmented; they exhibit insomnia (frequent wakening) at night. This fragmentation occurs across 24 h, and, thus, the loss of hypocretin signaling likely plays a role in this vigilance stage stability (see Saper et al., 2001), but other mechanism may also be involved in EDS in narcoleptic subjects. One of the most

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important characteristics of EDS in narcolepsy is that sleepiness is reduced, and patients feel refreshed after a short nap; however, but this does not last long and patients become sleepy within a short period of time. Hypocretin-1 levels in the extracellular space and in the CSF of rats fluctuate significantly across 24 h and build up toward the end of the active periods (Yoshida et al., 2001). Several manipulations (such as sleep deprivation, exercise, and long-term food depriva­ tion) are also known to increase hypocretin tonus (Fujiki et al., 2001; Yoshida et al., 2001). Thus, the lack of this hypocretin build-up (or increase), caused by circadian time and by various alerting stimulations, may also play a role in EDS associated with hypocretin deficient narcolepsy. Mechanisms for cataplexy and REM sleep abnormalities, associated with impaired hypocretin neurotransmission, have been studied. Hypocretin signifi­ cantly inhibits REM sleep in vivo, but could activate all brainstem REM-off LC neurons, REM-off raphe neurons, and REM-on cholinergic neurons as well as local GABAnergic neurons in vitro preparations. It is proposed that disinhibition (rather than disfacillitation) of REM-on cholinergic neurons, which are mediated through disfacillitation of inhibitory GABAnergic interneurons together with disfacillitation of REM-off monoaminergic neurons are responsible for the occurrences of abnormal manifestations of REM sleep in hypocretin deficient narcolepsy (Koyama, a personal communication).

VIII. Considerations for the Pathophysiology of Narcolepsy with Normal Hypocretin Levels

The pathophysiology of narcolepsy with normal hypocretin levels is currently debated. Over 90% patients with narcolepsy without cataplexy exhibit normal CSF hypocretin levels, yet they also present REM sleep abnormalities (i.e., SOREMS). Moreover, even when strict criteria for narcolepsy-cataplexy are applied, up to 10% of patients with narcolepsy-cataplexy show normal CSF hypocretin levels. Considering the fact that occurrence of cataplexy is tightly associated with hypocretin deficiency, impaired hypocretin neurotransmission is still likely involved in narcolepsy with normal CSF hypocretin levels. Concep­ tually, there are two potential explanations for these mechanisms: (1) specific impairment of hypocretin receptor and their downstream pathway and (2) partial/localized loss of hypocretin ligand (yet exhibition of normal CSF levels). A good example for the first explanation is provided by Hcrtr 2-mutated narco­ leptic dogs, which exhibit normal CSF hypocretin-1 levels (Ripley et al., 2001a), while having full blown narcolepsy. Thannickal et al. (2009) recently reported one narcolepsy without cataplexy patient, who had an overall loss of 33% of hypocretin cells (compared to normal) with maximal cell loss in the posterior

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hypothalamus. This result is more supportive of the second hypothesis, but further case studies are needed.

IX. Changes in Other Neurotransmitter Systems in Narcolepsy and Idiopathic

Hypersomnia

A. NARCOLEPSY

IN

DOGS

AND

HUMANS

Studies in humans with narcolepsy have shown a decrease in DA concentration in the CSF (Montplaisir et al., 1982). Studies on Hcrtr 2-mutated narcoleptic dogs, performed before and after probenecid administration, demonstrated an altered monoamine turnover with significantly less free homovanillic acid (HVA), dihy­ droxyphenylacetic acid (DOPAC), 3-methoxy-4-hydroxyphenylglycol (MHPG), and 5-hydroxyindoleacetic acid (5-HIAA) (Faull et al., 1986). The lower concentration of 5-HIAA in the CSF of narcoleptic dogs suggests a decreased concentration of the parent amine 5-HT, a decreased turnover of 5-HT in the brain, or both. Similarly, the lower steady-state CSF of HVA and DOPAC, as well as the reduced accumulation of DOPAC and HVA after probenecid, suggests decreased DA concentration, decreased turnover, or both. Finally, the lower concentration of MHPG after probenecid administration suggests decreased NE activity. Analyses of both human and animal narcoleptic brain tissue also suggest dopaminergic dysfunction. In postmortem human autoradiographic studies, striatal DA D2 receptor binding was increased in narcolepsy, more so than D1 receptors. (Aldrich et al., 1992) However, most in vivo studies with single-photon emission computed tomography (Hublin et al., 1994) and positron emission tomography (Rinne et al., 1995) found no increase in striatal D2 receptor binding in narcolepsy. Pharmacological studies demonstrated that narcoleptic canines are very sensitive to alpha-1b blockade and alpha-2 stimulation (as well as DA D2/D3 stimulation) and exhibit cataplexy (Nishino and Mignot, 1997). Also, they are sensitive to cholinergic M2/3 stimulation and exhibit cataplexy, and upregula­ tion of muscarinic receptors in the pons was reported (see Nishino and Mignot, 1997). Three independent studies reported altered catecholamine contents in the brains of narcoleptic dogs (Faull et al., 1986; Mefford et al., 1983). These studies found increases in DA and NE in many brain structures, especially DA in the amygdala and NE in the pontis reticularis oralis (Faull et al., 1986; Mefford et al., 1983). These changes are not due to the reduction in the turnover of these monoamines in the brain, since the turnover of these monoamines is either rather

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high or not altered (Nishino et al., 2001). Considering the fact that the drugs, which enhance dopaminergic neurotransmission (such as amphetamine-like sti­ mulants and modafinil [for EDS]) and NE neurotransmission (such as noradrena­ line uptake blockers [for cataplexy]), are needed to treat the symptoms in these animals (Nishino and Mignot, 1997), increases in DA and NE contents in the brain may be compensatory—mediated either by Hcrtr 1 or by other neuro­ transmitter systems; however, these findings are not consistent with the CSF findings. Most of these abnormalities are likely secondary to the deficiency in hypocretin neurotransmission, but alterations in these systems may actively mediate some of sleep-related symptoms of narcolepsy. The most recent neurochemical studies in canine narcolepsy specifically pointed to the involvement of histamine in narcolepsy. Histamine content in the brain was measured in genetically narcoleptic (n = 9) and control Dobermans (n = 9). As a reference, contents of DA, NE, and 5HT and their metabolites were also measured (Nishino et al., 2001). The histamine content in the cortex and thalamus (the areas important in the control of wakefulness via histami­ nergic input) was significantly lower in narcoleptic Dobermans compared to controls (Fig. 4). Considering the fact that hypocretins strongly excite TMN histaminergic neurons in vitro through Hcrtr2 stimulation (Eriksson et al., 2001; Yamanaka et al., 2002), the decrease in histaminergic content, found in narcoleptic dogs, may be due to the lack of excitatory input of hypocretin on TMN histaminergic neurons. Uncompensated low histamine levels in narcolepsy may suggest that the hypocretin system may be the major excitatory input to histaminergic neurons (through Hcrtr2). Histamine in the brains was also measured in three sporadic (ligand-deficient) narcoleptic dogs, and it was found that the histamine content in these animals was also as low as the Hcrtr2-mutated narcoleptic Dobermans (Nishino et al., 2001), thus suggesting that a decrease in histamine neurotransmission may also exist in ligand-deficient human narcolepsy.

B. IDIOPATHIC HYPERSOMNIA CSF analyses in idiopathic hypersomnia have shown normal cell counts, cytology, and protein content. Montplaisir and coworkers found a decrease in DA and indoleacetic acid in both patients with idiopathic hypersomnia and those with narcolepsy (Montplaisir et al., 1982). Faull and colleagues found similar mean concentrations of monoamine metabolites in subjects with narcolepsy or idiopathic hypersomnia and with controls; however, using a principal component

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analysis, they also found a dysregulation of the DA system in narcolepsy and of the NE system in idiopathic hypersomnia (Faull et al., 1983). These metabolic data may support the hypothesis of a primary deficient arousal system in patients with idiopathic hypersomnia.

X. Involvements of Histaminergic Neurotransmission in Human Narcolepsy and Other

Hypersomnia

Research evidence suggests that central histaminergic neurotransmission is involved in the control of vigilance (see Lin, 2000 for review). Clinically, it is widely known that histaminergic H1 blockers, such as promethazine or diphen­ hydramine, produce sedation, sleep, and temporal disruptions of attention and cognition. These effects are less prominent with the 2nd generation of H1 blockers with low central penetration. The 1st generation H1 blockers, such as diphenhydramine and doxylamine, are available as over-the-counter hypnotics. Histamine neurons are located exclusively in the TMN of the posterior hypothalamus, from where they project to practically all brain regions, including areas important for vigilance control, such as the hypothalamus, basal forebrain, thalamus, cortex, and brainstem structures (see Haas and Panula, 2003 for review). A series of experimental evidence had suggested that Hcrtr 2-mediated function plays more critical roles (over hcrtr1-mediated function) in generating narcoleptic symptoms in animals (Lin et al., 1999; Ripley et al., 2001a). The TMN exclusively expresses Hcrtr 2 (Marcus et al., 2001), and a series of electrophysio­ logical studies consistently demonstrated that hypocretin potently excites TMN histaminergic neurons through Hcrtr 2 (Eriksson et al., 2001; Yamanaka et al., 2002). Furthermore, it has been demonstrated that the wake-promoting effects of hypocretins were totally abolished in histamine H1 receptor KO mice, suggesting that the wake-promoting effects of hypocretin are dependent on the histaminergic neurotransmission (Huang et al., 2001). Extracellular histamine levels in the hypothalamus of rats show a clear diurnal variation: high during the active period and low during the resting period (Mochizuki et al., 1992). Histamine levels in the preoptic anterior hypothalamus in cats were also high during sleep deprivation and became lower during recovery sleep (Strecker et al., 2002), the findings similar to those of extracellular levels of hypocretin-1 (Yoshida et al., 2001). The animal experiment demonstrated that the changes in the brain extracellular histamine levels associated with diurnal and sleep/wake changes are also reflected in the CSF histamine levels (Soya et al., 2008), suggesting that CSF histamine levels at least partially reflect the central histamine neurotransmission and vigilance state changes. There were two clinical studies that evaluated the CSF histamine in

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human narcolepsy. The first study included narcolepsy with low CSF hypocretin-1 (£110 pg/ml, n = 34, 100% with cataplexy), narcolepsy without low CSF hypocretin-1 (n = 24, 75% with cataplexy), and normal controls (n = 23) (Nishino et al., 2009b). Narcoleptic subjects with and without hypocretin deficiency were included in order to determine if histamine neurotransmission is dependent on the hypocretin status of each subject. A significant reduction of CSF histamine levels was found in the cases with low CSF hypocretin-1, and levels were intermediate in other narcolepsy cases: Mean CSF histamine levels were 133.2 + 20.1 pg/ml in narcoleptic subjects with low CSF hypocretin­ 1, 233.3 + 46.5 pg/ml in patients with normal CSF hypocretin-1, and 300.5 + 49.7 pg/ml in controls. The results suggest the impaired histaminergic neurotransmission in human narcolepsy, but this is not entirely dependant on the hypocretin deficient status. We also examined CSF histamine levels in narcolepsy and other sleep disorders in a Japanese population. This second clinical study included 67 narcolepsy subjects, 26 idiopathic hypersomnia (IHS) subjects, 16 obstructive sleep apnea syndrome (OSAS) subjects, and 73 neurological controls (Kanbayashi et al., 2009a). We found significant reductions in CSF histamine levels in hypocretin deficient narcolepsy with cataplexy (mean + SEM; 176.0 +25.8 pg/ml), hypocretin non-deficient narcolepsy with cataplexy (97.8 + 38.4 pg/ml), hypocretin non-deficient narcolepsy without cataplexy (113.6 + 16.4 pg/ml), and idiopathic hypersomnia (161.0 + 29.3 pg/ml), while the levels in OSAS (259.3 + 46.6 pg/ ml) did not statistically differ from those in the controls (333.8 +22.0 pg/ml) (Fig. 5). Low CSF histamine levels were mostly observed in non-medicated patients, and significant reductions in histamine levels were evident in nonmedicated patients with hypocretin deficient narcolepsy with cataplexy (112.1 + 16.3 pg/ml) and idiopathic hypersomnia (143.3 + 28.8 pg/ml), while the levels in the medicated patients were in the normal range. Similar degrees of reduction, as seen in hypocretin deficient narcolepsy with cataplexy, were also observed in hypocretin non-deficient narcolepsy and in idiopathic hypersomnia, while those in OSAS (non-central nervous system hypersomnia) were not altered. These results confirmed the result of the first study, but further suggest that an impaired histaminergic system may be involved in mediating sleepiness in a much broader category of patients with EDS than hypocretin deficient narcolepsy cases. The decrease in histamine in these subjects was more specifically observed in non-medicated subjects, suggesting CSF histamine is a biomarker, reflecting the degree of hypersomnia of central origin. It is not known if decreased hista­ mine could either passively reflect or partially mediate daytime sleepiness in these pathologies. Further studies are essential, since central histaminomimetic com­ pounds, such as H3 antagonists, may be developed as a new class of wakepromoting compounds for EDS with various etiologies.

250

NISHINO AND SAGAWA CSF histamine levels (pg/ml)

CSF Hcrt-1 levels (pg/ml) 0

100

200

300

400

500

600

0

200

400

600

800

1000

(A)Neurological controls (B1)Hcrt- / N / C / med­

**

(B2)Hcrt- / N / C / med+ (C)Hcrt+ / N / C / med­

**

(D1)Hcrt- / N / woC / med­ (D2)Hcrt- / N / woC / med+

(F1)IHS / med­

** **

(E)Hcrt+ / N / woC / med­

(F2)IHS / med+ (G)OSAS

Medicated patient group

** p<0.01 ANOVA with post-hoc, vs. N. Controls

FIG. 5. CSF Hcrt-1 and histamine values for each individual with sleep disorders. CSF Hcrt-1 (i: left panel) and histamine (ii: right panel) values for each individual are plotted. The patient groups are indicated as Group A to Group G from above. The results of the subjects with CNS stimulants (shadowed) and without CNS stimulants medication are presented separately in the figure. The vertical lines show mean values. The cutoff value of CSF hypocretin-1 level (less than or equal to 110 pg/ml) clearly segregated hypocretin deficiency from non-deficiency. None of the patients with idiopathic hypersomnia and OSAS showed hypocretin deficiency. We found significant reductions in CSF histamine levels in hypocretin deficient (B: 176 + 25.8 pg/ml) and non-deficient narcolepsy with cataplexy (C: 97.8 + 38.4 pg/ml), hypocretin non-deficient narcolepsy without cataplexy (E: 113.6 + 16.4 pg/ml) and idiopathic hypersomnia (F: 161.0 + 29.3 pg/ml), while those in hypocretin deficient narcolepsy without cataplexy (D: 273.6 + 105 pg/ml) and OSAS (G: 259.3 + 46.6 pg/ml) were not statistically different from those in the control range (A: 333.8 + 22.0 pg/ml). The low CSF histamine levels were mostly observed in nonmedicated patients, and significant reductions in histamine levels were observed only in non-medicated patients with hypocretin deficient narcolepsy with cataplexy (B1: 112.1 + 16.3 pg/ml) and idiopathic hypersomnia (F1: 143.3 + 28.8 pg/ml). The levels in the medicated subjects are in the normal range (B2: 256.6 + 51.7 pg/ml and F2: 259.5 + 94.9 pg/ml). Non-medicated subjects had a tendency for low CSF histamine levels in hypocretin deficient narcolepsy without cataplexy (D1: 77.5 + 11.5 pg/ml) (adapted from Kanbayashi et al., 2009a). XI. Conclusion

This review described the current understanding of the neurochemistry for EDS with various etiologies. Although prevalence of primary hypersomnia, such as narcolepsy and idiopathic hypersomnia, is not high, prevalence of sympto­ matic EDS is considerably high, and the pathophysiology of symptomatic EDS likely overlaps with that of primary hypersomnia. Although much progress has been made regarding the pathophysiology and neurochemistry of EDS, this new knowledge, such as hypocretin replacement or

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histaminomimetic treatments, is not yet incorporated in the development of new treatments, rendering further research absolutely critical. Acknowledgments The authors thank Carl-Francis A. Deguzman for editing the manuscript.

