Sleep: Circuits and Functions

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SLEEP CIRCUITS AND FUNCTIONS EDITED BY

Pierre-Hervé Luppi, Ph.D. Université Claude Bernard Lyon I Lyon, France

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Library of Congress Cataloging-in-Publication Data Sleep : circuits & functions / edited by Pierre-Hervé Luppi. p. cm. — (Methods & new frontiers in neuroscience series) Includes bibliographical references and index. ISBN 0-8493-1519-0 (alk. paper) 1. Sleep—Physiological aspects. 2. Neurophysiology. I. Luppi, Pierre-Hervé. II. Series. QP425.S6735 2004 612.8¢21—dc22

2004054498

This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. All rights reserved. Authorization to photocopy items for internal or personal use, or the personal or internal use of specific clients, may be granted by CRC Press LLC, provided that $1.50 per page photocopied is paid directly to Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923 USA. The fee code for users of the Transactional Reporting Service is ISBN 0-8493-15190/05/$0.00+$1.50. The fee is subject to change without notice. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from CRC Press LLC for such copying. Direct all inquiries to CRC Press LLC, 2000 N.W. Corporate Blvd., Boca Raton, Florida 33431. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe.

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Methods & New Frontiers in Neuroscience Our goal in creating the Methods & New Frontiers in Neuroscience series is to present the insights of experts on emerging experimental techniques and theoretical concepts that are or will be at the vanguard of the study of neuroscience. Books in the series cover topics ranging from methods to investigate apoptosis to modern techniques for neural ensemble recordings in behaving animals. The series also covers new and exciting multidisciplinary areas of brain research, such as computational neuroscience and neuroengineering, and describes breakthroughs in classical fields such as behavioral neuroscience. We want these to be the books every neuroscientist will use in order to get acquainted with new methodologies in brain research. These books can be given to graduate students and postdoctoral fellows when they are looking for guidance to start a new line of research. Each book is edited by an expert and consists of chapters written by the leaders in a particular field. Books are richly illustrated and contain comprehensive bibliographies. Chapters provide substantial background material relevant to the particular subject; hence, they are not only “methods” books. They contain detailed tricks of the trade and information as to where these methods can be safely applied. In addition, they include information about where to buy equipment and about Web sites that are helpful in solving both practical and theoretical problems. We hope that as the volumes become available, the effort put in by us, by the publisher, by the book editors, and by the individual authors will contribute to the further development of brain research. The extent to which we achieve this goal will be determined by the utility of these books. Sidney A. Simon, Ph.D. Miguel A.L. Nicolelis, M.D., Ph.D. Series Editors

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Foreword This book presents up-to-date research on sleep mechanisms and functions. Each chapter describes the latest findings and provides a synthesis and bibliography. The chapter by Luppi et al. concerns the anatomical network of sleep, and it summarizes the newest model of the neural system responsible for paradoxical sleep (PS) or rapid eye movement (REM) sleep. In the rat the neurons responsible for PS onset and maintenance seem to be clustered in a sphere of tissue smaller than 1 mm3, centered on the sublaterodorsal nucleus of the pontine reticular formation. It is well known that a discrete bilateral lesion of this nucleus is followed by the disappearance during many months of the tonic aspect of PS (fast cortical activity and decrease of muscle tone), whereas pontine-geniculate-occipital (PGO) activity may still occur. Why such an important state, which is responsible for dreaming, is dependent upon such a small system without recuperation is still unknown, because for waking the systems responsible are quite diffuse and redundant (locus coeruleus, posterior hypothalamus, hypocretin neurons, etc.) so that lesion of one or even several of them is followed by recovery of EEG arousal. The same is true for slow-wave sleep because either lesion of the ventro-lateral preoptic region (VLPO) or prebulbar transection (midpontine preparation) is followed by some recovery of slow cortical activity after 1 or 2 weeks. Fort et al. have identified in vitro the presumed sleep-promoting neurons localized in the VLPO. They have been able to establish firmly that two subtypes of sleep-promoting neurons may be segregated according to their modulation by 5-HT. Their results resuscitate the almost moribund 5-HT theory of sleep in suggesting “that 5-HT released during waking in the preoptic area may participate concomitantly to seemingly opposite mechanisms by strengthening arousal through the inhibition of Type I neurons and prepare sleep via the subthreshold excitation of Type II neurons.” Fort et al. describe in detail a model in which PGD2 and adenosine would contribute to the homeostatic regulation of sleep. The molecular mechanisms of sleep-wake regulation by PGD2 and PGE2 have been described by Osamu Hayaishi, who dedicated 20 years of his life to the exploration of the role of prostaglandins in the sleep-waking cycle. In his chapter, he summarizes the experiments which have permitted to localize PGD synthase in the arachnoid membranes and the choroids plexuses, and the PGD2 receptors at the ventral surface of the rostral basal forebrain. Then, the binding of PGD2 to its receptor is followed by a transduction by adenosine through the adenosine A2 receptor. This is a good example of the cascade of events that start in the leptomeninges (a system unknown by the majority of electrophysiologists) and end in the ventral hypothalamus. In addition to PGD2 and adenosine, new players have entered the arena of sleepor waking-inducing substances. In his paper De Lecea emphasizes that “small is beautiful and interesting.” Given the extraordinary cellular complexity of the brain,

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it can be estimated that a few hundred mRNAs are expressed in a small population of cells (less than 106 neurons). The expression of these rare mRNAs would confer particular physiological properties to the neurons that produce them. By studying small populations, De Lecea has been able to isolate by differential gene expression analysis two peptidergic systems that are well-known newcomers in the jet set of waking- or sleep-responsible substances: Corticostatin at the cortical level is implicated in slow-wave activity and the famous hypocretins (or orexins) in the lateral hypothalamus. This latter system is responsible for waking and for inhibiting PS, because of the discoveries that hypocretins knockout (KO) mice present narcoleptic attacks, and narcoleptic dogs display a mutation in the hypocretin receptor 2 gene. De Lecea also describes new methods for deciphering the mechanisms of action of these new peptides: • • • •

The use of cholera toxin A, which is equivalent to placing a stimulating electrode in situ The utilization of pseudorabies viruses expressing green fluorescent protein (GFP), which may be used for the mapping of afferents The use of expression cameleon to monitor changes in intracellular calcium The development of MRI for rodents in vivo that will give moving images in three spatial planes

All of these techniques will provide the “how” but not necessarily the “why” of the function of sleep. The approach to the function of sleep states is the aim of numerous recent genetic studies. This field, which was pioneered by Valatx in 1972, is now fully developed, as demonstrated by the review written by Tafti et al: “A revolution in the understanding of the molecular basis of circadian rhythm has led to the identification of a number of clock genes and of their interaction to generate a circadian rhythm.” Moreover the recent progresses in molecular genetics have permitted the identification of genetic factors responsible for the pathology of sleep disorders (narcolepsy and advanced sleep-phase syndrome). In rodents it has also been shown that theta oscillations during sleep may be modulated by the metabolic fatty-acid beta-oxidation pathway. Tononi and Cirelli have followed another genetic approach. They screened 20,000 transcripts expressed in the cerebral cortex during sleep, wake, or sleep deprivation in the rat, and they found that about 100 genes related to protein synthesis and neural plasticity increase their expression during sleep. During sleep deprivation Tononi and Cirelli found an increase in the expression of the mRNA for the arylsulfotransferase enzyme (regulating a major step in the catabolism of catecholamines), which led them to the hypothesis that “sleep function might be to interrupt the continuous catecholaminergic activity that occurs during waking.” They have also thoroughly investigated the rest-activity cycle in the fruit fly. They bring conclusive evidence that the rest of a fly is sleeplike, because it is modulated by both the circadian clock and the need for sleep as evidenced by the homeostatic increase of rest after rest deprivation. These fly hypnologists also have been able to obtain by mutation a very short sleep line (3 H out of 24 H), which can be

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Contents Chapter 1

Sleep and Neuronal Plasticity: Cellular Mechanisms of Corticothalamic Oscillations

Mircea Steriade Chapter 2

Role of Basalo-Cortical System in Modulating Cortical Activity and Sleep-Wake States

Maan Gee Lee and Barbara E. Jones Chapter 3

In Vitro Identification of the Presumed Sleep-Promoting Neurons of the Ventrolateral Preoptic Nucleus (VLPO)

Patrice Fort, Pierre-Hervé Luppi, and Thierry Gallopin Chapter 4

Molecular Mechanisms of Sleep-Wake Regulation: A Role of Prostaglandin D2 and Adenosine

Osamu Hayaishi Chapter 5

The Network Responsible for Paradoxical Sleep Onset and Maintenance: A New Theory Based on the Head-Restrained Rat Model

Pierre-Hervé Luppi, Romuald Boissard, Damien Gervasoni, Laure Verret, Romain Goutagny, Christelle Peyron, Denise Salvert, Lucienne Léger, Bruno Barbagli, and Patrice Fort Chapter 6

Reverse Genetics and the Study of Sleep-Wake Cycle: The Hypocretins and Cortistatin

Luis de Lecea Chapter 7

Genetic Regulation of Sleep

Yves Dauvilliers, Paul Franken, and Mehdi Tafti Chapter 8

Searching for Sleep Mutants of Drosophila melanogaster

Chiara Cirelli and Giulio Tononi

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Chapter 9

Sleep Phylogeny: Clues to the Evolution and Function of Sleep

Jerome M. Siegel Chapter 10 Sleep, Synaptic Plasticity, and the Developing Brain Marcos Gabriel Frank Chapter 11 Changes in Brain Gene Expression between Sleep and Wakefulness Giulio Tononi and Chiara Cirelli Chapter 12 Neuronal Reverberation and the Consolidation of New Memories across the Wake-Sleep Cycle Sidarta Ribeiro, Damien Gervasoni, and Miguel A.L. Nicolelis Chapter 13 Cerebral Functional Segregation and Integration during Human Sleep Pierre Maquet, Fabien Perrin, Steven Laureys, Tahn Dang-Vu, Martin Desseilles, Mélanie Boly, and Philippe Peigneux

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1

Sleep and Neuronal Plasticity: Cellular Mechanisms of Corticothalamic Oscillations Mircea Steriade

CONTENTS Introduction Sleep Rhythms: Their Grouping by Cortical Slow Oscillation into Unified Activities Spindles, a Thalamic Rhythm under Cortical Influence Clock-Like Thalamic Delta, an Intrinsic Cell Oscillation Synchronized by Cortical Activity The Slow Cortical Oscillation and Its Actions in Grouping Other Sleep Rhythms Sleep Rhythms Leading to Neuronal Plasticity in Cortical Networks Intrathalamic and Thalamocortical Neuronal Circuits Underlying Augmenting Responses Neuronal Plasticity Outlasting Augmenting Responses and Sleep Spindles Functional Significance of Sleep Oscillations Acknowledgments References

INTRODUCTION Three pioneers of sleep research, Frédéric Bremer, Giuseppe Moruzzi, and Michel Jouvet, developed their concepts based on data from experiments conducted on various brainstem neuronal systems.1–3 All three eventually reached the conclusion that sleep is an active process, but Bremer and Moruzzi initially considered the stage of sleep with highly synchronized brain electrical activity as a deafferented, passive 0-8493-1519-0/05/$0.00+$1.50 © 2005 by CRC Press LLC

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state, due to a fall in the cerebral tonus produced by disconnection from sensory systems1 or decreased activity in the brainstem reticular core.2 These two views are not irreconcilable, because corticipetal activities in discrete sensory systems contribute to widespread forebrain activation by neocortical projections to the brainstem reticular formation.4 The passive theory of sleep genesis by brain deafferentation is still alive and well because some data that pointed to actively hypnogenic neurons did not yet elucidate the multiple targets and chemical codes of the presumably sleep-promoting elements. Neuronal systems that are hypothesized to induce sleep would exert their inhibitory actions on neurons located within ascending activating systems, thus disconnecting the forebrain, as postulated in passive sleep theories. Slow-wave sleep (SWS) is far from being a resting or inactive state associated with general inhibition of cortex and subcortical systems,5 which would give rise to an “abject annihilation of consciousness.”6 Recent studies using intracellular recordings in naturally sleeping animals7 demonstrate intense activity of neocortical neurons during SWS (Figure 1.1) and suggest that brain oscillations during SWS are actively implicated in the consolidation during this sleep state of memory traces acquired during the wakefulness. This chapter discusses the experimental basis of this hypothesis.

SLEEP RHYTHMS: THEIR GROUPING BY CORTICAL SLOW OSCILLATION INTO UNIFIED ACTIVITIES In the intact brain there are no pure rhythms, as those generated by neuronal networks in simplified preparations, such as thalamic or cortical slices maintained in vitro. Instead a coalescence of different oscillatory types is observed during SWS, due to the impact of cortically generated slow oscillation (~0.5–1 Hz) upon neuronal synaptic interactions in the thalamus that give rise to spindles (7–15 Hz) and upon the interplay between intrinsic currents of thalamocortical neurons that produce clocklike delta waves (1–4 Hz). Moreover, fast rhythms in the beta-to-gamma frequency range (20–60 Hz), conventionally regarded as only characteristic for the behavioral states of waking and REM sleep, occur during the active (depolarizing) phase of the slow cortical oscillation in SWS. This combination of low-frequency (<15 Hz) and fast (>20 Hz) rhythms defies a strict dissociation between different sleep and waking rhythms, and justifies our concept8 that sleep oscillations are generated in interconnected neuronal loops between the cerebral cortex and thalamus under the control of generalized modulatory systems arising in the brainstem core, hypothalamus, and basal forebrain. This condition can only be investigated in vivo9 and, at best, using intracellular recordings in naturally awake and sleeping preparations. This section discusses: • • •

Neuronal circuitry that underlies different sleep oscillations Comparison of the results of experiments conducted in vivo and in vitro9 Morphological and physiological bases of SWS rhythms’ coalescence by the slow cortical oscillation

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FIGURE 1.1 Natural slow-wave sleep (SWS) characterized by prolonged hyperpolarizations in neocortical neurons but rich spontaneous firing during depolarizing phases of slow oscillation. Chronically implanted cat. Five traces in top panel depict EEG from depth of left cortical areas 4 (motor) and 21 (visual association), intracellular recording from area 21 regular-spiking neuron (resting membrane potential is indicated), electro-oculogram (EOG), and electromyogram (EMG). Part marked by horizontal bar is expanded below left (arrow). Note relation between hyperpolarizations and depth-positive EEG field potentials. Below right histograms of membrane potential (10-sec epochs) during period of transition from waking to SWS depicted above. Note membrane potential around –64 mV during the 20 sec of waking and progressively increased tail of hyperpolarizations, up to –90 mV, during SWS. Data from experiments by M. Steriade, I. Timofeev and F. Grenier (details in Steriade et al.7).

•

Human studies that corroborate experimental work and together emphasize the role of SWS oscillations in neuronal plasticity and learning

SPINDLES,

A

THALAMIC RHYTHM

UNDER

CORTICAL INFLUENCE

Sleep spindles are generated in thalamic networks and are initiated in thalamic reticular neurons (Figure 1.2). Briefly, GABAergic reticular neurons impose spikebursts in the frequency range of spindles onto thalamocortical neurons, which display

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FIGURE 1.2 Spindle oscillations in reticular thalamic (RE), thalamocortical (Th-Cx, ventrolateral nucleus), and cortical (Cx, motor area) neurons. A, circuit of three neuronal types. B, two rhythms (7–14 Hz and 0.1–0.2 Hz) of spindle oscillations in cortical EEG. C, one EEG spindle sequence is depicted below with intracellular recordings in cats under barbiturate anesthesia. See explanations in text. Modified from Steriade and Deschênes (1988).

rhythmic inhibitory postsynaptic potentials (IPSPs) that de-inactivate the Ca2+dependent current (IT) and produce low-threshold spikes (LTSs) crowned by highfrequency bursts consisting of fast, Na+-mediated action potentials. These spikebursts are transferred to cortical neurons, where they elicit excitatory postsynaptic potentials (EPSPs), occasionally leading to action potentials.

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The pacemaking role of thalamic reticular GABAergic neurons was demonstrated by absence of spindles in thalamocortical systems after lesions of thalamic reticular perikarya or transections separating this inhibitory nucleus from the remaining thalamus10 and by preservation of spindles in the deafferented thalamic reticular nucleus.11 The failure to record spindles in isolated thalamic slices from the posterior part of the reticular nucleus was explained by the absence of an intact collection of reticular cells in that experimental condition.12 The slicing procedure may cut the very long dendrites of these neurons, which generate spindles through an avalanche of dendro-dendritic synaptic interactions.11 The requirement of high-density IT in intact dendrites of thalamic reticular neurons for the production of spindles, similar to those seen during natural SWS, was demonstrated in combined in vivo, in vitro, and in computo work.13 Because the thalamic origin of spindles was discussed at length in previous reviews and a monograph,14 here emphasis is placed on the cortical control of thalamically generated spindles, which explains discrepant results between extremely simplified and intact-brain preparations. One of the dissimilarities between in vitro and in vivo results is the systematic propagation of spindles in thalamic slices15 versus the quasi-simultaneity of spindle sequences over widespread thalamic and cortical territories during natural SWS in animals and humans.16,17 It is known that the most efficient experimental method to elicit spindles are corticofugal volleys, applied either ipsilaterally, which directly activate thalamic reticular neurons,18 or contralaterally (through callosal and corticothalamic pathways), to avoid antidromic invasion of thalamocortical cells’ axons and axon-reflex activation of pacemaking reticular neurons.19 It was natural to hypothesize that spindle propagation in thalamic slices was due to the absence of cortex in the isolated thalamus. Indeed, decortication prevented the simultaneity of spindle sequences and disorganized their spatio-temporal coherence.16,17 The role of cortex in spindles’ simultaneity is also shown by diminished coherence of spindles during cortical spreading depression, during which corticothalamic neurons display no or negligible spontaneous activity.20 The powerful role of corticothalamic projections in the high synchronization of spindle oscillations was demonstrated in humans by showing that cortical-damaged patients display significantly reduced coherence spectra from derivations ipsilateral to the lesion.21 Corticothalamic activity is not only implicated in the long-range synchronization of spindles but also in the termination of individual spindle sequences. The termination of spindle sequences is due, at least partially, to asynchrony in the thalamic circuit, stemming from the different durations of spindle-related IPSPs in thalamocortical cells, resulting in different times at which postinhibitory rebound spikebursts are fired, so that the synchrony in the circuit between thalamocortical and thalamic reticular neurons is disrupted and spindles are terminated. During the late phase of spindles, neocortical neurons become tonically depolarized leading to firing, and spike-triggered-averages by cortical neurons do not reveal a phase relationship between cortical and thalamocortical neurons.22 The sustained depolarization of cortical neurons during the late part of a spindle sequence may be effective in desynchronizing thalamic networks and terminate spindles. Another intrinsic cellular factor may be the depolarizing action of a hyperpolarization-activated cation current (IH).23 This hypothesis was tested in a computational model, but the

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isolated network between thalamocortical and thalamic reticular neurons oscillated infinitely, and up-regulation of IH alone was not sufficiently strong to terminate spindling; however, with the addition of the corticothalamic feedback, spindles in the thalamic network were shorter.22 To sum up, • •

• •

The first part of a spindle sequence is generated in the pacemaker thalamic reticular nucleus. During the first two-to-four IPSPs composing the spindles, thalamocortical neurons do not display rebound spike-bursts, do not return signals to thalamic reticular neurons, and do not contribute to this phase of spindles. The middle part of a spindle sequence is due to the activity in the reciprocal loop between thalamic reticular and thalamocortical neurons. The termination of spindles is due to the depolarizing action of corticothalamic neurons, possibly assisted by the depolarizing action of IH.

Importantly, although generated within the thalamus, spindles can be triggered by the synchronous firing of corticothalamic neurons, as naturally occurring during the depolarizing phase of the slow sleep oscillation, which is associated with depthnegative field potentials in cortex (Figure 1.3 A). This combination gives rise to slowly oscillatory cycles that include both the cortically generated slow oscillation and the thalamically generated spindles.

FIGURE 1.3 (See facing page.) Coalescence of cortical slow oscillation with other slowwave sleep (SWS) rhythms generated in the thalamus. In left column two traces represent field potential from depth of cortical association area 5 and intracellular recording from thalamic reticular neuron (top and bottom traces, respectively); below traces represent field potential from depth of cortical area 5 and intracellular recording from thalamocortical neuron in ventrolateral nucleus. In right column circuits involved in generation of respective SWS pattern. Synaptic projections are indicated with small letters, corresponding to arrows at left, which indicate time sequence of events. (A) Combination of slow oscillation with a spindle sequence. Depolarizing phase of field slow oscillation (depth-negative, downward deflection, also called K-complex) in cortex (Cx) travels through corticothalamic pathway (a) and triggers in thalamic reticular nucleus (RE) a spindle sequence that is transferred to thalamocortical cells (ThCx) of dorsal thalamus (b) and back to cortex (c), where it shapes tail of slow oscillatory cycle. (B) Modulation of slow oscillation by a sequence of clock-like delta waves originating in thalamus by interplay between two inward currents (IH and IT) of thalamocortical neurons. Synchronous activity of cortical neurons during slow oscillation (depth-negative peak of cortical field potential) travels along corticothalamic pathway (a’) eliciting an EPSP, curtailed by an IPSP produced along cortico-RE (a) and RE-ThCx (b) projections. Hyperpolarization of thalamocortical cell generates a sequence of low-threshold potentials crowned by high-frequency spike-bursts at delta frequency that may reach cortex through thalamocortical link (c). Diagrams modified from Amzica and Steriade,30 with intracellular staining of three neuronal types, and intracellular recordings by Steriade et al.29 and Contreras and Steriade.18

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CLOCK-LIKE THALAMIC DELTA, AN INTRINSIC CELL OSCILLATION SYNCHRONIZED BY CORTICAL ACTIVITY The thalamic component of delta waves has a clock-like pattern and depends on two inward currents of thalamocortical neurons: the hyperpolarization-activated current, IH, carried by Na+ and K+, which is expressed as a depolarizing sag of membrane potential toward rest, and a transient Ca2+ current, IT, underlying the LTS. The mechanisms of generation and synchronization of this intrinsic-cell thalamic oscillation were revealed using intracellular studies in vitro24–26 and in vivo.27,28 The prerequisite

FIGURE 1.3

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for the appearance of the clock-like rhythm is the hyperpolarization of thalamocortical neurons to levels more negative than –65 or –70 mV, and their depolarization leads to abolition of the rhythm. In contrast to the spindle oscillation that is generated by synaptic interactions that necessarily include the thalamic reticular nucleus, the delta oscillation is an intrinsic oscillation of thalamocortical neurons. However intrinsically generated, the thalamic delta oscillation is subject to the influence of corticothalamic synaptic volleys, which excite thalamic reticular neurons that set the membrane potential of thalamocortical neurons at adequate levels of hyperpolarization, at which clock-like delta is generated, and synchronize pools of thalamocortical cells.27 In turn the thalamic component of sleep delta waves is projected to cortex and sculpts the slow oscillation29,30 (Figure 1.3 B). Then a thalamic oscillation generated by intrinsic neuronal properties becomes expressed at the cortical level as a result of synchronization among different thalamocortical neurons due to synaptic activities evoked by corticofugal volleys.

THE SLOW CORTICAL OSCILLATION AND ITS ACTIONS IN GROUPING OTHER SLEEP RHYTHMS The slow oscillation was first described using intracellular recordings of cortical neurons in anesthetized animals as well as EEG recordings in human sleep.31 Its cortical origin was demonstrated by survival after thalamectomy,32 presence in large isolated cortical slabs in vivo,33 in cortical slices maintained in vitro,34 and absence in the thalamus of decorticated animals.35 The slow oscillation was also described using extracellular36 and intracellular7 recordings of cortical neurons during natural SWS in animals as well as in EEG37–38 and magnetoencephalographic (MEG)39 recordings during night SWS in humans. The slow oscillation consists of prolonged depolarizations, associated with brisk firing (~10–40 Hz), and long-lasting hyperpolarizations during which neurons are silent (Figure 1.1). Generally, the depolarization lasts for ~0.3–0.6 sec and consists of non-NMDA- and NMDA-mediated EPSPs, fast prepotentials (FPPs), a voltagedependent persistent Na+ current (INa(p)), and fast IPSPs induced in pyramidal neurons by synaptically coupled GABAergic local-circuit cortical cells.31 The presence of fast waves within the beta and gamma frequency bands (generally 20–60 Hz) over the depolarizing phase of the slow sleep oscillation may be surprising for those who think that these fast rhythms are necessarily associated with consciousness and states of cognition. In reality, fast rhythms are voltage-dependent and occur as a function of membrane depolarization in cortical neurons. The transition from beta to gamma oscillations may take place over short (0.5–1 sec) time periods36 without being related to change in behavioral or cognitive state. There is no need to distinguish between these two types of fast rhythms unless intracellular recordings in behaving and performing animals would demonstrate their distinction and the depolarization phase associated with fast rhythms characterizes the slow oscillation during natural SWS or deep anesthesia.36 As to the hyperpolarizing phase of the slow oscillation, it is not due to the action of inhibitory interneurons but to disfacilitation (removal of synaptic, mainly excitatory, inputs) in intracortical and thalamocortical networks, and to some K+ currents.

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Several pieces of evidence support this conclusion: •

•

•

•

Neurons identified morphologically as basket (aspiny or sparsely spiny) cells or electrophysiologically characterized as fast-spiking (presumably GABAergic) cells, during either natural sleep7 or ketamine-xylazine anesthesia,40 behave in phase with regular-spiking (pyramidal) neurons; i.e., they fire during the depolarizing phase and are silent during the hyperpolarizing phase. Intracellular recordings with Cl-filled pipettes during naturally sleeping animals do not affect the prolonged hyperpolarizations of the slow oscillation in SWS.7,41 Recordings with Cs+-filled pipettes strikingly reduce or abolish the hyperpolarizations.41 As Cs+ blocks nonspecifically K+ currents, hyperpolarizations during the slow oscillation are produced, at least partially, by a series of K+ currents, most probably IK(Ca). Disfacilitation in cortical networks is the other factor accounting for the prolonged hyperpolarizations, under anesthesia as well as during natural SWS, as the apparent input resistance was almost double during the hyperpolarizing phase of the slow oscillation in SWS, compared to the depolarizing phase of this oscillation.7,42

The disfacilitation is explained by a progressive depletion of [Ca2+]o during the depolarizing phase of the slow oscillation,43 which would produce a decrease in synaptic efficacy that would eventually lead to the functional disconnection of cortical networks. Realistic models of the SWS slow oscillation in corticothalamic systems propose that summation of miniature EPSPs during the hyperpolarizing (silent) phase of the slow oscillation activates INa(p) and depolarizes the membrane of pyramidal neurons sufficiently for triggering spikes and generating the next depolarizing phase.33,44 The transition from the SWS slow oscillation to brainactivated states is produced by the erasure of prolonged hyperpolarizing phases in cortical neurons45 and their increased input resistance as tested during the behavioral state of wakefulness.7 The concept of grouped SWS rhythms, mainly slow and spindle oscillations but also slow and fast oscillations, derived from animal and human studies7,8,32,36,38,40 (Figure 1.4), is corroborated by recent studies using d.c. EEG signals during stages 2 and 3 of human sleep and showing the grouping of slow oscillation with spindles and beta rhythms.46

SLEEP RHYTHMS LEADING TO NEURONAL PLASTICITY IN CORTICAL NETWORKS Plasticity is defined as a short- or long-term alteration in neuronal responsiveness that depends on the history of a given neuronal network, a change that may evolve from the transient strengthening or depression of synapses to permanent formation of new connections. Besides synaptic activities in neuronal networks, which depend

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on the behavioral state of vigilance, the mechanisms underlying plasticity include modifications in the release of neurotransmitters and postsynaptic sensitivity47 and changes in intrinsic currents that modify neuronal responsiveness.48,49 The impact of network synaptic activity on voltage- and transmitter-gated conductances of single thalamic and neocortical neurons, and the transformation of firing patterns produced by intrinsic cellular properties during shifts in natural states of vigilance, are discussed elsewhere.8,9 Moruzzi50 proposed the first hypothesis relating sleep to plasticity by postulating that SWS does not concern the fast recovery processes in routine synapses underlying stereotyped activities but the slow recovery of learned synapses. During the past decade the development from brain oscillations occurring spontaneously during SWS

FIGURE 1.4

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or their experimental models to neuronal plasticity was investigated in thalamocortical systems. Here we focus on short- and medium-term increases in cellular responsiveness that appear as a consequence of augmenting responses that mimic naturally occurring sleep spindles. This leads to a discussion of data from humans and animal experiments showing the role of sleep oscillations in memory and learning.

INTRATHALAMIC AND THALAMOCORTICAL NEURONAL CIRCUITS UNDERLYING AUGMENTING RESPONSES Augmenting responses are the experimental classical model of sleep spindles51 and are defined as thalamically evoked cortical potentials that grow in size during the first stimuli at a frequency of 5 to 15 Hz, usually ~10 Hz, like the waxing of waves at the onset of spontaneously occurring spindle sequences. Although augmentation occurs in the thalamus of decorticated animals52 (like spindles) and in the intact cortex of athalamic preparations32 or even in isolated cortical slabs in vivo53 and in cortical slices maintained in vitro,54 the full development of augmenting responses, leading to self-sustained activities, requires interacting thalamic and cortical networks. The old idea that incremental thalamocortical responses are of two basically different types, augmenting and recruiting, was suggested by invoking a different cortical layer distribution and a longer latency of recruiting responses that was ascribed to a “diffuse multineuronal system” with intralaminar nuclei serving as an intrathalamic association system.55 It is now known that augmenting responses may precede recruiting responses, or vice versa, within the same sequence of rhythmic potentials (because of the multi-laminar distribution of thalamic projections to cortex), that some cortical recruiting (depth-positive) responses may display latencies as short as those of augmenting (depth-negative) responses, and that the longer latency of cortical recruiting responses is not due to the intrathalamic spread of activity but to slower conduction velocities of axons from some thalamic nuclei.8 FIGURE 1.4 (See facing page.) Cortical slow oscillation groups thalamically generated spindles. CAT (top), intracellular recording in cat under urethane anesthesia from area 7 (1.5 mm depth). Electrophysiological identification (at right) shows orthodromic response to stimulation of thalamic centrolateral (CL) intralaminar nucleus and antidromic response to stimulation of lateroposterior (LP) nucleus. Neuron and related EEG wave oscillation is slow. One cycle of slow oscillation is framed in dots. Part marked by horizontal bar below intracellular trace (at left) is expanded above (right) to show spindles following depolarizing envelope of slow oscillation. CAT (bottom left), dual simultaneous intracellular recordings from right and left cortical area 4. Note spindle during depolarizing envelope of slow oscillation and synchronization of EEG when both neurons synchronously display prolonged hyperpolarizations. HUMAN, the K-complex (KC) in natural sleep. Scalp monopolar recordings with respect to contralateral ear are shown (see figurine). Traces show a short episode from a stage 3 non-REM sleep. The two arrows point to two K-complexes, consisting of a surface-positive wave, followed (or not) by a sequence of spindle (sigma) waves. Note synchrony of K-complexes in all recorded sites. At right, frequency decomposition of electrical activity from C3 lead (see 1) into three frequency bands: slow oscillation (S, 0 to 1 Hz), delta waves (D, 1 to 4 Hz) and spindles (s, 12 to 15 Hz). Modified from Steriade et al.,32 Contreras and Steriade40 (CAT), and Amzica and Steriade38 (HUMAN).

