Science of awakening, Volume 93 (International Review of Neurobiology.) - PDF Free Download (2024)

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

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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 hom*ogenous 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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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

**

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

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

100

200

ms

−0.8 Fpz Fz Cz

40-Hz response

Awake in bed

0.8

0 −0.4

100

200

ms

−0.8

Light sleep

0.8

−0.4

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

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

WHAT KEEPS US AWAKE?

59

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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61

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 Psychom*otor 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

65

WHAT KEEPS US AWAKE?

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 Psychom*otor 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 (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 (

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