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INDEX

A A2a receptor agonist, 12 Adenosine, 12, 65, 66, 67 sleep-generating effects of, 12 Adrenal steroids, 92 Adrenocorticotropic hormone (ACTH), 44, 113 dissociation of, 97 producing cells, 94 Alerting effects of light, 69 Alertness and cognitive performance, 73–74 AQP4, 242 AQP super-family, 242 Arcuate nucleus (ARC), 98 Arginine vasopressin (AVP), inhibitory effect of, 95 Aristotelian method of review, pathology of awakenings

correlation with Spielman factors, 210

efficient causes, 216–221

formal causes, 211–216

nuclear structures, 211–213 relevant network and neuron structures, 214–216 thalamocortical circuit elements, 214 relating theories from different conceptual levels, 210–211

substantial causes, 211

telic causes, 221–223

Arousal(s) awakenings, distinguished from, 25 defining, 26–27 states, homeostatic regulation of, 8–11 Arousability on attention in sleep, 41–42

40-Hz response, 42

N350, 42–44

P3, 42

behavioral reactivity, 28–29

individual differences in, 36–41

from sleep, factors influencing, 30

sleep-stage-specific effects, 29–31

NREM stages, 31 stage REM sleep, 31–33 Ascending arousal system, 3 “Ascending reticular activating system,” 3

Auditory-evoked potentials (AEP), 41 Auditory stimuli in waking, 39 Autonomic nervous system, 113–114 Awake, reason for being, 57–59 circadian and homeostatic impetus on wakefulness, 59–60 brain circuitry underlying circadian and homeostatic influences, 63–67 human sleep–wake cycle, 60–63 light on human wakefulness, 67–68 alerting effects of light, 69 dose- and wavelength, 69–72 light switches on clock and hourglass, 68–69 neuroanatomical underpinnings, 73–74 non-clinical applications of light, 74–77 melatonin on human sleep and wakefulness, 77 effects of exogenous melatonin, 79–80 endogenous melatonin and human circadian sleep–wake cycle, 77–79 treatment of insomnia and circadian rhythm disorders, 81–82 Awakenings, 23 ambiguity of, 200–201 of cardiovascular system, 100–102 defining, 26–27 distinguished from arousals, 25 lack of clear definition, 27 from NREM sleep, 137 process, CAR as, 160–161 shifts in attention, 26 Awakening, neurochemistry of, 229–251 changes in other neurotransmitter systems in narcolepsy, 246–248 histaminergic neurotransmission in human narcolepsy, 248–250 hypocretin deficiency and postnatal cell death of hypocretin neurons, 234–237 hypocretin ligand deficiency cause narcolepsy phenotype, 243 hypocretin/orexin deficiency and narcoleptic phenotype, 244–245 hypocretin/orexin system and sleep regulation, 243–244 pathophysiology of narcolepsy with normal hypocretin levels, 245–246 257

258

INDEX

Awakening, neurochemistry of (Continued) hypocretin non-deficient primary hypersomnia, 237–239 idiopathic hypersomnia, 247–248 narcolepsy in dogs and humans, 246–247 narcolepsy and symptoms of narcolepsy, 232–233 neurobiology of wakefulness, 231–232 symptomatic narcolepsy and hypersomnia, 239–242 Awakening onset process (AOP) researchers, 197 Awakening (pre and post), EEG changes, 23–25 critical remarks, 25–26 defining arousals and awakenings, 26–27 EEG changes following awakening partial awakenings, 46–48 state-related effects on cognition and behavior, 44–46 EEG changes preceding awakening activity in sleep and behavioral arousal thresholds, 33–35 on attention in sleep, 41–44 behavioral reactivity, 28–29 behavioral responsiveness and PGO waves, 35–36 individual differences in arousability, 36–41 sleep-stage-specific effects, 29–33 waking up to external stimuli, 27–28 B Bedtime settling routines, 179–80 Behavioral arousal thresholds, 33–35 Behavioral awakening, and CAR, 165–166 Behavioral responsiveness, 26, 49 and PGO waves, 35–36 “Behavioral sleep state,” 202 Breastfeeding, night awakenings in childhood, 181–182 C c-Fos, expression of, 6, 7 c-Fos immunoreactive neurons (IRN), 6–7 Fos-immunoreactivity in MnPN GABAergic neurons, 9 c-Fos-immunoreactivity, patterns of, 8 Cardiovascular system, 100 awakenings of, 100–102 Cataplexy and REM sleep abnormalities, 245 Childhood, night awakenings in, 177–188

developmental problems and diagnoses, 187 parent–child interactions and attachment, 186–187 temperament, 186 Cholinergic neurons, 5, 231 Chronic insomnia, 194 classification of “pure” insomnias, 195–196 clinical context, 194 defined syndromes of chronic insomnia, 196–200 DSM-AU7 IV generic definition of, 195 Circadian and homeostatic impetus on wakefulness, 59–60 circadian and homeostatic influences on human cognition, 63–67

diagrammatic representation of, 131

human sleep–wake cycle, 60–63

Circadian and sleep episode duration influences, 130–131 different measures of cognitive functioning, 142–145 length of sleep episode and SI, 137–141 time-of-day and cognition, 130–133 time-of-day effects and waking up, 133–137 Circadian aspects of sleepiness, 135 Circadian clock, 60 circadian modulation, 61 circadian regulation of alertness, 63 Circadian factors, 138 Circadian rhythm of core body temperature (CBT), 68 and homeostatic process self-awakening, factors of successful, 122–123 Clocks and hourglasses, light, and melatonin, role of, 57–59 circadian and homeostatic impetus on wakefulness, 59–60 from basic arousal states to controlled cognitive behavior, 60–63 homeostatic influences on human cognition, 63–67 effects of light on human wakefulness, 67–68 alerting effects of light, 69 effect of light on alertness and cognitive performance, 73–74 of light exposure and alertness, 69–72 light switches on clock and hourglass, 68–69 non-clinical applications of light, 74–77 melatonin on human sleep and wakefulness, 77 effects of exogenous melatonin, 79–80

INDEX

endogenous melatonin and human circadian sleep–wake cycle, 77–79 treatment of insomnia and circadian rhythm disorders, 81–82 Co-sleeping, night awakenings in childhood, 180–181

Cognition, time-of-day and, 130–133

Cognitive ability, 129

Cognitive awakening, and CAR, 161–163

Cognitive functioning, different measures of,

142–145

Colonic motility, 44

Conscious awareness, 40

Corticotropin-releasing hormone (CRH), 94

Cortisol awakening response (CAR), 153

average citations per year for papers published on, 157

as awakening process, 160–161

and behavioral awakening, 165–166

and cognitive awakening, 161–163

in context, 153–154

history of investigation of, 154–158

and HPA activity, 156

and immunological awakening, 164–165

measurement of, 166–169

peer-reviewed publications about, 156

regulation of CAR and SCN, 158–160

sensitive to non-psychological factors, 158

Cortisol/corticosterone awakening rise, 94–97

CSF Hcrt-1 and histamine values, 250

Cued reaction time task (CRTT), 144

Culture, 183

Cyclic alternating pattern (CAP), 217

grades of, 220

“microarousals,” 220

D Daytime naps, 115

Daytime sleepiness, 115

Decision-making performance after arousal, 46

Dehydroepiandrosterone (DHEA), 168

Delayed sleep phase syndrome (DSPS), 81

Descending subtraction task (DST), 136

Digit Symbol Substitution Test (DSST), 133

Dim light melatonin onset (DLMO), 75

“Disorder of awakenings,” 195

Distal vasoconstriction, 35

Dorsomedial hypothalamic nucleus (DMH), 63

“Dynamic state,” 201–202

259

E EEG changes pre and post awakening, 23–25 critical remarks, 25–26 defining arousals and awakenings, 26–27 following awakening partial awakenings, 46–48 sleep inertia or state-related effects, 44–46 preceding awakening activity in sleep and behavioral arousal thresholds, 33–35 behavioral reactivity, 28–29 behavioral responsiveness and PGO waves, 35–36 event-related potential studies on attention in sleep, 41–44 individual differences in arousability, 36–41 sleep-stage-specific effects, 29–33 waking up to external stimuli, 27–28 ELISA determination of Trib2-specific antibodies in sera, 238

Endocrine, anticipatory changes of, 113

Endogenous “melatonin cycle,” 59

Environmental factors

in control of sleep/wakefulness and intensity/

quality, 58

self-awakening, factors of successful, 122

Event-related potential (ERP), 28

Excessive sleepiness (EDS), 229

Exogenous melatonin, 79

on human sleep and wakefulness, 79–80 F “Failure-of-anticipation” theory, 101

Family context, night awakenings in childhood,

183

parental psychopathology, 184–185

socioeconomics, 183–184

“Forced awakening” (FA), 110

“Forced desynchronization (FD) protocols,” 132

Forced-desynchrony protocol, 80

G Gamma-aminobutyric acid (GABA), 1

Glucocorticoids, 154

Glucose homeostasis, 98

Gonadotropin-releasing hormone (GnRH)

neurons, 93

GABAergic neurons, 7, 15

260

INDEX

H Histaminergic neurotransmission, 248–250

Homeostatic impetus on wakefulness, 59–67

Homeostatic process, 60

diagrammatic representation of, 131

Homeostatic regulation of arousal states, 8–11

Homeostatic sleep pressure, 11, 66

Human sleep–wake cycle, 60–63

Hybrid CR/FDprocedure, 135

“Hyperarousal,” 208–209

Hypersomnia

idiopathic, 247–248

hypocretin non-deficient primary

hypersomnia, 237–239

narcolepsy in dogs and humans, 246–247

with sleep drunkenness, 239

symptomatic narcolepsy and, 239–243

Hypocretin deficiency, 229, 234–237

with cataplexy, 236

and narcoleptic phenotype, 244–245

in narcoleptic subjects, 236

Hypocretin ligand deficiency and narcolepsy phenotype, 243

hypocretin/orexin deficiency, 244–245

hypocretin/orexin system, 243–244

pathophysiology of narcolepsy, 245–246

Hypocretin neurons, 4–5 structures of mature hypocretin-1 and hypocretin-2 peptides, 235

Hypocretin non-deficient narcolepsy, 229

Hypocretin/orexin system and sleep regulation,

243–244

Hypocretin targeted therapy, 237

Hypothalamic SCN, 92, 155, 159

Hypothalamo-pituitary-adrenal (HPA) axis, 94,

153

I ICD-10, 196

Idiopathic hypersomnia, 247–248

hypocretin non-deficient primary

hypersomnia, 237–239

narcolepsy in dogs and humans, 246–247

Illuminance and subjective alertness, 70

Immunological awakening, and CAR, 164–165

“Independent sleep drunkenness,” 239

Insomnia, 81

and circadian rhythm disorders, 81–82

from multiple theory levels, 193–224

primary, 198–199

therapies, mid-level therapeutic theories of,

205–209

Intrinsic photosensitive retinal ganglion cell

(ipRGC), 67

K Karolinska Sleepiness Scale (KSS), dynamics of

subjective sleepiness on, 62

L Laterodorsal (LDT), 3

Light

exposure/alertness, dose/wavelength response

relationship of, 69–72

on human wakefulness, effects of, 67–68

alerting effects of light, 69

light switches on clock and hourglass, 68–69

neuroanatomical underpinnings, 73–74

non-clinical applications of light, 74–77

non-clinical applications of, 74–77

switches on clock and hourglass, 68–69

wavelength of, and its alerting response, 71

Lucid dreaming

40 Hz activity in, 48

coherences in, 49

M Median preoptic nucleus (MnPN), 1, 5

activation of GABAergic neurons, 5

expression of c-Fos, 9

Melanopsin-containing ipRGC, 67

Melatonin, 77

endogenous melatonin and human circadian

sleep–wake cycle, 77–79

on human sleep and wakefulness, 77, 79–80

increase in secretion in evening, 78

in insomniacs, 81

role in regulation human sleep–wake

behavior, 77

secretion, 134

treatment of insomnia and circadian rhythm

disorders, 81–82

Metaphysics (Aristotle), 210

“Micro-awakenings” , gamma-band, 204

Mid-level therapeutic theories of insomnia

therapies, 205–209

Miller Behavioral Style Scale (MBSS), 37

INDEX

MnPN GABAergic neurons, 9 nonREM and REM sleep, strong response, 10 Monitoring and blunting, 37 finger-lift response for, 38 Monoamine neurons, 231 Monoaminergic cell groups, 4 Morningness, sleep habit and, 117–118 Motivation and self-efficacy, 121–122 N Narcolepsy, 232–233, 237 changes in neurotransmitter systems in, 246–248 in dogs and humans, 246–247 with normal hypocretin levels, pathophysiology of, 245–246

sleepiness in, 244

symptoms, 232–233, 239, 240

“Natural awakening” (NA), 110 Nature Neuroscience, 157 Neuroanatomical underpinnings, 73–74 Neurobiology of wakefulness, 231–232 Neurochemistry of awakening, 229–231 changes in other neurotransmitter systems in narcolepsy, 246–248 histaminergic neurotransmission in human narcolepsy, 248–250 hypocretin deficiency and postnatal cell death, 234–237 hypocretin ligand deficiency cause narcolepsy phenotype, 243 hypocretin/orexin deficiency and narcoleptic phenotype, 244–245 hypocretin/orexin system and sleep regulation, 243–244 narcolepsy with normal hypocretin levels, 245–246 idiopathic hypersomnia, 247–248 narcolepsy in dogs and humans, 246–247 idiopathic hypersomnia, hypocretin nondeficient primary hypersomnia, 237–239 narcolepsy and symptoms of narcolepsy, 232–233 neurobiology of wakefulness, 231–232 symptomatic narcolepsy and hypersomnia, 239–242 Neuronal processes, theory level of, 209 Neuropeptide-Y (NPY)-containing neurons, 98 Newtonian conception, 203 Night awakenings, 177

261

child characteristics, 185 developmental problems and diagnoses, 187 parent–child interactions and attachment, 186–187 temperament, 186

in early childhood, 177–179

family context, 183

parental psychopathology, 184–185 socioeconomics, 183–184 parenting practices, 179

bedtime settling routines, 179–180

breastfeeding, 181–182

co-sleeping, 180–181

culture, 183

sleep aid use, 182

Non-visual (or non-image forming (NIF)) effects, 67 Nonignorable psychologically based mid-level theories, 206–209 discriminate stimuli as leading to awakenings, 206–207 limit-cycling cognitions affecting sleep, 207 mid-level theory dependencies on notion of “hyperarousal,” 208–209 sleep behaviors and cognitions follow operant principles of reinforcement, 207–208 NonREM sleep/NREM sleep, 3 awakenings from, 137 neurons during, 5 stages, 31 NREM–REM cycles, 29, 39, 112, 122, 123 O On Sleep and Sleeplessness (Aristotle), 59 Orexin deficiency. See Hypocretin deficiency P Parasomnias, 200 Paraventricular nucleus (PVN), 94 Parental psychopathology, 184–185 Parent–child interactions and attachment, 186–187 Parenting practices, 179 developmental progression of factors, 179 night awakenings in childhood, 179

bedtime settling routines, 179–180

breastfeeding, 181–182

co-sleeping, 180–181

culture, 183

sleep aid use, 182

262

INDEX

Partial awakenings, 46–48

frequency-specific activity, 47

Pathology of awakenings, 193–194

Aristotelian method of review

correlation with Spielman factors, 210

efficient causes, 216–221

formal causes, 211–216

relating theories from different conceptual

levels, 210–211

substantial causes, 211

telic causes, 221–223

chronic insomnia

classification of “pure” insomnias, 195–196

clinical context, 194

limitation of explanatory ambitions for

defined syndromes of chronic insomnia, 196–200 mid-level therapeutic theories of insomnia therapies, 205–209 realities about sleep “awakening” and “sleep,” ambiguity of, 200–201 metrological ambiguities of “sleep state,” 201–202 mnemonic and integrative duties of sleep, 204–205 process S and process C, 202–203 temporalizations relevant to understanding chronic insomnia patients, 204

theoretical vaguenesses and

incommensurate temporalizations, 203

Spielman 3-factor high-level model of

insomnia

implications for cognitive-behavioral

therapists, 205–206

nonignorable psychologically based mid-

level theories, 206–209

theory level of neuronal processes, 209

Pedunculopontine (PPT), 3

Perifornical region of lateral hypothalamus

(PFLH), 14

PGO waves, 32

behavioral responsiveness and, 35–36

Pineal-hormone melatonin, 59

Post-traumatic stress disorder (PTSD), 206

Postnatal cell death of hypocretin neurons, 234–237

Preoptic sleep regulatory systems, 8–11

Preparation for awakening, 109–110

definitions, 110–111

effects of attempt to self-awaken on sleep, 111

anticipatory changes of autonomic nervous system, 113–114

anticipatory changes of endocrine, 113

changes of sleep, 111–113

factors of successful self-awakening circadian rhythm and homeostatic process, 122–123

environmental factors, 122

motivation and self-efficacy, 121–122

psychological stress, 120–121

success rate of self-awakening, 119–120

time perception, 123

habit and ability of self-awakening

ability to self-awake, 118

habit of self-awakening, 115–117

sleep habit and morningness, 117–118

schematic model of self-awakening, 123–125 self-awakening and daytime functions

daytime naps, 115

daytime sleepiness, 115

sleep inertia, 114

Preparatory changes in pre-awakening period,

109–125

Primary insomnia, 194

DSM-IV definition of, 222

Psychological stress, 120–121

Q Quasi-Newtonian conception, 203

R Rapid eye movement (REM) sleep, 3

Reaction time (RT) task, 134

RT and SEM, 139

Realities about sleep ambiguity of “awakening” and “sleep,” 200–201 metrological ambiguities of “sleep state,” 201–202 mnemonic and integrative duties of sleep, 204–205 process S and process C, 202–203 theoretical vaguenesses and incommensurate temporalizations, 203

understanding chronic insomnia patients, 204

“REM-off,” 4, 13

REM sleep

40 Hz activity in, 48

coherences in, 49

cortical activation during waking and, 4

INDEX

mismatch negativity (MMN) during, 43

neurons during, 5

PGO waves, 32

relationship between SA and, 112

secondary consciousness, 24

stage, 31–33

Reticular formation, 2

S SCN. See Suprachiasmatic nuclei (SCN)

Secondary consciousness, 24

Self-awaken on sleep, effects of attempt to, 111

autonomic nervous system, changes in, 113–114

changes of sleep, 111–113

endocrine changes, 113

Self-awakening, 109

ability to, 118

and daytime functions

daytime naps, 115

daytime sleepiness, 115

sleep inertia, 114

with different age groups, 116

factors of successful

circadian rhythm and homeostatic process,

122–123

environmental factors, 122

motivation and self-efficacy, 121–122

psychological stress, 120–121

success rate of self-awakening, 119–120

time perception, 123

habit and ability of

ability to self-awake, 118

habit of self-awakening, 115–117

sleep habit and morningness, 117

morningness score and ratio of, 117

relationship between REM sleep and, 112

schema of, 124

schematic model of, 123–125

sleep–wake habit of university students, 118

vs. forced awakening, 109–125

Self-efficacy, 121–122

Sensory gating, 25

Simple RT, 135–136, 140

from neutral-flanker trials, 139

“Sleep,” ambiguity of, 200

Sleep aid use, 182

“Sleep atonia,” 165

Sleep disorders

international classification of, 196

narcolepsy, findings from, 229–251

263

“Sleep drunkenness,” 130

Sleep EEG, 8

Sleep endstate, 201

Sleep episode

duration influences, circadian and, 130–131 of cognitive functioning, 142–145 length of sleep episode and SI, 137–141 time-of-day and cognition, 130–133 time-of-day effects and waking up, 133–137 and SI, length of, 137–141

Sleep homeostasis, 8

Sleep inertia, 26, 45, 114, 130, 162

diagrammatic representation of, 131

or state-related effects on cognition and

behavior, 44–48

Sleep onset process (SOP), 196

Sleep-promoting circuitry, 6

Sleep regulation, hypocretin/orexin system

and, 243–244 Sleep-regulatory neurons neuronal activity in preoptic area, 11–13 in preoptic area, 13–15 in preoptic hypothalamus, 5–8 Sleep-regulatory regions, 6

Sleep stages (SS)

40-Hz response, 42

changes, 30

effects of stimulus probability/task relevance/

stimulus salience, 41

mean behavioral arousal thresholds across,

33, 34

N350, 42

P3, 42

specific effects, 29–31

ultradian rhythm of NREM and REM, 30

Sleep states, 165

metrological ambiguities of term, 201–202

Sleep-wakefulness cycles, 1–2

arousal states and preoptic sleep regulatory

systems, 8–11

integration of sleep-regulatory neuronal

activity, 11–13

rest phases in, 28

sleep-regulating neurons in preoptic

hypothalamus, 5–8 by sleep-regulatory neurons in preoptic area, 13–15 wakefulness-regulating systems, 2–5