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The distinction between augmenting and recruiting responses is no longer necessary and we simply designate these responses as augmenting or incremental. In the thalamus of decorticated animals, thalamocortical neurons display two types of augmenting responses to local thalamic stimulation at 10 Hz52: One type is associated with progressively decreased IPSPs elicited by successive stimuli in the train and with progressive depolarization of neurons leading to high-threshold spike-bursts with increasing number of action potentials and spike inactivation (Figure 1.5 A). The other type of intrathalamic augmenting responses is based on progressively increased LTSs, which are deinactivated by the increasing hyperpolarization produced by repetitive stimuli in the train (Figure 1.5 B). This type of augmentation (with progressively increased LTSs and rebound spike-bursts) is due to the parallel excitation, whereas the high-threshold form of augmenting is due to decremental responses, in a pool of thalamic reticular GABAergic neurons.56 As augmenting responses mimic spindles, and spindles have been recorded in the deafferented RE nucleus,11 augmenting responses as well as spindles were also obtained in computational models of isolated RE nucleus, with synaptic interconnections including GABAA and GABAB components.57,58 Augmenting responses are also generated within the neocortical circuitry, as demonstrated by stimulating the callosal pathway in thalamectomized cats.32 Rhythmic pulse-trains with the frequency range of sleep spindles (10 Hz) eventually lead to intrinsically bursting cell’s depolarization and dramatic increase in the number of action potentials within each evoked spike-burst (Figure 1.5 C). As in other forms of augmenting responses, such an enhancement in neuronal responsiveness may lead to self-sustained activities and, in some instances, to various patterns of electrographic seizures32 (see below).

NEURONAL PLASTICITY OUTLASTING AUGMENTING RESPONSES SLEEP SPINDLES

AND

This section discusses the self-sustained activity that follows evoked responses in the frequency range of spindles within the thalamus of decorticated animals52 and FIGURE 1.5 (See facing page.) Intrathalamic and corticocortical augmenting responses leading to neuronal plasticity. (A, B) Unilaterally decorticated cats under ketamine-xylazine anesthesia. Intracellular recordings from thalamocortical neurons in ventrolateral (VL) nucleus. Stimulation in VL nucleus (pulse-trains of 5 stimuli at 10 Hz). (A) Pulse-train at 10 Hz evoked high-threshold spike-bursts containing progressively more action potentials, with spike inactivation. (B) Low-threshold augmenting responses developing from progressive increase in IPSP-rebound sequences and followed by a self-sustained spindle. Arrow indicates expanded spike-burst (action potentials truncated). Part marked by horizontal bar and indicating augmenting responses is expanded at right. (C) Cat with extensive thalamic lesion by kainate lesion, unilateral to cortical recording in area 7. Repetitive callosal stimulation (10 Hz) of homotopic point in contralateral hemisphere. Responses to pulse-trains (each consisting of 5 stimuli at 10 Hz), repeated every 3 sec, applied to contralateral area 7. Intracortical augmenting responses to first and eighth pulse-trains are illustrated. Depolarization is about 7 mV, and action potentials within bursts are increased in number after repetitive stimulation. Modified from Steriade and Timofeev52 (A, B) and Steriade et al.32 (C).

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presents evidence of memory processes in the more complex corticothalamic networks after prolonged and rhythmic stimuli that mimic spindles.59 The self-sustained activity is virtually identical to that of responses during the prior period of stimulation. Similar changes have previously been reported in amygdalo-hippocampal synaptic networks and were followed by self-sustained seizures in those circuits.60

FIGURE 1.5

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Finally, medium-term neuronal plasticity, lasting several minutes, will be discussed from experiments on neocortical neurons.53 During repetitive (10 Hz) thalamic stimuli in decorticated animals, the IPSPs of thalamocortical neurons are progressively diminished, and the depolarization area of augmenting responses increases continuously (Figure 1.6 A). In intact thalamocortical networks, cortical augmenting responses to thalamic volleys are characterized by the appearance of a secondary depolarization that mainly depends on spike-bursts generated by an intrinsic property (the de-inactivation of IT) of thalamocortical neurons,61 however the cortex has the necessary equipment to develop some forms of augmentation even after thalamectomy (see Figure 1.5 C).52 The self-sustained oscillations following internally generated incoming signals during SWS61 suggest that this deafferented behavioral state may sustain mental events. Indeed repeated spike-bursts evoked by volleys applied to corticothalamic pathways as well as occurring during spontaneous oscillations may lead to self-sustained activity patterns resembling those evoked in the late stages of stimulation (Figure 1.6 B). Such changes are due to resonant activities in closed loops, as in memory processes. During the depolarizing envelope of spindle sequences, associated with firing in neocortical neurons, cortical stimuli elicit an enhancement of the control response, which may last from tens of seconds to several minutes (Figure 1.7). Repeated pulsetrains giving rise to augmenting responses produce progressively reduced in amplitude of the IPSP of the control response and its replacement by depolarization. Moreover single stimuli applied after the rhythmic pulse-trains elicit exclusively depolarizing responses whose enhancement remained unchanged for several minutes (see bottom panel in Figure 1.7). Similar phenomena occur in cortical neurons when testing cortical stimuli are applied during the depolarizing phases of naturally occur-

FIGURE 1.6 (See facing page.) Short-term plasticity from repetitive intrathalamic augmenting responses of high-threshold type, and development from corticothalamic augmenting responses to self-sustained activity. (A) Intracellular recording of thalamocortical neuron in VL nucleus of cat with ipsilateral hemidecortication and callosal cut. Ketamine-xylazine anesthesia. Progressive and persistent increase in area of depolarization by repeating pulsetrains. Pulse-trains consisting of 5 stimuli at 10 Hz were applied to VL every 2 sec. Responses to four pulse-trains (1–4) are illustrated (1 and 2 were separated by 2 sec; 3 and 4 were also separated by 2 sec and followed 14 sec after 2). Responses to 5-shock train consisted of an early antidromic spike, followed by orthodromic spikes displaying progressive augmentation and spike inactivation. With repetition of pulse-trains, IPSPs elicited by preceding stimuli in train were progressively reduced until their complete obliteration and spike-bursts contained more action potentials with spike inactivation. Increased area of depolarization from first to fifth responses in each pulse-train as well as from pulse-train 1 to pulse-trains 3 and 4. (B) Brainstem-transected cat. Cortically evoked spike-bursts in thalamic VL neuron (1). Motor cortex stimulation was applied with pulse-trains at 10 Hz delivered every 1.3 sec. In 1 the pattern of cortically evoked responses at onset of rhythmic pulse-trains (faster speed than in 2–4). Responses in 2–4 at later stages of stimulation. Stimuli are marked by dots. In 2–4 stimuli and evoked spike-bursts are aligned. Spontaneous spike-bursts appear progressive, resembling evoked ones, as a form of memory in corticothalamic circuit. Modified from Steriade and Timofeev,52 and Steriade.59

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ring spindle sequences.53 Among the mechanisms that may explain the long-term increased responsiveness is the high-frequency firing in response to repeated pulsetrains that may result in activation of high-threshold Ca2+ currents and enhanced [Ca2+]i that may activate protein kinase A62 or Ras/mitogen-activated protein kinase,63 which are thought to be involved in memory consolidation.

FIGURE 1.6

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FIGURE 1.7 Cortical augmenting responses lead to long-lasting enhancement of depolarizing responses in intact cortex. Cat under barbiturate anesthesia. Intracellular recording from electrophysiologically (left upper panel) and morphologically (left middle panel) identified area 7 pyramidal regular-spiking neuron with thin spike (see expanded action potential close to stained neuron). Right panel shows (from top to bottom): control response to a single stimulus to cortex, early responses to pulse-trains at 10 Hz, responses to pulse-train with same parameters applied 12 min later, and response to a single stimulus applied 16 min after onset of rhythmic stimulation. Below amplitude of stimulus-evoked response at 20 ms after stimulus onset. Initially hyperpolarizing responses became depolarizing after pulse-trains at 10 Hz. From Timofeev et al.53

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In addition to sleep spindles, the cortically generated slow oscillation is also implicated in neuronal plasticity. Oscillations within the frequency range of the slow (0.5–1 Hz) and delta (1–4 Hz) rhythms are implicated in cortical plasticity in the developing visual cortex, as microelectrode recording and optical imaging show that the effects of monocular deprivation on cortical responses are increased by a 6-hr SWS period in the dark, and SWS deprivation blocks this enhancement.64 A puzzling issue is the development of paroxysmal activity, such as epileptiform seizures with spike-wave (SW) complexes at ~3 Hz or SW and polyspike-wave (PSW) complexes intermingled with fast (10–20 Hz) runs, after the progressive enhancement of neocortical cells’ responsiveness induced by rhythmic stimulation in the frequency range of spindles that evoke augmenting responses.65,66 Such seizures are generated intracortically because thalamocortical neurons are steadily hyperpolarized and silent, due to the powerful inhibition exerted by thalamic reticular neurons that faithfully follow each paroxysmal depolarization of corticothalamic neurons.67,68 This transformation from normal phenomena (sleep spindles or augmenting responses leading to neuronal plasticity that may be implicated in memory) into pathological episodes (seizures) was unexpected, as epilepsy is a state during which memory is suspended. There is a subtle threshold beyond which augmentation and enhanced responsiveness to control stimuli are rapidly transformed into epileptiform patterns. The mechanisms and significance of this development are now under investigation.

FUNCTIONAL SIGNIFICANCE

OF

SLEEP OSCILLATIONS

Sleep oscillations may determine the behavioral quiescence during this behavioral state, rather than being simple electrical signs of it. Indeed data show that the neuronal substrates of widely synchronized thalamic and cortical sleep oscillations are the same as those that produce the disconnection and unresponsiveness to signals from the external world, which are the defining features of SWS. The brain oscillations that define the transition from wakefulness to SWS and occur during early stages of SWS, such as spindles, are associated with long periods of hyperpolarization69 and increased membrane conductance70 in thalamocortical cells, with the consequence that the incoming messages are blocked71 and the cerebral cortex is deprived of information from the outside world.59,68 The thalamus is the first relay station in which afferent signals are obliterated from the very onset of SWS. The role of spindles in disconnecting the brain from external stimuli was also demonstrated by investigating event-related-potentials in humans and showing that this thalamically generated oscillation gates information processing and protects the sleeper from disturbing stimuli.72 Following the appearance of these initial signs, other oscillatory types mark the late stage of SWS and they further deepen the unresponsiveness of thalamic and cortical neurons, disconnecting the brain from the external world. Spindles and slow oscillations are not only operational in passively deafferenting thalamocortical systems, but are also implicated in active cerebral functions. During spindles, rhythmic and synchronized spike-bursts of thalamocortical neurons depolarize the dendrites of neocortical neurons, which is associated with massive Ca2+

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entry.73 It was hypothesized74 that the massive Ca2+ entry in cortical cells’ dendrites may provide an effective signal to efficiently activate Ca2+ calmodulin-dependent protein kinase II (CaMKII), which is implicated in synaptic plasticity of excitatory synapses in cortex.75 Similar phenomena occur in SWS during the rhythmic spiketrains associated with oscillations in the frequency band of the slow (0.5–1 Hz) oscillation, and could provide the mechanisms that have been hypothesized to consolidate memory traces acquired during the state of wakefulness.29 This idea is supported by human studies demonstrating that the improvement of discrimination tasks and formation of procedural memory depends on SWS76–78 and that training on a declarative learning task leads to a significant enhancement of spindles’ density in humans.79 Similar relations between SWS and memory consolidation have been postulated in work on the hippocampus. The hypothesis that neuronal synchrony associated with sharp potentials during SWS consolidates and transfers information to neocortical fields80,81 was worked out and dendritic recordings from CA1 pyramidal neurons82 suggested that sleep patterns are important for preservation of experienceinduced synaptic changes.81 The firing rate of a hippocampal “place cell” and the correlation between neuronal pairs during wakefulness are increased during subsequent SWS epochs.83 All the above data show that, far from being a period of complete inactivity, SWS oscillations are implicated in mental processes. Dreaming mentation appears also during SWS, the content of dreams is closer to real life events84 than dreaming during REM sleep, the recall rate of dreaming mentation in SWS is quite high,85 and the suggestion has been made that cortically consolidated memories, stored during SWS by rhythmic spike-trains associated with neocortically generated oscillations29,68 as well as the information outflow from the hippocampus, would be integrated with other stored memories during REM sleep.86

ACKNOWLEDGMENTS Personal experiments discussed in this chapter have been supported by grants from the Canadian Institutes for Health Research (MT-3689 and MOP-36545), Human Frontier Science Program (RG-0131), and National Institutes of Health-USA (RO1NS40522). I thank the following collaborators for their creative work: F. Amzica, D. Contreras, R. Curró Dossi, F. Grenier, A. Nuñez, D. Paré, and I. Timofeev.

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63. Dolmetsch, R.E., Pajvani, U., Fife, K., Spotts, J.M., and Greenberg, M.E., Signaling to the nucleus by an L-type calcium channel-calmodulin complex through the MAP kinase pathway, Science, 294, 333–339, 2001. 64. Frank, M.G., Issa, N.P., and Stryker, M.P., Sleep enhances plasticity in the developing visual cortex, Neuron, 30, 275–287, 2001. 65. Steriade, M. and Amzica, F., Dynamic coupling among neocortical neurons during evoked and spontaneous spike-wave seizure activity, J. Neurophysiol., 72, 2051–2069, 1994. 66. Steriade, M., Amzica, F., Neckelmann, D., and Timofeev, I., Spike-wave complexes and fast runs of cortically generated seizures, II, Extra- and intracellular patterns. J. Neurophysiol., 80, 1456–79, 1998. 67. Steriade, M. and Contreras, D., Relations between cortical and thalamic cellular events during transition from sleep pattern to paroxysmal activity, J. Neurosci., 15, 623–642, 1995. 68. Steriade, M., Neuronal Substrates of Sleep and Epilepsy, Cambridge Univ. Press, Cambridge (UK), 2003. 69. Hirsch, J.C., Fourment, A., and Marc, M.E., Sleep-related variations of membrane potential in the lateral geniculate body relay neurons of the cat, Brain Research, 259, 308–312, 1983. 70. Timofeev, I., Contreras, D., and Steriade, M., Synaptic responsiveness of cortical and thalamic neurons during various phases of slow oscillation in cat, J. Physiol. (Lond.), 494, 265–278, 1996. 71. Steriade, M., Iosif, G., and Apostol, V., Responsiveness of thalamic and cortical motor relays during arousal and various stages of sleep, J. Neurophysiol., 32, 251–265, 1969. 72. Elton, M., Winter, O., Heslenfeld, D., Loewy, D., Campbell, K., and Kok, A., Eventrelated potentials to tones in the absence and presence of sleep spindles, J. Sleep Res., 6, 78–83, 1997. 73. Yuste, R. and Tank, D.W., Dendritic integration in mammalian neurons, a century after Cajal, Neuron, 16, 701–716, 1996. 74. Sejnowski, T.J. and Destexhe, A., Why do we sleep?, Brain Res., 886, 208–223, 2000. 75. Soderling, T.R. and Derkach, V.A., Postsynaptic protein phosphorylation and LTP, Trends Neurosci., 23, 75–80, 2000. 76. Gais, S., Plihal, W., Wagner, U., and Born, J., Early sleep triggers memory for early visual discrimination skills, Nat. Neurosci., 3, 1335–1339, 2000. 77. Stickgold, R., James, L., and Hobson, J.A., Visual discrimination learning requires sleep after training, Nat. Neurosci., 3, 1237–1238, 2000. 78. Stickgold, R., Whitbee, D., Schirmer, B., Patel, V., and Hobson, J.A., Visual discrimination improvement. A multi-step process occurring during sleep, J. Cogn. Neurosci., 12, 246–254, 2000. 79. Gais, S., Mölle, M., Helms, K., and Born, J., Learning-dependent increases in sleep density, J. Neurosci., 22, 6830–6834, 2002. 80. Buzsáki, G., Two-stage model of memory trace formation: a role for “noisy” brain states, Neuroscience, 31, 551–570, 1989. 81. Buzsáki, G., Memory consolidation during sleep: a neurophysiological perspective, J. Sleep Res., 7 (Suppl. 1), 17–23, 1998. 82. Kamondi, A., Acsády, L., and Buzsáki, G., Dendritic spikes are enhanced by cooperative network activity in the intact hippocampus, J. Neurosci., 18, 3919–3928, 1998. 83. Wilson, M.A. and McNaughton, B.L., Reactivation of hippocampal ensemble memories during sleep, Science, 265, 676–679, 1994.

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84. Hobson, J.A., Pace-Schott, E., and Stickgold, R., Dreaming and the brain: toward a cognitive neuroscience of conscious states, Brain Behav. Sci., 23, 793–842, 2000. 85. Nielsen, T., Cognition in REM and NREM sleep, Brain Behav. Sci., 23, 851–866, 2000. 86. Hobson, J.A. and Pace-Schott, E.F., The cognitive neuroscience of sleep: neuronal systems, consciousness and learning, Nat. Rev. Neurosci., 3, 679–693, 2002.

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2

Role of Basalo-Cortical System in Modulating Cortical Activity and Sleep-Wake States Maan Gee Lee and Barbara E. Jones

CONTENTS Introduction Discharge Properties of Identified Cholinergic and GABAergic Basal Forebrain Neurons in Anesthetized Rats Discharge Properties of Basal Forebrain Neurons in Head-Fixed Rats Role of Cholinergic and GABAergic Basal Forebrain Neurons Summary Acknowledgments References

INTRODUCTION Cortical activity varies in association with behavior and sleep-wake states. It is predominantly fast during waking and paradoxical sleep (PS or rapid eye movement sleep, REMS) and slow during quiet or slow wave sleep (SWS; see Figure 2.1). The fast activity reflects cortical activation that is stimulated and maintained by subcortical activating systems. These systems originate in the rostral brainstem where glutamatergic neurons of the reticular formation, cholinergic pontomesencephalic tegmental neurons, and noradrenergic locus coeruleus neurons collectively comprise critical activating systems.1,2 They project forward through a dorsal pathway to the midline and intralaminar thalamic nuclei that form the nonspecific thalamo-cortical projection system. Excited by brainstem inputs, this thalamo-cortical system stimulates in turn widespread cortical activation3 (see for review Jones4 and Steriade, this volume). In parallel with this pathway, ascending projections from the brainstem pass ventrally through the hypothalamus to reach the basal forebrain. Excited by the brainstem inputs, the basalo-cortical system also stimulates widespread cortical

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FIGURE 2.1 Cortical activation and slow wave (SW) promoting systems. Schematic sagittal view of rat brain showing the major relays of the arousal systems and their excitatory pathways (arrows) involved in promoting EEG fast activity (bottom EEG trace) characteristic of waking and paradoxical sleep (PS or REMS). The major ascending pathways emerge from the brainstem reticular formation to ascend along a dorsal trajectory into the thalamus where they terminate upon nuclei of the nonspecific thalamo-cortical projection system and a ventral trajectory through the lateral hypothalamus up to the basal forebrain, where they terminate upon neurons of the basalo-cortical projection system (in the substantia innominata). From the basal forebrain neurons containing acetylcholine (ACh, circle) give rise to widespread projections to the cerebral cortex to excite cortical neurons and promote fast activity. Evidence is presented to show that the cholinergic neurons discharge (On) in association with cortical activation (bottom EEG trace). They are excited by inputs from the brainstem arousal systems, including glutamatergic neurons of the reticular formation, noradrenergic neurons of the locus coeruleus and cholinergic neurons of the laterodorsal and pedunculopontine tegmental nuclei, as well as histaminergic and orexinergic neurons of the tuberomammillary nucleus and posterior hypothalamus. Neurons containing GABA (triangle) are also located in the basal forebrain. They give rise to inhibitory projections (ending as blocks) that go to the cortex, the posterior hypothalamus, or brainstem as well as to local neurons in the basal forebrain. Evidence is presented to show that particular GABAergic neurons discharge maximally with cortical slow waves (top EEG trace) and minimally with cortical activation (Off). Particular GABAergic cell groups can thus promote SWS by directly modulating cortical activity or by indirectly attenuating cortical activation through inhibition of hypothalamic and brainstem arousal systems or local cholinergic neurons. Illustration adapted with permission from Jones.53 Abbreviations: 7g, 7th nerve genu; ac, anterior commissure; CPu, caudate-putamen; Cx, cortex; Cu, cuneate nucleus; GP, globus pallidus; Hi, hippocampus; ic, internal capsule; LC, locus coeruleus; LDTg, laterodorsal tegmental nucleus; opt, optic tract; PH, posterior hypothalamus; POA, preoptic area; PPTg, pedunculopontine tegmental nucleus; RF Gi, gigantocellular reticular formation; RF Mes, mesencephalic reticular formation; RF PnC, pontis caudalis reticular formation; RF PnO, pontis oralis reticular formation; Rt, reticularis nucleus of the thalamus; s, solitary tract; scp, superior cerebellar peduncle; SI, substantia innominata; Sol, solitary tract nucleus; SN, substantia nigra; Th, thalamus; TM, tuberomammillary nucleus; VN, vestibular nuclei; VTA, ventral tegmental area.

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activation. This ventral extra-thalamic relay to the cerebral cortex can even be sufficient since lesion of the thalamus does not eliminate cortical activation.4,5 The basalo-cortical projection is composed importantly of cholinergic neurons6,7 (Figure 2.1 and Figure 2.2). Acetylcholine depolarizes and excites cortical neurons to stimulate tonic firing and resulting fast cortical activity.8,9 Pharmacological block

FIGURE 2.2 Basal forebrain cells and Neurobiotin (Nb)-labeled cell. (A) Distribution of cholinergic cells (small open circles) and GABAergic cells (small open triangles) in basal forebrain. (Adapted with permission from Gritti et al.21) Identified cells are indicated that were recorded and labeled in urethane-anesthetized rats as Nb+/ChAT+ (filled circle, see Figure 2.3) and Nb+/GAD+ Off (burst) (filled triangle, see Figure 2.4). Units recorded in unanesthetized head-fixed rats are indicated that were “like” the identified Nb+/ChAT+ cells (large open circle, see Figure 2.5) and Nb+/GAD+ Off (burst) cells (large open triangle, see Figure 2.6). Magnification bar = 1 mm. (B) A cell recorded and juxtacellularly labeled in the head-fixed rat. The Nb-labeled cell was revealed in fluorescence using Streptavidin-Cy2. (Adapted with permission from Lee et al.32) Magnification bar = 20 mm. Abbreviations: CPu, caudate-putamen; GP, globus pallidus; FStr, fundus striatum; LPO, lateral preoptic area; MCPO, magnocellular preoptic nucleus; MPO, medial preoptic nucleus; OTu, olfactory tubercle; Pir, piriform cortex; SIa, substantia innominata pars anterior.

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of muscarinic receptors with atropine diminishes fast cortical activity which is replaced by slow wave activity.10,11 Yet the way in which cholinergic neurons specifically modulate the cerebral cortex is not known, since single-unit recording has found that basal forebrain neurons comprise a very heterogeneous population of cells. Indeed, although many units discharge at their highest rate in association with cortical fast activity, others discharge at their highest rate in association with cortical slow wave activity.12–14 Although electrical stimulation can evoke cortical activation,15 it can also induce slow wave EEG activity and SWS.16,17 Although lesions have been reported to diminish cortical activation,11,18 they have also been shown to decrease sleep.19,20 Collectively, these results suggest the presence of sleeppromoting in addition to wake- or cortical activation-promoting neurons in the basal forebrain that would presumably correspond to noncholinergic and cholinergic neurons, respectively. In addition to cholinergic neurons, a large population of GABAergic neurons is distributed through the basal forebrain21 (Figure 2.1 and Figure 2.2 A). These cells are heterogeneous in their size and projections, magnocellular neurons projecting together with the cholinergic neurons to the cerebral cortex, smaller neurons projecting caudally to the posterior hypothalamus or brainstem, and others presumably projecting locally onto cholinergic and other basal forebrain cells.22–24 GABAergic neurons could thus play varied and different roles as compared to cholinergic neurons in the modulation of cortical activity and sleep–wake states. Clearly, knowing the transmitter phenotype and projection pathway of recorded units is critical for understanding the specific roles of GABAergic and cholinergic basal forebrain neurons in cortical modulation and sleep–wake states.

DISCHARGE PROPERTIES OF IDENTIFIED CHOLINERGIC AND GABAERGIC BASAL FOREBRAIN NEURONS IN ANESTHETIZED RATS The prominent properties of cholinergic and noncholinergic basal forebrain neurons were originally studied in vitro by intracellular recording and labeling with Neurobiotin (Nb) for subsequent identification using immunohistochemical staining for choline acetyltransferase (ChAT).25,26 The distinctive intrinsic properties manifested by cholinergic neurons in vitro provided clues for their subsequent identification in vivo. For the in vivo studies, extracellular recordings were performed using glass micropipettes, and recorded units were labeled with Nb by applying the juxtacellular technique27,28 (Figure 2.2 B). Equivalent to single cell electroporation,29 juxtacellular labeling is achieved by passing current pulses through an Nb-filled micropipette that is touching or in close proximity to the membrane of the recorded cell. Sections were immunostained for ChAT or glutamic acid decarboxylase (GAD) to determine whether the Nb-labeled cells were cholinergic or GABAergic. Using urethane anesthesia, it was possible to characterize units according to their discharge properties and the relationship of their discharge to slow wave versus stimulation-induced faster EEG activity.28,30

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FIGURE 2.3 Discharge pattern of Nb+/ChAT+ neuron in the MCPO (see Figure 2.2 A) recorded in a urethane-anesthetized rat. As shown by the EEG (in A) and unit discharge rate (from the peristimulus histogram, PSH, in B), the average spike rate increases in association with somatosensory stimulation-evoked cortical activation. In the expanded traces (C and D) for the prestimulation (left) and stimulation (right) conditions, it is evident that the unit discharge changes from irregular tonic discharge in association with cortical slow wave activity to rhythmic bursting discharge in association with cortical activation characterized by theta-like activity together with enhanced gamma activity. (Copied with permission from Manns, I.D., Alonso, A., and Jones, B.E., J. Neurosci., 20, 1505–1518, 2000.)

In urethane-anesthetized rats, all identified cholinergic neurons increased their discharge rate in association with stimulation-induced cortical activation (identified as Nb+/ChAT+ “On” cells28 (Figure 2.2 A and Figure 2.3). Cortical activation was marked by the replacement of irregular slow wave activity with rhythmic theta-like activity upon which was riding high frequency gamma activity (30–60 Hz, Figure 2.3 C). The majority of cholinergic neurons not only increased their rate of discharge with cortical activation but also changed their pattern of discharge from an irregular slow pattern to a rhythmic bursting discharge (Figure 2.3 D, with an average intraburst frequency of ~70 Hz). This bursting discharge likely emerged from the low threshold calcium spikes that had been shown in vitro to provide the cholinergic neurons with the capacity to discharge in rhythmically recurring high frequency spike bursts.26 In vivo the rhythmic bursting was correlated with the rhythmic thetalike EEG activity. These results suggested that cholinergic basal forebrain neurons

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could elicit cortical activation by stimulating high-frequency gamma activity together with theta through rhythmic modulation of the cerebral cortex (Figure 2.1). In the same preparation, identified GABAergic neurons were found to be heterogeneous in their discharge profiles and properties.30 A substantial group of GABAergic neurons (40%) increased their discharge rate in association with cortical activation and discharged tonically at relatively high frequencies in the gamma EEG range of activity (identified as Nb+/GAD+ “On” [tonic] cells). Some of these could be antidromically activated from the cerebral cortex. However, the majority of GABAergic neurons (60%) decreased their rate of discharge in association with cortical activation (identified as Nb+/GAD+ “Off” cells). Some of these discharged in a very slow, irregular tonic manner (<10 Hz) during slow-wave EEG activity and turned off during cortical activation (called Nb+/GAD+ Off [tonic] cells). None of these could be antidromically activated from the cerebral cortex and were thus thought to exert their influence either caudally on the posterior hypothalamus or brainstem or locally in the basal forebrain. Other GABAergic neurons discharged in bursts of spikes (~200 Hz) in association with cortical slow wave activity to turn off with stimulation-induced cortical activation (called Nb+/GAD+ Off [burst] cells, Figure 2.2 A and Figure 2.4). Their discharge was correlated with the irregular slow waves. They could be antidromically activated from the cerebral cortex and accordingly exert a direct inhibitory influence upon cortical neurons during slow wave EEG activity. It was thus hypothesized that particular GABAergic basal forebrain neurons could be more active with slow wave EEG during SWS than with cortical activation during waking and could thus inhibit the discharge of other subcortical or cortical neurons involved in stimulating cortical activation (Figure 2.1). Recording of identified cholinergic and GABAergic neurons in relation to EEG activity in urethane-anesthetized rats thus revealed different cell groups that could differentially modulate cortical activity and sleep-wake states. However, study of their discharge profiles in unanesthetized animals is necessary to determine their different roles in modulating natural EEG activity and sleep-wake states. For this purpose recording and juxtacellular labeling were undertaken in head-fixed rats.