Sleep–wake regulation, two-process model of, 122

Slow wave activity (SWA), 28

264

INDEX

Slow wave sleep (SWS), 26, 138

Socioeconomics, night awakenings in childhood,

183–184

Spielman 3-factor high-level model of insomnia

implications for cognitive-behavioral

therapists, 205–206

nonignorable psychologically based mid-level

theories, 206–209

“Spontaneous awakening,” 110

Stimulation (react to) and not wake up, 29

Stimulus processing, during sleep, 44

Suprachiasmatic GABAergic/glutamatergic

neurons, 99

Suprachiasmatic nuclei (SCN), 60

and autonomic nervous system, 91–93

awakening of cardiovascular system,

100–102 cortisol/corticosterone awakening rise, 94–97 dawn phenomenon, 97–100 SCN output rhythms, 93–94 circadian control of corticosterone release, 95

circadian oscillations in, 63

demonstrated and putative connections of, 96

distinct regulation of CAR and relationship

with, 158–160

neuropeptides, 101

promoting sleep, 61

Symptomatic narcolepsy and hypersomnia, 239–242 T “Temporal chimeras,” 93

Time-of-day

and cognition, 130–133 effects and waking up, 133–137

Time perception, 123

TMN neurons, 13

U Ultradian rhythm of NREM and REM sleep

stage (SS), 30

Unihemispheric sleep, 24

V

Vasoactive intestinal polypeptide (VIP), 92–93 Ventral lateral preoptic area (VLPO), 1, 5

activation of GABAergic neurons, 5

expression of c-Fos, 9

GABAergic neurons, 14

Visual evoked potentials (VEP), 162

VLPO GABAergic neurons, 9

nonREM and REM sleep, moderate

response, 10

W Wakefulness, 232

regulating systems, 2–5

Waking

40 Hz activity in, 48

coherences in, 49

to external stimuli, EEG, 27–28

time-of-day effects and, 133–137

Waking after sleep onset (WASO), 111

CONTENTS OF RECENT VOLUMES

Volume 37

Implicit Knowledge: New Perspectives on Unconscious Processes Daniel L. Schacter

Section I: Selectionist Ideas and Neurobiology Selectionist and Instructionist Ideas in Neuroscience Olaf Sporns

Section V: Psychophysics, Psychoanalysis, and Neuropsychology

Population Thinking and Neuronal Selection: Metaphors or Concepts? Ernst Mayr

Phantom Limbs, Neglect Syndromes, Repressed Memories, and Freudian Psychology V. S. Ramachandran

Selection and the Origin of Information Manfred Eigen

Neural Darwinism and a Conceptual Crisis in Psychoanalysis Arnold H. Modell

Section II: Development and Neuronal Populations

A New Vision of the Mind Oliver Sacks

Morphoregulatory Molecules and Selectional Dynamics during Development Kathryn L. Crossin

INDEX

Exploration and Selection in the Early Acquisi­ tion of Skill Esther Thelen and Daniela Corbetta Population Activity in the Control of Movement Apostolos P. Georgopoulos Section III: Functional Segregation and Integra­ tion in the Brain Reentry and the Problem of Cortical Integration Giulio Tononi Coherence as an Organizing Principle of Corti­ cal Functions Wolf Singerl Temporal Mechanisms in Perception Ernst Po¨ppel Section IV: Memory and Models Selection versus Instruction: Use of Computer Models to Compare Brain Theories George N. Reeke, Jr. Memory and Forgetting: Long-Term and Gradual Changes in Memory Storage Larry R. Squire

Volume 38 Regulation of GABAA Receptor Function and Gene Expression in the Central Nervous System A. Leslie Morrow Genetics and the Organization of the Basal Ganglia Robert Hitzemann, Yeang Olan, Stephen Kanes, Katherine Dains, and Barbara Hitzemann Structure and Pharmacology of Vertebrate GABAA Receptor Subtypes Paul J. Whiting, Ruth M. McKeman, and Keith A. Wafford Neurotransmitter Transporters: Molecular Biol­ ogy, Function, and Regulation Beth Borowsky and Beth J. Hoffman Presynaptic Excitability Meyer B. Jackson Monoamine Neurotransmitters in Invertebrates and Vertebrates: An Examination of the Diverse

265

266

CONTENTS OF RECENT VOLUMES

Enzymatic Pathways Utilized to Synthesize and Inactivate Biogenic Amines B. D. Sloley and A. V. Juorio

Changes in Ionic Fluxes during Cerebral Ischemia Tibor Kristian and Bo K. Siesjo

Neurotransmitter Systems in Schizophrenia Gavin P. Reynolds

Techniques for Examining Neuroprotective Drugs in Vitro A. Richard Green and Alan J. Cross

Physiology of Bergmann Glial Cells Thomas Mu¨ ller and Helmut Kettenmann INDEX

Volume 39 Modulation of Amino Acid-Gated Ion Channels by Protein Phosphorylation Stephen J. Moss and Trevor G. Smart Use-Dependent Regulation of GABAA Receptors Eugene M. Barnes, Jr. Synaptic Transmission and Modulation in the Neostriatum David M. Lovinger and Elizabeth Tyler The Cytoskeleton and Neurotransmitter Receptors Valerie J. Whatley and R. Adron Harris Endogenous Opioid Regulation of Hippocampal Function Michele L. Simmons and Charles Chavkin Molecular Neurobiology of the Cannabinoid Receptor Mary E. Abood and Billy R. Martin Genetic Models in the Study of Anesthetic Drug Action Victoria J. Simpson and Thomas E. Johnson Neurochemical Bases of Locomotion and Etha­ nol Stimulant Effects Tamara J. Phillips and Elaine H. Shen Effects of Ethanol on Ion Channels Fulton T. Crews, A. Leslie Morrow, Hugh Criswell, and George Breese INDEX

Volume 40 Mechanisms of Nerve Cell Death: Apoptosis or Necrosis after Cerebral Ischemia R. M. E. Chalmers-Redman, A. D. Fraser, W. Y. H. Ju, J. Wadia, N. A. Tatton, and W. G. Tatton

Techniques for Examining Neuroprotective Drugs in Vivo Mark P. Goldberg, Uta Strasser, and Laura L. Dugan Calcium Antagonists: Their Role in Neuro­ protection A. Jacqueline Hunter Sodium and Potassium Channel Modulators: Their Role in Neuroprotection Tihomir P. Obrenovich NMDA Antagonists: Their Role in Neuroprotection Danial L. Small Development of the NMDA Ion-Channel Blocker, Aptiganel Hydrochloride, as a Neuro­ protective Agent for Acute CNS Injury Robert N. McBurney The Pharmacology of AMPA Antagonists and Their Role in Neuroprotection Rammy Gill and David Lodge GABA and Neuroprotection Patrick D. Lyden Adenosine and Neuroprotection Bertil B. Fredholm Interleukins and Cerebral Ischemia Nancy J. Rothwell, Sarah A. Loddick, and Paul Stroemer Nitrone-Based Free Radical Traps as Neuropro­ tective Agents in Cerebral Ischemia and Other Pathologies Kenneth Hensley, John M. Carney, Charles A. Stewart, Tahera Tabatabaie, Quentin Pye, and Robert A. Floyd Neurotoxic and Neuroprotective Roles of Nitric Oxide in Cerebral Ischemia Turgay Dalkara and Michael A. Moskowitz A Review of Earlier Clinical Studies on Neuroprotective Agents and Current Approaches Nils-Gunnar Wahlgren INDEX

CONTENTS OF RECENT VOLUMES

Volume 41 Section I: Historical Overview

Verbal Fluency and Agrammatism Marco Molinari, Maria G. Leggio, and Maria C. Silveri

Rediscovery of an Early Concept Jeremy D. Schmahmann

Classical Conditioning Diana S. Woodruff-Pak

Section II: Anatomic Substrates

Early Infantile Autism Margaret L. Bauman, Pauline A. Filipek, and

Thomas L. Kemper

The Cerebrocerebellar System Jeremy D. Schmahmann and Deepak N. Pandya Cerebellar Output Channels Frank A. Middletan and Peter L. Strick Cerebellar-Hypothalamic Axis: Basic Circuits and Clinical Observations Duane E. Haines, Espen Dietrichs,

Gregory A. Mihaileff, and

E. Frank McDonald Section III. Physiological Observations Amelioration of Aggression: Response to Selec­ tive Cerebellar Lesions in the Rhesus Monkey Aaron J. Berman Autonomic and Vasomotor Regulation Donald J. Reis and Eugene V. Golanov Associative Learning Richard F. Thompson, Shaowen Bao, Lu Chen,

Benjamin D. Cipriano, Jeffrey S. Grethe, Jeansok

J. Kim, Judith K. Thompson, Jo Anne Tracy, Martha S. Weninger, and David J. Krupa

267

Olivopontocerebellar Atrophy and Fried-reich’s Ataxia: Neuropsychological Consequences of Bilateral versus Unilateral Cerebellar Lesions Th�e r�e se Botez-Marquard and Mihai I. Botez Posterior Fossa Syndrome Ian F. Pollack Cerebellar Cognitive Affective Syndrome Jeremy D. Schmahmann and Janet C. Sherman Inherited Cerebellar Diseases Claus W. Wallesch and Claudius Bartels Neuropsychological Abnormalities in Cerebellar Syndromes—Fact or Fiction? Irene Daum and Hermann Ackermann Section VI: Theoretical Considerations Cerebellar Microcomplexes Masao Ito Control of Sensory Data Acquisition James M. Bower

Visuospatial Abilities Robert Lalonde

Neural Representations of Moving Systems Michael Paulin

Spatial Event Processing Marco Molinari, Laura Petrosini, and Liliana G. Grammaldo Section IV: Functional Neuroimaging Studies

How Fibers Subserve Computing Capabilities: Similarities between Brains and Machines Henrietta C. Leiner and

Alan L. Leiner

Linguistic Processing Julie A. Fiez and Marcus E. Raichle

Cerebellar Timing Systems Richard Ivry

Sensory and Cognitive Functions Lawrence M. Parsons and Peter T. Fox

Attention Coordination and Anticipatory Control Natacha A. Akshoomoff, Eric Courchesne, and

Jeanne Townsend

Skill Learning Julien Doyon Section V: Clinical and Neuropsychological Observations Executive Function and Motor Skill Learning Mark Hallett and Jordon Grafman

Context-Response Linkage W. Thomas Thach Duality of Cerebellar Motor and Cognitive Functions James R. Bloedel and Vlastislav Bracha

268

CONTENTS OF RECENT VOLUMES

Section VII: Future Directions Therapeutic and Research Implications Jeremy D. Schmahmann Volume 42 Alzheimer Disease Mark A. Smith Neurobiology of Stroke W. Dalton Dietrich Free Radicals, Calcium, and the Synaptic Plasticity-Cell Death Continuum: Emerging Roles of the Trascription Factor NF�B Mark P. Mattson AP-I Transcription Factors: Short- and LongTerm Modulators of Gene Expression in the Brain Keith Pennypacker Ion Channels in Epilepsy Istvan Mody Posttranslational Regulation of Ionotropic Glu­ tamate Receptors and Synaptic Plasticity Xiaoning Bi, Steve Standley, and Michel Baudry Heritable Mutations in the Glycine, GABAA, and Nicotinic Acetylcholine Receptors Provide New Insights into the Ligand-Gated Ion Chan­ nel Receptor Superfamily Behnaz Vafa and Peter R. Schofield INDEX

Volume 43 Early Development of the Drosophila Neuromus­ cular Junction: A Model for Studying Neuronal Networks in Development Akira Chiba Development of Larval Body Wall Muscles Michael Bate, Matthias Landgraf, and Mar Ruiz Gmez Bate Development of Electrical Properties and Synap­ tic Transmission at the Embryonic Neuro-mus­ cular Junction Kendal S. Broadie

Ultrastructural Correlates of Neuromuscular Junction Development Mary B. Rheuben, Motojiro Yoshihara, and Yoshiaki Kidokoro Assembly and Maturation of the Drosophila Lar­ val Neuromuscular Junction L. Sian Gramates and Vivian Budnik Second Messenger Systems Underlying Plasticity at the Neuromuscular Junction Frances Hannan and Ti Zhong Mechanisms of Neurotransmitter Release J. Troy Littleton, Leo Pallanck, and Barry Ganetzky Vesicle Recycling at the Drosophila Neuromuscu­ lar Junction Daniel T. Stimson and Mani Ramaswami Ionic Currents in Larval Muscles of Drosophila Satpal Singh and Chun-Fang Wu Development of the Adult Neuromuscular System Joyce J. Femandes and Haig Keshishian Controlling the Motor Neuron James R. Trimarchi, Ping Jin, and Rodney K. Murphey Volume 44 Human Ego-Motion Perception A. V. van den Berg Optic Flow and Eye Movements M. Lappe and K.-P. Hoffman The Role of MST Neurons during Ocular Tracking in 3D Space K. Kawano, U. Inoue, A. Takemura, Y. Kodaka, and F. A. Miles Visual Navigation in Flying Insects M. V. Srinivasan and S.-W. Zhang Neuronal Matched Filters for Optic Flow Proces­ sing in Flying Insects H. G. Krapp A Common Frame of Reference for the Analysis of Optic Flow and Vestibular Information B. J. Frost and D. R. W. Wylie Optic Flow and the Visual Guidance of Locomo­ tion in the Gat H. Sherk and G. A. Fowler

269

CONTENTS OF RECENT VOLUMES

Stages of Self-Motion Processing in Primate Pos­ terior Parietal Cortex F. Bremmer, J.-R. Duhamel, S. B. Hamed,

and W. Graf

Cortical Reorganization and Seizure Generation in Dysplastic Cortex G. Avanzini, R. Preafico, S. Franceschetti, G. Sancini, G. Battaglia, and V. Scaioli

Optic Flow Analysis for Self-Movement Perception C. J. Duffy

Rasmussen’s Syndrome with Particular Refer­ ence to Cerebral Plasticity: A Tribute to Frank Morrell Fredrick Andermann and Yuonne Hart

Neural Mechanisms for Self-Motion Perception in Area MST R. A. Andersen, K. V. Shenoy, J. A. Crowell,

and D. C. Bradley

Computational Mechanisms for Optic Flow Analysis in Primate Cortex M. Lappe Human Cortical Areas Underlying the Percep­ tion of Optic Flow: Brain Imaging Studies M. W. Greenlee What Neurological Patients Tell Us about the Use of Optic Flow L. M. Vaina and S. K. Rushton INDEX

Volume 45

Structural Reorganization of Hippocampal Net­ works Caused by Seizure Activity Daniel H. Lowenstein Epilepsy-Associated Plasticity in gamma-Amnio­ butyric Acid Receptor Expression, Function and Inhibitory Synaptic Properties Douglas A. Coulter Synaptic Plasticity and Secondary Epilepto­ genesis Timothy J. Teyler, Steven L. Morgan, Rebecca N. Russell, and Brian L. Woodside Synaptic Plasticity in Epileptogenesis: Cellular Mechanisms Underlying Long-Lasting Synaptic Modifications that Require New Gene Expression Oswald Steward, Christopher S. Wallace, and Paul F Worley

Mechanisms of Brain Plasticity: From Normal Brain Function to Pathology Philip. A. Schwartzkroin

Cellular Correlates of Behavior Emma R. Wood, Paul A. Dudchenko, and Howard Eichenbaum

Brain Development and Generation of Brain Pathologies Gregory L. Holmes and Bridget McCabe

Mechanisms of Neuronal Conditioning Dcwid A. T King, David J. Krupa, Michael R. Foy, and Richard F. Thompson

Maturation of Channels and Receptors: Conse­ quences for Excitability David F Owens and Arnold R. Kriegstein

Plasticity in the Aging Central Nervous System C. A. Barnes

Neuronal Activity and the Establishment of Nor­ mal and Epileptic Circuits during Brain Development John W. Swann, Karen L. Smith, and Chong L. Lee The Effects of Seizures of the Hippocampus of the Immature Brain Ellen F Sperber and Solomon L. Moshe Abnormal Development and Catastrophic Epi­ lepsies: The Clinical Picture and Relation to Neuroimaging Harry T. Chugani and Diane C. Chugani

Secondary Epileptogenesis, Kindling, and Intractable Epilepsy: A Reappraisal from the Perspective of Neuronal Plasticity Thomas P. Sutula Kindling and the Mirror Focus Dan C. Mclntyre and Michael 0. Poulter Partial Kindling and Behavioral Pathologies Robert E. Adamec The Mirror Epileptogenesis B. J. Wilder

Focus

and

Secondary

270

CONTENTS OF RECENT VOLUMES

Hippocampal Lesions in Epilepsy: A Historical RobertNaquet Robert Naquet Clinical Evidence for Secondary Epileptogensis Hans 0. Luders Epilepsy as a Progressive (or Nonprogressive "Benign") Disorder John A. Wada Pathophysiological Aspects of Landau-Kleffher Syndrome: From the Active Epileptic Phase to Recovery Marie-Noelle Metz-Lutz, Pierre Maquet, Annd De Saint Martin, Gabrielle Rudolf, Norma Wioland, Edouard Hirsch, and Chriatian Marescaux Local Pathways of Seizure Propagation in Neocortex Barry W. Connors, David J. Pinto, and

Albert E. Telefeian

Multiple Subpial Assessment C. E. Polkey

Transection:

A

Clinical

The Legacy of Frank Morrell Jerome Engel, Jr. Volume 46 Neurosteroids: Beginning of the Story Etienne E. Baulieu, P. Robel, and M. Schumacher Biosynthesis of Neurosteroids and Regulation of Their Synthesis Synthia H. Mellon and Hubert Vaudry Neurosteroid 7-Hydroxylation Products in the Brain Robert Morfin and Luboslav St�arka Neurosteroid Analysis Ahmed A. Alomary, Robert L. Fitzgerald, and Robert H. Purdy Role of the Peripheral-Type Benzodiazepine Receptor in Adrenal and Brain Steroidogenesis Rachel C. Brown and Vassilios Papadopoulos Formation and Effects of Neuroactive Steroids in the Central and Peripheral Nervous System Roberto Cosimo Melcangi, Valeria Magnaghi,

Mariarita Galbiati, and Luciano Martini

Neurosteroid Modulation of Recombinant and Synaptic GABAA Receptors Jeremy J. Lambert, Sarah C. Homey, Delia Belelli, and John A. Peters GABAA-Receptor Plasticity during Long-Term Exposure to and Withdrawal from Progesterone Giovanni Biggio, Paolo Follesa, Enrico Sanna, Robert H. Purdy, and Alessandra Concas Stress and Neuroactive Steroids Maria Luisa Barbaccia, Mariangela Sena,

Robert H. Purdy, and Giovanni Biggio

Neurosteroids in Learning and Memory Processes Monique Vall�ee, Willy Mayo, George F. Koob, and Michel Le Moal Neurosteroids and Behavior Sharon R. Engel and Kathleen A. Grant Ethanol and Neurosteroid Interactions in the Brain A. Leslie Morrow, Margaret J. VanDoren, Rebekah Fleming, and Shannon Penland Preclinical Development of Neurosteroids as Neuroprotective Agents for the Treatment of Neurodegenerative Diseases Paul A. Lapchak and Dalia M. Araujo Clinical Implications of Circulating Neuroster­ oids Andrea R. Genazzani, Patrizia Monteleone,

Massimo Stomati, Francesca Bernardi,

Luigi Cobellis, Elena Casarosa, Michele Luisi,

Stefano Luisi, and Felice Petraglia

Neuroactive Steroids and Central Nervous Sys­ tem Disorders Mingde Wang, Torbjorn Ba¨ckstro¨m,

Inger Sundstrom, Go¨ran Wahlstro¨m,

Tommy Olsson, Di Zhu, Inga-Maj Johansson,

Inger Bjo¨rn, and Marie Bixo

Neuroactive Steroids in Neuropsychopharma­ cology Rainer Rupprecht and Florian Holsboer Current Perspectives on the Role of Neuroster­ oids in PMS and Depression Lisa D. Griffin, Susan C. Conrad, and Synthia H. Mellon INDEX

CONTENTS OF RECENT VOLUMES

Volume 47 Introduction: Studying Gene Expression in Neural Tissues by in Situ Hybridization W. Wisden and B. J. Morris Part I: In Situ Hybridization with Radiolabelled Oligonucleotides In Situ Hybridization with Oligonucleotide Probes Wl. Wisden and B. J. Morris

271

Nonradioactive in Situ Hybridization: Simplified Procedures for Use in Whole Mounts of Mouse and Chick Embryos Linda Ariza-McNaughton and Robb Krumlauf INDEX

Volume 48

Cryostat Sectioning of Brains Victoria Revilla and Alison Jones

Assembly and Intracellular GABAA Receptors Eugene Barnes

Processing Rodent Embryonic and Early Post­ natal Tissue for in Situ Hybridization with Radi­ olabelled Oligonucleotides David J. Laurie, Petra C. U. Schrotz, Hannah Monyer, and Ulla Amtmann

Subcellular Localization and Regulation of GABAA Receptors and Associated Proteins Bernhard Liischer and Jean-Marc Fritschy D1 Dopamine Receptors Richard Mailman

Processing of Retinal Tissue for in Situ Hybridization Frank Miiller

Molecular Modeling of Ligand-Gated Ion Chan­ nels: Progress and Challenges Ed Bertaccini and James R. Trudel

Processing the Spinal Cord for in Situ Hybridiza­ tion with Radiolabelled Oligonucleotides A. Berthele and T. R. Tolle

Alzheimer’s Disease: Its Diagnosis and Patho­ genesis Jillian J. Kril and Glentla M. Halliday

Processing Human Brain Tissue for in Situ Hybri­ dization with Radiolabelled Oligonucleotides Louise F B. Nicholson

DNA Arrays and Functional Genomics in Neurobiology Christelle Thibault, Long Wang, Li Zhiang, and Michael F Miles

In Situ Hybridization of Astrocytes and Neurons Cultured in Vitro L. A. Arizza-McNaughton, C. De Felipe,

and S. P. Hunt

In Situ Hybridization on Organotypic Slice Cultures A. Gerfin-Moser and H. Monyer Quantitative Analysis of in Situ Hybridization Histochemistry Andrew L. Gundlach and Ross D. O’Shea Part II: Nonradioactive in Situ Hybridization Nonradioactive in Situ Hybridization Using Alkaline Phosphatase-Labelled Oligonucleotides S. J. Augood, E. M. McGowan, B. R. Finsen, B. Heppelmann, and P. C. Emson Combining Nonradioactive in Situ Hybridization with Immunohistological and Anatomical Techniques Petra Wahle

Trafficking

of

INDEX

Volume 49 What Is West Syndrome? Olivier Dulac, Christine Soujflet, Catherine Chiron, and Anna Kaminski The Relationship between encephalopathy and Abnormal Neuronal Activity in the Developing Brain Frances E. Jensen Hypotheses from Functional Neuroimaging Studies Csaba Juh�asz, Harry T. Chugani, Ouo Muzik, and Diane C Chugani Infantile Spasms: Unique Sydrome or General Age-Dependent Manifestation of a Diffuse Encephalopathy? M. A. Koehn and M. Duchowny

272

CONTENTS OF RECENT VOLUMES

Histopathology of Brain Tissue from Patients with Infantile Spasms Harry V. Vinters

Brain Malformation, Epilepsy, and Infantile Spasms M. Elizabeth Ross

Generators of Ictal and Interictal Electroence­ phalograms Associated with Infantile Spasms: Intracellular Studies of Cortical and Thalamic Neurons M. Steriade and L Timofeeu

Brain Maturational Aspects Relevant to Patho­ physiology of Infantile Spasms G. Auanzini, F. Panzica, and S. Franceschetti

Cortical and Subcortical Generators of Normal and Abnormal Rhythmicity David A. McCormick Role of Subcortical Structures in the Patho-gen­ esis of Infantile Spasms: What Are Possible Sub­ cortical Mediators? F. A. Lado and S. L. Mosh�e

Gene Expression Analysis as a Strategy to Understand the Molecular Pathogenesis of Infantile Spasms Peter B. Crino Infantile Spasms: Criteria for an Animal Model Carl E. Stafstrom and Gregory L. Holmes INDEX

What Must We Know to Develop Better Therapies? Jean Aicardi

Volume 50

The Treatment of Infantile Spasms: An Evidence-Based Approach Mark Mackay, Shelly Weiss, and 0. Carter Snead III

Part I: Primary Mechanisms

ACTH Treatment of Infantile Spasms: Mechan­ isms of Its Effects in Modulation of Neuronal Excitability K. L. Brunson, S. Avishai-Eliner, and T. Z. Baram Neurosteroids and Infantile Spasms: The Deox­ ycorticosterone Hypothesis Michael A. Rogawski and Doodipala S. Reddy Are there Specific Anatomical and/or Transmit­ ter Systems (Cortical or Subcortical) That Should Be Targeted? Phillip C. Jobe Medical versus Surgical Treatment: Which Treatment When W. Donald Shields Developmental Outcome with and without Suc­ cessful Intervention Rochelle Caplan, Prabha Siddarth, Gary Mathem, Harry Vinters, Susan Curtiss, Jennifer Levitt, Robert Asarnow, and W. Donald Shields Infantile Spasms versus Myoclonus: Is There a Connection? Michael R. Pranzatelli Tuberous Sclerosis as an Underlying Basis for Infantile Spasm Raymond S. Yeung

How Does Glucose Generate Oxidative Stress In Peripheral Nerve? Irina G. Obrosova Glycation in Diabetic Neuropathy: Characteris­ tics, Consequences, Causes, and Therapeutic Options Paul J. Thomalley Part II: Secondary Changes Protein Kinase C Changes in Diabetes: Is the Concept Relevant to Neuropathy? Joseph Eichberg Are Mitogen-Activated Protein Kinases Glucose Transducers for Diabetic Neuropathies? Tertia D. Purves and David R. Tomlinson Neurofilaments in Diabetic Neuropathy Paul Fernyhough and Robert E. Schmidt Apoptosis in Diabetic Neuropathy Aviva Tolkovsky Nerve and Ganglion Blood Flow in Diabetes: An Appraisal Douglas W. Zochodne Part III: Manifestations Potential Mechanisms of Neuropathic Pain in Diabetes Nigel A. Calcutt

273

CONTENTS OF RECENT VOLUMES

Electrophysiologic Measures of Diabetic Neuro­ pathy: Mechanism and Meaning Joseph C. Arezzo and Elena Zotova Neuropathology and Pathogenesis of Diabetic Autonomic Neuropathy Robert E. Schmidt Role of the Neuropathy Luke Eckersky

Schwann

Cell

in

Diabetic

and

Diabetic

Glucose Transporter Protein Syndromes Darryl C. De Vivo, Dong Wang, Juan M. Pascual, and Yuan Yuan Ho Glucose, Stress, and Hippocampal Neuronal Vulnerability Lawrence P. Reagan

Part IV: Potential Treatment Polyol Pathway Neuropathy Peter J. Oates

CNS Sensing and Regulation of Peripheral Glu­ cose Levels Barry E. Levin, Ambrose A. Dunn-Meynell, and Vanessa H. Routh

Peripheral

Nerve Growth Factor for the Treatment of Dia­ betic Neuropathy: What Went Wrong, What Went Right, and What Does the Future Hold? Stuart C. Apfel Angiotensin-Converting Enzyme Inhibitors: Are there Credible Mechanisms for Beneficial Effects in Diabetic Neuropathy: Rayaz A. Malik and David R. Tomlinson Clinical Trials for Drugs Against Diabetic Neu­ ropathy: Can We Combine Scientific Needs With Clinical Practicalities? Dan Ziegler and Dieter Luft INDEX

Glucose/Mitochondria in Neurological Conditions John P. Blass Energy Utilization in the Ischemic/Reperfused Brain John W. Phillis and Michael H. O’Regan Diabetes Mellitus and the Central Nervous System Anthony L. McCall Diabetes, the Brain, and Behavior: Is There a Biological Mechanism Underlying the Associa­ tion between Diabetes and Depression? A. M. Jacobson, J. A. Samson, K. Weinger, and C. M. Ryan Schizophrenia and Diabetes David C. Henderson and Elissa R. Ettinger Psychoactive Drugs Affect Glucose Transport and the Regulation of Glucose Metabolism Donard S. Dwyer, Timothy D. Ardizzone, and Ronald J. Bradley

Volume 51

INDEX

Energy Metabolism in the Brain Leif Hertz and Gerald A. Dienel

Volume 52

The Cerebral Glucose-Fatty Acid Cycle: Evolu­ tionary Roots, Regulation, and (Patho) physiolo­ gical Importance Kurt Heininger

Neuroimmune Relationships in Perspective Frank Huckkbridge and Angela Clow

Expression, Regulation, and Functional Role of Glucose Transporters (GLUTs) in Brain Donard S. Dwyer, Susan J. Vannucci, and Ian A. Simpson Insulin-Like Growth Factor-1 Promotes Neu­ ronal Glucose Utilization During Brain Develop­ ment and Repair Processes Carolyn A. Bondy and Clara M. Cheng

Sympathetic Nervous System Interaction with the Immune System Virginia M. Sanders and Adam P. Kohm Mechanisms by Which Cytokines Signal the Brain Adrian J. Dunn Neuropeptides: Modulators of Responses in Health and Disease David S. Jessop

Immune

274

CONTENTS OF RECENT VOLUMES

Brain—Immune Interactions in Sleep Lisa Marshall and Jan Born Neuroendocrinology of Autoimmunity Michael Harbuz Systemic Stress-Induced Th2 Shift and Its Clin­ ical Implications IbiaJ. Elenkov

Section II: Primary Respiratory Chain Disorders Mitochondrial Disorders of the Nervous System: Clinical, Biochemical, and Molecular Genetic Features Dominic Thyagarqjan and Edward Byrne Section III: Secondary Respiratory Chain Disorders

Neural Control of Salivary S-IgA Secretion Gordon B. Proctor and Guy H. Carpenter

Friedreich’s Ataxia J. M. Cooper andj. L. Bradley

Stress and Secretory Immunity Jos A. Bosch, Christopher Ring Eco J. C. de Geus, Enno C. I. Veerman, and Arie V. Nieuw Amerongen

Wilson Disease C. A. Davie and A. H. V. Schapira

Cytokines and Depression Angela Clow Immunity and Schizophrenia: Autoimmunity, Cytokines, and Immune Responses Fiona Gaughran Cerebral Lateralization and the Immune System Pierre J. Neveu Behavioral Conditioning of the Immune System Frank Huckkbridge Psychological and Neuroendocrine Correlates of Disease Progression Julie M. Turner-Cobb The Role of Psychological Intervention in Mod­ ulating Aspects of Immune Function in Relation to Health and Well-Being J. H. Gruzelier INDEX

Volume 53 Section I: Mitochondrial Structure and Function Mitochondrial DNA Structure and Function Carlos T. Moraes, Sarika Srivastava, Ilias Krkinezos, Jose Oca-Cossio, Corina van Waveren, Markus Woischnick, and Francisca Diaz Oxidative Phosphorylation: Structure, Function, and Intermediary Metabolism Simon J. R. Heales, Matthew E. Gegg, and John B. Clark Import of Mitochondrial Proteins Matthias F. Bauer, Sabine Hofmann, and Walter Neupert

Hereditary Spastic Paraplegia Christopher J. McDerrmott and Pamela J. Shaw Cytochrome c Oxidase Deficiency Giacomo P. Comi, Sandra Strazzer, Sara Galbiati, and Nereo Bresolin Section IV: Toxin Induced Mitochondrial Dysfunction Toxin-Induced Mitochondrial Dysfunction Susan E. Browne and M. Flint Beal Section V: Neurodegenerative Disorders Parkinson’s Disease L.V.P. Korlipara and A. H. V. Schapira Huntington’s Disease: The Mystery Unfolds? ˚ sa Peters�en and Patrik Brundin A Mitochondria in Alzheimer’s Disease Russell H. Swerdlow and Stephen J. Kish Contributions of Mitochondrial Alterations, Resulting from Bad Genes and a Hostile Envir­ onment, to the Pathogenesis of Alzheimer’s Disease Mark P. Mattson Mitochondria and Amyotrophic Lateral Sclerosis Richard W. Orrell and Anthony H. V. Schapira Section VI: Models of Mitochondrial Disease Models of Mitochondrial Disease Danae Liolitsa and Michael G. Hanna Section VII: Defects of � Oxidation Including Carnitine Deficiency Defects of � Oxidation Including Carnitine Deficiency K. Bartlett and M. Pourfarzam

CONTENTS OF RECENT VOLUMES

Section VIII: Mitochondrial Involvement in Aging The Mitochondrial Theory of Aging: Involve­ ment of Mitochondrial DNA Damage and Repair Nadja C. de Souza-Pinto and Vilhelm A. Bohr INDEX

Volume 54 Unique General Anesthetic Binding Sites Within Distinct Gonformational States of the Nicotinic Acetylcholine Receptor Hugo R. Ariaas, William, R. Kem, James R. Truddell, and Michael P. Blanton Signaling Molecules and Receptor Transduction Cascades That Regulate NMDA ReceptorMediated Synaptic Transmission Suhas. A. Kotecha and John F. MacDonald Behavioral Measures of Alcohol Self-Administra­ tion and Intake Control: Rodent Models Herman H. Samson and Cristine L. Czachowski Dopaminergic Mouse Mutants: Investigating the Roles of the Different Dopamine Receptor Sub­ types and the Dopamine Transporter Shirlee Tan, Bettina Hermann, and Emiliana Borrelli Drosophila melanogaster, A Genetic Model System for Alcohol Research Douglas J. Guarnieri and Ulrike Heberlein

275

Problems in the Use of Herpes Simplex Virus as a Vector L. T. Feldman Lentiviral Vectors J. Jakobsson, C. Ericson, JV. Rosenquist, and C. Lundberg Retroviral Vectors for Gene Delivery to Neural Precursor Cells K. Kageyama, H. Hirata, andj. Hatakeyama Section II: Gene Therapy with Virus Vectors for Specific Disease of the Nervous System The Principles of Molecular Therapies for Glioblastoma G. Karpati and J. Nalbatonglu Oncolytic Herpes Simplex Virus J. C. C. Hu and R. S. Coffin Recombinant Retrovirus Vectors for Treatment of Brain Tumors N. G. Rainov and C. M. Kramm Adeno-Associated Viral Vectors for Parkinson’s Disease I. Muramatsu, L. Wang K. Ikeguchi, K-i Fujimoto, T. Okada, H. Mizukami, T. Hanazono, A. Kume, I. J. Vakano, and K. Ozawa HSV Vectors for Parkinson’s Disease D. S. Latchman Gene Therapy for Stroke K. Abe and W. R. Zhang Gene Therapy for Mucopolysaccharidosis A. Bosch and J. M. Heard

INDEX

INDEX

Volume 55

Volume 56

Section I: Virsu Vectors For Use in the Nervous System

Behavioral Mechanisms and the Neurobiology of Conditioned Sexual Responding Mark Krause

Non-Neurotropic Adenovirus: a Vector for Gene Transfer to the Brain and Gene Therapy of Neurological Disorders P. R. Lowenstein, D. Suwelack, J. Hu, X. Yuan, M. Jimenez-Dalmaroni, S. Goverdhama, and M.G. Castro Adeno-Associated Virus Vectors E. Lehtonen and L. Tenenbaum

NMDA Receptors in Alcoholism Paula L. Hoffman Processing and Representation of Species-Specific Communication Calls in the Auditory System of Bats George D. Pollak, Achim Klug, and Erie E. Bauer

276

CONTENTS OF RECENT VOLUMES

Central Nervous System Control of Micturition Gert Holstege and Leonora J. Mouton The Structure and Physiology of the Rat Audi­ tory System: An Overview Manuel Malmierca

Postsynaptic Density Scaffolding Proteins at Excitatory Synapse and Disorders of Synaptic Plasticity: Implications for Human Behavior Pathologies Andrea de Bartolomeis and Germane Fiore

Neurobiology of Cat and Human Sexual Behavior Gert Holstege and J. R. Georgiadis

Prostaglandin-Mediated Signaling in Schizo­ phrenia S. Smesny

INDEX

Volume 57

Mitochondria, Synaptic Plasticity, and Schizo­ phrenia Dorit Ben-Shachar and Daphna Laifenfeld

Cumulative Subject Index of Volumes 1–25

Membrane Phospholipids and Cytokine Interac­ tion in Schizophrenia Jeffrey K. Yao and Daniel P. van Kammen

Volume 58

Neurotensin, Schizophrenia, and Antipsychotic Drug Action Becky Kinkead and Charles B. Nemeroff