DISCHARGE PROPERTIES OF BASAL FOREBRAIN NEURONS IN HEAD-FIXED RATS In order to record and label neurons during natural sleep–wake states, rats were implanted with a u-frame, along with chronically indwelling EEG and EMG electrodes, to fix their heads to a special carriage in the stereotaxic frame and permit recording of single units together with other polygraphic variables.31 Following an ~1-week adaptation to the head-fixation, recording could be performed during the afternoon when the rat passes naturally through states of wake, SWS, and PS. Except during very large movements, units could usually be held through at least one full sleep–wake cycle to be labeled with Nb at the end of at least one complete cycle. Although many units could be recorded in this manner in one animal, only

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FIGURE 2.4 Discharge pattern of Nb+/GAD+ Off (burst) neuron (see Figure 2.2 A) recorded in a urethane-anesthetized rat. As shown by the EEG (in A) and unit discharge rate (from the peristimulus histogram, PSH, in B), the average spike rate decreases in association with somatosensory stimulation-evoked cortical activation. In the expanded traces (C and D) for the prestimulation (left) and stimulation (right) conditions, it is evident that the unit discharges in an irregular bursting pattern in association with cortical slow wave activity and stops firing in association with cortical activation. (Copied with permission from Manns, I.D., Alonso, A., and Jones, B.E., J. Neurosci., 20, 9252–9263, 2000.)

one unit could be labeled with Nb per side to insure unequivocal identification of the recorded unit. Applying this procedure a large sample of units has been recorded in the basal forebrain cholinergic cell area and a small sample of those labeled with Nb using the juxtacellular technique (Figure 2.2). In this sampling, a diverse population of cells was characterized according to discharge profiles in relation to sleep–wake states.32 The vast majority (90%) manifested significant variation in their average discharge rate as a function of state and showed maximum and minimum rates in different states to form (12) multiple distinct cell groups. Some of these shared properties with the cells previously identified in the urethane-anesthetized rats as the cholinergic cells that discharged with cortical activation (Nb+/ChAT+ On) and others with the GABAergic cells which discharged maximally with slow wave activity (Nb+/GAD+ Off). Awaiting immunohistochemical identification of an ade-

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quate number of Nb-labeled cells with these properties, we describe these two cell types here as “cholinergic-like” and “GABAergic-Off-like.” The major proportion of cells (~75%) in the basal forebrain (MCPO and SI area, Figure 2.2 A) discharged maximally during wake or PS in association with cortical activation. Comprising a small proportion (<10%), one group of cells discharged in rhythmic bursts of spikes in association with EEG theta activity during wake and PS in the head-fixed rats (Figure 2.2 A and Figure 2.5 A–C32) in a manner similar to the identified cholinergic cells recorded in urethane-anesthetized rats (above). According to the frequency of the primary mode of the interspike interval histogram (ISIH, Figure 2.5 D), the average instantaneous firing frequency within the bursts was remarkably similar to that of identified cholinergic cells in the anesthetized rats (above). According to the autocorrelation histogram (ACH, Figure 2.5 E), their discharge was rhythmic during waking and PS or transition to PS, when theta occurred. Their average discharge rate was highest in PS (thus classified as P-max), lower in active wake (aW) and lowest in SWS (Figure 2.5 F). Across epochs it was significantly positively correlated with gamma and theta and negatively correlated with delta EEG activity, while not being significantly correlated with EMG activity (see Figure 2.5 G–J). These “cholinergic-like” cells thus discharge in rhythmic bursts in association with theta activity during active

FIGURE 2.5 (See facing page.) Discharge properties and profile of a unit firing with cortical activation of PS and aW in the head-fixed rat and having similar discharge properties as identified Nb+/ChAT+ cells in the anesthetized rat (see Figure 2.3). Classified as P-max (from group 11: wsP [Fast, Phasic, Rhythmic] [#u210]), it discharges on average at an intermediate rate during aW (A), a minimal rate during SWS (B), and a maximal rate during PS (C). It fires in bursts of spikes during PS (with a peak mode of ~68 Hz in the ISIH, D) that recur rhythmically at a theta frequency (with a frequency of 7.0 Hz in the ACH, E). The rhythmic bursting also occurs with the appearance of theta during the transition into PS (tPS). It is correlated with the theta EEG activity of the retrosplenial cortex (see expanded trace of 1-sec period of unit activity and RS EEG in C (a) during PS, tPS and active periods of wake showing theta. The average spike rate (F) is moderately high in aW (7.28 Hz), minimal in SWS (0.74 Hz) and maximal in PS (11.23 Hz). The spike rate is significantly positively correlated with gamma (G, r = 0.43, n = 84 observations, p <.001) and theta (H, r = 0.50), significantly negatively correlated with delta (I, r = – 0.80), while being weakly positively correlated with EMG (J, r = 0.26). In this and Figure 2.6, the unit discharge is presented with EEG and EMG activity for 10 sec epochs of aW (A), SWS (B), and PS (C). Spike amplitude is cut at 1 mV. The unit discharge is analyzed for instantaneous firing frequency (from the peak of the primary mode of the interspike interval histogram, ISIH, D) and rhythmicity of discharge (from the autocorrelation histogram, ACH, E). Average spike rate (per second displayed on a linear scale, F), gamma EEG power (30–58 Hz, G), ratio of theta (4.5–8 Hz)/delta (1–4 Hz) EEG power (H), delta EEG power (1–4.5 Hz, I) and EMG amplitude (30–100 Hz, J) are displayed per state together with the s.e.m.s. Abbreviations: active wake (aW), quiet wake (qW), transition to SWS (tSWS), slow wave sleep (SWS), transition to PS (tPS) and paradoxical sleep (PS). (Copied with permission from Lee, M.G., Manns, I.D., Alonso, A., and Jones, B.E., J. Neurophysiol., in press.)

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waking and PS and may accordingly stimulate theta in addition to gamma during these states. A small proportion of cells (<10%) recorded in the basal forebrain discharged at lower average rates with cortical activation than cortical slow wave activity and thus at lower rates during wake and/or PS than SWS in head-fixed rats32 in a manner similar to that of identified GABAergic Off cells in urethane-anesthetized rats (above). Commonly discharging maximally in SWS (called S-max), these cells nonetheless had varying properties. Half of them discharged in an irregular tonic fashion like the Nb+/GAD+ Off (tonic) cells, showing slow average rates and

FIGURE 2.5

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instantaneous firing frequencies (<10 Hz) during SWS and lower rates (~50% on average) during wake and PS. Another half discharged in high frequency bursts of spikes with cortical slow wave activity (Figure 2.2 A and Figure 2.6 A–C) in a manner similar to that of the identified Nb+/GAD+ Off (burst) cells in the anesthetized animals. According to the frequency of the primary mode of the ISIH (Figure 2.6 D), their average instantaneous firing frequency within the bursts was within the range of that of the identified Nb+/GAD+ Off (burst) cells (above). According to the ACH (Figure 2.6 E), their burst discharge was not rhythmic but irregular. Their average discharge rate was highest in SWS and equivalently low in active wake (aW) and PS (Figure 2.6 F). Across epochs, it was significantly positively correlated with delta and negatively correlated with gamma and theta EEG activity, while not correlated with EMG activity (see Figure 2.6 G–J). These GABAergic Off (burst)-like cells accordingly discharge in irregular bursts in association with delta activity during SWS and could thus contribute to modulating this cortical slow activity. Collectively, particular putative GABAergic cell groups could promote SWS by inhibiting neurons of the posterior hypothalamus, brainstem or basal forebrain activating systems and also by directly inhibiting cortical neurons to attenuate cortical activation and promote slow wave EEG activity (Figure 2.1).

ROLE OF CHOLINERGIC AND GABAERGIC BASAL FOREBRAIN NEURONS Neurotoxic lesions of the basal forebrain have been reported to diminish cortical activation on the one hand11 and SWS20 on the other. Such different effects may be explained by the diverse cell population of the basal forebrain comprised by cortical activation On and promoting neurons on the one hand and cortical activation Off and slow wave promoting cells on the other. Evidence is presented here that these

FIGURE 2.6 (See facing page.) Discharge properties and profile of a unit firing with slow wave activity during SWS in the head-fixed rat and in a manner similar to Nb+/GAD+ Off (burst) cells in the anesthetized rat (see Figure 2.4). Classified as an S-max unit (from group 6: wSp [Fast, Phasic] unit [#c16u02]), it discharges on average at a minimal rate during aW (A), a maximal rate during SWS (B), and equivalent minimal rate during PS (C). As evident in the recording (see expanded trace of 500 msec period of unit activity in B [a]), the ISIH (D) and ACH (E), the unit discharges in a distinctly phasic manner with high frequency bursts (114 Hz peak frequency of the principal mode of the ISIH). This bursting occurs maximally during SWS, although it is also evident (by central peak in ACHs) during other states with a much lower incidence. The average spike rate (F) increased from aW (1.6 Hz) in the tSWS to be highest during SWS (3.8 Hz) and decreased in tPS to be equivalently low in PS (1.8 Hz) as in aW. The spike rate was not significantly correlated with gamma (G, r = 0.13), was significantly negatively correlated with theta (H, r = –0.30), and was significantly positively correlated with delta EEG power (I, r = 0.53, n = 180 observations, p <.001), while being significantly negatively correlated with EMG amplitude (J, r = –0.38). See Figure 2.5 for general details. (Copied with permission from Lee, M.G., Manns, I.D., Alonso, A., and Jones, B.E., J. Neurophysiol., in press.)

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two seemingly opposing cell groups are comprised of cholinergic and particular GABAergic neurons, respectively. Through both muscarinic and nicotinic receptors, cholinergic neurons would excite cortical neurons,9,33 whereas GABAergic neurons would inhibit cortical neurons. Such opposing actions from basal forebrain have been demonstrated by electrical stimulation, whereby cortical neuronal discharge with acetylcholine (ACh) release was evoked from some sites and inhibition of discharge with no ACh release was evoked from adjacent sites.34 These different effects can be explained by differential stimulation of intermingled yet clustered cholinergic and GABAergic basalo-cortical projecting neurons. The way in which

FIGURE 2.6

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these two opponent cell groups might modulate cortical activity and sleep-wake states is revealed here by their specific activity profiles. In both the anesthetized and naturally sleeping–waking rat, we have documented an increased rate and altered pattern of discharge in cholinergic and cholinergic-like neurons in association with cortical activation of waking and PS. Moreover the neurons were found to discharge in rhythmic bursts cross-correlated with cortical theta activity, which these neurons are thus hypothesized to stimulate. This modulation could be effected in the cerebral cortex by fast nicotinic actions of ACh upon some interneurons,33,35 which in turn would pace the activity of pyramidal neurons in the cortex as they do in the hippocampus in the production of theta.36 The slower muscarinic actions of ACh could also excite certain interneurons along with pyramidal cells to stimulate high frequency gamma activity that rides upon theta.36,37 The bursting discharge by the cholinergic cells would maximize release of ACh38 to attain large populations of cortical neurons and thus provide a means for synchronizing activity across distributed cortical networks during the cortical activation of active waking and PS. Our pharmacological experiments in freely moving rats have provided support for the role of cholinergic basalo-cortical neurons in generating theta and gamma in the cerebral cortex along with the states of wake and PS.39,40 From the pharmacological results of in vitro studies upon identified cholinergic neurons, it is known that cholinergic neurons are excited by the transmitters of the major arousal systems, including glutamate, noradrenaline (NA), histamine and orexin.41–44 The effect of these transmitters is through particular receptors upon the cholinergic cells, such as the a1–adrenergic receptor (a1–AR), which evoke membrane depolarization and excitation. Microinjections of these transmitters, including importantly NA or their agonists into the basal forebrain cholinergic cell area increase gamma and theta EEG activity while suppressing delta EEG activity and concomitantly stimulate waking while suppressing sleep.40,45,46 The peptide, neurotensin, which was shown in vitro to selectively excite and elicit rhythmic bursting in the cholinergic cells,47 was found when injected into the basal forebrain to evoke theta together with gamma activity while suppressing delta and SWS but enhancing PS in addition to waking.39 These results clearly demonstrate the robust influence of cholinergic basalo-cortical neurons in stimulating theta and gamma EEG activity and promoting waking and PS states (Figure 2.1). GABAergic basal forebrain neurons are heterogeneous in their projections and properties and thus may fulfill different roles in modulating EEG activity and sleep–wake states. By fast inhibitory postsynaptic potentials (IPSPs) produced through GABAA receptors, GABA can serve to pace activity in pyramidal or other neurons in the cortex. It can also stimulate slow IPSPs through GABAB receptors. It can thus serve to pace activity of fast or slow waves or to inhibit activity. Also depending upon their prime target in the cortex,48,49 the GABAergic projection neurons could facilitate pyramidal cell discharge by inhibiting particular GABAergic interneurons or could inhibit pyramidal cell discharge by inhibiting other GABAergic interneurons, given the interconnections of cortical interneuronal networks. We provide evidence here that different groups of GABAergic neurons may facilitate cortical activation as On cells, whereas others may attenuate cortical acti-

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vation as Off cells. The Nb+/GAD+ Off (burst) cells recorded in the anesthetized rat and Nb+/GAD+ Off (burst)-like cells recorded in the head-fixed rat would appear to have the capacity to pace or modulate slow cortical activity or to inhibit cortical fast activity (Figure 2.1). The Nb+/GAD+ Off (tonic) cells could also via projections to cholinergic basal forebrain neurons or to other neurons in the posterior hypothalamus or brainstem attenuate cortical activation by inhibiting those activating cells (Figure 2.1). The SW- or SWS-promoting GABAergic basal forebrain neurons represent particular subgroups of GABAergic cells that likely would be modulated in particular ways by transmitters of the arousal systems. Indeed, in vitro pharmacological studies revealed a small group of non-cholinergic neurons that were inhibited by NA.50 Previous in vivo studies had also found that SWS-active neurons in the preoptic area and basal forebrain were inhibited by NA through an a2 -adrenergic receptor (a2 –AR).51 In our in vivo recording studies in urethane-anesthetized rats, we found that identified Nb+/GAD+ Off cells were immunostained for the a2 –AR.52 Together with the results presented here for the Nb+/GAD+ Off and -like cells, these results suggest that the GABAergic SWS-active neurons would be inhibited by NA during waking to become disinhibited and active during SWS. Studies examining the effect of NA microinjections into the basal forebrain (above) could be interpreted in light of these findings, since NA suppressed delta and SWS during the day when the rat normally sleeps the majority of the time, while also evoking gamma and theta with waking.45 Our results support the role in the promotion of slow wave activity and SWS of particular GABAergic cell groups that bear a2 –AR and would thus be inhibited by NA released from the locus coeruleus neurons of the brainstem arousal systems. Through differential modulation by the transmitters of the brainstem arousal systems, cholinergic cortical activation (On) and GABAergic SW-promoting (Off) basal forebrain neurons, respectively, serve to stimulate cortical activation with wake and PS and reciprocally promote cortical slow wave activity and SWS (Figure 2.1). The basal forebrain thus has the capacity to regulate both the cortical activity and sleep–wake state of the animal across the sleep–wake cycle.

SUMMARY The basal forebrain is known to serve as the ventral extra-thalamic relay to the cerebral cortex from the brainstem activating systems and thus to stimulate cortical activation. Yet it is also known to have the capacity to promote slow wave cortical activity and SWS. By using juxtacellular labeling with Nb in association with extracellular recording of neurons in urethane-anesthetized and in head-fixed rats, we have identified particular cell groups which discharge at their highest rate with cortical activation as cholinergic or cholinergic-like. Others discharge at their highest rate with slow wave activity and SWS as GABAergic or GABAergic-like. The cholinergic cells discharge in rhythmic bursts with rhythmic theta activity and stimulate theta and gamma EEG activity with waking and PS. Particular GABAergic cells discharge in arrhythmic bursts with slow, irregular EEG activity and may modulate this cortical slow wave activity while promoting SWS. Other GABAergic cells discharge in a slow, irregular

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tonic manner with slow wave activity and may attenuate cortical activation by inhibiting other neurons of the activating systems, including the local cholinergic neurons or the neurons in the posterior hypothalamus or brainstem. These cortical activation and slow wave promoting cell groups are differentially modulated by transmitters of the activating systems, including importantly NA, which excites cholinergic cells and inhibits the slow wave promoting GABAergic cells. Through the cholinergic cells, the basal forebrain thus serves as a relay for the activating influences of the brainstem yet also directly modulates activity of the cerebral cortex and promotes wake or PS states. Through particular GABAergic cell groups, the basal forebrain may close the relay by inhibiting cholinergic and other neurons of the activating systems and also directly modulate slow cortical activity and promote SWS. The basal forebrain thus has the capacity to regulate cortical activity and sleep–wake states across the sleep–wake cycle.

ACKNOWLEDGMENTS This research was supported by grants from the Canadian Institute of Health Research (13458) and National Institute of Mental Health (RO1 MH-60119-01A).

REFERENCES 1. Jones, B.E., Reticular formation. Cytoarchitecture, transmitters, and projections, in The Rat Nervous System, 2nd ed., Paxinos, G., Academic Press Australia, New South Wales, 1995, pp. 155–171. 2. Jones, B.E., Arousal systems, Front. Biosci., 8, S438–S451, 2003. 3. Steriade, M., Curro Dossi, R., Pare, D., and Oakson, G., Fast oscillations (20–40Hz) in thalamocortical systems and their potentiation by mesopontine cholinergic nuclei in the cat, Proc. Natl. Acad. Sci. USA, 88, 4396–4400, 1991. 4. Jones, B.E., Basic Mechanisms of Sleep-wake States, in Principles and Practice of Sleep Medicine, 3rd ed., Kryger, M.H., Roth, T., and Dement, W.C. Eds., W.B. Saunders, Philadelphia, 2000, pp. 134–154. 5. Vanderwolf, C.H. and Stewart, D.J., Thalamic control of neocortical activation: a critical re-evaluation, Brain Res. Bull., 20, 529–538, 1988. 6. Rye, D.B., Wainer, B.H., Mesulam, M.-M., Mufson, E.J., and Saper, C.B., Cortical projections arising from the basal forebrain: a study of cholinergic and noncholinergic components employing combined retrograde tracing and immunohistochemical localization of choline acetyltransferase, Neuroscience, 13, 627–643, 1984. 7. Jones, B.E., Activity, modulation and role of basal forebrain cholinergic neurons innervating the cerebral cortex, Prog. Brain Res., 145, 157–169, 2004. 8. Metherate, R., Cox, C.L., and Ashe, J.H., Cellular bases of neocortical activation: modulation of neural oscillations by the nucleus basalis and endogenous acetylcholine, J. Neurosci., 12, 4701–4711, 1992. 9. McCormick, D.A., Neurotransmitter actions in the thalamus and cerebral cortex and their role in neuromodulation of thalamocortical activity, Prog. Neurobiol., 39, 337–388, 1992. 10. Longo, V.G., Behavioral and electroencephalographic effects of atropine and related compounds, Pharmacol. Rev., 18, 965–996, 1966.

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11. Stewart, D.J., MacFabe, D.F., and Vanderwolf, C.H., Cholinergic activation of the electrocorticogram: Role of the substantia innominata and effects of atropine and quinuclidinyl benzilate, Brain Res., 322, 219–232, 1984. 12. Detari, L. and Vanderwolf, C.H., Activity of identified cortically projecting and other basal forebrain neurones during large slow waves and cortical activation, Brain Res., 437, 1–8, 1987. 13. Szymusiak, R. and McGinty, D., Sleep-related neuronal discharge in the basal forebrain of cats, Brain Res., 370, 82–92, 1986. 14. Szymusiak, R. and McGinty, D., Sleep-waking discharge of basal forebrain projection neurons in cats, Brain Res. Bull., 22, 423–430, 1989. 15. Starzl, T.E., Taylor, C.W., and Magoun, H.W., Ascending conduction in reticular activating system, with special reference to the diencephalon, J. Neurophysiol., 14, 461–477, 1951. 16. Sterman, M.B. and Clemente, C.D., Forebrain inhibitory mechanisms: Sleep patterns induced by basal forebrain stimulation in the behaving cat, Exp. Neurol., 6, 103–117, 1962. 17. Sterman, M.B. and Clemente, C.D., Forebrain inhibitory mechanisms: Cortical synchronization induced by basal forebrain stimulation, Exp. Neurol., 6, 91–102, 1962. 18. Buzsaki, G., Bickford, R.G., Ponomareff, G., Thal, L.J., Mandel, R., and Gage, F.H., Nucleus basalis and thalamic control of neocortical activity in the freely moving rat, J. Neurosci., 8, 4007–4026, 1988. 19. McGinty, D.J. and Sterman, M.B., Sleep suppression after basal forebrain lesions in the cat, Science, 160, 1253–1255, 1968. 20. Szymusiak, R. and McGinty, D., Sleep suppression following kainic acid-induced lesions of the basal forebrain, Exp. Neurol., 94, 598–614, 1986. 21. Gritti, I., Mainville, L., and Jones, B.E., Codistribution of GABA — with acetylcholine-synthesizing neurons in the basal forebrain of the rat, J. Comp. Neurol., 329, 438–457, 1993. 22. Gritti, I., Mainville, L., Mancia, M., and Jones, B.E., GABAergic and other noncholinergic basal forebrain neurons project together with cholinergic neurons to mesoand iso-cortex in the rat, J. Comp. Neurol., 383, 163–177, 1997. 23. Gritti, I., Mainville, L., and Jones, B.E., Projections of GABAergic and cholinergic basal forebrain and GABAergic preoptic-anterior hypothalamic neurons to the posterior lateral hypothalamus of the rat, J. Comp. Neurol., 339, 251–268, 1994. 24. Zaborszky, L. and Duque, A., Local synaptic connections of basal forebrain neurons, Behav. Brain Res., 115, 143–158, 2000. 25. Alonso, A., Khateb, A., Fort, P., Jones, B.E., and Muhlethaler, M., Differential oscillatory properties of cholinergic and noncholinergic nucleus basalis neurons in guinea pig brain slice, Eur. J. Neurosci., 8, 169–182, 1996. 26. Khateb, A., Muhlethaler, M., Alonso, A., Serafin, M., Mainville, L., and Jones, B.E., Cholinergic nucleus basalis neurons display the capacity for rhythmic bursting activity mediated by low threshold calcium spikes, Neuroscience, 51, 489–494, 1992. 27. Pinault, D., A novel single-cell staining procedure performed in vivo under electrophysiological control: morpho-functional features of juxtacellularly labeled thalamic cells and other central neurons with biocytin or Neurobiotin, J. Neurosci. Methods, 65, 113–136, 1996. 28. Manns, I.D., Alonso, A., and Jones, B.E., Discharge properties of juxtacellularly labeled and immunohistochemically identified cholinergic basal forebrain neurons recorded in association with the electroencephalogram in anesthetized rats, J. Neurosci., 20, 1505–1518, 2000.

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29. Haas, K., Sin, W.C., Javaherian, A., Li, Z., and Cline, H.T., Single-cell electroporation for gene transfer in vivo, Neuron, 29, 583–591, 2001. 30. Manns, I.D., Alonso, A., and Jones, B.E., Discharge profiles of juxtacellularly labeled and immunohistochemically identified GABAergic basal forebrain neurons recorded in association with the electroencephalogram in anesthetized rats, J. Neurosci., 20, 9252–9263, 2000. 31. Souliere, F., Urbain, N., Gervasoni, D., Schmitt, P., Guillemort, C., Fort, P., Renaud, B., Luppi, P.H., and Chouvet, G., Single-unit and polygraphic recordings associated with systemic or local pharmacology: a multi-purpose stereotaxic approach for the awake, anesthetic-free, and head-restrained rat, J. Neurosci. Res., 61, 88–100, 2000. 32. Lee, M.G., Manns, I.D., Alonso, A., and Jones, B.E., Sleep-wake related discharge of basal forebrain neurons recorded with micropipettes in head-fixed rats, J. Neurophysiol., in press. 33. Porter, J.T., Cauli, B., Tsuzuki, K., Lambolez, B., Rossier, J., and Audinat, E., Selective excitation of subtypes of neocortical interneurons by nicotinic receptors, J. Neurosci., 19, 5228–5235, 1999. 34. Jimenez-Capdeville, M.E., Dykes, R.W., and Myasnikov, A.A., Differential control of cortical activity by the basal forebrain in rats: a role for both cholinergic and inhibitory influences, J. Comp. Neurol., 381, 53–67, 1997. 35. Xiang, Z., Huguenard, J.R., and Prince, D.A., Cholinergic switching within neocortical inhibitory networks, Science, 5379, 985–988, 1998. 36. Soltesz, I. and Deschenes, M., Low- and high-frequency membrane potential oscillations during theta activity in CA1 and CA3 pyramidal neurons of the rat hippocampus under ketamine-xylazine anesthesia, J. Neurophysiol., 70, 97–116, 1993. 37. McCormick, D.A. and Prince, D.A., Mechanisms of action of acetylcholine in the guinea-pig cerebral cortex in vitro, J. Physiol., 375, 169–194, 1986. 38. Lisman, J.E., Bursts as a unit of neural information: making unreliable synapses reliable, Trends Neurosci., 20, 38–43, 1997. 39. Cape, E.G., Manns, I.D., Alonso, A., Beaudet, A., and Jones, B.E., Neurotensininduced bursting of cholinergic basal forebrain neurons promotes gamma and theta cortical activity together with waking and paradoxical sleep, J. Neurosci., 20, 8452–8461, 2000. 40. Cape, E.G., and Jones, B.E., Effects of glutamate agonist versus procaine microinjections into the basal forebrain cholinergic cell area upon gamma and theta EEG activity and sleep-wake state, Eur. J. Neurosci., 12, 2166–2184, 2000. 41. Eggermann, E., Serafin, M., Bayer, L., Machard, D., Saint-Mleux, B., Jones, B.E., and Muhlethaler, M., Orexins/hypocretins excite basal forebrain cholinergic neurones, Neuroscience, 108, 177–181, 2001. 42. Fort, P., Khateb, A., Pegna, A., Muhlethaler, M., and Jones, B.E., Noradrenergic modulation of cholinergic nucleus basalis neurons demonstrated by in vitro pharmacological and immunohistochemical evidence in the guinea pig brain, Eur. J. Neurosci., 7, 1502–1511, 1995. 43. Khateb, A., Fort, P., Pegna, A., Jones, B.E., and Muhlethaler, M., Cholinergic nucleus basalis neurons are excited by histamine in vitro, Neuroscience, 69, 495–506, 1995. 44. Khateb, A., Fort, P., Serafin, M., Jones, B.E., and Muhlethaler, M., Rhythmical bursts induced by NMDA in cholinergic nucleus basalis neurones in vitro, J. Physiol. (Lond.), 487.3, 623–638, 1995.

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45. Cape, E.G., and Jones, B.E., Differential modulation of high frequency gamma electroencephalogram activity and sleep-wake state by noradrenaline and serotonin microinjections into the region of cholinergic basalis neurons, J. Neurosci., 18, 2653–2666, 1998. 46. Espana, R.A., Baldo, B.A., Kelley, A.E., and Berridge, C.W., Wake-promoting and sleep-suppressing actions of hypocretin (orexin): basal forebrain sites of action, Neuroscience, 106, 699–715, 2001. 47. Alonso, A., Faure, M.-P., and Beaudet, A., Neurotensin promotes oscillatory bursting behavior and is internalized in basal forebrain cholinergic neurons, J. Neurosci., 14, 5778–5792, 1994. 48. Freund, T.F. and Meskenaite, V., Gamma-aminobutyric acid-containing basal forebrain neurons innervate inhibitory interneurons in the neocortex, Proc. Natl. Acad. Sci. USA, 89, 738–742, 1992. 49. Freund, T.F. and Gulyas, A.I., GABAergic interneurons containing calbindin D28K or somatostatin are major targets of GABAergic basal forebrain afferents in the rat neocortex, J. Comp. Neurol., 314, 187–199, 1991. 50. Fort, P., Khateb, A., Serafin, M., Muhlethaler, M., and Jones, B.E., Pharmacological characterization and differentiation of non-cholinergic nucleus basalis neurons in vitro, NeuroReport, 9, 1–5, 1998. 51. Osaka, T. and Matsumura, H., Noradrenaline inhibits preoptic sleep-active neurons through a2-receptors in the rat, Neurosci. Res., 21, 323–330, 1995. 52. Manns, I.D., Lee, M.G., Modirrousta, M., Hou, Y.P., and Jones, B.E., Alpha 2 adrenergic receptors on GABAergic, putative sleep-promoting basal forebrain neurons, Eur. J. Neurosci., 18, 723–727, 2003. 53. Jones, B.E., Neurotransmitter systems regulating sleep-wake states, in Biological Psychiatry, D’Haenen, D., den Boer, J.A., and Willner, P., Eds., John Wiley & Sons, New York, 2002, pp. 1215–1228.