Cumulative Subject Index of Volumes 26–50

Volume 59 Loss of Spines and Neuropil Liesl B. Jones Schizophrenia as a Disorder of Neuroplasticity Robert E. McCullumsmith, Sarah M. Clinton, and James H. Meador-Woodruff The Synaptic Pathology of Schizophrenia: Is Aberrant Neurodevelopment and Plasticity to Blame? Sharon L. Eastwood Neurochemical Basis for an Epigenetic Vision of Synaptic Organization E. Costa, D. R. Grayson, M. Veldic,

and A. Guidotti

Muscarinic Receptors in Schizophrenia: Is There a Role for Synaptic Plasticity? Thomas J. Raedler

Schizophrenia, Vitamin D, and Brain Development Alan Mackay-Sim, Franc¸ois Feron, Dartyl Eyles, Thomas Bume, and John McGrath Possible Contributions of Myelin and Oligo­ dendrocyte Dysfunction to Schizophrenia Daniel G. Stewart and Kenneth L. Davis Brain-Derived Neurotrophic Factor and the Plasticity of the Mesolimbic Dopamine Pathway Oliver Guillin, Nathalie Griffon, Jorge Diaz, Bernard Le Foil, Erwan Bezard, Christian Gross, Chris Lammers, Holger Stark, Patrick Carroll, Jean-Charles Schwartz, and Pierre Sokoloff S100B in Schizophrenic Psychosis Matthias Rothermundt, Gerald Ponath, and Volker Arolt Oct-6 Transcription Factor Maria Ilia NMDA Receptor Function, Neuroplasticity, and the Pathophysiology of Schizophrenia Joseph T. Coyle and Guochuan Tsai INDEX

Serotonin and Brain Development Monsheel S. K Sodhi and Elaine Sanders-Bush

Volume 60

Presynaptic Proteins and Schizophrenia William G. Honer and Clint E. Young

Microarray Platforms: Introduction and Appli­ cation to Neurobiology Stanislav L. Karsten, Lili C. Kudo, and

Daniel H. Geschwind

Mitogen-Activated Protein Kinase Signaling Svetlana V. Kyosseva

277

CONTENTS OF RECENT VOLUMES

Experimental Design and Low-Level Analysis of Microarray Data B. M. Bolstad, F. Collin, K M. Simpson, R. A. Irizarry, and T. P. Speed Brain Gene Expression: Genomics and Genetics ElissaJ. Chester and Robert W. Williams

Proteomics Analysis in Alzheimer’s Disease: New Insights into Mechanisms of Neurodegeneration D. Allan Butterfield and Debra Boyd-Kimball Proteomics and Alcoholism Frank A. Witzmann and Wendy N. Strother

DNA Microarrays and Animal Models of Learn­ ing and Memory Sebastiano Cavallaro

Proteomics Studies of Traumatic Brain Injury Kevin K. W. Wang, Andrew Ottens, William Haskins, Ming Cheng Liu, Firas Kobeissy, Nancy Denslow, SuShing Chen, and Ronald L. Hayes

Microarray Analysis of Human Nervous System Gene Expression in Neurological Disease Steven A. Greenberg

Influence of Huntington’s Disease on the Human and Mouse Proteome Claus Zabel and Joachim Klose

DNA Microarray Analysis of Postmortem Brain Tissue K�aroly Mirnics, Pat Levitt, and David A. Lewis

Section V: Overview of the Neuroproteome

INDEX

Proteomics—Application to the Brain Katrin Marcus, Oliver Schmidt, Heike Schaefer, Michael ˚ van Hall, and Helmut E. Meyer Hamacher, AndrA INDEX

Volume 61 Section I: High-Throughput Technologies Biomarker Discovery Using Molecular Profiling Approaches Stephen J. Walker and Arron Xu Proteomic Analysis of Mitochondrial Proteins Mary F. Lopez, Simon Melov, Felicity Johnson, Nicole Nagulko, Eva Golenko, Scott Kuzdzal, Suzanne Ackloo, and Alvydas Mikulskis Section II: Proteomic Applications NMDA Receptors, Neural Pathways, and Pro­ tein Interaction Databases Holger Husi Dopamine Transporter Network and Pathways Rajani Maiya and R. Dayne Mayjield

Volume 62 GABAA Receptor Structure–Function Studies: A Reexamination in Light of New Acetylcholine Receptor Structures Myles H. Akabas Dopamine Mechanisms and Cocaine Reward Aiko Ikegami and Christine L. Duvauchelle Proteolytic Dysfunction in Neurodegenerative Disorders Kevin St. P. McNaught Neuroimaging Studies in Bipolar Children and Adolescents Rene L. Olvera, David C. Glahn, Sheila C. Caetano, Steven R. Pliszka, andjair C. Soares

Proteomic Approaches in Drug Discovery and Development Holly D. Soares, Stephen A. Williams, Peter J. Snyder, Feng Gao, Tom Stiger, Christian Rohljf, Athula Herath, Trey Sunderland, Karen Putnam, and W. Frost White

Chemosensory G-Protein-Coupled Signaling in the Brain Geoffrey E. Woodard

Receptor

Section III: Informatics Proteomic Informatics Steven Russell, William Old, Katheryn Resing, and Lawrence Hunter

The Use of Caenorhabditis elegans in Molecular Neuropharmacology Jill C. Bettinger, Lucinda Carnell, Andrew G. Davies, and Steven L. McIntire

Section IV: Changes in the Proteome by Disease

INDEX

Disturbances of Emotion Regulation after Focal Brain Lesions Antoine Bechara

278

CONTENTS OF RECENT VOLUMES

Volume 63

Volume 65

Mapping Neuroreceptors at work: On the Defi­ nition and Interpretation of Binding Potentials after 20 years of Progress Albert Gjedde, Dean F. Wong, Pedro Rosa-Neto, and Paul Cumming

Insulin Resistance: Causes and Consequences Zachary T. Bloomgarden

Mitochondrial Dysfunction in Bipolar Disorder: From 31P-Magnetic Resonance Spectroscopic Findings to Their Molecular Mechanisms Tadafumi Kato Large-Scale Microarray Studies of Gene Expres­ sion in Multiple Regions of the Brain in Schizo­ phrenia and Alzeimer’s Disease Pavel L. Katsel, Kenneth L. Davis, and Vahram Haroutunian Regulation of Serotonin 2C Receptor PRE­ mRNA Editing By Serotonin Claudia Schmauss The Dopamine Hypothesis of Drug Addiction: Hypodopaminergic State Miriam Melis, Saturnino Spiga, and Marco Diana Human and Animal Spongiform Encephalopa­ thies are Autoimmune Diseases: A Novel Theory and Its supporting Evidence Bao Ting Zhu Adenosine and Brain Function Bertil B. Fredholm, Jiang-Fan Chen, Rodrigo A. Cunha, Per Svenningsson, and Jean-Marie Vaugeois INDEX

Volume 64 Section I. The Cholinergic System John Smythies Section II. The Dopamine System John Symythies

Antidepressant-Induced Manic Conversion: A Developmentally Informed Synthesis of the Literature Christine J. Lim, James F. Leckman, Christopher Young, and Andr�e s Martin Sites of Alcohol and Volatile Anesthetic Action on Glycine Receptors Ingrid A. Lobo and R. Adron Harris Role of the Orbitofrontal Cortex in Reinforce­ ment Processing and Inhibitory Control: Evi­ dence from Functional Magnetic Resonance Imaging Studies in Healthy Human Subjects Rebecca Elliott and Bill Deakin Common Substrates of Dysphoria in Stimulant Drug Abuse and Primary Depression: Therapeu­ tic Targets Kate Baicy, Carrie E. Bearden, John Monterosso, Arthur L. Brody, Andrew J. Isaacson, and Edythe D. London The Role of cAMP Response Element–Binding Proteins in Mediating Stress-Induced Vulner­ ability to Drug Abuse Arati Sadalge Kreibich and Julie A. Blendy G-Protein–Coupled Receptor Deorphanizations Yumiko Saito and Olivier Civelli Mechanistic Connections Between Glucose/ Lipid Disturbances and Weight Gain Induced by Antipsychotic Drugs Donard S. Dwyer, Dallas Donohoe, Xiao-Hong Lu, and Eric J. Aamodt Serotonin Firing Activity as a Marker for Mood Disorders: Lessons from Knockout Mice Gabriella Gobbi INDEX

Section III. The Norepinephrine System John Smythies Section IV. The Adrenaline System John Smythies

Volume 66

Section V. Serotonin System John Smythies

Brain Atlases of Normal and Diseased Populations Arthur W. Toga and Paul M. Thompson

INDEX

CONTENTS OF RECENT VOLUMES

Neuroimaging Databases as a Resource for Scientific Discovery John Darrell Van Horn, John Wolfe,

Autumn Agnoli, Jeffrey Woodward,

Michael Schmitt, James Dobson,

Sarene Schumacher, and Bennet Vance

Modeling Brain Responses Karl J. Friston, William Penny, and Olivier David Voxel-Based Morphometric Analysis Using Shape Transformations Christos Davatzikos

279

Neuroimaging in Functional Somatic Syndromes Patrick B. Wood Neuroimaging in Multiple Sclerosis Alireza Minagar, Eduardo Gonzalez-Toledo, James Pinkston, and Stephen L. Jaffe Stroke Roger E. Kelley and Eduardo Gonzalez-Toledo Functional MRI in Pediatric Neurobehavioral Disorders Michael Seyffert and F. Xavier Castellanos

Quantification of White Matter Using DiffusionTensor Imaging Hae-Jeong Park

Structural MRI and Brain Development Paul M. Thompson, Elizabeth R. Sowell,

Nitin Gogtay, Jay N. Giedd, Christine

N. Vidal, Kiralee M. Hayashi, Alex Leow,

Rob Nicolson, Judith L. Rapoport, and

Arthur W. Toga

Perfusion fMRI for Functional Neuroimaging Geoffrey K. Aguirre, John A. Detre, and Jiongjiong Wang

Neuroimaging and Human Genetics Georg Winterer, Ahmad R. Hariri, David Goldman, and Daniel R. Weinberger

Functional Near-Infrared Spectroscopy: Poten­ tial and Limitations in Neuroimaging Studies Toko Hoshi

Neuroreceptor Imaging in Psychiatry: Theory and Applications W. Gordon Frankle, Mark Slifstein, Peter S. Talbot, and Marc Laruelle

The Cutting Edge of fMRI and High-Field fMRI Dae-Shik Kim

Neural Modeling and Functional Brain Imaging: The Interplay Between the Data-Fitting and Simulation Approaches Barry Horwitz and Michael F. Glabus Combined EEG and fMRI Studies of Human Brain Function V. Menon and S. Crottaz-Herbette INDEX

Volume 67 Distinguishing Neural Substrates of Heterogene­ ity Among Anxiety Disorders Jack B. Nitschke and Wendy Heller Neuroimaging in Dementia K. P. Ebmeier, C. Donaghey, and N. J. Dougall Prefrontal and Anterior Cingulate Contributions to Volition in Depression Jack B. Nitschke and Kristen L. Mackiewicz Functional Imaging Research in Schizophrenia H. Tost, G. Ende, M. Ruf, F. A. Henn, and A. Meyer-Lindenberg

INDEX

Volume 68 Fetal Magnetoencephalography: Viewing the Developing Brain In Utero Hubert Preissl, Curtis L. Lowery, and Hari Eswaran Magnetoencephalography in Studies of Infants and Children Minna Huotilainen Let’s Talk Together: Memory Traces Revealed by Cooperative Activation in the Cerebral Cortex Jochen Kaiser, Susanne Leiberg, and Werner

Lutzenberger

Human Communication Investigated With Magnetoencephalography: Speech, Music, and Gestures Thomas R. Kno¨sche, Burkhard Maess, Akinori

Nakamura, and Angela D. Friederici

280

CONTENTS OF RECENT VOLUMES

Combining Magnetoencephalography Functional Magnetic Resonance Imaging Klaus Mathiak and Andreas J. Fallgatter

and

Across-Channel Spectral Processing John H. Grose, Joseph W. Hall III, and Emily Buss

Beamformer Analysis of MEG Data Arjan Hillebrand and Gareth R. Barnes Functional Connectivity Analysis Magnetoencephalography Alfons Schnitzler and Joachim Gross

Basic Psychophysics of Human Spectral Processing Brian C. J. Moore

in

Human Visual Processing as Revealed by Mag­ netoencephalographys Yoshiki Kaneoke, Shoko Watanabe, and Ryusuke Kakigi A Review of Clinical Applications of Magnetoencephalography Andrew C. Papanicolaou, Eduardo M. Castillo, Rebecca Billingsley-Marshall, Ekaterina Pataraia, and Panagiotis G. Simos INDEX

Volume 69 Nematode Neurons: Anatomy and Anatomical Methods in Caenorhabditis elegans David H Hall, Robyn Lints, and Zeynep Altun Investigations of Learning and Memory in Cae­ norhabditis elegans Andrew C. Giles, Jacqueline K. Rose, and Catharine H. Rankin Neural Specification and Differentiation Eric Aamodt and Stephanie Aamodt Sexual Behavior of the Caenorhabditis elegans Male Scott W. Emmons The Motor Circuit Stephen E. Von Stetina, Millet Treinin, and David M. Miller III Mechanosensation in Caenorhabditis elegans Robert O’Hagan and Martin Chalfie Volume 70 Spectral Processing by the Peripheral Auditory System Facts and Models Enrique A. Lopez-Poveda

Speech and Music Have Different Requirements for Spectral Resolution Robert V. Shannon Non-Linearities and the Representation of Audi­ tory Spectra Eric D. Young, Jane J. Yu, and Lina A. J. Reiss Spectral Processing in the Inferior Colliculus Kevin A. Davis Neural Mechanisms for Spectral Analysis in the Auditory Midbrain, Thalamus, and Cortex Monty A. Escabi and Heather L. Read Spectral Processing in the Auditory Cortex Mitchell L. Sutter Processing of Dynamic Spectral Properties of Sounds Adrian Rees and Manuel S. Malmierca Representations of Spectral Coding in the Human Brain Deborah A. Hall, PhD Spectral Processing Determination Donal G. Sinex

and

Sound

Source

Spectral Information in Sound Localization Simon Carlile, Russell Martin, and Ken McAnally Plasticity of Spectral Processing Dexter R. F. Irvine and Beverly A. Wright Spectral Processing In Cochlear Implants Colette M. McKay INDEX

Volume 71 Autism: Neuropathology, Alterations of the GA-BAergic System, and Animal Models Christoph Schmitz, Imke A. J. van Kooten, Patrick R. Hof, Herman van Engeland, Paul H. Patterson, and Harry W. M. Steinbusch The Role of GABA in the Early Neuronal Development Marta Jelitai and Emi ’lia Madarasz

CONTENTS OF RECENT VOLUMES

GABAergic Signaling Cerebellum Chitoshi Takayama

in

the

Developing

Shared Chromosomal Susceptibility Regions Between Autism and Other Mental Disorders Yvon C. Chagnon index

Insights into GABA Functions in the Developing Cerebellum Mo 0 nica L. Fiszman

INDEX

Role of GABA in the Mechanism of the Onset of Puberty in Non-Human Primates Ei Terasawa

Volume 72

Rett Syndrome: A Rosetta Stone for Understanding the Molecular Pathogenesis of Autism Janine M. LaSalle, Amber Hogart, and Karen N. Thatcher GABAergic Cerebellar System in Autism: A Neu-ropathological and Developmental Perspec­ tive Gene J. Blatt Reelin Glycoprotein Schizophrenia S. Hossein Fatemi

in

Autism

and

Is There A Connection Between Autism, PraderWilli Syndrome, Catatonia, and GABA? Dirk M. Dhossche, Yaru Song, and Yiming Liu Alcohol, GABA Receptors, and Neurodevelop­ mental Disorders Ujjwal K. Rout Effects of Secretin on Extracellular GABA and Other Amino Acid Concentrations in the Rat Hippocampus Hans-Willi Clement, Alexander Pschibul, and Eberhard Schulz Predicted Role of Secretin and Oxytocin in the Treatment of Behavioral and Developmental Disorders: Implications for Autism Martha G. Welch and David A. Ruggiero Immunological Findings in Autism Hari Har Parshad Cohly and Asit Panja Correlates of Psychomotor Symptoms in Autism Laura Stoppelbein, Sara Sytsma-Jordan, and Leilani Greening GABRB3 Gene Deficient Mice: A Potential Model of Autism Spectrum Disorder Timothy M. DeLorey The Reeler Mouse: Anatomy of a Mutant Gabriella D’Arcangelo

281

Classification Matters for Catatonia and Autism in Children Klaus-Ju¨ rgen Neuma¨rker A Systematic Examination of Catatonia-Like Clinical Pictures in Autism Spectrum Disorders Lorna Wing and Amitta Shah Catatonia in Individuals with Autism Spectrum Disorders in Adolescence and Early Adulthood: A Long-Term Prospective Study Masataka Ohta, Yukiko Kano, and Yoko Nagai Are Autistic and Catatonic Regression Related? A Few Working Hypotheses Involving GABA, Purkinje Cell Survival, Neurogenesis, and ECT Dirk Marcel Dhossche and Ujjwal Rout Psychomotor Development and Psychopath­ ology in Childhood Dirk M. J. De Raeymaecker The Importance of Catatonia and Stereotypies in Autistic Spectrum Disorders Laura Stoppelbein, Leilani Greening, and Angelina Kakooza Prader–Willi Syndrome: Atypical Psychoses and Motor Dysfunctions Willem M. A. Verhoeven and Siegfried Tuinier Towards a Valid Nosography and Psychopath­ ology of Catatonia in Children and Adolescents David Cohen Is There a Common Neuronal Basis for Autism and Catatonia? Dirk Marcel Dhossche, Brendan T. Carroll, and Tressa D. Carroll Shared Susceptibility Region on Chromosome 15 Between Autism and Catatonia Yvon C. Chagnon Current Trends in Behavioral Interventions for Children with Autism Dorothy Scattone and Kimberly R. Knight

282

CONTENTS OF RECENT VOLUMES

Case Reports with a Child Psychiatric Explora­ tion of Catatonia, Autism, and Delirium Jan N. M. Schieveld ECT and the Youth: Catatonia in Context Frank K. M. Zaw Catatonia in Autistic Spectrum Disorders: A Medical Treatment Algorithm Max Fink, Michael A. Taylor, and Neera Ghaziuddin Psychological Approaches to Chronic CatatoniaLike Deterioration in Autism Spectrum Disorders Amitta Shah and Lorna Wing Section V: Blueprints Blueprints for the Assessment, Treatment, and Future Study of Catatonia in Autism Spectrum Disorders Dirk Marcel, Dhossche, Amitta Shah, and Lorna Wing INDEX

Volume 73 Chromosome 22 Deletion Syndrome and Schizophrenia Nigel M. Williams, Michael C. O’Donovan, and Michael J. Owen Characterization of Proteome of Human Cere­ brospinal Fluid Jing Xu, Jinzhi Chen, Elaine R. Peskind, Jinghua Jin, Jimmy Eng, Catherine Pan, Thomas J. Montine, David R. Goodlett, and Jing Zhang Hormonal Pathways Regulating Intermale and Interfemale Aggression Neal G. Simon, Qianxing Mo, Shan Hu,

Carrie Garippa, and Shi-Fang Lu

Neuronal GAP Junctions: Expression, Function, and Implications for Behavior Clinton B. McCracken and David C. S. Roberts Effects of Genes and Stress on the Neurobiology of Depression J. John Mann and Dianne Currier Quantitative Imaging with the Micropet SmallAnimal Pet Tomograph Paul Vaska, Daniel J. Rubins, David L. Alexoff, and Wynne K. Schiffer

Understanding Myelination through Studying its Evolution Ru¨ diger Schweigreiter, Betty I. Roots, Christine Bandtlow, and Robert M. Gould INDEX

Volume 74 Evolutionary Neurobiology and Art C. U. M. Smith Section I: Visual Aspects Perceptual Portraits Nicholas Wade The Neuropsychology of Visual Art: Conferring Capacity Anjan Chatterjee Vision, Illusions, and Reality Christopher Kennard Localization in the Visual Brain George K. York Section II: Episodic Disorders Neurology, Synaesthesia, and Painting Amy Ione Fainting in Classical Art Philip Smith Migraine Art in the Internet: A Study of 450 Contemporary Artists Klaus Podoll Sarah Raphael’s Migraine with Aura as Inspira­ tion for the Foray of Her Work into Abstraction Klaus Podoll and Debbie Ayles The Visual Art of Contemporary Artists with Epilepsy Steven C. Schachter Section III: Brain Damage Creativity in Painting and Style in BrainDamaged Artists Julien Bogousslavsky Artistic Changes in Alzheimer’s Disease Sebastian J. Crutch and Martin N. Rossor Section IV: Cerebrovascular Disease Stroke in Painters H. Ba¨zner and M. Hennerici

CONTENTS OF RECENT VOLUMES

283

Visuospatial Neglect in Lovis Corinth’s SelfPortraits Olaf Blanke

Transmitter Release at the Neuromuscular Junction Thomas L. Schwarz

Art, Constructional Apraxia, and the Brain Louis Caplan

Vesicle Trafficking and Recycling at the Neuro­ muscular Junction: Two Pathways for Endocytosis Yoshiaki Kidokoro

Section V: Genetic Diseases Neurogenetics in Art Alan E. H. Emery A Naı¨ ve Artist of St Ives F. Clifford Rose Van Gogh’s Madness F. Clifford Rose Absinthe, The Nervous System and Painting Tiina Rekand Section VI: Neurologists as Artists Sir Charles Bell, KGH, FRS, FRSE (1774–1842) Christopher Gardner-Thorpe Section VII: Miscellaneous Peg Leg Frieda Espen Dietrichs The Deafness of Goya (1746–1828) F. Clifford Rose INDEX

Volume 75 Introduction on the Use of the Drosophila Embryonic/Larval Neuromuscular Junction as a Model System to Study Synapse Development and Function, and a Brief Summary of Pathfind­ ing and Target Recognition Catalina Ruiz-Can˜ada and Vivian Budnik

Glutamate Receptors at the Drosophila Neuro­ muscular Junction Aaron DiAntonio Scaffolding Proteins at the Drosophila Neuromus­ cular Junction Bulent Ataman, Vivian Budnik, and Ulrich Thomas Synaptic Cytoskeleton at the Neuromuscular Junction Catalina Ruiz-Can˜ada and Vivian Budnik Plasticity and Second Messengers During Synapse Development Leslie C. Griffith and Vivian Budnik Retrograde Signaling that Regulates Synaptic Development and Function at the Drosophila Neuromuscular Junction Guillermo Marqu�e s and Bing Zhang Activity-Dependent Regulation of Transcription During Development of Synapses Subhabrata Sanyal and Mani Ramaswami Experience-Dependent Potentiation of Larval Neuromuscular Synapses Christoph M. Schuster Selected Methods for the Anatomical Study of Drosophila Embryonic and Larval Neuromuscular Junctions Vivian Budnik, Michael Gorczyca, and Andreas Prokop INDEX

Development and Structure of Motoneurons Matthias Landgraf and Stefan Thor The Development of the Drosophila Larval Body Wall Muscles Karen Beckett and Mary K. Baylies Organization of the Efferent System and Struc­ ture of Neuromuscular Junctions in Drosophila Andreas Prokop Development of Motoneuron Electrical Proper­ ties and Motor Output Richard A. Baines

Volume 76 Section I: Physiological Correlates of Freud’s Theories The ID, the Ego, and the Temporal Lobe Shirley M. Ferguson and Mark Rayport ID, Ego, and Temporal Lobe Revisited Shirley M. Ferguson and Mark Rayport

284

CONTENTS OF RECENT VOLUMES

Section II: Stereotaxic Studies Olfactory Gustatory Responses Evoked by Elec­ trical Stimulation of Amygdalar Region in Man Are Qualitatively Modifiable by Interview Con­ tent: Case Report and Review Mark Rayport, Sepehr Sani, and Shirley M. Ferguson Section III: Controversy in Definition of Beha­ vioral Disturbance Pathogenesis of Psychosis in Epilepsy. The "Seesaw" Theory: Myth or Reality? Shirley M. Ferguson and Mark Rayport Section IV: Outcome of Temporal Lobectomy Memory Function After Temporal Lobectomy for Seizure Control: A Comparative Neuropsy chiatric and Neuropsychological Study Shirley M. Ferguson, A. John McSweeny, and Mark Rayport Life After Surgery for Temporolimbic Seizures Shirley M. Ferguson, Mark Rayport, and Carolyn A. Schell

Neurogenesis and Neuroenhancement in the Pathophysiology and Treatment of Bipolar Disorder Robert J. Schloesser, Guang Chen, and Husseini K. Manji Neuroreplacement, Growth Factor, and Small Molecule Neurotrophic Approaches for Treating Parkinson’s Disease Michael J. O’Neill, Marcus J. Messenger, Viktor Lakics, Tracey K. Murray, Eric H. Karran, Philip G. Szekeres, Eric S. Nisenbaum, and Kalpana M. Merchant Using Caenorhabditis elegans Models of Neuro­ degenerative Disease to Identify Neuroprotective Strategies Brian Kraemer and Gerard D. Schellenberg Neuroprotection and Enhancement of Neurite Outgrowth With Small Molecular Weight Com­ pounds From Screens of Chemical Libraries Donard S. Dwyer and Addie Dickson INDEX

Appendix I Mark Rayport Appendix II: Conceptual Foundations of Studies of Patients Undergoing Temporal Lobe Surgery for Seizure Control Mark Rayport

Volume 78

INDEX

Neurobiology of Dopamine in Schizophrenia Olivier Guillin, Anissa Abi-Dargham, and Marc Laruelle

Volume 77

The Dopamine System and the Pathophysiology of Schizophrenia: A Basic Science Perspective Yukiori Goto and Anthony A. Grace

Regenerating the Brain David A. Greenberg and Kunlin Jin Serotonin and Brain: Evolution, Neuroplasticity, and Homeostasis Efrain C. Azmitia

Glutamate and Schizophrenia: Phencyclidine, N-methyl-D-aspartate Receptors, and Dopamine–Glutamate Interactions Daniel C. Javitt Deciphering the Disease Process of Schizo­ phrenia: The Contribution of Cortical GABA Neurons David A. Lewis and Takanori Hashimoto

Therapeutic Approaches to Promoting Axonal Regeneration in the Adult Mammalian Spinal Cord Sari S. Hannila, Mustafa M. Siddiq, and Marie T. Filbin

Alterations of Serotonin Schizophrenia Anissa Abi-Dargham

Evidence for Neuroprotective Effects of Antipsy­ chotic Drugs: Implications for the Pathophysiol­ ogy and Treatment of Schizophrenia Xin-Min Li and Haiyun Xu

Serotonin and Dopamine Interactions in Rodents and Primates: Implications for Psychosis and Antipsychotic Drug Development Gerard J. Marek

Transmission

in

CONTENTS OF RECENT VOLUMES

Cholinergic Circuits and Signaling in the Patho­ physiology of Schizophrenia Joshua A. Berman, David A. Talmage, and

Lorna W. Role

Schizophrenia and the �7 Nicotinic Acetylchol­ ine Receptor Laura F. Martin and Robert Freedman Histamine and Schizophrenia Jean-Michel Arrang Gannabinoids and Psychosis Deepak Cyril D’Souza Involvement of Neuropeptide Systems in Schizo­ phrenia: Human Studies Ricardo C�aceda, Becky Kinkead, and

Charles B. Nemeroff

Brain-Derived Neurotrophic Factor in Schizo­ phrenia and Its Relation with Dopamine Olivier Guillin, Caroline Demily, and

Florence Thibaut

Schizophrenia Susceptibility Genes: In Search of a Molecular Logic and Novel Drug Targets for a Devastating Disorder Joseph A. Gogos INDEX

Volume 79 The Destructive Alliance: Interactions of Leuko­ cytes, Cerebral Endothelial Cells, and the Immune Cascade in Pathogenesis of Multiple Sclerosis Alireza Minagar, April Carpenter, and J. Steven Alexander Role of B Cells in Pathogenesis of Multiple Sclerosis Behrouz Nikbin, Mandana Mohyeddin Bonab,

Farideh Khosravi, and Fatemeh Talebian

The Role of CD4 T Cells in the Pathogenesis of Multiple Sclerosis Tanuja Chitnis The CD8 T Cell in Multiple Sclerosis: Suppres­ sor Cell or Mediator of Neuropathology? Aaron J. Johnson, Georgette L. Suidan,

Jeremiah McDole, and Istvan Pirko

285

Immunopathogenesis of Multiple Sclerosis Smriti M. Agrawal and V. Wee Yong Molecular Mimicry in Multiple Sclerosis Jane E. Libbey, Lori L. McCoy, and

Robert S. Fujinami

Molecular “Negativity” May Underlie Multiple Sclerosis: Role of the Myelin Basic Protein Family in the Pathogenesis of MS Abdiwahab A. Musse and George Harauz Microchimerism and Stem Cell Transplantation in Multiple Sclerosis Behrouz Nikbin, Mandana Mohyeddin Bonab, and Fatemeh Talebian The Insulin-Like Growth Factor System in Mul­ tiple Sclerosis Daniel Chesik, Nadine Wilczak, and

Jacques De Keyser

Cell-Derived Microparticles and Exosomes in Neuroinflammatory Disorders Lawrence L. Horstman, Wenche Jy, Alireza Minagar, Carlos J. Bidot, Joaquin J. Jimenez, J. Steven Alexander, and Yeon S. Ahn Multiple Sclerosis in Children: Clinical, Diag­ nostic, and Therapeutic Aspects Kevin Rost�asy Migraine in Multiple Sclerosis Debra G. Elliott Multiple Sclerosis as a Painful Disease Meghan Kenner, Uma Menon, and Debra Elliott Multiple Sclerosis and Behavior James B. Pinkston, Anita Kablinger, and Nadejda Akkseeva Cerebrospinal Fluid Analysis in Multiple Sclerosis Francisco A. Luque and Stephen L. Jaffe Multiple Sclerosis in Isfahan, Iran Mohammad Saadatnia, Masoud Etemadifar, and Amir Hadi Maghzi Gender Issues in Multiple Sclerosis Robert N. Schwendimann and Nadejda Alekseeva Differential Diagnosis of Multiple Sclerosis Halim Fadil, Roger E. Kelley, and Eduardo

Gonzalez-Toledo

Prognostic Factors in Multiple Sclerosis Roberto Bergamaschi

286

CONTENTS OF RECENT VOLUMES

Neuroimaging in Multiple Sclerosis Robert Zivadinov and Jennifer L. Cox

Volume 80

Detection of Cortical Lesions Is Dependent on Choice of Slice Thickness in Patients with Multi­ ple Sclerosis Ondrej Dolezal, Michael G. Dwyer, Dana Horakova,

Eva Havrdova, Alireza Minagar,

Srivats Balachandran, Niels Bergsland, Zdenek Seidl,

Manuela Vaneckova, David Fritz, Jan Krasensky,

and Robert Zjvadinov

Epilepsy in the Elderly: Scope of the Problem Ilo E. Leppik

The Role of Quantitative Neuroimaging Indices in the Differentiation of Ischemia from Demyelina­ tion: An Analytical Study with Case Presentation Romy Hoque, Christina Ledbetter, Eduardo GonzalezToledo, Vivek Misra, Uma Menon, Meghan Kenner, Alejandro A. Rabinstein, Roger E. Kelley, Robert Zjvadinov, and Alireza Minagar

Life and Death of Neurons in the Aging Cerebral Cortex John H. Morrison and Patrick R. Hof

HLA-DRB1*1501, -DQB1*0301, -DQB l*0302, -DQB1*0602, and -DQB1*0603 Alleles Are Associated with More Severe Disease Outcome on MRI in Patients with Multiple Sclerosis Robert Zivadinov, Laura Uxa, Alessio Bratina, Antonio Bosco, Bhooma Srinivasaraghavan, Alireza Minagar, Maja Ukmar, Su yen Benedetto, and Marino Zorzon Glatiramer Acetate: Mechanisms of Action in Multiple Sclerosis Tjalf Ziemssen and Wiebke Schrempf Evolving Therapies for Multiple Sclerosis Elena Korniychuk, John M. Dempster, Eileen O’Connor, J. Steven Alexander, Roger E. Kelley, Meghan Kenner, Uma Menon, Vivek Misra, Romy Hoque, Eduardo C. Gonzalez-Toledo, Robert N. Schwendimann, Stacy Smith, and Alireza Minagar Remyelination in Multiple Sclerosis Divya M. Chari Trigeminal Neuralgia: A Modern-Day Review Kelly Hunt and Ravish Patwardhan Optic Neuritis and the Neuro-Ophthalmology of Multiple Sclerosis Paramjit Kaur and Jeffrey L. Bennett Neuromyelitis Optica: Pathogenesis Dean M. Wingerchuk INDEX

New

Findings

on

Animal Models in Gerontology Research Nancy L. Nadon Animal Models of Geriatric Epilepsy Lauren J. Murphree, Lynn M. Rundhaugen, and Kevin M. Kelly

An In Vitro Model of Stroke-Induced Epilepsy: Elucidation of the Roles of Glutamate and Cal­ cium in the Induction and Maintenance of Stroke-Induced Epileptogenesis Robert J. DeLorenzo, David A. Sun, Robert E. Blair, and Sompong Sambati Mechanisms of Action of Antiepileptic Drugs H. Steve White, Misty D. Smith, and Karen S. Wilcox Epidemiology and Outcomes of Status Epilepti­ cus in the Elderly Alan R. Towne Diagnosing Epilepsy in the Elderly R. Eugene Ramsay, Flavia M. Macias, and A. James Rowan Pharmacoepidemiology in Community-Dwelling Elderly Taking Antiepileptic Drugs Dan R. Berlowitz and Mary Jo V. Pugh Use of Antiepileptic Medications in Nursing Homes Judith Garrard, Susan L. Harms, Lynn E. Eberly, and Ilo E. Leppik Differential Diagnosis of Multiple Sclerosis Halim Fadil, Roger E. Kelley, and Eduardo

Gonzalez-Toledo

Prognostic Factors in Multiple Sclerosis Roberto Bergamaschi Neuroimaging in Multiple Sclerosis Robert Zivadinov and Jennifer L. Cox Detection of Cortical Lesions Is Dependent on Choice of Slice Thickness in Patients with Multi­ ple Sclerosis Ondrej Dolezal, Michael G. Dwyer, Dana Horakova, Eva Havrdova, Alireza Minagar, Srivats

CONTENTS OF RECENT VOLUMES

Balachandran, Niels Bergsland, Zdenek Seidl, Manuela Vaneckova, David Fritz, Jan Krasensky, and Robert Zivadinov The Role of Quantitative Neuroimaging Indices in the Differentiation of Ischemia from Demyelination: An Analytical Study with Case Presentation Romy Hoque, Christina Ledbetter, Eduardo GonzalezToledo, Vivek Misra, Uma Menon, Meghan Kenner, Alejandro A. Rabinstein, Roger E. Kelley, Robert Zivadinov, and Alireza Minagar HLA-DRB l*1501,-DQB l*0301,-DQB l*0302, -DQB 1*0602, and -DQB 1*0603 Alleles Are Associated with More Severe Disease Outcome on MRI in Patients with Multiple Sclerosis Robert Zivadinov, Laura Uxa, Alessio Bratina, Antonio Bosco, Bhooma Srinivasaraghavan, Alireza Minagar, Maja Ukmar, Su yen Benedetto, and Marino Zorzon

287

Animal Models of Geriatric Epilepsy Lauren J. Murphree, Lynn M. Rundhaugen, and Kevin M. Kelly Life and Death of Neurons in the Aging Cerebral Cortex John H. Morrison and Patrick R. Hof An In Vitro Model of Stroke-Induced Epilepsy: Elucidation of the Roles of Glutamate and Cal­ cium in the Induction and Maintenance of Stroke-Induced Epileptogenesis Robert J. DeLorenzo, David A. Sun, Robert E. Blair, and Sompong Sambati Mechanisms of Action of Antiepileptic Drugs H. Steve White, Misty D. Smith, and Karen S. Wilcox Epidemiology and Outcomes of Status Epilepti­ cus in the Elderly Alan R. Towne

Glatiramer Acetate: Mechanisms of Action in Multiple Sclerosis Tjalf Ziemssen and Wiebke Schrempf

Diagnosing Epilepsy in the Elderly R. Eugene Ramsay, Flavia M. Macias, and A. James Rowan

Evolving Therapies for Multiple Sclerosis Elena Komiychuk, John M. Dempster, Eileen O’Connor, J. Steven Alexander, Roger E. Kelley, Meghan Kenner, Uma Menon, Vivek Misra, Romy Hoque, Eduardo C. Gonzalez-Toledo, Robert N. Schwendimann, Stacy Smith, and Alireza Minagar

Pharmacoepidemiology in Community-Dwelling Elderly Taking Antiepileptic Drugs Dan R. Berlowitz and Mary Jo V. Pugh