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3

In Vitro Identification of the Presumed Sleep-Promoting Neurons of the Ventrolateral Preoptic Nucleus (VLPO) Patrice Fort, Pierre-Hervé Luppi, and Thierry Gallopin

CONTENTS The Discovery of the VLPO as a Potent Sleep Center of the Brain Serotonin Modulation Reveals Two Types of Presumed Sleep Promoting Neurons in the VLPO Hypnogenic Substances Differentially Modulate Type-1 and Type-2 Neurons: An Emphasis on the PGD2-Adenosine Axis A Functional Model of the Neuronal Network Responsible for Sleep Promotion Summary Acknowledgments References

THE DISCOVERY OF THE VLPO AS A POTENT SLEEP CENTER OF THE BRAIN Between World War I and World War II, von Economo proposed that a basal forebrain area would be the place for a brain center involved in sleep regulation. He reported that comatose patients struck down with an encephalitis lethargica had prominent parenchyma injury at the level of the preoptic area (POA) near the optic tract.1 These seminal clinical studies, indicating that an intact rostral hypothalamus is critical for

0-8493-1519-0/05/$0.00+$1.50 © 2005 by CRC Press LLC

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the production of normal sleep, definitely represents a founding step for the research aimed at discovering the neurobiological mechanisms regulating behavioral states (namely wakefulness, slow waves sleep, and paradoxical sleep). During the subsequent period until early 1990, the experimental process for basic research, using standard lesion, neuronal unit recording, neuropharmacological, and neurochemical approaches in animals, led to the statement of a few fundamental concepts, which can be summarized as follows: 1. In lines with early predictions by von Economo, POA, more especially its lateral part, is the unique brain structure that fulfilled necessary and sufficient criteria for an hypnogenic center containing neurons that directly promote sleep.2–6 2. This simplicity highly contrasts with the redundant network responsible for arousal, involving numerous brain areas and neurotransmitter systems such as acetylcholine-, noradrenaline-, serotonin-, histamine-, and recently discovered orexin-containing neurons, with widespread projections from rhombencephalon to cerebral mantle. Collectively these components, with an activity specific to wakefulness, form the so-called ascending reticular activating system (ARAS) that regulates cortical activation during waking.7–10 3. Soon as drowsiness begins, the hypnogenic center would put out of function the ARAS system through sustained inhibitions. 4. The sleep pressure as well as drowsiness would be owed to the conjunction of homeostatic and circadian processes that are able to directly modulate the sleep center.11,12 Despite these crude consensual concepts and the real progress of our knowledge since von Economo’s proposal, we have to admit that basic neurobiological mechanisms involved in sleep promotion and the harmonious succession of behavioral states remain largely underestimated. Indeed, LPOA is a vast forebrain region that contains multiple contingents of intermingled and loosely arranged neurons, governing vital functions. This cytoarchitectonic configuration and the lack of a precise plotting of the sleep-promoting neurons have hindered the decoding of cellular, synaptic, or molecular mechanisms used by the sleep center to play its functional role. However, a decisive stage was exceeded in 1996 by the individuation of the ventrolateral preoptic nucleus (VLPO), a small neuronal core (radius 300 mm) located in the most ventral part of the LPOA. This was made possible by means of a functional neuroimaging paradigm at the cellular level, using the expression of the early gene c-Fos as a marker of neuronal activity in rats having slept for a long period before sacrifice.13 This hypersomnia, also coined sleep-rebound, is the typical behavioral response following sleep deprivation in rats. While neurons specifically activated during sleep and immunostained for c-Fos (c-Fos+) were diffusely distributed in LPOA, they were more densely packed within the VLPO. Furthermore, the density of c-Fos+ neurons correlated closely with the sleep quantities during the last two hours preceding sacrifice. This labeling pattern of sleep-active neurons would be related rather to the production of sleep itself than

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to a homeostatic regulation induced by its deprivation. Although drowsiness markedly increased in deprived rats, little staining was observed in rats sacrificed before the sleep-rebound.13,14 By the same functional approach, it has been demonstrated that VLPO and suprachiasmatic nucleus (SCN) have synchronized activity.15 Further, they are interconnected and received inputs from retina ganglionic cells. Similarly the dorso-median hypothalamic nucleus, a SCN relay, projects strongly to the VLPO. Considered together, these anatomical data suggests that circadian- and photic-linked information may be conveyed to modulate the VLPO activity across the nyctemeral period.16–23 Electrophysiological experiments in freely-moving rats have shown that neurons that doubled their firing rate at sleep onset are more frequently recorded in VLPO than in other LPOA parts.24 Furthermore, their recruitment and firing activation are positively correlated to both sleep depth and duration. Of particular functional interest, this sleep-specific activity of VLPO neurons (i.e., sleep-on neuron) is inverse to that of wake-active neurons.25–28 Functionally the bilateral neurotoxic destruction of VLPO neurons (more than 70%) is followed by a profound and long-lasting insomnia with a reduction of 56% of sleep quantities in rats.29 In line with these data, we further demonstrated that iontophoretic application of carbachol, a cholinergic agonist, targeted to the VLPO suppressed sleep in anesthetic-free head-restraint rats.30,31 These physiological data support the necessity of VLPO for producing normal sleep. Retrograde and anterograde tract-tracing studies indicate that VLPO neurons are reciprocally connected with cerebral areas containing wake-active neurons such as the histaminergic tuberomammillary nucleus (TMN), serotonergic midbrain raphe nuclei (RN), noradrenergic locus coeruleus (LC), cholinergic pontine (LDT/PPT) and magnocellular preoptic (NB) nuclei, as well as orexinergic perifornical area of the lateral hypothalamus.13,14,32–37 More than 90% of c-Fos+ sleep-active neurons in VLPO express galanin mRNA while 80% of neurons projecting to the TMN contain both galanin and GAD, the GABA-synthesizing enzyme, suggesting that projections to the waking systems are inhibitory in nature.13,14,36 Taken together these data indicate that the VLPO plays a key role in coordinating the inhibition of arousal systems to promote sleep and thus occupies a privileged place within the complex neuronal network involved in behavioral states. Its individuation had opened new fields for investigation of the underlying regulatory mechanisms of sleep. For us the fact that VLPO neurons are: • • •

Specifically active during sleep Endowed with reciprocal inhibitory connections with the wake-promoting areas Densely packed in a small-sized nucleus

offers a unique opportunity and evident methodological advantage to study at cellular, synaptic and molecular levels the neurons responsible for sleep. A special effort to characterize neurotransmitters and pathways that control VLPO sleep-active neurons would thus contribute to understand the mechanisms that manage their excitability across the sleep-waking cycle and should provide key insight into the regulation of behavioral states.

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To strengthen our proposal, we have undertaken during the last years electrophysiological recordings of VLPO neurons in rat brain slices. This in vitro experimental approach is proved to be suitable for exploring electrophysiological, pharmacological, and chemo-morphological properties of neurons and thus for drawing up the so-called functional ID card of the sleep-active neurons. One of our primary objectives was to determine whether neurons inhibited by neurotransmitters released from wake-promoting areas could be frequently recorded in VLPO. In close collaboration with our colleagues of the Geneva University (Switzerland), we thus identified successfully a homogeneous neuronal group with a specific set of intrinsic membrane properties and a clear-cut chemo-morphology that are inhibited by the major neurotransmitters of waking. Their high proportion (80% of the recorded neurons), matching that of cells active during sleep in VLPO, and their pharmacological profile represent convincing arguments about their presumed status as sleeppromoting neurons (PSP). We showed that PSP neurons are GABAergic, multipolar, triangular shaped, and endowed with a potent low threshold calcium potential. These neurons are inhibited by noradrenaline (NA).38 Recent works determined that this inhibitory effect is mediated by post-synaptic alpha-2 adrenoceptors.39,40 We further revealed that NA-inhibited neurons are also inhibited by acetylcholine (ACH). In contrast histamine (HA) and orexin (Ox) did not modulate PSP neurons, although an inhibitory influence was expected.38,41 However, it should be noted that TMN neurons contain both histamine and GABA42 and are thus in position, as noradrenergic and cholinergic drives, to inhibit PSP neurons during waking. Considering their unique profile of neuromodulation (since the remaining recorded cells are excited by NA, ACH, HA, and Ox), the overall inhibition of the PSP neurons by neurotransmitters of waking is in agreement with their inactivity during waking.24 We previously suggested that the reciprocal inhibitory interaction of PSP neurons with the multiple waking systems to which they project is a key factor for promoting sleep by coordinating their inhibition at sleep onset.38 More recently a consensual model has been proposed suggesting that this reciprocity of projections is analogous to a flip-flop switch electrical circuit.6 When VLPO neurons start to fire at sleep onset and fire rapidly during sleep, they would inhibit the waking-promoting neurons allowing for their own disinhibition and reinforced firing. During arousal, waking-promoting neurons fire at a high rate, thus inhibiting VLPO neurons and resulting in the disinhibition of their own firing. Either sleep or waking is self-reinforcing when its component neurons are sufficiently active. The reciprocal inhibitory interaction of these systems provides a mechanism for the maintenance of one of the two stable configurations. Accordingly, disruption of wake- and sleep-promoting pathways would result in behavioral instability due to a destabilization of the reciprocal inhibitory interactions. This is likely the case in murine models of narcolepsy, a human sleep pathology, with functional failure of the orexin system concomitant to pronounced vigilance disturbances and sudden transitions in behavioral states.43,44 An increasing number of data agreed that orexin-containing neurons would play a major role for the maintenance of arousal. The widespread excitatory projection to waking-promoting neurons provides to this neuronal system an ideal position to orchestrate their respective activity.45 Turn on during waking, orexin-containing neurons would strengthen the activity of the wake-

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promoting neurons, which in turn, via their inhibitory projections to PSP neurons, would prevent sleep onset and thus stabilize waking.6,45–47 Functional properties of the flip-flop switch model may easily support the production by a simple neuronal network of stable states of wakefulness and sleep and an important resistance to switching by limiting inappropriate changes when inputs to VLPO or wake-promoting areas fluctuate. In great contrast, this model does not take account for the necessary instability or un-balanced relationship between wakeand sleep-promoting neurons that should occur for rapid transitions between sleep and waking (drowsiness or awaking), switching events that are frequently encountered across the sleep-waking cycle (75% of all transition states in rats). In this context mechanisms responsible for the firing increase of sleep-on neurons just before or at sleep onset remain unknown. They would be the result of a disinhibition linked to a decreased activity of wake-promoting neurons, thus releasing PSP neurons from potent inhibitions during waking or an increase of a sleep-dependent excitatory drive, thus inducing the inhibition of the wake-promoting neurons and reinforcing sleep. It is tempting to hypothesize that such excitatory drive would be related to thermoregulation48 or homeostatic process, involving hypnogenic factors that directly excite PSP neurons. In an attempt to test this second hypothesis, we thus pursued the pharmacological and molecular characterization of the PSP neurons in VLPO slices with a special interest for their interactions with presumed sleep neuromodulators. Numerous substances contributing to the sleep homeostasis have been described.49–51 The following parts are focused on two well-recognized sleep factors, namely serotonin and adenosine.

SEROTONIN MODULATION REVEALS TWO TYPES OF PRESUMED SLEEP PROMOTING NEURONS IN THE VLPO In the subsequent experiments we performed recordings in loose cell-attach configuration to study the effect of multiple drug applications on a single PSP cell. In contrast to intracellular and patch-clamp electrophysiological techniques, this mode allows stable recordings of healthy neurons for long periods of time necessary to complete pharmacological experiments. Infrared differential interference videomicroscopy was used to locate VLPO neurons according to their typical size and triangular shape. Cell-attached recordings were made from the soma with patch micropipettes filled with ACSF and attached to an electric microdrive to place it under visual control in contact with the soma of the cell chosen (Figure 3.1). In this mode cells were classified as PSP neurons when application of NA and ACH induced a decrease of their firing rate.38 We thus showed that 47% of the PSP neurons are also inhibited by 5-HT (Type-1 cells) while 53% are excited (Type-2 cells).52 Our data indicate that the modulation of PSP neurons by 5-HT is complex and that two types of PSP neurons are present in VLPO according to their pharmacological profile (Figure 3.1). This unexpected segregation led us to resume the characterization of both types of neurons to arrest their respective neuronal and functional specificities regarding

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FIGURE 3.1 Pharmacological identification of the Type-1 and Type-2 neurons of the VLPO. (A1–B1) Microphotographies showing the typical morphology (triangular shaped) of the Type-1 and Type-2 neurons respectively, as observed in slices by means of the infrared differential interference videomicroscopy. Bars: 20mm. (A2–A3) Firing frequency versus time diagrams illustrating a Type-1 neuron extracellularly recorded in loose-attached configuration since its firing rate is strongly reduced after the bath application of NA (A2) and 5-HT (A3). (B2–B3) Firing frequency versus time diagrams illustrating a Type-2 neuron since it is inhibited by NA (B2) but reversibly excited following 5-HT application (B3).

sleep production. First assessed as Type-1 or Type-2 cells according to their responses to NA and 5-HT, PSP neurons were then systematically re-recorded in the whole-cell patch-clamp configuration. We found that the standard electrophysiological parameters such as the resting potential, membrane resistance, amplitude and duration of the sodium-dependant action potential, as well as duration of the post-hyperpolarization (AHP) are not significantly different in both types of PSP neurons (Figure 3.2). The only difference was the higher AHP amplitude in Type2 versus Type-1 neurons. In agreement with our previous data,38 an electrophysiological landmark of both types of cells is the presence of a powerful low threshold calcium spike, due to an IT current. We further demonstrated that both types of PSP neurons present abnormal membrane rectifications underlying the activation of sodium persisting (INaP) and potassium time-dependant (Ih) currents (Figure 3.2). For the morphological identification, PSP neurons were filled with biocytin during the recording sessions. These staining experiments demonstrate that both types of neurons were recorded in the

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FIGURE 3.2 Intrinsic membrane properties of both types of presumed sleep-promoting neurons recorded in the VLPO. (A) Response of a neuron submitted to a depolarizing pulse at rest or held hyperpolarized by continuous negative current injection. Notice the presence of a potent low threshold spike (LTS, *), probably calcium-dependent because it remained following TTX treatment and sodium spikes disappeared. (B) At the resting membrane potential, the application of short-lasting depolarizing current pulses revealed the presence of a plateau due to the activation of a persistent sodium current (trace 1), which in some cases is able to reach the spike threshold (trace 2). (C) Response of a neuron following injection of hyperpolarizing current pulses with progressive amplitude increase showing the presence of a time-dependent rectification of the membrane potential (sag indicated by filled versus open circle). (D) Current-voltage curves obtained for the cell illustrated in D (I–V). Note the diminution of the membrane resistance during the current pulses revealing the presence of an Ih current in PSP neurons. Arrowheads indicated the level of the resting membrane potential.

VLPO proper, avoiding the extended VLPO36 or adjacent basal forebrain structures (lateral or median preoptic areas, nucleus basalis, supraoptic nucleus). Both types of cells were morphologically undistinguishable since they were typically mediumsized and mainly triangular shaped with three primary dendrites (Figure 3.3). Biocytin-labeled dendrites occasionally extended ventrally over long distances, where they exit the parenchyma and/or travel along the brain surface.20 A required feature to ensure that both types are PSP neurons was to determine their respective neurochemical nature, by evaluating first the well-known cellular markers of neurons that are sleep-active, namely galanin and GABA. By doublefluorescent staining, we demonstrated that biocytin-filled Type-1 and Type-2 neurons both contain galanin (Figure 3.3). Furthermore, we applied the single-cell RTPCR technique53 coupled to patch-clamp recordings of neurons beforehand assessed by their responses to NA and 5-HT. We thus determined that Type-1 and Type-2 neurons express mRNAs coding for GAD 65 and GAD 67 (Figure 3.4). It is thus likely that Type-1 and Type-2 neurons match up the VLPO neurons that are GABA and galanin in nature, selectively activated during sleep and that project directly to the waking systems.14,36

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Type 1

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B GA D6 5 5-H T1 a SS -in tr GA D6 7

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GA D6 5 5-H T5 a 5-H T2 c SS -i n G A tr D6 7

FIGURE 3.3 Both types of presumed sleep promoting neurons contain galanine as a neurotransmitter. (A1–B1) Photomicrographies of one Type-1 (C1) and one Type-2 (D1) neuron, both filled with neurobiotine revealed using a streptavidine-Cy3 fluorochrome (yellow). (C2–D2) The same slices were submitted simultaneously to galanin immunodetection by using a secondary antibody labeled with Cy2 fluorochrome (green). The superposition evidenced that both types of PSP neurons are double-labeled, indicating that they match the galanin VLPO neurons that are activated during sleep. Bars: 20mM.

603 bp 310 bp

FIGURE 3.4 Molecular characterization of the Type-1 (A) and Type-2 (B) neurons by the multiplex single-cell RT-PCR technique. Agarose gel of PCR products showing expression of GAD65, GAD67, and 5-HT1a receptors in one Type-1 neuron (A) and GAD65, GAD67, and serotonergic 5-HT2c and 5-HT5a in one Type-2 neuron (B4). F is a marker for relative molecular mass. Genomic DNA amplification, which could occur if the nucleus was harvested during the patch-clamp recordings, was systematically assessed using a somatostatin gene intron (ss-intr) as a genomic control. These data indicate that both types of neurons are likely GABAergic in nature but expressed different sets of serotonergic postsynaptic receptors.

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Inhibition No effect Excitation

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FIGURE 3.5 Pharmacological characterization of VLPO Type-1 and Type-2 neurons recorded in loose cell-attach configuration. Following systematic applications of both NA and 5-HT to assess the subtype of the recorded cell, the effect on its spontaneous firing rate was examined following by PGD2 (A1, B1, C1), ADO (B1, C1, D1), CGS, an A2A agonist (C1, C2, C3), and galanin (D1, D2, D3) applications. Note that although ADO clearly inhibited the Type-2 neurons, CGS application was followed by a reversible increase of the firing rate. The experiments clearly demonstrated that Type-1 and Type-2 neurons present a specific pharmacological profile and thus expressed different sets of postsynaptic receptors for these different sleep factors.

It has been shown that galanin inhibited noradrenergic LC neurons,54 serotonergic midbrain raphe neurons55 and histaminergic TMN neurons.56 Besides, galanin is also co-localized with 5-HT and NA.57,58 It seems that galanin could participate in combination with amines to the inhibition during waking of PSP neurons. Partly supporting this hypothesis, we observed that galanin inhibits the firing activity of Type-2 neurons but had no effect on Type-1 neurons (Figure 3.5). Although three types of post-synaptic receptors have been cloned (GalR1, GalR2 and GalR3), only GalR2 are specifically expressed in the VLPO, while GalR1 and GalR2 are widely distributed in the POA.59 Taken together our data indicate that the neuromodulation by 5-HT would be a relevant criterion for the pharmacological segregation of the PSP neurons in VLPO. Numerous 5-HT receptors have already been identified and dispatched in seven major classes according to their functional properties.26 The extreme diversity of the pharmacological effects observed with 5-HT highly supports its duality of modulation of PSP neurons. We thus attempted to identify the subtypes of 5-HT receptors

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responsible for the excitatory and inhibitory effects. For this purpose we used the single-cell multiplex RT-PCR technique for an initial screening of 5-HT receptor mRNAs expressed in PSP neurons. Although preliminary, this study evidences that Type-1 and Type-2 neurons express different sets of 5-HT receptors. Type-2 neurons express mRNA for 5-HT2c, 5-HT5a and probably 5-HT4 receptors. Previous studies have shown that 5-HT2c receptors mediate excitatory effects in numerous brain regions60 and would be involved in sleep regulation. Indeed, intraperitoneal administration of 5-HT2a/2c or 5-HT2c (m-CPP) agonists decreased sleep quantities, in contrast to 5-HT2a/2c antagonist (ritanserine).61–63 Furthermore, ritanserine reversed the effect produced by 5-HT2a/2c agonist but not that induced by 5-HT2c agonist, rather supporting the crucial role of 5-HT2a in sleep regulation.61,62,64,65 With regard to the Type-1 neurons, they express mRNAs coding for 5-HT6, 5-HT5b, 5-HT1d and probably 5-HT1a receptors (Figure 3.4). The presence of 5-HT1a receptors was assessed because 8-OH-DPAT, a 5-HT1a agonist, mimicked the inhibition induced by 5-HT. It has been already demonstrated that activation of 5-HT1a receptors hyperpolarized neurons in many brain regions. Their involvement in the 5-HT-induced inhibition of Type-1 neurons is consistent with data reporting that systemic administration of 8-OH-DPAT is followed by a decrease of sleep quantities in rats.66–68 Although our present data shed new light on the functional links between VLPO neurons and sleep, they however raise new questions regarding the specific role of the two populations of PSP neurons, differently modulated by 5-HT, and their integration within the flip-flop switch model.6,38 The firing rate of 5-HT neurons is maximal during waking and greatly decreases during sleep to become silent during paradoxical sleep.27,69 In addition the 5-HT release is lower during sleep versus waking in target areas of the midbrain raphe nuclei.70,71 This suggests that 5-HT would act preferentially as a waking neurotransmitter. In lines with this statement, the inhibition of Type-1 neurons highly supports the participation of 5-HT during waking to the inhibition of the VLPO sleep-active neurons. On the other hand, our unexpected findings that 5-HT may excite a subset of PSP neurons revive the Jouvet’s hypothesis, suggesting a major contribution of 5-HT to sleep production.72–74 This proposal was initially supported by the profound insomnia induced by lesion of midbrain raphe nuclei.72,75 Similar behavioral effects have been obtained following the down-regulation of 5-HT synthesis with systemic para-chloro-phenylalanine treatment (PCPA) in cats and rats.76 In PCPA-treated insomniac animals, sleep can be restored by intraventricular or systemic administration of 5-HTP, the precursor of 5-HT.77–79 A potential target for sleep induction is the ventral LPOA that a posteriori includes VLPO, because 5-HTP microinjection in this forebrain area restored, with a delayed latency (1 hour), natural sleep in PCPA-treated insomniac cats, while injections in neighboring zones were unable to reverse such insomnia.80 These data suggest that the 5HT release in VLPO during waking could not be responsible for sleep onset, through direct excitation of PSP neurons, but would rather prepare the local physiological conditions necessary for sleep to occur. This diachronic action as stated by Jouvet’s team74,81,82 would facilitate the 5-HT-dependant synthesis of hypnogenic factors (see in the following) during waking that could mediate the activation of PSP neurons.

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Considering this hypothesis, Type-2 neurons would stay inactive during waking due to combined NA and ACH inhibitions, despite the 5-HT subthreshold excitatory drive that would participate to intracellular mechanisms downstream post-synaptic serotonergic receptors and prepare their firing activation for sleep onset. Both functional neuroanatomy and electrophysiology in awake animals indicate that the activation of sleep-on neurons in VLPO is related rather to the sleep occurrence than to the sleep need associated to long-lasting awaking.13,24 However, a subset of VLPO neurons increased their firing rate and anticipated for a few seconds the sleep onset (drowsiness).24 At the same moment, 5-HT release increased within the LPOA due to either a sudden firing acceleration of afferent raphe neurons or presynaptic mechanisms monitoring local 5-HT release.83 The obvious temporal correlation between these two events led us to suggest that Type-2 neurons could match this subset of VLPO sleep-on neurons and would thus be directly involved, rather than Type-1 neurons, in 5-HT mechanisms of sleep induction.52 In conclusion, our present data suggest that 5-HT, released during waking in the VLPO, may participate concomitantly to seemingly opposite mechanisms, by strengthening arousal through the inhibition of Type-1 neurons and preparing sleep via the subthreshold excitation of Type-2 neurons.

HYPNOGENIC SUBSTANCES DIFFERENTIALLY MODULATE TYPE-1 AND TYPE-2 NEURONS: AN EMPHASIS ON THE PGD2-ADENOSINE AXIS Among processes that are also inclined to modulate the activity of PSP neurons, homeostatic mechanisms had long been thought to play a crucial role in sleep triggering.11,12,49,50 The homeostasis is supposed to explain the sleep pressure and sleep-rebound following its deprivation, due to the synthesis and accumulation of natural factors during prolonged awakening. Their increasing concentration during waking in wake and sleep-promoting areas up to a critical threshold would contribute the preparation and promotion of sleep. Prostaglandin D2 (PGD2) and adenosine (ADO) have long been functionally implicated in sleep regulation, although their respective targets and mechanisms of action remain largely unknown.84 In this context we tried to determine whether these sleep factors would directly modulate PSP neurons in a way that supported their role in sleep. We observed that the spontaneous firing of Type-1 and Type-2 neurons was not modified by PGD2 (Figure 3.5), suggesting that its behavioral effects are not due to direct activation of PSP neurons. However previous in vivo studies showed that PGD2 when injected LPOA85 promoted sleep and excited one third of sleep-on neurons in rats.86,87 Other studies rather support an indirect mechanism by blocking the brainstem noradrenergic inputs that strongly inhibit sleep-on neurons during waking.88,89 To test this hypothesis, it turns out necessary to determine in vitro whether PGD2 would modulate noradrenergic inhibitory post-synaptic potentials in Type-1 and Type-2 cells. Another possibility is that PGD2 would promote sleep by inducing meningeal targets to release paracrine-signaling molecules.84 PGD2 concentration in the CSF increases during waking and sleep deprivation.90,91 Furthermore

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PGD2 infusion into the subarachnoid space ventral to the basal forebrain, namely the PGD2-sensitive zone, induced sleep92 and consequently c-Fos expression in VLPO neurons.93 Because PSP neurons are unresponsive to PGD2 and likely do not express PGD2 receptors, their activation during sleep would require a secondary messenger to transmit the PGD2-mediated signal into the brain parenchyma. Among these messengers ADO is a concrete candidate because: • • •

Its local concentration increased following PGD2 infusion into the subarachnoid space.93,94 Its infusion or that of specific A2a receptors (A2aR) agonists into the same space mimicked the PGD2 effects.95–98 Sleep quantities are greatly reduced in mice deleted for functional A2aR.99

In the following, we thus tried to determine whether ADO modulates Type-1 and Type-2 neurons. We observed that ADO inhibited the firing of both types of PSP neurons (Figure 3.5). These effects involved A1R since a specific agonist, CPA, reproduces the ADO effect. It is not easy to reconcile the inhibition by ADO of PSP neurons with its hypnogenic properties. However ADO inhibits ubiquitously the neuronal activity, via A1R, by combining post-synaptic hyperpolarization and pre-synaptic inhibition of neurotransmitter release.100–102 ADO acts also as a homeostatic regulator to slow down cell metabolism and is usually considered as a witness of neuronal energy use.103,104 Indeed, ADO cerebral concentrations are generally low, but considerably increase in conditions of ischemia or massive energy depletion.105 It is then possible that, in our experimental conditions, ADO would be interpreted by recorded PSP neurons as a signal of energy deficiency, leading to homeostatic protection by their own firing inhibition without relevance with sleep regulation. In wake-promoting areas as cholinergic forebrain and pontine nuclei, ADO concentration is higher during waking versus sleep and increases during prolonged awakening.106–108 Thus ADO via A1R would facilitate sleep by reducing the cellular metabolism of wakingpromoting neurons according to homeostatic mechanisms.109 Specific A2aR agonists and antagonists were used to determine their potential role in the modulation of PSP neurons. We demonstrated that Type-1 neurons express exclusively A1R since the antagonist, DPCPX, reversed totally the ADO-induced inhibition. In contrasts, DPCPX treatment revealed a reversible firing increase in Type-2 neurons, due to post-synaptic A2aR. In fact, this excitatory effect is similarly observed following application of CGS 21680, a specific agonist (Figure 3.5) and reversed in presence of a specific antagonist, ZM 241385. Excitatory post-synaptic effects mediated by A2aR had already been described in several brain regions110–113 but never in LPOA or VLPO. Our data clearly indicate that Type-2 neurons: • • •

Are under a permanent inhibitory control by ADO Express A2aR in addition to A1R Are able to be turned on by ADO

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This the first tangible experimental demonstration supporting the consensual hypothesis that ADO would promote sleep through direct excitation of VLPO sleepactive, likely Type-2, neurons expressing A2aR.6,51,84,114 Previous microdialysis experiments failed to evidence an increase of ADO concentration in LPOA, in contrast to wake-promoting areas, during prolonged awakening.115 However this technique is not suitable to measure faint variations of ADO concentration in LPOA, where PSP neurons are loosely arranged. To date the kinetics of ADO release in VLPO across the sleep-waking cycle remain unknown. We already reported that PGD2 infusion in the subarachnoid space is followed simultaneously by sleep increase, c-Fos expression in VLPO neurons and local ADO release.94 Similar effects are obtained following CGS 21680 infusion in the PGD2sensitive zone.98 It is tempting to hypothesize that ADO in the CSF may rapidly diffuse until nearby A2aR-expressing PSP neurons in VLPO. It is therefore interesting to recall that PSP neurons have primary dendrites that course and travel along the brain floor to exit in some cases the cerebral parenchyma into the subarachnoid space. Although more experiments are needed to work out detailed mechanisms, our data suggest that Type-2 neurons would function as a neuronal probe to detect local increases of ADO concentration. During waking ADO would be gradually accumulated in CSF, diffusing into the neighboring parenchyma. When the critical ADO concentration is attained in VLPO, Type-2 neurons would be turned on by direct excitation or presynaptic mechanisms of local neurotransmitter release. As 5-HT, ADO may have seemingly opponent actions in VLPO, both promoting waking, via inhibition of Type-1 neurons, and preparing sleep via excitation of Type-2 neurons.

A FUNCTIONAL MODEL OF THE NEURONAL NETWORK RESPONSIBLE FOR SLEEP PROMOTION Our initial in vitro work led to the first identification of PSP neurons within VLPO with characteristics matching with their potential role in sleep promotion. It has been subsequently suggested that alternance of behavioral states is settled to reciprocal inhibitory interactions between PSP and waking-promoting neurons. The PSP neurons would be strongly inhibited during arousal but totally released from this inhibition and thus active during sleep. The recently acquired data, pursuing this successful experimental process to complete the functional ID card of PSP neurons, establish firmly that two subtypes of PSP neurons can be segregated, based on their differential pharmacological profile regarding two major sleep neuromodulators. However major unknowns in the matter remain to be elucidated for an accurate understanding of the events leading to sleep. The most obvious concern is the respective functional contribution of Type-1 and Type-2 neurons and their anatomical integration within the flip-flop switching model. It has been previously shown that VLPO galanin-containing neurons projecting to the TMN or LC are less numerous than c-Fos+ galanin-containing neurons encountered following sleep rebound.14 These data support our proposal that two types of sleep-active neurons are present in VLPO, one assigned to the inhibition of waking-promoting areas during sleep, the other with unknown efferent projection and function. A priority is to

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determine whether Type-1 or Type-2 neurons send their axons to the wakingpromoting areas. While this question is currently under investigation in our laboratory, it is already possible to propose some preliminary clues. The reciprocal inhibitory interactions and flip-flop switching models both supposed that, during waking, PSP neurons are strongly inhibited by wake-active cells. Therefore it is more likely that Type-1 neurons would project to the waking-promoting areas because they should be maintained completely silent during waking, when inhibitory neurotransmitters NA, 5-HT, ACH, and GABA are maximally released in VLPO. The consolidated inhibition of Type-1 neurons during waking allows the required stability for the system. In contrast, Type-2 neurons are able to procure a subtle instability required for behavioral switching (drowsiness). Although under NA and ACH inhibitory drives during waking, Type-2 neurons would be less hyperpolarized and thus more excitable because they are responsive for homeostatic signals. They could start firing before Type-1 neurons, when the release of sleep factors increased in VLPO during longlasting awaking. Supporting this hypothesis, a contingent of VLPO sleep-active neurons begins to fire during drowsiness, just before sleep onset. It is finally possible that Type-2 neurons, secondary to their own excitation, stimulate, by direct excitation or desinhibition, the neighboring Type-1 neurons. The increased activity of Type-1 neurons would initiate the down-regulation of waking-promoting areas, definitely releasing all PSP neurons from their powerful inhibition. Our current functional model of the neuronal network responsible switching of behavioral states is summarized by the scheme given in Figure 3.6. During waking PSP neurons (Type-1 and Type-2) are maintained inactive by combined inhibitions of NA and ACH waking-promoting systems. Simultaneously 5-HT and ADO are released in VLPO. In concert with homeostatic, metabolic, and circadian drives, both compounds gradually activate Type-2 neurons and drowsiness occurs. Likely by presynaptic mechanisms, Type-2 neurons in turn stimulate neighboring Type-1 neurons, which inhibit the waking-promoting systems, thus reinforcing their own firing to finally promote sleep. We propose that Type-2 neurons would be responsible for the preparation and initiation of sleep (permissive neurons) and Type-1 neurons would be responsible for sleep maintenance (executive neurons).

SUMMARY In agreement with our initial statement, the main contribution of the present work is the concrete identification of neurons that are responsible for sleep and the disclosure that VLPO is a suitable and simple model for additional basic research to approach at cellular and molecular levels the neurobiological processes underlying sleep production and regulation.

ACKNOWLEDGMENTS CNRS FRE 2469 and UMR5167, INSERM U480, Université Claude Bernard Lyon 1 and CNRS UMR 7637 ESPCI in Paris supported this work.

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FIGURE 3.6 (See color insert following page 108.) Model of the neuronal network responsible for reciprocal interactions between sleep- and waking-promoting areas, regulating the sleep-waking switching. Inhibitory pathways are shown in red and the excitatory pathways in green. The black circle indicates the brain areas involved in sleep regulation. As traffic lights, circles are colored in green when areas are strongly active, in red when they are inactive, and yellow when they are in a transitory state (either increasing or decreasing their firing rate). Abbreviations: 5-HT, serotonin; Ach, acetylcholine; ADO, adenosine; RN, mesencephalic raphe nuclei; Gal, galanine; Hist, histamine; LC, locus coeruleus; LDT, laterodorsal tegmental nuclei; NA, noradrenalin; NB, nucleus basalis; TMN, tuberomammillary nucleus; VLPO, ventrolateral preoptic nucleus.

REFERENCES 1. Von Economo, C., Schlaftheorie, Ergeb. Physiol 55, 121-135, 1929. 2. Szymusiak, R., Steininger, T., Alam, N., and McGinty, D., Preoptic area sleepregulating mechanisms, Arch Ital Biol 139 (1-2), 77-92, 2001. 3. McGinty, D. and Szymusiak, R., Hypothalamic regulation of sleep and arousal, Front Biosci 8, 1074-1083, 2003. 4. Shiromani, P., Scammell, T., Sherin, J. E., and Saper, C. B., Hypothalamic Regulation of Sleep, in Handbook of Behavioral State Control: Cellular and Molecular Mechanisms, Lydic, R. and Baghdoyan, H. A., CRC Press, Boca Raton, FL, 1999, pp. 311-325.

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5. Schmidt, M. H., Valatx, J. L., Sakai, K., Fort, P., and Jouvet, M., Role of the lateral preoptic area in sleep-related erectile mechanisms and sleep generation in the rat, J Neurosci 20 (17), 6640-6647, 2000. 6. Saper, C. B., Chou, T. C., and Scammell, T. E., The sleep switch: hypothalamic control of sleep and wakefulness, Trends Neurosci 24 (12), 726-731, 2001. 7. Moruzzi, G., The sleep-waking cycle, Ergeb Physiol 64, 1-165, 1972. 8. Moruzzi, G. and Magoun, H. W., Brain stem reticular formation and activation of the EEG., J Neuropsychiatry Clin Neurosci 7 (2), 251-267, 1949. 9. Jones, B., The organization of central cholinergic systems and their functional importance in sleep-waking states, in Cholinergic Function and Dysfunction. Progress in Brain Research., Cuello, A.C., Ed., Elsevier, Amsterdam, 1993, p. 61. 10. Jones, B. E., Basic mechanisms of sleep-wakes states, in Principles and Practice of Sleep Medecine, Kryger, M. H., Roth, T., and Dement, W. C., Eds., W.B. Saunders, Philadelphia, 1994, pp. 145-162. 11. Borbely, A. A., From slow waves to sleep homeostasis: new perspectives, Arch Ital Biol 139 (1-2), 53-61, 2001. 12. Borbely, A. A., Sleep regulation. Introduction, Hum Neurobiol 1 (3), 161-162, 1982. 13. Sherin, J. E., Shiromani, P. J., McCarley, R. W., and Saper, C. B., Activation of ventrolateral preoptic neurons during sleep, Science 271 (5246), 216-219, 1996. 14. Sherin, J. E., Elmquist, J. K., Torrealba, F., and Saper, C. B., Innervation of histaminergic tuberomammillary neurons by GABAergic and galaninergic neurons in the ventrolateral preoptic nucleus of the rat, J Neurosci 18 (12), 4705-4721, 1998. 15. Novak, C. M. and Nunez, A. A., Daily rhythms in Fos activity in the rat ventrolateral preoptic area and midline thalamic nuclei, Am J Physiol 275, R1620-R1626, 1998. 16. Thompson, R. H., Canteras, N. S., and Swanson, L. W., Organization of projections from the dorsomedial nucleus of the hypothalamus: a PHA-L study in the rat, J Comp Neurol 376 (1), 143-173, 1996. 17. Chou, T. C., Bjorkum, A. A., Gaus, S. E., Lu, J., Scammell, T. E., and Saper, C. B., Afferents to the ventrolateral preoptic nucleus, J. Neurosci 22 (3), 977-990, 2002. 18. Chou, T. C., Scammell, T. E., Gooley, J. J., Gaus, S. E., Saper, C. B., and Lu, J., Critical role of dorsomedial hypothalamic nucleus in a wide range of behavioral circadian rhythms, J Neurosci 23 (33), 691-702, 2003. 19. Watts, A. G., Swanson, L. W., and Sanchez-Watts, G., Efferent projections of the suprachiasmatic nucleus: I. Studies using anterograde transport of Phaseolus vulgaris leucoagglutinin in the rat, J Comp Neurol 258 (2), 204-229, 1987. 20. Sun, X., Whitefield, S., Rusak, B., and Semba, K., Electrophysiological analysis of suprachiasmatic nucleus projections to the ventrolateral preoptic area in the rat, Eur J Neurosci 14 (8), 1257-1274, 2001. 21. Novak, C. M. and Nunez, A. A., A sparse projection from the suprachiasmatic nucleus to the sleep active ventrolateral preoptic area in the rat, Neuroreport 11 (1), 93-96, 2000. 22. Lu, J., Shiromani, P., and Saper, C. B., Retinal input to the sleep-active ventrolateral preoptic nucleus in the rat, Neuroscience 93 (1), 209-214, 1999. 23. Deurveilher, S., Burns, J., and Semba, K., Indirect projections from the suprachiasmatic nucleus to the ventrolateral preoptic nucleus: a dual tract-tracing study in rat, Eur J Neurosci 16 (7), 1195-213, 2002. 24. Szymusiak, R., Alam, N., Steininger, T. L., and McGinty, D., Sleep-waking discharge patterns of ventrolateral preoptic/anterior hypothalamic neurons in rats, Brain Res 803 (1-2), 178-188, 1998.

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25. Aston-Jones, G. and Bloom, F. E., Activity of norepinephrine-containing locus coeruleus neurons in behaving rats anticipates fluctuations in the sleep-waking cycle, J Neurosci 1 (8), 876-886, 1981. 26. Barnes, N. M. and Sharp, T., A review of central 5-HT receptors and their function, Neuropharmacology 38 (8), 1083-1152, 1999. 27. Jacobs, B., Overview of the activity of brain monoaminergic neurons across the sleepwake cycle, in Sleep: Neurotransmitters and Neuromodulators, Wauquier A. M. J., Gaillard J.M, et al. Eds., Raven Press, New York, 1985, p. 1. 28. Sakai, K., Central mechanisms of paradoxical sleep, Brain Dev 8 (4), 402-407, 1986. 29. Lu, J., Greco, M. A., Shiromani, P., and Saper, C. B., Effect of lesions of the ventrolateral preoptic nucleus on NREM and REM sleep, J Neurosci 20 (10), 38303842, 2000. 30. Schmidt, M. H., Gervasoni, D., Luppi, P. H., and Fort, P., Carbachol administration into the lateral preoptic area induces penile erections and wakefulness, Neurosci Abstr 522.19, 2001. 31. Schmidt, M. H., Gervasoni, D., Luppi, P. H., and Fort, P., The ventrolateral preoptic area: role and origin of cholinergic input in the control of wakefulness and penile erections, Sleep 25 (supp), 2002. 32. Steininger, T. L., Gong, H., McGinty, D., and Szymusiak, R., Subregional organization of preoptic area/anterior hypothalamic projections to arousal-related monoaminergic cell groups, J Comp Neurol 429, 638-653, 2001. 33. Fort, P., Gervasoni, D., Peyron, C., Rampon, C., Boissard, R., and Luppi, P. H., GABAergic projections to the magnocellular preoptic area and substantia innominata in the rat, Neurosci Abstr 1998. 34. Schmidt, M. H., Gervasoni, D., Luppi, P. H., and Fort, P., Quantitative analysis of cholinergic afferents to the ventrolateral preoptic area: role in waking mechanisms, Sleep 26 (supp), 0089, 2003. 35. Luppi, P. H., Aston-Jones, G., Akaoka, H., Chouvet, G., and Jouvet, M., Afferent projections to the rat locus coeruleus demonstrated by retrograde and anterograde tracing with cholera-toxin B subunit and Phaseolus vulgaris leucoagglutinin, Neuroscience 65 (1), 119-160, 1995. 36. Lu, J., Bjorkum, A. A., Xu, M., Gaus, S. E., Shiromani, P. J., and Saper, C. B., Selective activation of the extended ventrolateral preoptic nucleus during rapid eye movement sleep, J Neurosci 22 (11), 4568-4576, 2002. 37. Gervasoni, D., Peyron, C., Rampon, C., Barbagli, B., Chouvet, G., Urbain, N., Fort, P., and Luppi, P. H., Role and origin of the GABAergic innervation of dorsal raphe serotonergic neurons, J Neurosci 20 (11), 4217-4225, 2000. 38. Gallopin, T., Fort, P., Eggermann, E., Cauli, B., Luppi, P. H., Rossier, J., Audinat, E., Mühlethaler, M., and Serafin, M., Identification of sleep-promoting neurons in vitro, Nature 404 (6781), 992-995, 2000. 39. Gallopin, T., Luppi, P. H., Rambert, F., Frydman, A., and Fort, P., Effect of the wake promoting agent modafinil on sleep promoting neurons from the ventrolateral preoptic nucleus: an in-vitro pharmacologic study, Sleep 27 (1), 19-25, 2004. 40. Matsuo, S., Jang, I. S., Nabekura, J., and Akaike, N., alpha 2-Adrenoceptor-mediated presynaptic modulation of GABAergic transmission in mechanically dissociated rat ventrolateral preoptic neurons, J Neurophysiol 89 (3), 1640-1648, 2003. 41. Eggermann, E., Serafin, M., Bayer, L., Machard, D., Saint-Mleux, B., Jones, B. E., and Mühlethaler, M., Orexins/hypocretins excite basal forebrain cholinergic neurones, Neuroscience 108 (2), 177-181, 2001.

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42. Airaksinen, M. S., Alanen, S., Szabat, E., Visser, T. J., and Panula, P., Multiple neurotransmitters in the tuberomammillary nucleus: comparison of rat, mouse, and guinea pig, J Comp Neurol 323 (1), 103-116, 1992. 43. Nishino, S., Ripley, B., Overeem, S., Lammers, G. J., and Mignot, E., Hypocretin (orexin) deficiency in human narcolepsy, Lancet 355 (9197), 39-40, 2000. 44. Lin, L., Faraco, J., Li, R., Kadotani, H., Rogers, W., Lin, X., Qiu, X., de Jong, P. J., Nishino, S., and Mignot, E., The sleep disorder canine narcolepsy is caused by a mutation in the hypocretin (orexin) receptor 2 gene, Cell 98 (3), 365-376, 1999. 45. Peyron, C., Tighe, D. K., van den Pol, A. N., de Lecea, L., Heller, H. C., Sutcliffe, J. G., and Kilduff, T. S., Neurons containing hypocretin (orexin) project to multiple neuronal systems, J Neurosci 18 (23), 9996-10015, 1998. 46. de Lecea, L., Kilduff, T. S., Peyron, C., Gao, X., Foye, P. E., Danielson, P. E., Fukuhara, C., Battenberg, E. L., Gautvik, V. T., Bartlett, F. S., Frankel, W. N., van den Pol, A. N., Bloom, F. E., Gautvik, K. M., and Sutcliffe, J. G., The hypocretins: hypothalamus-specific peptides with neuroexcitatory activity, Proc Natl Acad Sci U S A 95 (1), 322-327, 1998. 47. Kilduff, T. S. and Peyron, C., The hypocretin/orexin ligand-receptor system: implications for sleep and sleep disorders, Trends Neurosci 23 (8), 359-65, 2000. 48. McGinty, D., Alam, M. N., Szymusiak, R., Nakao, M., and Yamamoto, M., Hypothalamic sleep-promoting mechanisms: coupling to thermoregulation, Arch Ital Biol 139 (1-2), 63-75, 2001. 49. Krueger, J. M., Cytokines and Sleep Regulation, in Handbook of Behavioral State Control: Cellular and Molecular Mechanisms, Lydic, R. and Baghdoyan, H. A., Eds., CRC Press, Boca Raton, FL, 1999, pp. 609-622. 50. Krueger, J. M. and Obal, F., Jr., Sleep function, Front Biosci 8, d511-9, 2003. 51. Obal, F., Jr. and Krueger, J. M., Biochemical regulation of non-rapid-eye-movement sleep, Front Biosci 8, d520-50, 2003. 52. Gallopin, T., Cauli, B., Luppi, P. H., Rossier, J., Lambolez, B., and Fort, P., Serotonin modulation reveals two types of sleep-promoting neurons in the ventrolateral preoptic nucleus, FENS Abstr 1, 024-13, 2002. 53. Lambolez, B., Audinat, E., Bochet, P., Crepel, F., and Rossier, J., AMPA receptor subunits expressed by single Purkinje cells, Neuron 9 (2), 247-258, 1992. 54. Seutin, V., Verbanck, P., Massotte, L., and Dresse, A., Galanin decreases the activity of locus coeruleus neurons in vitro, Eur J Pharmacol 164 (2), 373-376, 1989. 55. Xu, Z. Q., Zhang, X., Pieribone, V. A., Grillner, S., and Hokfelt, T., Galanin-5hydroxytryptamine interactions: electrophysiological, immunohistochemical and in situ hybridization studies on rat dorsal raphe neurons with a note on galanin R1 and R2 receptors, Neuroscience 87 (1), 79-94, 1998. 56. Schonrock, B., Busselberg, D., and Haas, H. L., Properties of tuberomammillary histamine neurones and their response to galanin, Agents Actions 33 (1-2), 135-137, 1991. 57. Melander, T., Hokfelt, T., Rokaeus, A., Cuello, A. C., Oertel, W. H., Verhofstad, A., and Goldstein, M., Coexistence of galanin-like immunoreactivity with catecholamines, 5-hydroxytryptamine, GABA and neuropeptides in the rat CNS, J Neurosci 6 (12), 3640-3654, 1986. 58. Hokfelt, T., Xu, Z. Q., Shi, T. J., Holmberg, K., and Zhang, X., Galanin in ascending systems. Focus on coexistence with 5-hydroxytryptamine and noradrenaline, Ann N Y Acad Sci 863, 252-263, 1998. 59. Gundlach, A. L., Burazin, T. C., and Larm, J. A., Distribution, regulation and role of hypothalamic galanin systems: renewed interest in a pleiotropic peptide family, Clin Exp Pharmacol Physiol 28 (1-2), 100-105, 2001.

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60. Aghajanian, G. K., Electrophysiology of serotonin receptor subtypes and signal transduction pathways, in Psychopharmacology: The Fourth Generation of Progress, Bloom, F. R. and Kupfer, D. J., Eds., Raven Press, New York, 1995, pp. 1451-1459. 61. Dugovic, C., Functional activity of 5-HT2 receptors in the modulation of the sleep/wakefulness states, J Sleep Res 1 (3), 163-168, 1992. 62. Dugovic, C., Wauquier, A., Leysen, J. E., Marrannes, R., and Janssen, P. A., Functional role of 5-HT2 receptors in the regulation of sleep and wakefulness in the rat, Psychopharmacology (Berl) 97 (4), 436-442, 1989. 63. Martin, J. R., Bos, M., Jenck, F., Moreau, J., Mutel, V., Sleight, A. J., Wichmann, J., Andrews, J. S., Berendsen, H. H., Broekkamp, C. L., Ruigt, G. S., Kohler, C., and Delft, A. M., 5-HT2C receptor agonists: pharmacological characteristics and therapeutic potential, J Pharmacol Exp Ther 286 (2), 913-924, 1998. 64. Dugovic, C., Role of serotonin in sleep mechanisms, Rev Neurol (Paris) 157 (11 Pt 2), S16-S19, 2001. 65. Landolt, H. P., Meier, V., Burgess, H. J., Finelli, L. A., Cattelin, F., Achermann, P., and Borbely, A. A., Serotonin-2 receptors and human sleep: effect of a selective antagonist on EEG power spectra, Neuropsychopharmacology 21 (3), 455-466, 1999. 66. Bjorvatn, B., Fagerland, S., Eid, T., and Ursin, R., Sleep/waking effects of a selective 5-HT1A receptor agonist given systemically as well as perfused in the dorsal raphe nucleus in rats, Brain Res 770 (1-2), 81-88, 1997. 67. Monti, J. M. and Jantos, H., Stereoselective antagonism by the pindolol enantiomers of 8-OH-DPAT-induced changes of sleep and wakefulness, Neuropharmacology 33 (5), 705-708, 1994. 68. Monti, J. M. and Jantos, H., Dose-dependent effects of the 5-HT1A receptor agonist 8-OH-DPAT on sleep and wakefulness in the rat, J Sleep Res 1 (3), 169-175, 1992. 69. McGinty, D. J. and Harper, R. M., Dorsal raphe neurons: depression of firing during sleep in cats, Brain Res 101 (3), 569-575, 1976. 70. Houdouin, F., Cespuglio, R., and Jouvet, M., Effects induced by the electrical stimulation of the nucleus raphe dorsalis upon hypothalamic release of 5-hydroxyindole compounds and sleep parameters in the rat, Brain Res 565 (1), 48-56, 1991. 71. Puizillout, J. J., Gaudin-Chazal, G., Daszuta, A., Seyfritz, N., and Ternaux, J. P., Release of endogenous serotonin from “encephale isole” cats. II - Correlations with raphe neuronal activity and sleep and wakefulness, J Physiol (Paris) 75 (5), 531-537, 1979. 72. Jouvet, M., Biogenic amines and the states of sleep, Science 163 (862), 32-41, 1969. 73. Jouvet, M., The role of monoamines and acetylcholine-containing neurons in the regulation of the sleep-waking cycle, Ergeb Physiol 64, 166-307, 1972. 74. Jouvet, M., Sleep and serotonin: an unfinished story, Neuropsychopharmacology 21 (Suppl), 24S-27S, 1999. 75. Arpa, J. and De Andres, I., Re-examination of the effects of raphe lesions on the sleep/wakefulness cycle states in cats, J Sleep Res 2 (2), 96-102, 1993. 76. Delorme, F., Froment, J. L., and Jouvet, M., Suppression of sleep with p-chloromethamphetamine and p-chlorophenylalanine, C R Seances Soc Biol Fil 160 (12), 2347-2351, 1966. 77. Borbely, A. A., Neuhaus, H. U., and Tobler, I., Effect of p-chlorophenylalanine and tryptophan on sleep, EEG and motor activity in the rat, Behav Brain Res 2 (1), 1-22, 1981. 78. Petitjean, F., Buda, C., Janin, M., Sallanon, M., and Jouvet, M., Insomnia caused by administration of para-chlorophenylalanine: reversibility by peripheral or central injection of 5-hydroxytryptophan and serotonin, Sleep 8 (1), 56-67, 1985.

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79. Touret, M., Sarda, N., Gharib, A., Geffard, M., and Jouvet, M., The role of 5hydroxytryptophan (5-HTP) in the regulation of the sleep/wake cycle in parachlorophenylalanine (p-CPA) pretreated rat: a multiple approach study, Exp Brain Res 86 (1), 117-124, 1991. 80. Denoyer, M., Sallanon, M., Kitahama, K., Aubert, C., and Jouvet, M., Reversibility of para-chlorophenylalanine-induced insomnia by intrahypothalamic microinjection of L-5-hydroxytryptophan, Neuroscience 28 (1), 83-94, 1989. 81. Jouvet, M., Neuromediators and hypnogenic factors, Rev Neurol (Paris) 140 (6-7), 389-400, 1984. 82. Sallanon, M., Buda, C., and Janin, M., Implication of serotonin in sleep mechanisms: induction, facilitation?, in Sleep: Neurotransmitters and Neuromodulators, A. Wauquier, J.M. Monti, and J.M. Gaillard, Eds., Raven Press, New York, 1985, p. 136. 83. Python, A., Steimer, T., De Saint, H., Mikolajewski, R., and Nicolaidis, S., Extracellular serotonin variations during vigilance states in the preoptic area of rats: a microdialysis study, Brain Res 910 (1-2), 49-54, 2001. 84. Hayaishi, O., Molecular genetic studies on sleep-wake regulation, with special emphasis on the prostaglandin D(2) system, J Appl Physiol 92 (2), 863-868, 2002. 85. Ueno, R., Ishikawa, Y., Nakayama, T., and Hayaishi, O., Prostaglandin D2 induces sleep when microinjected into the preoptic area of conscious rats, Biochem Biophys Res Commun 109 (2), 576-582, 1982. 86. Koyama, Y. and Hayaishi, O., Modulation by prostaglandins of activity of sleeprelated neurons in the preoptic/anterior hypothalamic areas in rats, Brain Res Bull 33 (4), 367-372, 1994. 87. Koyama, Y. and Hayaishi, O., Firing of neurons in the preoptic/anterior hypothalamic areas in rat: its possible involvement in slow wave sleep and paradoxical sleep, Neurosci Res 19 (1), 31-38, 1994. 88. Osaka, T. and Matsumura, H., Noradrenergic inputs to sleep-related neurons in the preoptic area from the locus coeruleus and the ventrolateral medulla in the rat, Neurosci Res 19 (1), 39-50, 1994. 89. Osaka, T. and Matsumura, H., Noradrenaline inhibits preoptic sleep-active neurons through alpha 2-receptors in the rat, Neurosci Res 21, 323-330, 1995. 90. Pandey, H. P., Ram, A., Matsumura, H., and Hayaishi, O., Concentration of prostaglandin D2 in cerebrospinal fluid exhibits a circadian alteration in conscious rats, Biochem Mol Biol Int 37 (3), 431-437, 1995. 91. Ram, A., Pandey, H. P., Matsumura, H., Kasahara-Orita, K., Nakajima, T., Takahata, R., Satoh, S., Terao, A., and Hayaishi, O., CSF levels of prostaglandins, especially the level of prostaglandin D2, are correlated with increasing propensity towards sleep in rats, Brain Res 751 (1), 81-89, 1997. 92. Matsumura, H., Nakajima, T., Osaka, T., Satoh, S., Kawase, K., Kubo, E., Kantha, S. S., Kasahara, K., and Hayaishi, O., Prostaglandin D2-sensitive, sleep-promoting zone defined in the ventral surface of the rostral basal forebrain, Proc Natl Acad Sci U S A 91 (25), 11998-12002, 1994. 93. Scammell, T., Gerashchenko, D., Urade, Y., Onoe, H., Saper, C., and Hayaishi, O., Activation of ventrolateral preoptic neurons by the somnogen prostaglandin D2, Proc Natl Acad Sci U S A 95 (13), 7754-7759, 1998. 94. Mizoguchi, A., Eguchi, N., Kimura, K., Kiyohara, Y., Qu, W. M., Huang, Z. L., Mochizuki, T., Lazarus, M., Kobayashi, T., Kaneko, T., Narumiya, S., Urade, Y., and Hayaishi, O., Dominant localization of prostaglandin D receptors on arachnoid trabecular cells in mouse basal forebrain and their involvement in the regulation of nonrapid eye movement sleep, Proc Natl Acad Sci U S A 98 (20), 11674-11679, 2001.

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95. Satoh, S., Matsumura, H., and Hayaishi, O., Involvement of adenosine A2A receptor in sleep promotion, Eur J Pharmacol 351 (2), 155-162, 1998. 96. Satoh, S., Matsumura, H., Koike, N., Tokunaga, Y., Maeda, T., and Hayaishi, O., Region-dependent difference in the sleep-promoting potency of an adenosine A2A receptor agonist, Eur J Neurosci 11 (5), 1587-1597, 1999. 97. Satoh, S., Matsumura, H., Suzuki, F., and Hayaishi, O., Promotion of sleep mediated by the A2a-adenosine receptor and possible involvement of this receptor in the sleep induced by prostaglandin D2 in rats, Proc Natl Acad Sci U S A 93 (12), 5980-5984, 1996. 98. Scammell, T. E., Gerashchenko, D. Y., Mochizuki, T., McCarthy, M. T., Estabrooke, I. V., Sears, C. A., Saper, C. B., Urade, Y., and Hayaishi, O., An adenosine A2a agonist increases sleep and induces Fos in ventrolateral preoptic neurons, Neuroscience 107 (4), 653-663, 2001. 99. Urade, Y., Eguchi, N., Qu, W. M., Sakata, M., Huang, Z. L., Chen, J. F., Schwarzschild, M. A., Fink, J. S., and Hayaishi, O., Minireview: Sleep regulation in adenosine A(2A) receptor-deficient mice, Neurology 61 (Suppl 6), S94-6, 2003. 100. Barrie, A. P. and Nicholls, D. G., Adenosine A1 receptor inhibition of glutamate exocytosis and protein kinase C-mediated decoupling, J Neurochem 60 (3), 10811086, 1993. 101. Dunwiddie, T. V., The physiological role of adenosine in the central nervous system, Int Rev Neurobiol 27, 63-139, 1985. 102. Proctor, W. R. and Dunwiddie, T. V., Pre- and postsynaptic actions of adenosine in the in vitro rat hippocampus, Brain Res 426 (1), 187-190, 1987. 103. Mcllwain, H., Adenosine and its mononucleotides as regulatory and adaptative signals in the brain, in Physiological and Regulatory Functions of Adenosine and Adenine Nucleotides, Baer, H. P. and Drummond, G. I., Eds., Raven Press, New York, 1979, pp. 361-376. 104. Daval, J. L. and Nicolas, F., Non-selective effects of adenosine A1 receptor ligands on energy metabolism and macromolecular biosynthesis in cultured central neurons, Biochem Pharmacol 55 (2), 141-149, 1998. 105. Benington, J. H. and Heller, H. C., Restoration of brain energy metabolism as the function of sleep, Prog Neurobiol 45 (4), 347-360, 1995. 106. Alam, M. N., Szymusiak, R., Gong, H., King, J., and McGinty, D., Adenosinergic modulation of rat basal forebrain neurons during sleep and waking: neuronal recording with microdialysis, J Physiol (London) 521, 679-690, 1999. 107. Rainnie, D. G., Grunze, H. C., McCarley, R. W., and Greene, R. W., Adenosine inhibition of mesopontine cholinergic neurons: implications for EEG arousal, Science 263 (5147), 689-692, 1994. 108. Strecker, R. E., Morairty, S., Thakkar, M. M., Porkka-Heiskanen, T., Basheer, R., Dauphin, L. J., Rainnie, D. G., Portas, C. M., Greene, R. W., and McCarley, R. W., Adenosinergic modulation of basal forebrain and preoptic/anterior hypothalamic neuronal activity in the control of behavioral state, Behav Brain Res 115 (2), 2000. 109. Cunha, R. A., Adenosine as a neuromodulator and as a homeostatic regulator in the nervous system: different roles, different sources and different receptors, Neurochem Int 38 (2), 107-125, 2001. 110. Barajas-Lopez, C., Surprenant, A., and North, R. A., Adenosine A1 and A2 receptors mediate presynaptic inhibition and postsynaptic excitation in guinea pig submucosal neurons, J Pharmacol Exp Ther 258 (2), 490-495, 1991. 111. Umemiya, M. and Berger, A. J., Activation of adenosine A1 and A2 receptors differentially modulates calcium channels and glycinergic synaptic transmission in rat brainstem, Neuron 13 (6), 1439-1446, 1994.

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112. Li, H. and Henry, J. L., Adenosine A2 receptor mediation of pre- and postsynaptic excitatory effects of adenosine in rat hippocampus in vitro, Eur J Pharmacol 347 (23), 173-182, 1998. 113. Sebastiao, A. M. and Ribeiro, J. A., Adenosine A2 receptor-mediated excitatory actions on the nervous system, Prog Neurobiol 48 (3), 167-189, 1996. 114. Adrien, J., Adenosine in sleep regulation, Rev Neurol (Paris) 157 (11 Pt 2), S7-11, 2001. 115. Porkka-Heiskanen, T., Strecker, R. E., and McCarley, R. W., Brain site-specificity of extracellular adenosine concentration changes during sleep deprivation and spontaneous sleep: an in vivo microdialysis study, Neuroscience 99 (3), 507-517, 2000.

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4

Molecular Mechanisms of Sleep-Wake Regulation: A Role of Prostaglandin D2 and Adenosine Osamu Hayaishi

CONTENTS Prostaglandins and Sleep Prostaglandins Prostaglandin D2 Induces Physiological Sleep in Rats and Monkeys Experiments with Rodents Experiments with Primates PGE2 Promotes Wakefulness Mechanisms Underlying Sleep-Wake Regulation by PGD2 Prostaglandin D Synthase (PGDS) Structure and Function Localization of PGDS PGD Receptor (DPR, D-Type PG Receptor) Adenosine and A2A Receptor VLPO and TMN Molecular Genetic Studies TG Mice for PGDS KO Mice for PGDS and DPR A2AR KO Mice H1R KO Mice and EP4 Receptor Summary Acknowledgments References

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PROSTAGLANDINS AND SLEEP PROSTAGLANDINS The prostaglandins (PGs) are a family of naturally occurring lipids with a unique five-membered carbon ring and are derived from various polyunsaturated fatty acids such as arachidonic acid. These compounds are widely distributed in virtually all types of cells in mammalian tissues and organs, and exhibit numerous and diverse biological effects on a wide variety of physiological and pathological activities such as contraction and relaxation of muscles, platelet aggregation, inflammation, fever, and so forth; hence they are generally referred to as tissue hormones or local hormones. However relatively little attention was paid to the PGs in the central nervous system (CNS) of mammals until in early 1980s, when we found PGD2 to be the major prostanoid in the brain of rats1,2 and other mammals, including humans.3 PGD2 had long been considered as a minor and biologically relatively inactive prostanoid, and therefore our findings suggested that PGD2 may be a unique constituent of the brain of mammals and that it may play some important and possibly specific function in this organ.