Remyelination in Multiple Sclerosis Divya M. Chari

Age-Related Changes in Pharmacokinetics: Pre­ dictability and Assessment Methods Emilio Perucca

Trigeminal Neuralgia: A Modern-Day Review Kelly Hunt and Ravish Patwardhan

Use of Antiepileptic Medications in Nursing Homes Judith Garrard, Susan L. Harms, Lynn E. Eberly, and Ilo E. Leppik

Optic Neuritis and the Neuro-Ophthalmology of Multiple Sclerosis Paramjit Kaur and Jeffrey L. Bennett

Factors Affecting Antiepileptic Drug Pharmaco­ kinetics in Community-Dwelling Elderly James C. Cloyd, Susan Marino,

and Angela K. Bimbaum

Neuromyelitis Optica: Pathogenesis Dean M. Wingerchuk

Pharmacokinetics of Antiepileptic Drugs in Elderly Nursing Home Residents Angela K. Bimbaum

New

Findings

on

INDEX

Volume 81 Epilepsy in the Elderly: Scope of the Problem Ilo E. Leppik Animal Models in Gerontology Research Nancy L. Nadon

The Impact of Epilepsy on Older Veterans Maty Jo V. Pugh, Dan R. Berlowitz, and Lewis Kazis Risk and Predictability of Drug Interactions in the Elderly Rene H. Levy and Carol Collins Outcomes in Elderly Patients With Newly Diag­ nosed and Treated Epilepsy Martin J. Brodie and Linda J. Stephen

288

CONTENTS OF RECENT VOLUMES

Recruitment and Retention in Clinical Trials of the Elderly Flavia M. Macias, R. Eugene Ramsay, and A. James Rowan Treatment of Convulsive Status Epilepticus David M. Treiman Treatment of Nonconvulsive Status Epilepticus Matthew C. Walker Antiepileptic Drug Formulation and Treatment in the Elderly: Biopharmaceutical Considerations Barry E. Gidal INDEX

Volume 82 Inflammatory Mediators Leading to Protein Misfolding and Uncompetitive/Fast Off-Rate Drug Therapy for Neurodegenerative Disorders Stuart A. Lipton, Zezong Gu, and Tomohiro

Nakamura

Innate Immunity and Protective Neuroinflam­ mation: New Emphasis on the Role of Neuroim­ mune Regulatory Proteins M. Griffiths, J. W. Nead, and P. Gasque Glutamate Release from Astrocytes in Physiolo­ gical Conditions and in Neurodegenerative Dis­ orders Characterized by Neuroinflammation Sabino Vesce, Daniela Rossi, Liliana Brambilla, and Andrea Volterra The High-Mobility Group Box 1 Cytokine Induces Transporter-Mediated Release of Gluta­ mate from Glial Subcellular Particles (Gliosomes) Prepared from In Situ-Matured Astrocytes Giambattista Bonanno, Luca Raiteri, Marco Milanese, Simona Zappettini, Edon Melloni, Marco Pedrazzi, Mario Passalacqua, Carlo Tacchetti, Cesare Usai, and Bianca Sparatore The Role of Astrocytes and Complement System in Neural Plasticity Milos Pekny, Ulrika Wilhelmsson, Yalda Rahpeymai Bogestal, and Marcela Pekna New Insights into the Roles of Metalloprotei-nases in Neurodegeneration and Neuroprotection A. J. Turner and N. N. Nalivaeva

Relevance of High-Mobility Group Protein Box 1 to Neurodegeneration Silvia Fossati and Alberto Chiarugi Early Upregulation of Matrix Metalloproteinases Following Reperfusion Triggers Neuroinflam­ matory Mediators in Brain Ischemia in Rat Diana Amantea, Rossella Russo, Micaela Gliozzi, Vincenza Fratto, Laura Berliocchi, G. Bagetta, G. Bemardi, and M. Tiziana Corasaniti The (Endo)Cannabinoid System in Multiple Sclerosis and Amyotrophic Lateral Sclerosis Diego Centonze, Silvia Rossi, Alessandro

Finazzi-Agro, Giorgio Bemardi, and Mauro

Maccarrone

Chemokines and Chemokine Receptors: Multi­ purpose Players in Neuroinflammation Richard M. Ransohoff, LiPing Liu, and

Astrid E. Cardona

Systemic and Acquired Immune Responses in Alzheimer’s Disease Markus Britschgi and Tony Wyss-Coray Neuroinflammation in Alzheimer’s Disease and Parkinson’s Disease: Are Microglia Pathogenic in Either Disorder? Joseph Rogers, Diego Mastroeni, Brian Leonard, Jeffrey Joyce, and Andrew Grover Gytokines and Neuronal Ion Channels in Health and Disease Barbara Viviani, Fabrizio Gardoni, and Marina Marinovch Cyclooxygenase-2, Prostaglandin E2, and Micro­ glial Activation in Prion Diseases Luisa Minghetti and Maurizio Pocchiari Glia Proinflammatory Cytokine Upregulation as a Therapeutic Target for Neurodegenerative Diseases: Function-Based and Target-Based Discovery Approaches Linda J. Van Eldik, Wendy L. Thompson, Hantamalala Ralay Ranaivo, Heather A. Behanna, and D. Martin Watterson Oxidative Stress and the Pathogenesis of Neuro­ degenerative Disorders Ashley Reynolds, Chad Laurie, R. Lee Mosley, and Howard E. Gendelman

289

CONTENTS OF RECENT VOLUMES

Differential Modulation of Type 1 and Type 2 Gannabinoid Receptors Along the Neuro­ immune Axis Sergio Oddi, Paola Spagnuolo, Monica Bari,

Antonella D’Agostino, and Mauro Maccarrone

Effects of the HIV-1 Viral Protein Tat on Central Neurotransmission: Role of Group I Meta-botropic Glutamate Receptors Elisa Neri, Veronica Musante, and Anna Pittaluga Evidence to Implicate Early Modulation of Inter­ leukin-1/� Expression in the Neuroprotectdon Afforded by 17/�-Estradiol in Male Rats Under­ gone Transient Middle Cerebral Artery Occlusion Olga Chiappetta, Micaela Gliozzi, Elisa Siviglia, Diana Amantea, Luigi A. Morrone, Laura Berliocchi, G. Bagetta, and M. Tiziana Corasaniti ARoleforBrainCyclooxygenase-2andProstaglandin­ E2 in Migraine: Effects of Nitroglycerin Cristina Tassorelli, Rosaria Greco, Marie Ther�e se Armentero, Fabio Blandini, Giorgio Sandrini, and Giuseppe Nappi The Blockade of K+-ATP Channels has Neuro­ protective Effects in an In Vitro Model of Brain Ischemia Robert Nistic�o, Silvia Piccirilli, L. Sebastianelli, Giuseppe Nistic�o, G. Bernardi, and N. B. Mercuri Retinal Damage Caused by High Intraocular Pressure-Induced Transient Ischemia is Pre­ vented by Coenzyme Q10 in Rat Carlo Nucci, Rosanna Tartaglione, Angelica Cerulli, R. Mancino, A. Spano, Federica Cavaliere, Laura Rombol, G. Bagetta, M. Tiziana Corasaniti, and Luigi A. Morrone Evidence Implicating Matrix Metalloproteinases in the Mechanism Underlying Accumulation of IL-1 � and Neuronal Apoptosis in the Neocortex of HIV/gpl20-Exposed Rats Rossella Russo, Elisa Siviglia, Micaela Gliozzi, Diana Amantea, Annamaria Paoletti, Laura Berliocchi, G. Bagetta, and M. Tiziana Corasaniti Neuroprotective Effect of Nitroglycerin in a Rodent Model of Ischemic Stroke: Evaluation of Bcl-2 Expression Rosaria Greco, Diana Amantea, Fabio Blandini, Giuseppe Nappi, Giacinto Bagetta, M. Tiziana Corasaniti, and Cristina Tassorelli INDEX

Volume 83 Gender Differences in Pharmacological Response Gail D. Anderson Epidemiology and Classification of Epilepsy: Gender Comparisons John C. McHugh and Norman Delanty Hormonal Influences Neurobiology Cheryl A. Frye

on

Seizures:

Basic

Catamenial Epilepsy Patricia E. Penovich and Sandra Helmers Epilepsy in Women: Special Considerations for Adolescents Mary L. Zupanc and Sheryl Haut Contraception in Women with Epilepsy: Phar­ macokinetic Interactions, Contraceptive Options, and Management Caryn Dutton and Nancy Foldvary-Schaefer Reproductive Dysfunction in Women with Epi­ lepsy: Menstrual Cycle Abnormalities, Fertility, and Polycystic Ovary Syndrome Ju¨ rgen Bauer and Deirdre Cooper-Mahkorn Sexual Dysfunction in Women with Epilepsy: Role of Antiepileptic Drugs and Psychotropic Medications Mary A. Gutierrez, Romila Mushtaq, and Glen Stimmel Pregnancy in Epilepsy: Issues of Concern John DeToledo Teratogenicity and Antiepileptic Drugs: Poten­ tial Mechanisms Mark S. Yerby Antiepileptic Drug Teratogenesis: What are the Risks for Congenital Malformations and Adverse Cognitive Outcomes? Cynthia L. Harden Teratogenicity of Antiepileptic Drugs: Role of Pharmacogenomics Raman Sankar and Jason T. Lerner Antiepileptic Drug Therapy in Pregnancy I: Gesta­ tion-Induced Effects on AED Pharmacokinetics Page B. Pennell and Collin A. Hovinga Antiepileptic Drug Therapy in Pregnancy II: Fetal and Neonatal Exposure Collin A. Hovinga and Page B. Pennell

290

CONTENTS OF RECENT VOLUMES

Seizures in Pregnancy: Diagnosis Management Robert L. Beach and Peter W. Kaplan

and

Management of Epilepsy and Pregnancy: An Obstetrical Perspective Julian N. Robinson and Jane Cleary-Goldman Pregnancy Registries: Strengths, Weaknesses, and Bias Interpretation of Pregnancy Registry Data Marianne Cunnington and John Messenheimer Bone Health in Women With Epilepsy: Clinical Features and Potential Mechanisms Alison M. Pack and Thaddeus S. Walczak Metabolic Effects of AEDs: Impact on Body Weight, Lipids and Glucose Metabolism Raj D. Sheth and Georgia Montouris Psychiatric Gomorbidities in Epilepsy W. Curt Lafrance, Jr., Andres M. Kanner, and Bruce Hermann Issues for Mature Women with Epilepsy Cynthia L. Harden Pharmacodynamic and Pharmacokinetic Interac­ tions of Psychotropic Drugs with Antiepileptic Drugs Andres M. Kanner and Barry E. Gidal Health Disparities in Epilepsy: How PatientOriented Outcomes in Women Differ from Men Frank Gilliam INDEX

Volume 84 Normal Brain Aging: Clinical, Immunological, Neuropsychological, and Neuroimaging Features Maria T. Caserta, Yvonne Bannon, Francisco Fernandez, Brian Giunta, Mike R. Schoenberg, and Jun Tan

Contributions of Neuropsychology and Neuroi­ maging to Understanding Clinical Subtypes of Mild Cognitive Impairment Amy J. Jak, Katherine J. Bangen, Christina E. Wierenga, Lisa Delano-Wood,

Jody Corey-Bloom, and Mark W. Bondi

Proton Magnetic Resonance Spectroscopy in Dementias and Mild Cognitive Impairment H. Randall Griffith, Christopher C. Stewart, and Jan A. den Hollander Application of PET Imaging to Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment James M. Noble and Nikolaos Scarmeas The Molecular and Cellular Pathogenesis of Dementia of the Alzheimer’s Type: An Overview Francisco A. Luque and Stephen L. Jaffe Alzheimer’s Disease Genetics: Current Status and Future Perspectives Lars Bertram Frontotemporal Lobar Degeneration: Insights from Neuropsychology and Neuroimaging Andrea C. Bozoki and Muhammad U. Farooq Lewy Body Dementia Jennifer C. Hanson and Carol F. Lippa Dementia in Parkinson’s Disease Bradley J. Robottom and William J. Weiner Early Onset Dementia Halim Fadil, Aimee Borazanci, Elhachmia Ait Ben Haddou, Mohamed Yahyaoui, Elena Korniychuk, Stephen L. Jaffe, and Alireza Minagar Normal Pressure Hydrocephalus Glen R. Finney Reversible Dementias Anahid Kabasakalian and Glen R. Finney INDEX

Subcortical Ischemic Gerebrovascular Dementia Uma Menon and Roger E. Kelley Cerebrovascular and Cardiovascular Pathology in Alzheimer’s Disease Jack C. de la Torre

Volume 85

Neuroimaging of Cognitive Impairments in Vas­ cular Disease Carol Di Perri, Turi 0. Dalaker, Mona K. Beyer, and Robert Zivadinov

Solving Hajime Mushiake, Kazuhiro Sakamoto, Naohiro Saito, Toshiro Inui, Kazuyuki Aihara, and Jun Tanji

Involvement of the Prefrontal Cortex in Problem

CONTENTS OF RECENT VOLUMES

GluK l Receptor Antagonists and Hippocampal Mossy Fiber Function Robert Nistico, Sheila Dargan, Stephen M. Fitzjohn, David Lodge, David E. Jane, Graham L. Collingridge, and Zuner A. Bortolotto Monoamine Transporter as a Target Molecule for Psychostimulants Ichiro Sora, Bing Jin Li, Setsu Fumushima, Asami Fukui, Yosefu Arime, Yoshiyuki Kasahara, Hiroaki Tomita, and Kazutaka Ikeda Targeted Lipidomics as a Tool to Investigate Endocannabinoid Function Giuseppe Astarita, Jennifer Geaga, Faizy Ahmed, and Daniele Piomelli The Endocannabinoid System as a Target for Novel Anxiolytic and Antidepressant Drugs Silvana Gaetani, Pasqua Dipasquale, Adele Romano, Laura Righetti, Tommaso Cassano, Daniele Piomelli, and Vincenzo Cuomo GABAA Receptor Function and Gene Expres­ sion During Pregnancy and Postpartum Giovanni Biggio, Maria Cristina Mostallino, Paolo Follesa, Alessandra Concas, and Enrico Sanna Early Postnatal Stress and Neural Circuit Under­ lying Emotional Regulation Machiko Matsumoto, Mitsuhiro Yoshioka, and

Hiroko Togashi

Roles of the Histaminergic Neurotransmission on Methamphetamine-Induced Locomotor Sen­ sitization and Reward: A Study of Receptors Gene Knockout Mice Naoko Takino, Eiko Sakurai, Atsuo Kuramasu,

Nobuyuki Okamura, and Kazuhiko Yanai

Developmental Exposure to Cannabinoids Causes Subtle and Enduring Neurofunctional Alterations Patrizia Campolongo, Viviana Trezza, Maura

Palmery, Luigia Trabace, and Vincenzo Cuomo

Neuronal Mechanisms for Pain-Induced Aver­ sion: Behavioral Studies Using a Conditioned Place Aversion Test Masabumi Minami Bv8/Prokineticins and their Receptors: A New Pronociceptive System Lucia Negri, Roberta Lattanzi, Elisa Giannini, Michela Canestrelli, Annalisa Nicotra, and Pietro Melchiorri

291

P2Y6-Evoked Microglial Phagocytosis Kazuhide Inoue, Schuichi Koizumi, Ayako Kataoka, Hidetoshi Tozaki-Saitoh, and Makoto Tsuda PPAR and Pain Takehiko Maeda and Shiroh Kishioka Involvement of Inflammatory Mediators in Neu­ ropathic Pain Caused by Vincristine Norikazu Kiguchi, Takehiko Maeda, Yuka Kobayashi, Fumihiro Saika, and Shiroh Kishioka Nociceptive Behavior Induced by the Endogen­ ous Opioid Peptides Dynorphins in Uninjured Mice: Evidence with Intrathecal N-ethylmaleimide Inhibiting Dynorphin Degradation Kbichi Tan-No, Hiroaki Takahashi, Osamu Nakagawasai, Fukie Niijima, Shinobu Sakurada, Georgy Bakalkin, Lars Terenius, and Takeshi Tadano Mechanism of Allodynia Evoked by Intrathecal Morphine-3-Glucuronide in Mice Takaaki Komatsu, Shinobu Sakurada,

Sou Katsuyama, Kengo Sanai, and Tsukasa Sakurada

(–)-Linalool Attenuates Allodynia in Neuropathic Pain Induced by Spinal Nerve Ligation in C57/B16 Mice Laura Berliocchi, Rossella Russo, Alessandra Levato, Vincenza Fratto, Giacinto Bagetta, Shinobu Sakurada, Tsukasa Sakurada, Nicola Biagio Mercuri, and Maria Tiziana Corasaniti Intraplantar Injection of Bergamot Essential Oil into the Mouse Hindpaw: Effects on CapsaicinInduced Nociceptive Behaviors Tsukasa Sakurada, Hikari Kuwahata, Soh Katsuyama, Takaaki Komatsu, Luigi A. Morrone, M. Tiziana Corasaniti, Giacinto Bagetta, and Shi­ nobu Sakurada New Therapy for Neuropathic Pain Hirokazu Mizoguchi, Chizuko Watanabe, Akihiko Yonezawa, and Shinobu Sakurada Regulated Exocytosis from Astrocytes: Physiolo­ gical and Pathological Related Aspects Corrado Calii, Julie Marchaland, Paola Spagnuolo, Julien Gremion, and Paola Bezzi Glutamate Release from Astrocytic Gliosomes Under Physiological and Pathological Conditions Marco Milanese, Tiziana Bonifacino, Sitmona Zappettini, Cesare Usai, Carlo Tacchetti, Mario Nobile, and Giambattista Bonanno

292

CONTENTS OF RECENT VOLUMES

Neurotrophic and Neuroprotective Actions of an Enhancer of Ganglioside Biosynthesis Jin-ichi Inokuchi

Bidirectional Interfaces with the Peripheral Nervous System Silvestro Micera and Xavier Navarro

Involvement of Endocannabinoid Signaling in the Neuroprotective Effects of Subtype 1 Meta­ botropic Glutamate Receptor Antagonists in Models of Cerebral Ischemia Elisa Landucci, Francesca Boscia, Elisabetta Gerace, Tania Scartabelli, Andrea Cozzi, Flavio Moroni, Guido Mannaioni, and Domenico E. Pellegrini-Giampietro

Interfacing Insect Brain for Space Applications Giovanni Di Pino, Tobias Seidl, Antonella Benvenuto, Fabrizio Sergi, Domenico Campolo, Dino Accoto, Paolo Maria Rossini, and Eugenio Guglielmelli