PROSTAGLANDIN D2 INDUCES PHYSIOLOGICAL SLEEP IN RATS AND MONKEYS Experiments with Rodents In an attempt to elucidate the neural function of PGD2, we microinjected nmolar quantities of PGD2 in the preoptic area (POA) of rats and discovered that the amount of slow-wave sleep (SWS) was increased dose-dependently up to approximately sixfold with a concomitant drop in the colonic temperature.4 Subsequently we examined the somnogenic activity of PGD2 in more detail by using the continuous infusion circadian sleep bioassay system originally developed by Honda and Inoué.5 The effect of this PG was dose-dependent, and as little as 60 fmol/min of PGD2 was effective in inducing both SWS and paradoxical sleep (PS). The sleep-inducing effect of PGD2 was specific, and other PGs were much less effective or totally inactive. Sleep induced by PGD2 was indistinguishable from physiological sleep as judged from EEG and EMG recordings, heart rate, locomotor activities, and the general behavior of the rat.6 PGD2 was not pyrogenic and actually caused a slight decrease in the body temperature, as is observed to occur in physiological sleep. The PGD2 concentration in rat CSF showed a circadian change coupled to the sleep-wake cycle7 and elevates with an increase in sleep propensity during sleep deprivation.8 Furthermore as PGD2 was found to be actively synthesized and metabolized in the brain, it was tentatively concluded to be an endogenous regulator of physiological sleep.9 Experiments with Primates To extend our studies to primates, we then developed a continuous sleep bioassay system for use in monkeys and investigated the sleep-inducing activity of PGD2 in the rhesus monkey, Macaca mulatta.10 When PGD2 was infused into the lateral or the third ventricle of the cerebrum during the light period, the amount of total sleep increased

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up to three- to fourfold over the control level. As in the case of rats, sleep induced by PGD2 in this primate appears to be normal on the basis of the following criteria: •

•

• • •

Polygraphic characteristics such as EEG, EMG, EOG, and heart rate, as well as general behavior, were indistinguishable from those observed during natural sleep. Power spectral analyses were carried out with an analyzer equipped with a Fast Fourier Transform System. The data obtained were also similar to those collected during natural sleep. On the other hand, those data obtained after oral administration of benzodiazepine derivatives such as nitrazepam were clearly different from the latter in that there was a sharp peak in the d wave region about 0.5–3 Hz, and the q band about 4–7 Hz seen during natural sleep was almost nonexistent. Instead the so-called benzodiazepine fast wave with its peak about 20 Hz was clearly visible.10 Sleep occurred episodically, and both REM and NREM sleep were observed. Body temperature and heart rate fluctuated according to the change in sleep stages. The monkeys were easily aroused by external stimuli, indicating their sleep to be clearly different from that induced by common hypnotic agents such as barbiturates, benzodiazepines, and so forth.

PGD2 was reported to be involved in the pathogenesis of mastocytosis, a disorder characterized by massive, episodic, and endogenous production of PGD2 accompanied by lethargy and deep sleep episodes.11 Subsequently by using radioimmunoassay, Pentreath and coworkers12 determined the levels of PGD2, PGE2, and related compounds in samples of CSF from patients with African sleeping sickness, which is caused by Trypanosoma. PGD2 concentrations were selectively and progressively elevated in the advanced-stage patients. This correlation may indicate that sleep in the late stage of sleeping sickness may be caused, at least in part, by increased production of endogenous PGD2. The possibility that PGD2 is produced by enzymic action in the cell body of the parasites, such as Trypanosoma brucei gambiense, can not be completely ruled out, because recent available evidence indicates that PGs are not only produced and widely distributed in higher animals but also in parasites, such as cestodes, trematodes, nematodes, and protozoa. PGF2a synthetase, the enzymes catalyzing the synthesis of one of these PGs was highly purified from Trypanosoma brucei and characterized and PGD2 was also shown to be produced in and secreted from trypanosomes both in vitro and in vivo.13 These results show that PGD2 is a somnogenic agent not only in rats and monkeys but in humans also.

PGE2 PROMOTES WAKEFULNESS PGE2 and D2 are positional isomers (Figure 4.1) and are known to exhibit opposite biological effects. For example, PGD2 lowers body temperature, suppresses secretion of luteinizing hormone-releasing hormone, and decreases the transmucosal potential

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Cell membrane

Phospholipase A 2 COOH COOH O

OH

Cyclooxygenase O

COOH OH

PGD synthase

PGI2 OH

OH

PGF 2 α

OH

PGH2

COOH O

TXA 2

O

OH COOH

OH

COOH

O

O

O

OH

Arachidonic acid

OH

PGD2

COOH OH

OH

PGE 2

FIGURE 4.1 Arachidonate cascade system.

difference in rat colon mucosa, whereas PGE2 has the opposite effect; i.e., it causes an increase in body temperature, stimulates the hormone secretion, and increases the potential difference. As for the effect of the E-series PGs on sleep, reports by previous investigators have been inconsistent, probably due to differences in animal species, site of application, and other conditions. In 1988 Matumura et al.14 demonstrated that microinjection of PGE2 into the POA reduced the amount of diurnal sleep of rats, indicating that PGE2 may induce wakefulness. The awaking effect of this PG was further examined by use of a long term sleep bioassay system.15 Under more physiological conditions, both NREM and REM sleep were dose-dependently reduced. The rebound of both NREM and REM sleep was observed during the night after PGE2 infusion. NREM sleep reduction was due to the shortened duration of episodes while REM sleep reduction resulted from both the shortened duration and the decreased number of episodes. Under the experimental conditions, PGE2 also induced hyperthermia. However there seems to be no evidence to support the cause-effect relationships between changes in sleep-wake activities and temperature alterations. In order to investigate whether endogenous PGE2 is indeed involved in physiological regulation of sleep-wake cycle in rats, Matumura et al.16 tested the effect of AH6809 (6-isopropoxy-9-oxoxantheme-2-carboxylic acid, Glaxo), an antagonist of the PGE2 receptor. When AH 6809 was infused at a rate of 20 pmol/min into the third ventricle of a rat during the night, when the animal is normally awake, the amount of NREM sleep was increased by 22% over the control, and that of REM sleep, by 89%. If PGE2 induces wakefulness or inhibits sleep under physiological

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conditions, such a PGE2 antagonist should counteract the effect of endogenous PGE2 and increase the amount of sleep or decrease the amount of wakefulness, provided that it has no effect on the PGD2 system. Such results were interpreted to mean that PGE2 is involved in the maintenance of the arousal state under physiological conditions. It is interesting to note that the infusion of AH6809 increased the amount of sleep but that the brain temperature was hardly affected. When both PGE2 and AH6809 were infused simultaneously, the waking effect of PGE2 was completely neutralized, whereas the pyrogenic effect of PGE2 was not inhibited. These results indicate that the waking effect of PGE2 is probably independent of the pyrogenic effect and that the PGE2 receptors for waking activity and temperature regulation are probably different, one being sensitive to AH6809 and the other insensitive to this antagonist. These studies were further extended to rhesus monkeys. PGE2 was administered through a microdialysis probe into 11 brain loci and the promotion of wakefulness and elevation of brain temperature were monitored.17 The hyperthermic effect of PGE2 was dose-dependent and most potent in the POA. Its waking effect was also dose-dependent and was most pronounced in the tuberomammilary nucleus (TMN) region in the posterior hypothalamus (PH). The waking response of PGE2 was not correlated with the change in brain temperature. For example when a low dose of PGE2 (< 100 pmol/m) was administered into the TMN, the time spent awake during the infusion period increased up to 3.5-fold, and the amount of SWS decreased to 50% of that of the control level, with negligible changes in brain temperature.17 These results clearly indicate that PGE2 is a wakefulness inducing substance in monkeys also and that its arousal-promoting activity is independent of its hyperthermia effect, and is mediated in a specific site in the TMN/PH region. The earlier studies mentioned in this section up to April 1991 were reviewed mainly in two previous review articles.9,18

MECHANISMS UNDERLYING SLEEP-WAKE REGULATION BY PGD2 PROSTAGLANDIN D SYNTHASE (PGDS) Structure and Function PGs of the two series and thromboxane A2 are produced from a common substrate, arachidonic acid, through the arachidonate cascade system, as shown in Figure 4.1. In this pathway the oxygenation of arachidonate to PGH2 is catalyzed by cyclooxygenases (COX-I and -II), and this step has been believed to be the rate-limiting step in the production of the final metabolites such as PGD2, E2 etc. PGDS (EC 5. 3. 99. 2) catalyzes the isomerization of PGH2 to PGD2 (Figure 4.1). Because it is the key enzyme in sleep-wake regulation, we studied its structure, properties, and function in detail. The detailed account has been published in several previous reviews19–21 and so only a brief summary is presented here. Two distinct types of PGDS have been reported, one the lipocalin type PGDS (L-PGDS), also known as the brain-type or glutathione (GSH)-independent enzyme and the other

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hematopoietic PGDS, the spleen-type or GSH-requiring enzyme. Hereafter PGDS refers to L-PGDS unless specified otherwise. L-PGDS was purified from the brains of rats, humans and frogs. From cDNA cloning studies and sequence analyses of crystalline enzyme preparations, it was found to be an N-glycosylated monomeric protein with a molecular weight of about 25,000. The primary structure indicated it to be a member of the lipocalin superfamily and an ectoprotein that is easily secreted from the cells. The enzyme requires free sulfhydryl compounds for catalysis, but this requirement is not specific to GSH. All these catalytic and structural properties are intimately related to its function in sleep regulation, as will be described later. Although PGDS is a very stable enzyme and highly resistant to heat treatment, it is inhibited reversibly and specifically by relatively low concentrations of inorganic quadrivalent selenium (Se4+) compounds.22 This inhibition is probably due to their interaction with the free SH group in the active center, because this inhibition can be easily reversed by the addition of excess amounts of sulfhydryl compounds, such as GSH or diethyl-dithiothreitol (DTT). When picomolar quantities of SeCl4 were infused into the third ventricle of a rat during the day, both SWS and REMS were inhibited time- and dose-dependently. After about 2 h from the start of infusion, both types of sleep were almost completely inhibited. The effect was reversible, and when the infusion was interrupted or SH compounds, such as GSH or DTT, were infused simultaneously, either sleep was restored in agreement with results or the in vitro enzyme activity.23 Further studies showed that intravenous administration of tetravalent selenium compounds inhibited the sleep of freely moving rats.24 Recently intracerebroventricular infusion of a PGD receptor (DPR) antagonist (ONO4127) was also shown to inhibit sleep reversibly and dose-dependently.25 These results, taken together, clearly show that PGD2 is an endogenously produced, natural sleeppromoting substance, or a sleep hormone, and that PGDS and DPR play a crucial role in sleep regulation under physiological conditions. Localization of PGDS To determine the localization of PGDS in the rat brain, we employed three independent approaches, namely: (1) in situ hybridization to detect messenger RNA (mRNA) of PGDS, (2) immunohistochemical staining of the enzyme protein, and (3) the direct determination of enzyme activity. Our results yielded much important and some unexpected information, which gave us a new insight into the mechanism of the somnogenic activity of PGD2. The results of the in situ hybridization studies revealed that the mRNA was expressed intensely in the membrane system surrounding the brain rather than in the brain parenchyma, namely, in the leptomeninges; i.e., the arachnoid membrane of the brain and spinal cord, and also in the choroid plexus in the ventricles. The mRNA was only faintly and diffusely expressed in the brain parenchyma, mainly in the white matter rather than in the gray matter, especially in the corpus callosum.26 Immunohistochemical detection of the PGDS enzyme protein also revealed essentially the same results. The oligodendrocytes were positive for both mRNA and protein staining, but little, if any, of either was observed in other types of cells including neurons. Further studies on the mouse brain27 were in

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essential agreement with the results obtained with rats and clearly showed that mRNA for PGDS and the enzyme protein were mainly localized in the trabecular cells of the entire leptomeninges and also in the epithelial cells of the choroid plexus. We then determined the specific activity of PGDS in different parts of the rat brain parenchyma as well as in these membranous tissues.26 The specific activities of the PGDS of the choroid plexus and arachnoid membrane were several-fold higher than that activity in the whole brain. Both rat and human CSF contained a remarkably large amount of PGDS activity. In the meantime Zahn et al.28 and Hoffmann et al.29 independently and concurrently reported the amino acid sequence of human brain PGDS to be highly homologous to that of b-trace, a major protein of unknown function in the human CSF. Watanabe et al.30 quickly confirmed these findings and further established the fact that b-trace and PGDS are not only structurally but also enzymatically and immunologically identical. These results taken together were interpreted to mean that PGDS is mainly, if not exclusively, present in the membrane system surrounding the brain, namely the arachnoid membrane and choroid plexus, where PGD2 is dominantly produced. PGDS, being a secretory protein, is then secreted into the CSF to become b-trace. This b-trace and the PGD2 thus produced circulate in the CSF in the ventricular and subarachnoid space between the arachnoid membrane and pia mater.

PGD RECEPTOR (DPR, D-TYPE PG RECEPTOR) For determination of the site at which PGD2 acts to induce sleep, three different approaches were employed: (1) autoradiography to detect the PGD2 binding protein, (2) microinfusion of PGD2 via a microdialysis probe into different areas of the brain, and (3) immunohistochemical detection of the DPR. Autoradiographic image analyses by computerized densitometry and color coding of the binding protein with 3H-labeled PGD2 showed that the PGD2 binding occurred in the POA of the rat brain.31 In good agreement with this result, when a pmolar amount of PGD2 was infused through a microdialysis probe into more than 200 different areas in the rat brain, PGD2 failed to induce sleep in all parts of the brain parenchyma except in the POA, where a weak somnogenic activity was consistently observed. The most pronounced sleep-inducing activity was observed, however, when PGD2 was applied to the subarachnoid space in the medial ventral region of the rostral basal forebrain.32 Finally the location of DPR in the rat and mouse brains was visualized with antibody highly specific for DPR.27 The DPR immunoreactivity was localized almost exclusively in the leptomeninges on the ventral surface of the basal forebrain with weak immunoreactivity in the pia/arachnoid membrane in the choroid plexus of the lateral and third ventricles. In contrast, the PGDS immunoreactivity was localized in the leptomeninges surrounding the entire brain and in the choroid plexus, in good agreement with our previous preliminary studies with rat brain. The DPR immunoreactivity was not found in the dura mater, pia mater, or brain parenchyma. Electron microscopic studies on the mouse brain clearly showed that DPR-expressing cells were arachnoid trabecular cells and that the immunogold particles were mainly located on the plasma membranes and with less frequency on the intracellular membrane structures such as the vesicles

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and endoplasmic reticulum.27 Little, if any, immunoreactivity was seen in arachnoid barrier cells and pia mater cells. Most DPR-expressing cells were also positive for PGDS, indicating that PGD2 acts as an autocrine as well as paracrine agent, although PGD2 produced in other parts of the brain, such as CSF, may also contribute to promoting sleep. More than 700 serial coronal sections were used to find the exact location of DPR-expressing cells in the mouse brain.27 The DPR-positive cells were highly concentrated in the ventral surface of the rostral basal forebrain whereas other areas were almost completely negative. The region with concentrated DPRs was clearly defined as bilateral wings in the rostral basal forebrain lateral to the optic chiasm in the proximity of the ventrolateral preoptic (VLPO) area, a known sleep center, and the TMN, a known wake center. The rostral and main portions of this region were associated with the visual pathway composed of the optic nerves, optic chiasm, and optic tracts. PGD2 infusion into the lateral ventricle of mice increased preferentially NREM sleep rather than REM sleep27 in good agreement with our previous observation that NREM sleep was selectively induced by PGD2 infusion into the DPR-rich area in the basal forebrain of rats.32 In the DPR knockout (KO) mice, the amount of NREM sleep did not increase after PGD2 infusion into the brain.27 These results taken together clearly show that PGD2 produced in the leptomeninges and choroid plexus, and possibly in the CSF, circulates in the ventricular and subarachnoid space, and binds to DPRs in the basal forebrain to initiate the NREM sleep.

ADENOSINE

AND

A2A RECEPTOR

To find out how the sleep signal initiated by the binding of PGD2 to the DPR in the surface of the basal forebrain is transduced into the brain parenchyma, we applied numerous neurotransmitters, peptides, hormones, and other bioactive substances to the DPR-enriched sleep-promoting zone to see if any of these compounds could replace or mimic the somnogenic activity of PGD2. Among several hundred test compounds, only adenosine and adenosine A2A receptor (A2AR) agonists such as 2(4-(2-carboxyethyl) phenylethylamino) adenosine-5’-N-ethykcarboxamideadenosine (CGS21680) and 2-(4-(2-(2-aminoethylamino-carbonyl)ethyl) phenylethylamino) -5’-N-ethylcarboxamidoadenosine (APEC) were effective and induced NREM, but not REM sleep when infused into rats during the night.33 On the other hand, A1-receptor agonists, such as N6-cyclohexyladenosine (CHA) and N6-cyclopentyladenosine (CPA), were ineffective. In rats pretreated by intraperitoneal infusion of a selective antagonist of A2AR, KF17837, the sleep-inducing effects of both A2AR agonists and PGD2 were attenuated, indicating that the somnogenic effect of PGD2 may be mediated by adenosine via A2AR. The extracellular level of adenosine in the subarachnoid space of the basal forebrain was increased dose-dependently by the infusion of PGD2 or the DPR agonist BW245C in rats, and this effect was attenuated by the simultaneous treatment of the rats with a DPR antagonist, BWA868C. The PGD2-induced increase in extracellular adenosine was also found in wild type (WT) mice but was not observed in the DPR KO mice.27 More recent experimental results obtained from A2AR KO mice showed that PGD2 exerted its

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Basal forebrain

Preoptic area

Posterior hypothalamus

PGDS PGDS

A 2A R

VLPO

Histamine GABA Galanin TMN

DPR PGD2

PGE 2, Orexin

FIGURE 4.2 Schematic representation of the molecular mechanisms of sleep-wake regulation by PGD2, E2, adenosine, histamine, and orexin.

sleep-promoting effects in a manner at least partially dependent on the A2AR.34 We conclude that A2AR is involved in the sleep-promoting action of PGD2 and that adenosine plays an important role in the sleep promotion initiated by PGD2.

VLPO

AND

TMN

The immediate early gene product cFos (cellular feline osteosarcoma) has widely been used as a useful marker of neuronal activation. Fos, the protein encoded by the gene, is a transcription factor that triggers transcription in a cascade of cellular responses. To determine which neuron groups are involved in response to PGD2 or adenosine, especially A2A agonists, we examined c-Fos immunoreactivity.35–37 When PGD2 or the A2AR agonist CGS 21680 was infused for 2 h into the subarachnoid space in the PGD2-sensitive zone, a marked increase in the number of Fos-positive cells was observed in the leptomeningeal membrane on the ventral surface of the basal forebrain as well as in the VLPO area concomitant with the induction of NREM sleep. In contrast the number of Fos-positive neurons decreased markedly in the TMN of the PH. Using Fos immunoreactivity, Sherin et al.38 showed a discrete cluster of neurons in the VLPO to play a critical role in the generation of sleep. The VLPO is known to send specific inhibitory GABAergic and galaninergic efferents to the TMN, which neurons contain the ascending histaminergic arousal system (Figure 4.2). PGD2 does not induce sleep when infused into the TMN32 and therefore it is unlikely that putative wake neurons in the TMN are directly inhibited by PGD2. PGD2 induces sleep most effectively when infused into the subarachnoid space in the PGD2-sensitive zone. PGD2 increased the firing rates of sleep-active neurons in the POA,39 where these neurons are most abundant in the VLPO. The VLPO may induce sleep by inhibiting wake-promoting neurons in the TMN, for both GABA and galanin were shown to inhibit the firing rate of wake-active TMN neurons.38

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PGD2 also induced Fos-IR in the leptomeninges,35 which suggests that PGD2 activates VLPO via leptomeningeal DP receptors. These results taken together strongly indicate that PGD2 may bind to the DPRs in the PGD2-sensitive zone, where meningeal cells release paracrine signaling molecules such as adenosine, which subsequently excite nearby sleep-active VLPO neurons. These VLPO neurons may directly induce NREM sleep or send inhibitory signals to the TMN to down-regulate the wake neurons; thus sleep-wake cycle is regulated by a flip-flop mechanism involving the interaction between these two centers. The results of biochemical and pharmacological studies up to the end of 2002 described in this section have been reviewed in previous articles.40–42

MOLECULAR GENETIC STUDIES For elucidation of the exact role of the PGD2 system and possibly of the E2 one in sleep-wake regulation in vivo under physiological conditions and also for delineation of the genetic mechanisms involved in this process, the sleep behavior of KO mice for L-PGDS, DPR, adenosine A2AR, and histamine H1 receptor (H1R) generated in the laboratories of the author and others, was examined. Transgenic (TG) mice overexpressing the human PGDS gene generated in my laboratory were also tested. In all these experiments, the circadian profiles of sleep-wake patterns of WT and the genetically engineered mice were essentially identical under macroscopic examination. However, gross phenotypic changes were observed under certain specific conditions, such as extraneous physical stimuli, sleep deprivation, and so forth. These results may be interpreted to mean that sleep is essential for life and that the sleepregulatory system is composed of a complicated compensatory network in which the deficiency of one system may be effectively compensated by other systems during development.

TG MICE

FOR

PGDS

In the year 2000 we generated TG mice by incorporating the human PGDS gene into mice. Northern blot analysis clearly showed that human PGDS mRNA was expressed in almost all tissues and organs of these mice.43 We expected these mice to sleep all the time, but the mice appeared to be very healthy and to grow and sleep normally. As shown in Figure 4.3, there was no significant difference in the circadian sleep pattern between the WT and TG mice, but when the tails of these mice were clipped for DNA sampling at 8:00 PM, as indicated by the arrows, we found that the amount of SWS of the TG mice increased sharply and significantly. This effect lasted for several hours, and the amount of SWS returned to the control level after 5–6 h. The maximum increment was almost as high as the maximum amount of sleep during the daytime. The sleep pattern of the WT mice was essentially unaffected by the tail clipping, although the amount of SWS slightly decreased, probably due to the pain. In both cases the amount of REM sleep did not differ significantly. These somewhat unexpected and puzzling experimental observations may be explained on the basis of the sequence of enzyme reactions depicted in Figure 4.1.

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Wild type

TG

Amount of SWS (min/hr)

50

* 30

**

tail clip

* * * **

*

baseline after tc

baseline after tc

10 20:00

*

8:00

20:00 20:00

8:00

20:00

Clock time FIGURE 4.3 Increase in SWS in TG mice after tail clipping n = 7 for WT and n = 10 for TG mice. * P<0.05 ** P<0.01 Compared with the baseline day by the paired t test.

In the arachidonate cascade system, the enzyme cyclooxygenase, existing as constitutive COX I and inducible COX II, is generally believed to be the rate-limiting enzyme rather than the individual synthases under physiological conditions, which is why the sleep patterns of WT and TG mice were essentially identical. However, it is possible that the pain stimulus caused by the tail clipping elicited induction of the inducible COX II, which then produced an excessive amount of PGH2, the substrate for PGDS, so the PGDS step now became the rate-limiting step under these conditions, leading to the larger quantities of PGD2 in the TG mice than in the WT mice and ultimately to an increased amount of SWS in the TG mice. In order to prove this interpretation, we then measured the amount of PGD2 in the brains of WT and TG mice before and after the tail clipping.43 The amount of PGD2 in the brains of TG mice increased sharply and significantly for about 3 h after the tail clipping, and then started to decrease thereafter and returned to normal after about 6 h. The time course of the changes in the amount of PGD2 in the TG mice brain was essentially the same as that in the amount of SWS. In contrast, the PGD2 content in the brains of WT controls after clipping remained essentially the same as before it. Thus, it seems reasonable to conclude that the increase in SWS in the TG mice after tail clipping was probably due to the induction of COX II, or possibly to some other rate-limiting enzyme in the up-stream in response to the pain stimulus, resulting in the increased level of PGH2 in the brain, which prostanoid was then converted to PGD2 by the excessive amount of PGDS in these TG mice. These results are consistent with the notion that PGD2 is an endogenous sleep substance in the brain of mice and is primarily involved in the induction and maintenance of NREM sleep. Furthermore, they indicate that the PGDS gene is the first gene to be implicated in the homeostatic control of NREM sleep.

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Amount of NREM sleep (min/2hr)

Wild type

KO

100 after SD

after SD

** ** *

50

*

SD

SD

baseline

baseline

0

8:00

20:00

8:00 8:00

20:00

8:00

Clock time FIGURE 4.4 Effect of SD on NREM sleep in WT and PGDS-KO mice. * P<0.05 ** P<0.01 Compared with the baseline day by the paired t test.

KO MICE

FOR

PGDS

AND

DPR

PGDS KO mice were also generated.44 The circadian profiles of NREM and REM sleep in these KO mice were similar if not identical to those of the corresponding WT control mice. Furthermore, the duration and the episode number of each vigilance state in WT and KO mice were almost the same, indicating that the lack of PGDS does not seem to affect the phenotype. Because sleep is controlled as a function of prior wakefulness and sleep pressure increases during waking or sleep deprivation (SD), the effect of SD for 6 h in the late phase of the light period between 2 PM and 8:00 PM on NREM and REM sleep in WT and KO mice was examined. As shown in Figure 4.4, a strong rebound of NREM sleep (approximately 43% increase) was observed after SD in the WT mice. The rebound in KO mice after SD was only approximately 10%, indicating that endogenously produced PGD2 by PGDS is involved in the homeostasis of NREM sleep after SD. This assumption is further supported by the observation that the PGD2 content in the brains of the WT mice after SD was approximately twofold higher than that before SD, whereas the amount of PGD2 in the brains of KO mice was essentially unchanged after SD.45 The cDNA for the DPR has been isolated and identified as a 7-transmembrane GS protein-coupled rhodopsin-type receptor.46 Again the basal circadian sleep profiles of WT and DPR KO mice were essentially identical. Infusion of PGD2 into the lateral ventricle of the WT mice induced a dose-dependent increase in the amount of NREM sleep as in the case of rats, but essentially no increase was observed in the DPR KO mice.27 The extracellular adenosine level in the subarachnoid space of the basal forebrain, the DPR-enriched zone, was then determined after PGD2 perfusion through a microdialysis probe. In the WT mice, the adenosine level increased more than 100% after PGD2 perfusion of 400 pmol/min but did not increase at all

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in DPR KO mice after PGD2 perfusion or in either WT or KO mice after the vehicle perfusion. These results are in good agreement with our previous observation that the activation of DPRs in the arachnoid trabecular cells of the basal forebrain in rats triggers a local increase in the extracellular adenosine level.

A2AR KO MICE A2AR KO mice were generated in a study on the relationship between A2AR and the D2 dopamine receptor.47 The A2AR agonist CGS21680, at doses of 0.04, 0.2, 1 and 5 pmol/min dose-dependently increased NREM sleep in WT mice after infusion into the lateral ventricle, but not at all in A2AR KO mice, as compared with the value for the baseline day. In contrast, an A1 agonist, cyclopentyladenosine, did not affect sleep profiles in WT mice at a dose of 1 pmol/min and increased NREM sleep only slightly at a dose of 5 pmol/min.33 These results suggested that activation of mainly the A2AR was involved in sleep promotion effect of adenosine. When PGD2 was infused into the lateral ventricle of A2AR KO and WT mice, it dose-dependently induced sleep during 6-h PGD2 infusion and 4-h post-dosing. PGD2 at doses of 10 and 50 pmol/min increased NREM sleep in WT mice by 35% and 90.6%, and in A2AR KO mice by only 5.6% and 38.1%, respectively, as compared with the baseline-day value.34 These results indicate that PGD2 exerted its somnogenic effect in a manner at least partially dependent on the A2AR system, somewhat analogous to the interaction between A2AR and D2 dopamine receptors.47 Alternatively, PGD2 may directly activate sleep-active neurons as previously shown by in vivo experiments, in which PGD2 and E2 were applied ionophoretically on to various neurons in the POA/anterior hypothalamus of unanesthetized rats.39 PGD2 had an excitatory effect on about onethird of the sleep neurons and approximately the same percentage of wake-neurons were excited by PGE2. As mentioned earlier, the VLPO containing a dense population of sleep-active neurons is located in close proximity to the inner surface of the subarachnoid space and, therefore, it is possible that PGD2 may activate some of these neurons directly.

H1R KO MICE

AND

EP4 RECEPTOR

For exploration of the neural mechanisms involved in the PGE2-induced wakefulness in rats, the effect of PGE2 on the activity of the histaminergic system and the involvement of PGE2 receptor subtypes in the response were examined. The TMN of the posterior hypothalamus is the sole source of histaminergic innervation of the mammalian CNS, and this histaminergic system is considered to play a central role in mediating wakefulness. PGE2 perfusion of the TMN significantly increased both synthesis and release of histamine. Among the agonists of the four distinct subtypes of PGE2 receptors (EP1-4) tested, only the EP4 receptor-agonist (ONO-AE1-329) mimicked the excitatory effect of PGE2. In situ hybridization revealed that EP4 receptor mRNA was expressed in the TMN region. Furthermore, EP4 agonist perfusion of the TMN induced wakefulness. These findings thus indicate that PGE2 induced wakefulness through activation of the histaminergic system via EP4 receptors.48

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Other activators of the histaminergic system may also be involved in wakefulness. The neuropeptides orexin (hipocretin) A and B were isolated from rat hypothalamic extracts and identified, and a mutation in the orexin-2 receptor gene was found to be associated with canine narcolepsy. Mice lacking the orexin peptide display increases in REM and NREM sleep and a decrease in awake time during the active period of normal rodents. The exact role of orexin in physiological sleep and the mechanism involved have not yet been clarified. Orexin neurons are exclusively localized in the lateral hypothalamic area and project their fibers to the entire central nervous system, including the TMN, which is enriched in orexin-2 receptors. Perfusion of orexin A (5–25 pmol/min) for 1 h into the TMN of rats through a microdialysis probe promptly increased wakefulness, concomitant with a reduction in REM and NREM sleep.49 Microdialysis studies showed that orexin A increased histamine release from both the medial preoptic area and the frontal cortex by approximately twofold over the baseline in a dosedependent manner. Infusion of orexin A (1.5 pmol/min) for 6 h into the lateral ventricle of mice produced a significant increase in wakefulness during the 8 h after the start of infusion to the same level seen during the active period in WT mice; however in H1R KO mice no effect of orexin infusion was observed under the same conditions. These results indicate that orexin is a potent waking substance acting upon its receptor in the TMN and that the arousal effect of orexin A depends on the histaminergic neurotransmission mediated by H1R.49 Molecular genetic aspects of our publications up to the end of 2002, mentioned in this section were reviewed previously.50

SUMMARY The concept of humoral, rather than neural, regulation of sleep dates as far back as to almost 100 years ago. Kuniomi Ishimori and Henri Piéron independently and concurrently took samples of the CSF of sleep-deprived dogs and infused them into the brain of normal dogs. The recipient dogs soon fell asleep. Thus these two authors became the first to demonstrate the presence of endogenous sleep-promoting substances, but the chemical nature of their sleep substances was not identified. Although it is no longer possible to determine their chemical structure, available evidence indicates PGD2 as a most plausible candidate. During the next 90 years or so, nearly 50 endogenous sleep substances were reported by numerous investigators to be present in the brain, CSF, and other organs and tissues of mammals, although their physiological relevance has remained uncertain in most instances.51 This review has briefly summarized the highlights of the prostaglandin and sleep paradigm, the study of which has been carried out in my own and other laboratories over the past 20 years. Based upon results obtained by biochemical, physiological, and molecular biological studies, the following tentative conclusions have been drawn as a working hypothesis for future studies: •

PGD2 and E2 are endogenous sleep and wake substances, respectively, involved in the regulation of sleep and wakefulness under physiological

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•

•

•

conditions not only in rodents but also in monkeys and humans and possibly other mammals as well. PGD2 is produced by L-PGDS mainly present in the membrane system surrounding the brain, secreted into the CSF, and is bound to its receptor, DPR, also present in the outer surface of the rostral basal forebrain. The above experimental results strongly indicate the presence of a hitherto unknown signal transduction system in the CNS of mammals. During the past several decades, the mechanism of signal transduction has been extensively studied by a number of investigators at the cellular level. These studies indicated that most, if not all hormones, cytokines, and neurotransmitters do not penetrate the cell membrane. Instead they are bound to specific receptors on the cell surface, and the signals are then transmitted through these receptors via so-called second messengers such as cyclic AMP, Ca2+, and so forth. The mechanisms underlying the sleep regulation by PGD2 are somewhat reminiscent of the signal transduction mechanisms at the cellular level; namely, PGD2 is bound to its receptor on the surface of the meninges, which binding is followed by the transduction via adenosine through the adenosine A2A receptor. This signal is transmitted across the leptomeninges into the brain parenchyma into the VLPO, a putative sleep center, and further to TMN, a putative wake center. More recent studies with PGDS- and DPR-KO mice and others reveal that the PGD2 system plays a crucial role in the homeostatic regulation of NREM sleep.

We have thus witnessed a significant progress in sleep research on the humoral mechanisms of sleep regulation and opened up a new frontier by elucidating the interplay between the humoral regulation and the neural network. Obviously many more important questions remain to be answered. Hopefully the fruits of our studies described herein will provide a basis for further studies to solve the remaining formidable problems pertaining to the mystery of sleep.

ACKNOWLEDGMENTS The author is indebted to Y. Urade, N. Eguchi, Z.-L. Huang, and L. Frye for their help during the preparation of this manuscript and illustrations, and to N. Ueda for secretarial assistance. He also wishes to express his deep gratitude to all collaborators, past and present, on this project during the past 20 years. The work from this laboratory has been supported mainly by grants-in-aid from the Ministry of Health, Labor and Welfare of Japan; the Ministry of Education, Culture, Sports, Science, and Technology of Japan; and the Osaka Bioscience Institute.

REFERENCES 1. Narumiya, S. et al., Prostaglandin D2 in rat brain, spinal cord and pituitary: basal level and regional distribution, Life Sciences, 31, 2093, 1982.

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2. Hiroshima, O. et al., Basal level of prostaglandin D2 in rat brain by a solid-phase enzyme immunoassay, Prostaglandins, 32, 63, 1986. 3. Ogorochi, T. et al., Regional distribution of prostaglandins D2, E2, and F2a and related enzymes in postmortem human brain, J. Neurochem., 43, 71, 1984. 4. Ueno, R. et al., Prostaglandin D2 induced sleep when microinjected into the preoptic area of conscious rats, Biochem. Biophys. Res. Commun., 109, 576, 1982. 5. Honda, K. and Inoué, S., Establishment of a bioassay method for the sleep-promoting substance, Rep. Inst. Med. Dent. Eng., 12, 81, 1978. 6. Ueno, R. et al., Prostaglandin D2, a cerebral sleep-inducing substance in rats, Proc. Natl. Acad. Sci. USA, 80, 1735, 1983. 7. Pandy, H.P. et al. Concentration of prostaglandin D2 in cerebrospinal fluid exhibits a circadian alteration in conscious rats, Biochem. Mol. Biol. Int., 37, 431, 1995. 8. Ram, A. et al., CSF levels of prostaglandins, especially the level of prostaglandin D2, are correlated with increasing propensity towards sleep in rats, Brain Res., 751, 81, 1997. 9. Hayaishi, O., Sleep–wake regulation by prostaglandins D2 and E2, J. Biol. Chem., 263, 14593, 1988. 10. Onoe, H. et al., Prostaglandin D2, a cerebral sleep-inducing substance in monkeys, Proc. Natl. Acad. Sci. USA, 85, 4082, 1988. 11. Roberts, II, J.L. et al., Increased production of prostaglandin D2 in patients with systemic mastocytosis, New Engl. J. Med., 303, 1400, 1980. 12. Pentreath, V.W. et al., The somnogenic T lymphocyte suppressor prostaglandin D2 is selectively elevated in cerebrospinal fluid of advanced sleeping sickness patients, Trans. R. Soc. Trop. Med. Hyg., 84, 795, 1990. 13. Kubata, B. K., Identification of a novel prostaglandin F2a synthase in Trypanosoma brucei, J. Exp. Med., 192, 1327, 2000. 14. Matsumura, H. et al., Awaking effect of PGE2 microinjected into the preoptic area of rats, Brain Res., 444, 265, 1988. 15. Matumura, H. et al., Awaking effect of prostaglandin E2 in freely moving rats, Brain Res., 481, 242, 1989. 16. Matumura, H. et al., Evidence that brain prostaglandin E2 is involved in physiological sleep–wake regulation in rats, Proc. Natl. Acad. Sci. USA, 86, 5666, 1989. 17. Onoe, H. et al., Prostaglandin E2 exerts an awaking effect in the posterior hypothalamus at a site distinct from that mediating its febrile action in the anterior hypothalamus, J. Neurosi., 12, 2715, 1992. 18. Hayaishi, O., Molecular mechanisms of sleep–wake regulation: roles of prostaglandins D2 and E2, FASEB J., 5, 2575, 1991. 19. Urade, Y. and Hayaishi, O., Prostaglandin D synthase: structure and function, Vitamins and Hormones, 58, 89, 2000. 20. Urade, Y. and Hayaishi, O., Biochemical, structural, genetic physiological, and pathophysiological features of lipocalin prostaglandin D synthase, Biochim. Biophys. Acta, 1482, 259, 2000. 21. Urade, Y. and Eguchi, N., Lipocalin-type and hematopoietic prostaglandin D synthases as a novel example of functional convergence, prostaglandins, and other lipid mediators, 68–69, 375, 2002. 22. Islam, F. et al., Inhibition of rat brain prostaglandin D synthase by inorganic selenocompounds, Arch. Biochem. Biophys., 289, 161, 1991. 23. Matsumura, H., Takahata, R., and Hayaishi, O., Inhibition of sleep in rats by inorganic selenium compounds, inhibitors of prostaglandin D synthase, Proc. Natl. Acad. Sci. USA, 88, 9046, 1991.

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24. Takahata, R. et al., Intravenous administration of inorganic selenium compounds, inhibitors of prostaglandin D synthase, inhibits sleep in freely moving rats, Brain Res., 623, 65, 1993. 25. Huang, Z.-L., Urade, Y., and Hayaishi, O., unpublished observation. 26. Urade, Y. et al., Dominant expression of mRNA for prostaglandin D synthase in leptomeninges, choroids plexus, and oligodendrocytes of the adult rat brain, Proc. Natl. Acad. Sci. USA, 90, 9070, 1993. 27. Mizoguchi, A. et al., Dominant localization of prostaglandin D receptors on arachnoid trabecular cells in mouse basal forebrain and their involvement in the regulation of nonrapid eye movement sleep, Proc. Natl. Acad. Sci. USA, 98, 11674, 2001. 28. Zahn, M. et al., Purification and N-terminal sequence of b-trace, a protein abundant in human cerebrospinal fluid, Neurosci. Lett., 154, 93, 1993. 29. Hoffmann, A. et al., Purification and chemical characterization of b-trace protein from human cerebrospinal fluid: its identification as prostaglandin D synthase, J. Neurochem., 61, 451, 1993. 30. Watanabe, K. et al., Identification of b-trace as prostaglandin D synthase, Biochem. Biophys. Res. Commun., 203, 1110, 1994. 31. Yamashita, A., Watanabe, Y., and Hayaishi, O., Autoradiographic localization of a binding protein(s) specific for prostaglandin D2 in rat brain, Proc. Natl. Acad. Sci. USA, 80, 6113, 1983. 32. Matsumura, H. et al., Prostaglandin D2-sensitive, sleep-promoting zone defined in the ventral surface of the rostral basal forebrain, Proc. Natl. Acad. Sci. USA, 91, 11998, 1994. 33. Satoh, S., Matumura, H., and Hayaishi, O., Involvement of adenosine A2A receptor in sleep promotion, Eur. J. Pharmacol., 351, 155, 1998. 34. Qu, W.-M. et al., Adenosine A2A receptor deficiency attenuates the somnogenic effect of prostaglandin D2, Sleep Biol. Rhythms, 2, S55, 2004. 35. Scammell, T. et al., Activation of ventrolateral preoptic neurons by the somnogen prostaglandin D2, Proc. Natl. Acad. Sci. USA, 95, 7754, 1998. 36. Scammell, T.E. et al., An adenosine A2a agonist increases sleep and induces Fos in ventrolateral preoptic neurons, Neuroscience, 107, 653, 2001. 37. Satoh, S. et al., Region-dependent difference in the sleep-promoting potency of an adenosine A2A receptor agonist, Eur. J. Neurosci., 11, 1587, 1999. 38. Sherin, J.E. et al., Activation of ventrolateral preoptic neurons during sleep, Science, 271, 216, 1996. 39. Koyama, Y. and Hayaishi, O., Modulation by prostaglandins of activity of sleeprelated neurons in the preoptic/anterior hypothalamic areas in rats, Brain Res. Bull., 33, 367, 1994. 40. Hayaishi, O., Molecular mechanisms of sleep-wake regulation: a role of prostaglandin D2, Philos. Trans. R. Soc. Lond. B. Biol. Sci., 355, 275, 2000. 41. Hayaishi, O. and Urade, Y., Prostalglandin D2 in sleep-wake regulation: recent progress and perspectives, Neuroscientist, 8, 12, 2002. 42. Hayaishi, O., Unraveling the enigma of sleep — molecular mechanisms of sleep–wake regulation, in Oxygen and Life — Oxygenases, Oxidases, and Lipid Mediators, Ishimura, Y. et al., Eds., Elsevier, Amsterdam, 2002, 503. 43. Pinzar, E. et al., Prostaglandin D synthase gene is involved in the regulation of nonrapid eye movement sleep, Proc. Natl. Acad. Sci. USA, 97, 4903, 2000. 44. Eguchi, N. et al., Lack of tactile pain (allodynia) in lipocalin-type prostaglandin D synthase-deficient mice, Proc. Natl. Acad. Sci. USA, 96, 726, 1999.

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45. Eguchi, N. et al., Sleep in transgenic and gene-knockout mice for lipocalin-type prostaglandin D synthase, in Oxygen and Life — Oxygenases, Oxidases, and Lipid Mediators, Ishimura, Y., Eds., Elsevier, Amsterdam, 2002, 429. 46. Hirata et al., Molecular characterization of a mouse prostaglandin D receptor and functional expression of the cloned gene, Proc. Natl. Acad. Sci. USA, 91, 11192, 1994. 47. Chen, J. F. et al., The role of the D2 dopamine receptor (D2R) in A2A adenosine receptor (A2AR)-mediated behavioral and cellular responses as revealed by A2A and D2 receptor knockout mice, Proc. Natl. Acad. Sci. USA, 98, 1970, 2001. 48. Huang, Z.-L. et al., Prostaglandin E2 activates the histaminergic system via EP4 receptor to induce wakefulness in rats, J. Neurosci., 23, 5975, 2003. 49. Huang, Z.-L. et al., Arousal effect of orexin A depends on activation of the histaminergic system, Proc. Natl. Acad. Sci., USA, 98, 9965, 2001. 50. Hayaihsi, O., Molecular genetic studies on sleep-wake regulation, with special emphasis on the prostaglandin D2 system, J. Appl. Physiol., 92, 863, 2002. 51. Inoué, S., Biology of Sleep Substances, CRC Press, Boca Raton, FL, 1989.

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5

The Network Responsible for Paradoxical Sleep Onset and Maintenance: A New Theory Based on the Head-Restrained Rat Model Pierre-Hervé Luppi, Romuald Boissard, Damien Gervasoni, Laure Verret, Romain Goutagny, Christelle Peyron, Denise Salvert, Lucienne Léger, Bruno Barbagli, and Patrice Fort

CONTENTS Introduction: The Neuronal Network Responsible for Paradoxical Sleep before the Head-Restrained Rat Model The PS Neuronal Network Identified with the Head-Restrained Rat Model Identification and Pharmacology of a Pontine PS-Inducing Structure in Rats Efferents of the PS-On Neurons from the SLD GABAergic and Non-GABAergic Inputs to the SLD PS-On Neurons Evidence That GABA Is Responsible for the Inactivation of Monoaminergic Neurons during PS Localization of the GABAergic Neurons Responsible for the Tonic Inhibition of Monoaminergic Neurons during PS Conclusion: A New Network Model for PS Onset and Maintenance Acknowledgments References 0-8493-1519-0/05/$0.00+$1.50 © 2005 by CRC Press LLC

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INTRODUCTION: THE NEURONAL NETWORK RESPONSIBLE FOR PARADOXICAL SLEEP BEFORE THE HEAD-RESTRAINED RAT MODEL In the middle of the last century, a series of historical observations led to the discovery of a sleep phase in humans and other mammals characterized by a cortical activation and conspicuous rapid eye movements (REM) paradoxically associated with a complete disappearance of the muscle tone.1–3 This phase of sleep, coined paradoxical sleep (PS) or REM sleep was then shown to correlate with dream activity.1,4 A large number of studies in cats later demonstrated that the dorsal part of the pontine reticular formation plays a crucial role in PS onset and maintenance. It was first shown that PS persists following decortication, cerebellar ablation, or brain stem transections rostral to the pons. In contrast transection at the posterior limit of the pons suppresses PS.5 It was later shown that electrolytic and chemical lesions of the dorsal part of the pontis oralis (PnO) and caudalis (PnC) nuclei suppress PS.6–10 Since then a large number of results showed that the neurons responsible for PS are cholinergic or at least cholinoceptif and restricted to a small area of the dorsal part of these nuclei.11 Jouvet and Michel12 were the first to demonstrate that cholinergic mechanisms play a major role in PS generation because peripheral atropine administration suppressed PS, whereas anticholinesterase compounds increase PS. Then George et al.13 discovered that bilateral injections of carbachol, a cholinergic agonist, into the PnO and PnC promote PS. It was later shown that PS is induced with the shortest latency when carbachol is ejected in a small area of the dorsal PnO and PnC,14–18 named peri-locus coeruleus a (peri-LCa) by Sakai et al.19,20 Sakai and coworkers20–23 found that the great majority of the pontine neurons with a tonic activity specific during PS were localized in the peri-LCa. They recently divided these neurons into two populations23: The first population of neurons are located in the dorsal and rostral peri-LCa. They are inhibited by carbachol, a cholinergic agonist and project rostrally to the intralaminar thalamic nuclei of the thalamus, the posterior hypothalamus, and the basal forebrain. The second population of PS-on neurons is excited by carbachol, distributed in all parts of the peri-LCa, and caudally project to the nucleus reticularis magnocellularis (Mc) localized in the ventromedial bulbar reticular formation19,20 Based on these and other results, it has been proposed that the first type of neurons are cholinergic and responsible for the cortical activation during PS, whereas the second type of neurons are noncholinergic, possibly glutamatergic, and generate the muscle atonia observed during this sleep state via descending excitatory projection to glycinergic premotoneurons within the Mc.11,22–27 Supporting this hypothesis, intracellular recordings of motoneurons combined with strychnine applications demonstrated that glycine is responsible for the tonic hyperpolarization of the spinal, hypoglossal and trigeminal motoneurons.24,28–30 Further, our anatomical data showed that the great majority of the neurons in the peri-LCa projecting to the Mc are not cholinergic.25 In addition glutamate release in the Mc increases specifically during PS31 and injection of non-NMDA glutamate agonists in the Mc suppresses muscle tone.32 Spinal-projecting PS-on neurons have been recorded in the Mc33,34 and cyto-

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toxic lesions of this structure induced a decrease in PS quantities and an increase in muscle tone during PS.35 Further, we have shown that the Mc contains a large contingent of glycinergic neurons.26,27 Glycinergic neurons from the ventral gigantocellular reticular nucleus (the rat equivalent of the Mc) directly project to spinal motoneurons36 while those of the parvocellular and parvocellular alpha nuclei directly project to the trigeminal motor nucleus.37,38 It has been shown in cats following induction of PS by carbachol injections in the peri-LCa that Fos-labeled cells in the Mc project to the trigeminal motor nucleus.39 Several hypotheses have been proposed concerning the mechanisms responsible for the activation of the PS-on neurons from the peri-LCa at the onset and during PS. Hobson and McCarley40,41 and later on Sakai20 proposed that the activation of these neurons is due to an excitatory interaction between the PS-on and a reciprocal inhibition with the monoaminergic neurons. This well-accepted hypothesis was formulated following the findings that serotonergic neurons from the raphe nuclei and noradrenergic neurons from the locus coeruleus cease firing during PS.40,42–44 Supporting this theory, drugs enhancing serotonin and noradrenergic transmission in particular serotonin and norepinephrine reuptake blockers suppress PS (review in References 11 and 45). However the sites where the monoamines in particular serotonin exert their PS-suppressing effect have not been unambiguously identified. Applications of norepinephrine, epinephrine, or benoxathian (an a2 agonist) into the peri-LCa inhibit PS, but that of serotonin has no effect.46–48 Norepinephrine via a2-adrenoceptor inhibits the noncholinergic PS-on neurons but has no effect on the cholinergic PS-on neurons from the peri-LCa, and serotonin has no effect on both types of neurons.23 Monoamines could also act on PS-on neurons localized in structures other than the peri-LCa such as the Mc25 or the pedunculopontine tegmental (PPT) and laterodorsal tegmental cholinergic nuclei (LDT).49 The PPT and LDT have been indeed reported to contain PS-on neurons, although the great majority of the neurons from these nuclei are tonically active both during waking (W) and PS.50–53 In cats, a subset of these neurons is tonically active during W and exhibit phasic burst discharge just prior to and during ponto-geniculo-occipital (PGO) waves.54–57 Bilateral lesion of the LDT and PPT in cats leads to the disappearance of the PGO waves.58 These neurons have been antidromically activated following the stimulation of the intralaminar and lateral geniculate nuclei from the thalamus.56,57 It has been shown in combining retrograde tracing with cholineacetyltransferase immunostaining that LDT and PPT neurons projecting to these nuclei are cholinergic.56,59,60 These results indicate that cholinergic neurons from the LDT and PPT play an important role in the activation of the cortex during W and PS and are responsible in cats for the genesis of the PGO waves. In this species serotonin or norepinephrine depletion induce continuous PGO waves, suggesting that the monoamines are inhibitory on the PGO-on cholinergic neurons, but in vivo and in vitro pharmacological studies reported excitatory, inhibitory, or no effect of monoamines on cholinergic neurons from the LDT and PPT (review in Reference 61). Monoamines have also been proposed to contribute to the muscle atonia of PS by a disfacilitation of motoneurons, and it has been shown that serotonin and

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norepinephrine are excitatory on motoneurons.62,63 The role of monoamines might be particularly important for the hypoglossal motoneurons because the application of serotonin on hypoglossal motoneurons during PS reverses the atonia of the upper airway musculature.64 According to the classical reciprocal interaction model,20,41 the cessation of firing of the noradrenergic and serotonergic neurons at the onset of PS is the result of active PS-specific inhibitory processes originating from PS-on cells. These neurons were first hypothesized to be cholinergic and localized in the peri-LCa , LDT and PPT, but acetylcholine excites LC noradrenergic neurons and is only weakly inhibitory on serotonergic DRN neurons.65,66 It has therefore been suggested that they might use GABA or glycine, rather than acetylcholine, as an inhibitory neurotransmitter.67,68 It has recently been shown that application of non-NMDA agonists such as kainic acid or that of bicuculline, a GABAA antagonist into the peri-LCa region induces a strong increase in PS quantities.16,69–72 Altogether these recent results strongly pointed out that in addition to cholinergic and monoaminergic neurons, populations of glutamatergic and GABAergic neurons might play a crucial role in the onset and maintenance of PS. To test this hypothesis, we developed a new model combining single-unit recordings, precise and limited local pharmacology by micro-iontophoresis in unanaesthetized head-restrained rats, and anterograde and retrograde tracing combined with Fos and neurochemical identification of labeled cells.73–77 We decided to work with rats instead of cats because of the cost, the availability of the majority of recent data, and more limited ethical concerns. With this choice we were nevertheless facing the challenge to identify in rats the structures responsible for PS onset and maintenance already identified in cats, in particular the peri-LCa and the Mc. Data on the neuronal network responsible for PS onset and maintenance was lacking in this species. We succeeded in identifying this network in rats and present our results below in a first part. In a second part we present our results with the same rat model showing that monoaminergic neurons are silenced during PS by GABAergic neurons from the dorsal paragigantocellular reticular nucleus. Finally, based on all these results, we propose a new theory on the network responsible for PS onset and maintenance.

THE PS NEURONAL NETWORK IDENTIFIED WITH THE HEAD-RESTRAINED RAT MODEL IDENTIFICATION AND PHARMACOLOGY STRUCTURE IN RATS

OF A

PONTINE PS-INDUCING

We found that a long-lasting PS-like hypersomnia can be pharmacologically induced with a short latency in head-restrained rats by iontophoretic applications of bicuculline or gabazine, two GABAA antagonists specifically into a very small area of the dorso-lateral pontine tegmentum.76 We also recorded neurons in this region specifically active during PS and excited by bicuculline or gabazine iontophoresis78 (Figure 5.1). This region has been denominated the sublaterodorsal nucleus (SLD)

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A

gabazine

Spikes/sec Kainic acid 300

Kainic acid PS-like

200

100

0

seconds

B Spikes/s 200

carbachol

PS Kainic acid

100

0

seconds

FIGURE 5.1 Pharmacology of a PS-on neuron from the SLD. (A) Effect of the iontophoretic application of gabazine (100 nA, 265 sec) or kainic acid (50 nA, 3 or 5 sec) on the activity of a PS-on SLD neuron. Application of kainic acid during SWS induced an excitation of the neuron. Long gabazine application induced a very strong excitation of the neuron followed by the onset of a PS-like phase characterized by theta activity on the EEG and a complete atonia on the EMG. (B) On the same neuron as in A, the application of carbachol (50 nA, 30 sec) during SWS induced no change in the firing rate of the neuron, and that of kainic acid induced a strong excitation.

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by Swanson.79 It approximately corresponds to the dorsal subcoeruleus nucleus in Paxinos and Watson atlas80 and seems to be the equivalent in rats of the cat periLCa. Our results have been recently reproduced in freely moving rats81 and are in agreement with a recent study in cats showing that pressure injection of bicuculline and to a lesser extent phaclofen (a GABAB antagonist) in the dorsal portion of the nucleus pontis oralis (which roughly corresponds to the peri-LCa) induces a strong increase in PS quantities with short latencies, whereas the application of muscimol (a GABAA agonist) or baclofen (a GABAB agonist) induced W.70,71 These and our data imply that the onset of PS-on neurons of the SLD is mainly due to the removal of a tonic GABAergic input present during W and SWS. We also showed that kainic acid (a glutamate agonist) iontophoretic application into the SLD induces an activation of PS-on neurons (Figure 5.1) and is consistently associated with a transient PS-like state followed by W with an increase in muscle activity.76 The PS-like state induced by bicuculline was reversed by the application of kynurenate.76 In agreement with our results, it has been shown in cats that the administration of kainic acid in the peri-LCa using microdialysis induces a PS-like state.69 Altogether these results suggest that PS-on neurons in the SLD receive a tonic glutamatergic input during all sleep-waking states. Upon removal of the tonic GABAergic input at the onset of PS, the unmasked glutamatergic input would be responsible of the tonic activity on the PS-on SLD neurons during PS. Neurons responsible for PS onset and maintenance seem to be clustered in a sphere of tissue smaller than 1 mm3 centered on the SLD. Indeed Fos labeled neurons in the site of injection following 90-min bicuculline applications occupied less than 1mm3. Further, bicuculline, gabazine and kainic acid ejections just 500 mm away from the positive sites gave rise to W with increased muscle activity.76 The neurochemical nature of the PS-on neurons from the SLD remains to be determined. It is unlikely that they are cholinergic since in our Fos staining experiments, only occasional Fos positive neurons in the injection site were immunoreactive to cholineacetyltransferase. Rather, it is likely that at least part of these neurons are glutamatergic, because our Fos experiments highly suggest that they provide an excitatory pathway to glycinergic neurons from the medullary reticular formation and glutamate release is increased at this level during PS.31 Bicuculline or gabazine administration into the SLD induced a PS-like state characterized by the presence of muscle atonia, EEG activation with or without theta activity, and nonreactivity to surrounding stimuli but the absence of ocular movements or penile erections.76 The absence of these events may be due to the fact that SLD neurons responsible for phasic activities during PS do not receive tonic glutamatergic and GABAergic inputs during W and SWS. It is also possible that the SLD neurons are not involved in all PS-related phenomena. Cholinergic and non-cholinergic neurons from the LDT or neurons from other structures could be responsible for the missing phasic events. Supporting this hypothesis, neurons specifically active during PS-related erections have been recently recorded in the LDT,82 and an increased number of Fos positive neurons has been observed in the LDT following PS rebound.83 In our experiments, bicuculline injections into the LDT induce W in apparent contradiction with this hypothesis. This finding may be

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explained by the fact that the majority of the LDT neurons exhibit a high firing rate both during W and PS, whereas only a minority are specifically active during PS.84,85 We found that carbachol iontophoresis into the rat SLD induced a W state with increased muscle and that SLD PS-on neurons do not respond to carbachol iontophoresis76 (Figure 5.1). These results indicate important species differences between rats and cats in the pharmacological sensitivity of the pontine PS-on neurons. In agreement with our results, following carbachol administration into the rat pontine reticular formation, the enhancement of PS was of small magnitude86–89 or not reliably obtained.90 The increase in PS was less than 100%, compared to the 300% increase obtained in cats, the first PS episode appeared at least 50 min after the carbachol injection, and the duration of the episodes was similar to natural, spontaneous PS. In cats, however, PS is induced almost immediately after the injection and the episodes last longer than in control PS. The effective sites in rats were widely distributed in the pontine reticular formation. In contrast, the most effective site in cats is the peri-LCa that corresponds to the rat SLD.15 The absence of effect of carbachol ejection in the SLD does not rule out a role of cholinergic processes in PS onset and maintenance in the rat. It is indeed possible that PS-on neurons in the SLD have muscarinic or nicotinic receptors but that the activation of these receptors by carbachol is unable to modify their activity due to the strong GABAergic tonic inhibition revealed in our study. Supporting this hypothesis it has been shown that carbachol applications in the region of the SLD are able to induce with a short latency a long period of atonia in anesthetized or decerebrate rats models91,92 in which the GABAergic inhibitory tone on SLD neurons could be decreased or even absent. Another possibility is that the cholinergic system plays an important role in PS in rats via an action on populations of neurons controlling PS localized in other pontine regions than the SLD. An increase in the number of cholinergic neurons containing Fos has been indeed observed following PS recovery,83 and a strong enhancement in PS quantities was found following carbachol pressure ejection in the most ventral part of the oral pontine reticular formation.18,93

EFFERENTS

OF THE

PS-ON NEURONS

FROM THE

SLD

We recently attempted to determine the efferents of the SLD responsible for the cortical activation and the muscle atonia seen following bicuculline, gabazine, or kainic acid applications in the SLD combining Fos and glycine immunostainings with PHA-L anterograde tracing.76 Following PHA-L injection in the SLD, a large number of anterogradely labeled fibers was observed in the medullary reticular nuclei. In combining Fos, glycine, and PHA-L stainings in rats that received long bicuculline or gabazine applications in the SLD before perfusion, we observed in the caudal nucleus raphe magnus, ventral gigantocellular (rat equivalent of the cat Mc), parvocellular, and parvocellular alpha reticular nuclei a large number of anterogradely labeled fibers in the vicinity of Fos labeled cells and numerous Fos and glycine-immunoreactive double-labeled neurons.76 These results confirm and extend previous results demonstrating that PS-on neurons from the peri-LCa provide a

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strong excitatory putatively glutamatergic projection to glycinergic neurons from the Mc.22 These results and ours indicate that glycinergic neurons from the ventral gigantocellular nucleus and the parvocellular and parvocellular alpha reticular nuclei are responsible for the hyperpolarization of spinal and cranial motoneurons during PS, respectively. The role of the glycinergic neurons from the caudal nucleus raphe magnus, active during PS, is less clear. They could be responsible for the known attenuation of ascending sensory neurotransmission during PS. It has been shown that glycine mediates the inhibition during PS of ascending sensory transmission via Clarke’s column dorsal spinocerebellar tract neurons, and raphe magnus neurons are known to control sensory input via direct projections to sensory neurons from the dorsal horn of the spinal cord.94 In addition to descending projections to the medullary reticular formation, we also found that the SLD projects to rostral structures like the intralaminar thalamic nuclei, intrafascicular thalamic nucleus and zona incerta.76 In agreement with Jones and Yang,95 these ascending projections could be responsible for the cortical activation obtained during the administration of bicuculline, gabazine, or kainic acid into the SLD and more generally than seen during PS. Fos labeled neurons were observed in these structures following long lasting bicuculline or gabazine ejections into the SLD.76

GABAERGIC AND NON-GABAERGIC INPUTS PS-ON NEURONS

TO THE

SLD

Combining retrograde tracing with cholera toxin B subunit (CTb) and GAD immunostaining, we recently tried to identify the GABAergic neurons potentially responsible for the inhibition during W and SWS of the PS-on neurons localized in the SLD and the glutamatergic neurons responsible for their constant excitation across all vigilance states.77 Our results suggest that the GABAergic innervation of SLD neurons arises both from interneurons and distant neurons located in the pontine and deep mesencephalic reticular nuclei and to a minor extent hypothalamic and medullary structures.77 These results are in agreement with previous studies indicating that the GABAergic neurons responsible for the tonic inhibition during W and SWS of the PS-on neurons from the SLD could be within the SLD itself or in the pontine and deep mesencephalic reticular nuclei. A recent study by Xi et al.70 suggested that GABAergic interneurons might be the best candidates for the inhibition of PS-on SLD neurons. They found in cats that administration of antisense oligonucleotides against glutamic acid decarboxylase (GAD) mRNA in the nucleus pontis oralis (NPO), a region corresponding to the SLD, produces a significant decrease in W and an increase in PS. Maloney et al.96 found in rats that the number of Fos expressing GABAergic neurons in the rostral pontine reticular nucleus decreased following PS rebound, suggesting that GABAergic neurons from this structure are active during W and SWS and inactive during PS. It has been shown in cats97,98 and rats78 that muscimol injections in the most ventrolateral part of the periaqueductal gray and in the region of the deep mesencephalic reticular nucleus just ventral to it induce a strong increase in PS quantities

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Delta (1.5-4Hz)

Theta (4.5-8.5Hz)

Aq

Sigma (9-14Hz)

Gamma (30-50Hz)

DRN

4

1000µm

Muscle Hypnogram PS

Mus 90min

SWS W 30

60

90

120

150

180 210 min

FIGURE 5.2 Effect of a muscimol application in the region of the deep mesencephalic reticular nucleus just ventral to the periaqueductal gray. (A) Power spectrum analysis and hypnogram obtained in a rat before and during the iontophoretic application of muscimol (100 nA, 90 min). The application of muscimol induced a 250% increase in PS quantities as compared to Nacl. (B) The CTb injection site obtained by iontophoresis in the positive site of muscimol ejection with another barrel of the electrode.