NF-kappaB Dimers in the Regulation of Neuro­ nal Survival Ilenia Sarnico, Annamaria Lanzillotta, Marina Benarese, Manuela Alghisi, Cristina Baiguera, Leontino Battistin, PierFranco Spano, and Marina Pizzi Oxidative Stress in Stroke Pathophysiology: Vali­ dation of Hydrogen Peroxide Metabolism as a Pharmacological Target to Afford Neuroprotection Diana Amantea, Maria Cristina Marrone, Robert Nistic�o, Mauro Federici, Giacinto Bagetta, Giorgio Bernardi, and Nicola Biagio Mercuri Role of Akt and ERK Signaling in the Neuro­ genesis following Brain Ischemia Norifumi Shioda, Feng Han, and Kohji Fukunaga Prevention of Glutamate Accumulation and Upregulation of Phospho-Akt may Account for Neuroprotection Afforded by Bergamot Essential Oil against Brain Injury Induced by Focal Cere­ bral Ischemia in Rat Diana Amantea, Vincenza Fratto, Simona Maida, Domenicantonio Rotiroti, Salvatore Ragusa, Giuseppe Nappi, Giacinto Bagetta, and Maria Tiziana Corasaniti Identification of Novel Pharmacological Targets to Minimize Excitotoxic Retinal Damage Rossella Russo, Domenicantonio Rotiroti, Cristina Tassorelli, Carlo Nucci, Giacinto Bagetta, Massimo Gilberto Bucci, Maria Tiziana Corasaniti, and Luigi Antonio Morrone INDEX

Volume 86 Section One: Hybrid Bionic Systems EMG-Based and Gaze-Tracking-Based Man–Machine Interfaces Federico Carpi and Danilo De Rossi

Section Two: Meet the Brain Meet the Brain: Neurophysiology John Rothwell Fundamentals of Electroencefalography, Magne­ toencefalography, and Functional Magnetic Resonance Imaging Claudio Babiloni, Vittorio Pizzella, Cosimo Del

Gratta, Antonio Ferretti, and Gian Luca Romani

Implications of Brain Plasticity to Brain–Machine Interfaces Operation: A Potential Paradox? Paolo Maria Rossini Section Three: Brain Machine Interfaces, A New Brain-to-Environment Communication Channel An Overview of BMIs Francisco Sepulveda Neurofeedback and Brain–Computer Interface: Clinical Applications Niels Birbaumer, Ander Ramos Murguialday, Cornelia Weber, and Pedro Montoya Flexibility and Practicality: Graz Brain– Computer Interface Approach Reinhold Scherer, Gernot R. Mulkr-Putz, and

Gert Pfurtscheller

On the Use of Brain–Computer Interfaces Out­ side Scientific Laboratories: Toward an Applica­ tion in Domotic Environments F. Babiloni, F. Cincotti, M. Marciani, S. Salinari, L. Astolfi, F. Aloise, F. De Vico Fallani, and D. Mattia Brain–Computer Interface Research at the Wadsworth Center: Developments in Noninva­ sive Communication and Control Dean J. Krusienski and Jonathan R. Wolpaw Watching Brain TV and Playing Brain Ball: Exploring Novel BCL Strategies Using Real–Time Analysis of Human Intercranial Data Karim Jerbi, Samson Freyermuth, Lorella Minotti, Philippe Kahane, Alain Berthoz, and Jean-Philippe Lachaux

CONTENTS OF RECENT VOLUMES

Section Four: Brain-Machine Interfaces and Space Adaptive Changes of Rhythmic EEG Oscilla­ tions in Space: Implications for Brain–Machine Interface Applications G. Cheron, A. M. Cebolla, M. Petieau, A. Bengoetxea, E. Paknero-Soter, A. Leroy, and B. Dan Validation of Brain–Machine Interfaces During Parabolic Flight Jos�e del R. Mill�an, Pierre W. Ferrez, and Tobias Seidl Matching Brain–Machine Interface Perfor­ mance to Space Applications Luca Citi, Oliver Tonet, and Martina Marinelli Brain–Machine Interfaces for Space Applications —Research, Technological Development, and Opportunities Leopold Summerer, Dario Izzo, and Luca Rossini INDEX

Volume 87 Peripheral Nerve Repair and Regeneration Research: A Historical Note Bruno Battiston, Igor Papalia, Pierluigi Tos, and Stefano Geuna Development of the Peripheral Nerve Suleyman Kaplan, Ersan Odaci, Bunyami Unal, Bunyamin Sahin, and Michele Fornaro Histology of the Peripheral Nerve and Changes Occurring During Nerve Regeneration Stefano Geuna, Stefania Raimondo, Giulia Ronchi, Federka Di Scipio, Pierluigi Tos, Krzysztof Czaja, and Michete Fornaro Methods and Protocols in Peripheral Nerve Regeneration Experimental Research: Part I— Experimental Models Pierluigi Tos, Giulia Ronchi, Igor Papalia, Vera Sallen, Josette Legagneux, Stefano Geuna, and Maria G. Giacobini-Robecchi Methods and Protocols in Peripheral Nerve Regeneration Experimental Research: Part II— Morphological Techniques Stefania Raimondo, Michele Fornaro, Federica Di Scipio, Giulia Ronchi, Maria G. Giacobini-Robecchi, and Stefano Geuna

293

Methods and Protocols in Peripheral Nerve Regeneration Experimental Research: Part III— Electrophysiological Evaluation Xavier Navarro and Esther Udina Methods and Protocols in Peripheral Nerve Regeneration Experimental Research: Part IV— Kinematic Gait Analysis to Quantify Per­ ipheral Nerve Regeneration in the Rat Luis M. Costa, Maria J. Simes, Ana C. Mauricio and Artur S. P. Varejo Current Techniques and Concepts in Peripheral Nerve Repair Maria Siemionow and Grzegorz Brzezicki Artificial Scaffolds for Peripheral Reconstruction Valeria Chiono, Chiara Tonda-Turo, and

Gianluca Ciardelli

Nerve

Conduit Luminal Additives for Peripheral Nerve Repair Hede Yan, Feng Zhang, Michael B. Chen, and

William C. Lineaweaver

Tissue Engineering of Peripheral Nerves Bruno Battiston, Stefania Raimondo, Pierluigi Tos, Valentina Gaidano, Chiara Audisio, Anna Scevola, Isabelle Perroteau, and Stefano Geuna Mechanisms Underlying The End-to-Side Nerve Regeneration Eleana Bontioti and Lars B. Dahlin Experimental Results in End-To-Side Neurorrhaphy Alexandras E. Beris and Marios G. Lykissas End-to-Side Nerve Regeneration: From the Laboratory Bench to Clinical Applications Pierluigi Tos, Stefano Artiaco, Igor Papalia, Ignazio Marcoccio, Stefano Geuna, and Bruno Battiston Novel Pharmacological Approaches to Schwann Cells as Neuroprotective Agents for Peripheral Nerve Regeneration Valeria Magnaghi, Patrizia Procacci, and

Ada Maria Tata

Melatonin and Nerve Regeneration Ersan Odaci and Suleyman Kaplan Transthyretin: An Enhancer of Nerve Regeneration Carolina E. Fleming, Fernando Milhazes Mar, Filipa Franquinho, and Mnica M. Sousa

294

CONTENTS OF RECENT VOLUMES

Enhancement of Nerve Regeneration and Recovery by Immunosuppressive Agents Damien P. Kuffler

Dosing Time-Dependent Psychostimulants H. Manev and T. Uz

The Role of Collagen in Peripheral Nerve Repair Guide Koopmans, Birgit Hasse, and Nektarios Sinis

Dopamine-Induced Behavioral Changes and Oxidative Stress in Methamphetamine-Induced Neurotoxicity Taizo kita, Ikuko Miyazaki, Masato Asanuma, Mika Takeshima, and George C. Wagner

Gene Therapy Perspectives for Nerve Repair Serena Zacchigna and Mauro Giacca Use of Stem Cells for Improving Nerve Regeneration Giorgio Terenghi, Mikael Wiberg, and Paul J. Kingham Transplantation of Olfactory Ensheathing Cells for Peripheral Nerve Regeneration Christine Radtke, Jeffery D. Kocsis, and Peter M. Vogt Manual Stimulation of Target Muscles has Dif­ ferent Impact on Functional Recovery after Injury of Pure Motor or Mixed Nerves Nektarios Sinis, Thodora Manoli, Frank Werdin, Armin Kraus, Hans E. Schaller, Orlando GuntinasLichius, Maria Grosheva, Andrey Irintchev, Emanouil Skouras, Sarah Dunlop, and Doychin N. Angelov Electrical Stimulation for Improving Nerve Regeneration: Where do we Stand? Tessa Gordon, Olewale A. R. Sulaiman, and Adil Ladak Phototherapy in Peripheral Nerve Injury: Effects on Muscle Preservation and Nerve Regeneration Shimon Rochkind, Stefano Geuna, and Asher Shainberg Age-Related Differences in the Reinnervation after Peripheral Nerve Injury Uro Kovai, Janez Sketelj, and Fajko F. Bajrovi Neural Plasticity After Nerve Injury and Regeneration Xavier Navarro Future Perspective in Peripheral Nerve Reconstruction Lars Dahlin, Fredrik Johansson, Charlotta Lindwall, and Martin Kanje INDEX

Volume 88 Effects Of Psychostimulants On Neurotrophins: Implications For Psychostimulant-Induced Neurotoxicity Francesco Angelucci, Valerio Ricci, Gianfranco Spalletta, Carlo Caltagirone, Aleksander A. Math�e , and Pietro Bria

Actions

of

Acute Methamphetamine Intoxication: Brain Hyperthermia, Blood–Brain Barrier, Brain Edema, and morphological cell abnormalities Eugene A. Kiyatkin and Hari S. Sharma Molecular Bases of Methamphetamine-Induced Neurodegeneration Jean Lud Cadet and Irina N. Krasnova Involvement of Nicotinic Receptors in Metham­ phetamine- and MDMA-Induced Neurotoxicity: Pharmacological Implications E. Escubedo, J. Camarasa, C. Chipana, S. Garcia-Rates, and D.Pubill Ethanol Alters the Physiology of Neuron–Glia Communication Antonio Gonzalez and Gines M. Salido Therapeutic Targeting of “DARPP-32”: A Key Signaling Molecule in the Dopiminergic Pathway for the Treatment of Opiate Addiction Supriya D. Mahajan, Ravikumar Aalinkeel, Jessica L. Reynolds, Bindukumar B. Nair, Donald E. Sykes, Zihua Hu, Adela Bonoiu, Hong Ding, Paras N. Prasad, and Stanley A. Schwartz Pharmacological and Neurotoxicological Actions Mediated By Bupropion and Diethylpropion Hugo R. Arias, Abel Santamaria, and Syed F. Ali Neural and Cardiac Toxicities Associated With 3,4-Methylenedioxymethamphetamine (MDMA) Michael H. Baumann and Richard B. Rothman Cocaine-Induced Breakdown of the Blood–Brain Barrier and Neurotoxicity Hari S. Sharma, Dafin Muresanu, Aruna Sharma, and Ranjana Patnaik Cannabinoid Receptors in Brain: Pharmacoge­ netics, Neuropharmacology, Neurotoxicology, and Potential Therapeutic Applications Emmanuel S. Onaivi

CONTENTS OF RECENT VOLUMES

295

Intermittent Dopaminergic Stimulation causes Behavioral Sensitization in the Addicted Brain and Parkinsonism Francesco Fornai, Francesca Biagioni, Federica Fulceri, Luigi Muni, Stefano Ruggieri, Antonio Paparelli

Method and Validity of Transcranial Sonogra­ phy in Movement Disorders ˇ David Skoloud� ı k and Uwe Walter

The Role of the Somatotrophic Axis in Neuro­ protection and Neuroregeneration of the Addic­ tive Brain Fred Nyberg

Part II: Transcranial Sonography in Parkinsons Disease

INDEX

Volume 89 Molecular Profiling of Striatonigral and Striato­ pallidal Medium Spiny Neurons: Past, Present, and Future Mary Kay Lobo BAC to Degeneration: Bacterial Artificial Chro­ mosome (Bac)-Mediated Transgenesis for Model­ ing Basal Ganglia Neurodegenerative Disorders Xiao-Hong Lu Behavioral Outcome Measures for the Assess­ ment of Sensorimotor Function in Animal Mod­ els of Movement Disorders Sheila M. Fleming The Role of DNA Methylation in the Central Nervous System and Neuropsychiatric Disorders Jian Feng and Guoping Fan

Transcranial Sonography—Anatomy Heiko Huber

Transcranial Sonography in Relation to SPECT and MIBG Yoshinori Kajimoto, Hideto Miwa and Tomoyoshi Kondo Diagnosis of Parkinson’s Disease—Transcranial Sonography in Relation to MRI Ludwig Niehaus and Kai Boelmans Early Diagnosis of Parkinson’s Disease Alexandra Gaenslen and Daniela Berg Transcranial Sonography in the Premotor Diag­ nosis of Parkinson’s Disease Stefanie Behnke, Ute Schro¨der and Daniela Berg Pathophysiology of Transcranial Sonography Signal Changes in the Human Substantia Nigra K. L. Double, G. Todd and S. R. Duma Transcranial Sonography for the Discrimination of Idiopathic Parkinson’s Disease from the Aty­ pical Parkinsonian Syndromes A. E. P. Bouwmans, A. M. M. Vlaar, K. Srulijes, W. H. Mess AND W. E. J. Weber

Heritability of Structural Brain Traits: An Endo­ phenotype Approach to Deconstruct Schizophrenia Nil Kaymaz and J. Van Os

Transcranial Sonography in the Discrimination of Parkinson’s Disease Versus Vascular Parkinsonism Pablo Venegas-Francke

The Role of Striatal NMDA Receptors in Drug Addiction Yao-Ying Ma, Carlos Cepeda, and Cai-Lian Cui

TCS in Monogenic Forms of Parkinson’s Disease Kathrin Brockmann and Johann Hagenah

Deciphering Rett Syndrome With Mouse Genet­ ics, Epigenomics, and Human Neurons Jifang Tao, Hao Wu, and Yi Eve Sun INDEX

Part III—Transcranial Sonography in other Movement Disorders and Depression Transcranial Sonography in Brain Disorders with Trace Metal Accumulation Uwe Walter

Volume 90

Transcranial Sonography in Dystonia Alexandra Gaenslen

Part I: Introduction

Transcranial Sonography in Essential Tremor Heike Stockner and Isabel Wurster

Introductory Remarks on the History and Cur­ rent Applications of TCS Matthew B. Stern

VII—Transcranial Sonography in Restless Legs Syndrome Jana Godau and Martin Sojer

296

CONTENTS OF RECENT VOLUMES

Transcranial Sonography in Ataxia Christos Krogias, Thomas Postert and Jens Eyding Transcranial Sonography in Huntington’s Disease Christos Krogias, Jens Eyding and Thomas Postert Transcranial Sonography in Depression Milija D. Mijajlovic Part IV: Future Applications and Conclusion Transcranial Sonography-Assisted Stereotaxy and Follow-Up of Deep Brain Implants in Patients with Movement Disorders Uwe Walter

Intrinsic Ion Channels and Neurotransmitter Inputs Hitoshi Morikawa and Richard A. Morrisett Alcohol and the Prefrontal Cortex Kenneth Abernathy, L. Judson Chandler

and John J. Woodward

BK Channel and Alcohol, A Complicated Affair Gilles Erwan Martin

Conclusions Daniela Berg

A Review of Synaptic Plasticity at Purkinje Neurons with a Focus on Ethanol-Induced Cerebellar Dysfunction C. Fernando Valenzuela, Britta Lindquist

and Paula A. Zamudio-Bulcock

INDEX

INDEX

Volume 91

Volume 92

The Role of microRNAs in Drug Addiction: A Big Lesson from Tiny Molecules Andrzej Zbigniew Pietrzykowski

The Development of the Science of Dreaming Claude Gottesmann

The Genetics of Behavioral Alcohol Responses in Drosophila Aylin R. Rodan and Adrian Rothenfluh

Dreaming as Inspiration: Evidence from Religion, Philosophy, Literature, and Film Kelly Bulkeley

Neural Plasticity, Human Genetics, and Risk for Alcohol Dependence Shirley Y. Hill

Developmental Perspective: Dreaming Across the Lifespan and What This Tells Us Melissa M. Burnham and Christian Conte

Using Expression Genetics to Study the Neurobiology of Ethanol and Alcoholism Sean P. Farris, Aaron R. Wolen and Michael F. Miles

REM and NREM Sleep Mentation Patrick Mcnamara, Patricia Johnson, Deirdre McLaren, Erica Harris,Catherine Beauharnais and Sanford Auerbach

Genetic Variation and Brain Gene Expression in Rodent Models of Alcoholism: Implications for Medication Development Karl Bjo¨rk, Anita C. Hansson and Wolfgang H. Sommer

Neuroimaging of Dreaming: State of the Art and Limitations Caroline Kuss�e, Vincenzo Muto, Laura Mascetti, Luca Matarazzo, Ariane Foret, Anahita Shaffii-Le Bourdiec and Pierre Maquet

Identifying Quantitative Trait Loci (QTLs) and Genes (QTGs) for Alcohol-Related Phenotypes in Mice Lauren C. Milner and Kari J. Buck

Memory Consolidation, The Diurnal Rhythm of Cortisol, and The Nature of Dreams: A New Hypothesis Jessica D. Payne

Glutamate Plasticity in the Drunken Amygdala: The Making of an Anxious Synapse Brian A. Mccool, Daniel T. Christian, Marvin R. Diaz and Anna K. La¨ck

Characteristics and Contents of Dreams Michael Schredl

Ethanol Action on Dopaminergic Neurons in the Ventral Tegmental Area: Interaction with

Trait and Neurobiological Correlates of Indivi­ dual Differences in Dream Recall and Dream Content Mark Blagrove and Edward F. Pace-Schott

CONTENTS OF RECENT VOLUMES

Consciousness in Dreams David Kahn and Tzivia Gover The Underlying Emotion and the Dream: Relating Dream Imagery to the Dreamer‘s Underlying Emo­ tion can Help Elucidate the Nature of Dreaming Ernest Hartmann Dreaming, Handedness, and Sleep Architecture: Interhemispheric Mechanisms Stephen D. Christman and Ruth E. Propper

297

To What Extent Do Neurobiological Sleep-Wak­ ing Processes Support Psychoanalysis? Claude Gottesmann The Use of Dreams in Modern Psychotherapy Clara E. Hill and Sarah Knox INDEX