(Figure 5.2). More recently Sakai et al.22 reported that muscimol applications limited to the region of the deep mesencephalic reticular nucleus just ventral to the periaqueductal gray induced an increase in PS quantities but those in the ventrolateral periaqueductal gray had no effect. We reported a strong non-GABAergic projection to the SLD from the ventrolateral periaqueductal gray and a mixed GABAergic and non-GABAergic projection from the region of the deep mesencephalic reticular nucleus just ventral to the periaqueductal gray.77 Altogether from these results, we propose that GABAergic neurons located in the dorsal part of the deep mesencephalic reticular nucleus, the pontine reticular nucleus, and in the SLD itself project to and directly inhibit the PS-on neurons from the SLD specifically during W and SWS. Because acetylcholine was thought to be the main neurotransmitter responsible for the activation of the pontine PS-on neurons, the cholinergic input to the cat periLCa and the pontine reticular nucleus has attracted a lot of attention and several studies reported that it arises from the LDT and the PPT.99–103 Sakai104 found that the peri-LCa receives additional cholinergic inputs from the magnocellular, parvocellular and lateral paragigantocellular reticular nuclei. In our study, we only found a small non-GABAergic projection from the LDT and PPT. In agreement with our pharmacological results, these results suggest that the LDT and PPT cholinergic input to the SLD is rather a minor one.77

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Following a section between the pons and the medulla in the cat, a state of PS cannot be recorded on either side of the section.105,106 From these results it has been hypothesized that, in addition to the descending projections from the SLD to the medullary reticular nuclei responsible for muscle atonia, reciprocal ascending projections are crucial for generating PS. We found strong ascending projections to the SLD from the parvocellular and lateral paragigantocellular reticular nuclei and only small projections from the dorsal paragigantocellular and magnocellular reticular nuclei.77 These results suggest that these two later structures thought to contain respectively the GABA and the glycinergic neurons responsible for the inhibition of LC noradrenergic neurons107 and the motoneurons76 during PS, play minor roles in the control of the PS-on neurons from the SLD. In contrast, the parvocellular, and lateral paragigantocellular reticular nuclei could contain neurons controlling PSon neurons from the SLD. In the classical reciprocal interaction model, serotonin and norepinephrine are responsible for the inhibition of the PS-on neurons during W and SWS. Supporting this hypothesis, it has been previously shown in rats that the pontine reticular nucleus receives noradrenergic inputs from the LC and A5 and A7 noradrenergic groups and serotoninergic inputs from all pontine and medullary raphe nuclei and the B9 group.102 In general agreement with these results, we observed a small number of retrogradely labelled neurons in the dorsal raphe and locus coeruleus nuclei following CTb injections into the SLD.77 Additional studies in rats are now necessary to determine the exact role of the monoamines in the regulation of the activity of the PS-on neurons from the SLD. From our pharmacological results76 we also hypothesized that the PS-on neurons from the SLD are constantly excited across all vigilance states by a glutamatergic input. The majority of the glutamatergic neurons providing a constant excitatory input to SLD PS-on neurons should be located in the brainstem although forebrain glutamatergic neurons could also participate. The structures responsible for the onset and maintenance of PS are indeed restricted to the brainstem.5 Such glutamatergic inputs can arise from the numerous non-GABAergic neurons projecting to the SLD localized in the ventrolateral periaqueductal gray, the mesencephalic, pontine, and parvocellular reticular nuclei. Additional studies are necessary to determine which one of these structures provides a glutamatergic input to the SLD PS-on neurons. The histochemical nature and role of the strong non-GABAergic afferents to the SLD from the primary motor area of the frontal cortex, the bed nucleus of the stria terminalis, and central nucleus of the amygdala remains to be identified. Maquet et al.108 found that regional cerebral blood flow is positively correlated with PS in the amygdaloid complex, and electrical stimulation of the central nucleus of the amygdala increases the frequency of pontine waves recorded in or just dorsal to the SLD during PS.109 From these and our results, it might be hypothesized that the central nucleus of the amygdala and the functionally related bed nucleus of the stria terminalis provide excitatory glutamatergic projections to PS-on neurons from the SLD. The substantial predominantly non-GABAergic projection to the SLD from the lateral, perifornical and posterior hypothalamic areas could also play an important role in PS homeostasis. Reversible inactivation of the lateral hypothalamic area by

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muscimol (a GABAA agonist) applications induced a decrease in W and a disappearance of PS,110 and neurons specifically active during PS or W have been recorded in the same area.111,112 A number of recent studies indicate that two intermingled populations of neurons localized in this region containing the hypocretins (orexins) or melanin concentrating hormone (MCH) could be involved. It has first been shown that narcolepsy, a sleep disorder characterized by excessive daytime sleepiness and cataplexy, is due to the lack of hypocretin mRNA and peptides in humans113 or a disruption of the hypocretin receptor 2 or its ligand in dogs and mice.114,115 It has then been shown that intracerebroventricular infusion of hypocretin induce W while that of MCH increases PS and, to a minor extent, SWS amounts.116,117 Combining Fos and MCH or hypocretin immunostainings, we recently demonstrated that MCH but not hypocretin neurons are active during PS.116 Finally a substantial number of MCH and hypocretin immunoreactive fibers have been observed in the SLD.118,119 Altogether these data suggest that MCH and hypocretin neurons might directly project to PS-on neurons from the SLD. However, it is unlikely that their effects on PS is mediated by such projection. Indeed, hypocretins and MCH are excitatory and inhibitory peptides, respectively, and it is therefore more likely that hypocretins inhibit and MCH promotes PS respectively via an excitation and an inhibition of PS-off neurons. They could act on the GABAergic presumably PS-off SLD neurons but also on the GABAergic neurons located in the deep mesencephalic and pontine reticular nucleus. They could also influence PS via projections to the monoaminergic PS-off neurons from the locus coeruleus and raphe nuclei that contain a large number of MCH and hypocretin immunoreactive fibers118,119 (see below).

EVIDENCE THAT GABA IS RESPONSIBLE FOR MONOAMINERGIC NEURONS DURING PS

THE INACTIVATION OF

In anesthetized rats iontophoretic applications of GABA or glycine strongly inhibit LC and DRN neurons, and co-iontophoresis of bicuculline or strychnine (GABAA and glycine antagonists, respectively), antagonize these effects.67,120,121 In vitro studies on slices using focal stimulation and bath-applied bicuculline and strychnine revealed GABA- and glycine-mediated IPSPs in LC neurons, and GABA-mediated IPSPs in DRN cells.122–125 In agreement with these results, GABA-and glycineimmunoreactive varicose fibers as well as GABAA and glycine receptors have been found in the rat LC and DRN.67,68,126–128 Based on these results, Jones67,68 and we proposed that GABA or glycine might be responsible for the inhibition of monoaminergic neurons during both slow wave sleep (SWS) and PS. To test this hypothesis we determine the effect of iontophoretic applications of bicuculline and gabazine (two GABAA antagonists) and strychnine (a glycine antagonist) during W, SWS, and PS on the activity of LC noradrenergic and DRN serotonergic cells in the headrestrained unanesthetized rat.73–75 Iontophoretic application of bicuculline, gabazine, or strychnine during SWS or PS induced a tonic firing in LC noradrenergic and DRN serotonergic neurons73–75 (Figure 5.3). Application of these antagonists during W induced a sustained increase in discharge rate. These results indicate the existence of tonic GABA and glycinergic

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FIGURE 5.3 Effect of gabazine iontophoresis during PS on a serotonergic neurons of the DRN. The iontophoretic application of gabazine induced a reversible increase of the firing rate of the neuron recorded. Iontophoretic application of 8OH-DPAT, a classical 5HT1A agonist on this neuron induced a complete cessation of its activity highly suggesting its serotonergic nature.

inputs to the LC and DRN that are active during all vigilance states. Importantly we found that when the strychnine effect occurred during transitions between PS and W, the discharge rate of the LC or DRN neurons increased at the onset of W. In the same situation but after bicuculline administration, the discharge rate of a given neuron was not increased at the transition between PS and W. These results strongly suggest that the release of GABA but not that of glycine is responsible for the inactivation of LC noradrenergic neurons and DRN serotonergic during PS. At variance with our results, Levine and Jacobs129 found in cats that the iontophoretic application of bicuculline reversed the typical suppression of neuronal activity of DRN serotonergic neurons during SWS but not during PS. Sakai and Crochet130 did not find in cats an effect of bicuculline microdialysis infusion on DRN serotonergic neurons during PS and hypothesized that our results were due to a nonspecific excitatory action of bicuculline. This is unlikely because we reproduced the effect of bicuculline with gabazine, another specific GABAA antagonist (Figure 5.3). Our results are supported by those of Nitz and Siegel,131,132 who found in cats with the microdialysis technique a significant increase in GABA release in the DRN and LC during PS as compared to W and SWS and, in contrast, no detectable changes in glycine concentrations. Based on these and our results, we suggest that during W the LC and DRN cells are under a tonic GABAergic inhibition that increases during SWS, and even further during PS, and that the increase in GABAergic inhibition is responsible for the inactivation of these neurons during the sleep states. In contrast the glycinergic tonic inhibition would be constant across the sleep-waking cycle and thus control the general excitability of LC and DRN neurons. Instead of GABA, Sakai and Crochet130 proposed that the cessation of activity of the serotonergic neurons of the DRN is caused by a disfacilitation resulting from the cessation of discharge of norepinephrine or histamine containing neurons. They found that the cessation of discharge of presumed serotonergic DRN neurons during PS is reversed by either histamine or phenylephrine, an a1-adrenergic agonist, while

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FIGURE 5.4 Effect of prazosin iontophoresis during W on a serotonergic DRN neuron. The application of prazosin (150 nA, 82 sec) during W induced a significant decrease in the discharge rate of a serotonergic neuron from the DRN.

the application of a specific H1 histamine receptor antagonist or prazosin, a specific a1-adrenoceptor antagonist suppressed the spontaneous discharge of DRN serotonergic neurons during W and SWS. However, it was shown previously that systemic administration of prazosin or WB4101 in freely moving cats did not block the activity of DRN neurons.133 We also found in rats that the iontophoretic application of norepinephrine increases the activity of DRN serotonergic neurons during W, SWS, and PS, but that of prazosin did not completely suppress their tonic activity during W (Figure 5.4). We therefore propose that the inactivation of DRN serotonergic neurons and LC noradrenergic neurons during PS is due to a tonic GABAergic inhibition. The tonic activity of DRN serotonergic neurons during W would be due at least in part to excitatory noradrenergic and histaminergic inputs, and that of LC noradrenergic neurons would be mainly due to their intrinsic electrophysiological properties.123 It must be noted that other neuroactive substances such as the hypocretins, MCH, and serotonin might also play a role in the inactivation of monoaminergic neurons during PS. We recently showed that hypocretin neurons are inactive during PS while MCH neurons are strongly active.116 MCH- and hypocretin-containing fibers have been observed in the monoaminergic nuclei,118,119 and hypocretins strongly excite histaminergic neurons from the tuberomammillary nucleus,117,134 serotonergic neurons from the DRN,135,136 and noradrenergic neurons from the LC.137–139 MCH is known to be an inhibitory peptide,140 but its effects on monoaminergic neurons remain to be studied. It seems likely that an increase in MCH and a decrease in hypocretin releases, respectively, could also contribute to the cessation of activity of monoaminergic neurons during PS. Based on the measurement by voltammetry of 5HIAA, the metabolite of serotonin, it has also been proposed that a dendritic release of serotonin could be responsible for the cessation of activity of DRN serotonergic neurons during PS.141 However, other authors have shown by microdialysis that serotonin release in the DRN decreases during PS (review in Reference 142).

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LOCALIZATION OF THE GABAERGIC NEURONS RESPONSIBLE FOR THE TONIC INHIBITION OF MONOAMINERGIC NEURONS DURING PS Our results obtained with double-staining experiments indicate that the LC and DRN receive GABAergic inputs from neurons located in a large number of distant regions from the forebrain to the medulla.75,107 We observed a substantial number of GAD-immunoreactive neurons in the preoptic area, the lateral hypothalamic area, the mesencephalic and pontine periaqueductal gray, and the dorsal paragigantocellular reticular nucleus that project to the LC and DRN.75,107 These results indicate that the GABAergic innervation of these two monoaminergic nuclei arises from multiple, distant GABAergic groups in addition to interneurons. They suggest that the serotonergic neurons of the DRN and noradrenergic neurons of the LC could be inhibited by multiple populations of GABAergic neurons located in different structures, and raise the question of the functional significance of such redundancy. One possibility is that only some of these GABAergic afferents are destined to the serotonergic neurons of the DRN and the noradrenergic neurons of the LC. This seems likely for the DRN, which is an heterogeneous structure, but not for the LC which in rats contains nearly exclusively noradrenergic cells. Another possibility is that some of these afferents are postsynaptic and the others presynaptic, but the more likely explanation is that each of these afferents is active only under specific physiological conditions. Based on physiological and electrophysiological data (see above), we expect that one or several of these GABAergic afferents are turned on specifically at the onset of and during PS episodes and are responsible for the inhibition of brainstem monoaminergic neurons during PS. Although it has recently been proposed that GABAergic neurons located in the extended ventrolateral preoptic nucleus might also be involved,143 previous results highly suggest that brainstem GABAergic neurons are mostly involved. Indeed, it is well known that PS-like episodes occur in pontine or decerebrate cats.144 It has recently been shown in decerebrate animals that PS episodes induced by carbachol injections in the pons are still associated with a cessation of activity of serotonergic neurons of the raphe obscurus and pallidus nuclei.145 Among the brainstem GABAergic afferents revealed in our study, several are common to the DRN and the LC and are therefore good candidates for this role. We observed substantial GABAergic projections to the LC and DRN from the ventrolateral periaqueductal gray and the dorsal paragigantocellular nucleus.75,107 In agreement with these results, local application of bicuculline blocked the dorsal paragigantocellular-evoked inhibition of LC neurons,120 and focal iontophoretic application of NMDA in the ventral periaqueductal gray induced bicuculline sensitive IPSPs in DRN serotonergic neurons.146 The hypothesis that the GABAergic inhibition is coming from neurons located in the periaqueductal gray is further supported by two recent studies. Yamuy et al.147 showed that after a long period of PS induced by pontine injection of carbachol, a large number of Fos positive cells are visible in the DRN and a region lateral to it. Maloney et al.83 observed, after a PS rebound induced by deprivation, an increase in Fos-positive GAD immunoreactive neurons in the periaqueductal gray.

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To directly determine among the GABAergic afferents to the LC, those active during PS, we recently combined iontophoretic application of CTb in the LC with Fos staining in rats deprived of PS, rats with enhanced PS during rebound after PS deprivation, and control rats. Using this method we observed a large number of CTb and Fos double-immunostained neurons in the dorsal paragigantocellular reticular nucleus and a substantial number in the ventro-lateral periaqueductal gray and the lateral paragigantocellular reticular nucleus.148 We propose that the GABAergic neurons responsible for the inhibition of the LC noradrenergic neurons during PS are mainly but not exclusively localized in the dorsal paragigantocellular reticular nucleus. To further test this hypothesis, we recorded the spontaneous activity of neurons from the dorsal paragigantocellular reticular nucleus across the sleep-waking cycle in head-restrained rats. Neurons with an activity specific to PS (PS-on neurons) were found within this nucleus,149 further supporting that it contains the GABAergic neurons responsible for the cessation of activity of the noradrenergic neurons of the LC during PS. This hypothesis is also supported by a recent study showing that electrical stimulation of the area of the dorsal paragigantocellular reticular nucleus induces an increase in PS quantities.150

CONCLUSION: A NEW NETWORK MODEL FOR PS ONSET AND MAINTENANCE (FIGURE 5.5) We propose that the onset and maintenance of PS is due to the activation of PS-on glutamatergic neurons from the SLD. During W and SWS they would be hyperpolarized by tonic GABAergic inputs arising from GABAergic PS-off neurons localized in the SLD itself and the deep mesencephalic and pontine reticular nuclei. Noradrenergic and serotonergic PS-off neurons would also participate in the hyperpolarization of SLD neurons particularly during W. The cessation of activity of the monoaminergic neurons at the onset of and during PS would be due to an active inhibition by PS-on GABAergic neurons localized in the dorsal paragigantocellular reticular nucleus and the ventrolateral periaqueductal gray. Although the exact mechanism of the cessation of activity of the GABAergic PS-off neurons remains to be identified, we propose that the GABAergic PS-on neurons inhibiting the monoaminergic neurons could, at the same time, inhibit the GABAergic PS-off neurons. The activation of the SLD PS-on neurons at the onset of PS would be due to the strong glutamatergic excitatory input present during all vigilance states blocked during W and SWS by the inhibitory inputs from the GABAergic and monoaminergic PS-off neurons. It would arise from one or several of the non-GABAergic afferents to the SLD (e.g., the periaqueductal gray, the deep mesencephalic and pontine reticular nuclei, and the parvocellular reticular nucleus). Ascending SLD PS-on glutamatergic neurons would induce cortical activation via their projections to intralaminar thalamic relay neurons in collaboration with W/PS-on cholinergic and glutamatergic neurons from the LDT and PPT, mesencephalic and pontine reticular nuclei, and the basal forebrain. Descending PS-on glutamatergic SLD neurons would induce muscle atonia via their excitatory projections to glycinergic premotoneurons localized in the magnocellular and parvocellular reticular nuclei.

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W/SWS

PS

PeF, HLA Hcrt Pef/HLA

MCH

MCH

Thalamus EEG activation Glu

Thalamus EEG activation Glu

SLD PAG, DPMe, PRN

SLD

LC/DRN PAG, MRN, PRN

NA/5-HT

Glu

Glu

Glu

DPMe, PRN, SLD D

Ach

LC/DRN NA/5-HT

Glu

GABA

LDT/PPT

PeF, HLA Hcrt

Pef/HLA

DPMe, PRN, SLD GABA

LDT/PPT Ach

Mc

Mc

Muscle atonia Gly

Muscle atonia Gly vlPAG/DPGi

vlPAG/DPGi

GABA

PS-on

GABA

PS-off

PS-on

PS-off

FIGURE 5.5 (See color insert following page 108.) Model of the network responsible for PS onset and maintenance. The onset and maintenance of PS would result from the activation of PSon glutamatergic neurons from the SLD. The activation of these neurons would be due to the removal of tonic inhibitions arising from the monoaminergic PS-off neurons and GABAergic PSoff neurons localized in the SLD itself and the deep mesencephalic and pontine reticular nuclei. The cessation of activity of the PS-off neurons would be due to a tonic inhibition issued from GABAergic PS-on neurons localized in the dorsal paragigantocellular reticular nucleus and the ventrolateral periaqueductal gray. Abbreviations: DRN, dorsal raphe nucleus; 5-HT, serotonin; LC, locus coeruleus; NA, norepinephrine; LDT, laterodorsal tegmental nucleus; Ach, acetylcholine; Mc, magnocellular reticular nucleus; Gly, glycine; DPMe, deep mesencephalic reticular nucleus; PAG, periaqueductal gray; DPGi, dorsal paragigantocellular reticular nucleus; PPT, pedunculopontine nucleus; PRN, pontine reticular nucleus; SLD, sublaterodorsal nucleus; Glu, glutamate; Pef/HLA perifornical/lateral hypothalamic area; Hcrt, hypocretin (orexin).

In addition, two populations of hypothalamic neurons containing the hypocretins or MCH would participate to PS homeostasis through their direct excitatory and inhibitory actions respectively on the monoaminergic and GABAergic PS-off neurons and reciprocal inhibitory interactions.

ACKNOWLEDGMENTS This work was supported by CNRS (ERS 5645, FRE 2469, and UMR5167), INSERM (U480), Université Claude Bernard Lyon 1. The authors wish to thank C. Guillemort (GFG Co., Pierre-Bénite, France) for his help in designing the headrestraining system.

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3. Jouvet, M. and Michel, F., Corrélations électromyographiques du sommeil chez le chat décortiqué et mésencéphalique chronique, C.R. Soc. Biol., 153, 422-425, 1959. 4. Dement, W. and Kleitman, N., The relation of eye movements during sleep to dream activity: an objective method for the study of dreaming, Journal of Experimental Psychology. Learning, Memory, and Cognition, 53, 339, 1957. 5. Jouvet, M., Recherches sur les structures nerveuses et les mécanismes responsables des différentes phases du sommeil physiologique, Arch. Ital. Biol., 100, 125-206, 1962. 6. Carli, G. and Zanchetti, A., A study of pontine lesions suppressing deep sleep in the cat, Arch. Ital. Biol., 103 (4), 751-88, 1965. 7. Jouvet, M. and Delorme, F., Locus coeruleus et sommeil paradoxal, C.R. Seances Soc. Biol., 159, 895-899, 1965. 8. Webster, H.H. and Jones, B.E., Neurotoxic lesions of the dorsolateral pontomesencephalic tegmentum-cholinergic cell area in the cat. II. Effects upon sleep-waking states, Brain Research, 458 (2), 285-302, 1988. 9. Sastre, J.P., Sakai, K., and Jouvet, M., Are the gigantocellular tegmental field neurons responsible for paradoxical sleep?, Brain Research, 229 (1), 147-161, 1981. 10. Jouvet, M. and Mounier, D., Effets des lésions de la formation réticulée pontique sur le sommeil du chat, C.R. Seances Soc. Biol. Fil, 154, 2301-2305, 1960. 11. Jones, B.E., Paradoxical sleep and its chemical/structural substrates in the brain, Neuroscience, 40 (3), 637-656, 1991. 12. Jouvet, M. and Michel, F., Mise en evidence d`un “centre hypnique” au niveau du rhombencéphale chez le chat., C.R. Acad. Sci., 154, 2301-2305, 1960. 13. George, R., Haslett, W.L., and Jenden, D.J., A cholinergic mechanism in the brainstem reticular formation: induction of paradoxical sleep, Int. J. Neuropharmacol., 3, 541552, 1964. 14. Baghdoyan, H.A., Cholinergic mechanisms regulating REM sleep, in Sleep Science: Integrating Basic Research and Clinical Practice, Schwartz, W.J.S., Ed., Karger Publishing, Basel, 1997, pp. 88-116. 15. Vanni-Mercier, G., Sakai, K., Lin, J.S., and Jouvet, M., Mapping of cholinoceptive brainstem structures responsible for the generation of paradoxical sleep in the cat, Arch. Ital. Biol., 127 (3), 133-164, 1989. 16. Lai, Y.Y. and Siegel, J.M., Cardiovascular and muscle tone changes produced by microinjection of cholinergic and glutamatergic agonists in dorsolateral pons and medial medulla, Brain Research, 514 (1), 27-36, 1990. 17. Yamamoto, K., Mamelak, A.N., Quattrochi, J.J., and Hobson, J.A., A cholinoceptive desynchronized sleep induction zone in the anterodorsal pontine tegmentum: locus of the sensitive region, Neuroscience, 39 (2), 279-293, 1990. 18. Garzon, M., De Andres, I., and Reinoso-Suarez, F., Sleep patterns after carbachol delivery in the ventral oral pontine tegmentum of the cat, Neuroscience, 83 (4), 11371144, 1998. 19. Sakai, K., Sastre, J.P., Salvert, D., Touret, M., Tohyama, M., and Jouvet, M., Tegmentoreticular projections with special reference to the muscular atonia during paradoxical sleep in the cat: an HRP study, Brain Res., 176 (2), 233-254, 1979. 20. Sakai, K., Sastre, J.P., Kanamori, N., and Jouvet, M., State-specific neurones in the ponto-medullary reticular formation with special reference to the postural atonia during paradoxical sleep in the cat, in Brain Mechanisms of Perceptual Awareness and Purposeful Behavior, Pompeiano, O. and Aimone Marsan, C., Eds., Raven Press, New York, 1981, pp. 405-429.

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21. Sakai, K., Neurons responsible for paradoxical sleep, in Sleep: Neurotransmitters and Neuromodulators, Wauquier, A. and Janssen Research Foundation, Raven Press, New York, 1985, pp. 29-42. 22. Sakai, K., Crochet, S., and Onoe, H., Pontine structures and mechanisms involved in the generation of paradoxical (REM) sleep, Arch. Ital. Biol., 139 (1-2), 93-107, 2001. 23. Sakai, K. and Koyama, Y., Are there cholinergic and non-cholinergic paradoxical sleep-on neurones in the pons?, Neuroreport, 7 (15-17), 2449-2453, 1996. 24. Chase, M.H., Soja, P.J., and Morales, F.R., Evidence that glycine mediates the postsynaptic potentials that inhibit lumbar motoneurons during the atonia of active sleep, J. Neurosci., 9 (3), 743-51, 1989. 25. Luppi, P.H., Sakai, K., Fort, P., Salvert, D., and Jouvet, M., The nuclei of origin of monoaminergic, peptidergic, and cholinergic afferents to the cat nucleus reticularis magnocellularis: a double-labeling study with cholera toxin as a retrograde tracer, J. Comp. Neurol., 277 (1), 1-20, 1988. 26. Fort, P., Luppi, P.H., Wenthold, R., and Jouvet, M., Glycine immunoreactive neurons in the medulla oblongata in cats, C. R. Acad. Sci. III, 311 (5), 205-212, 1990. 27. Fort, P., Luppi, P.H., and Jouvet, M., Glycine-immunoreactive neurones in the cat brain stem reticular formation, Neuroreport, 4 (9), 1123-1126, 1993. 28. Soja, P.J., Lopez-Rodriguez, F., Morales, F.R., and Chase, M.H., The postsynaptic inhibitory control of lumbar motoneurons during the atonia of active sleep: effect of strychnine on motoneuron properties, J. Neurosci., 11 (9), 2804-2811, 1991. 29. Kohlmeier, K. A., Lopez-Rodriguez, F., Liu, R.H., Morales, F.R., and Chase, M.H., State-dependent phenomena in cat masseter motoneurons, Brain Res., 722 (1-2), 3038, 1996. 30. Yamuy, J., Fung, S.J., Xi, M., Morales, F.R., and Chase, M.H., Hypoglossal motoneurons are postsynaptically inhibited during carbachol-induced rapid eye movement sleep, Neuroscience, 94 (1), 11-15, 1999. 31. Kodama, T., Lai, Y.Y., and Siegel, J.M., Enhanced glutamate release during REM sleep in the rostromedial medulla as measured by in vivo microdialysis, Brain Res., 780 (1), 178-181, 1998. 32. Lai, Y.Y. and Siegel, J.M., Pontomedullary glutamate receptors mediating locomotion and muscle tone suppression, J. Neurosci., 11 (9), 2931-2937, 1991. 33. Siegel, J.M., Wheeler, R.L., and McGinty, D.J., Activity of medullary reticular formation neurons in the unrestrained cat during waking and sleep, Brain Res., 179, 4960, 1979. 34. Sakai, K., Kanamori, N., and Jouvet, M., Neuronal activity specific to paradoxical sleep in the bulbar reticular formation in the unrestrained cat, C.R. Seances Acad. Sci. D., 289 (6), 557-561, 1979. 35. Holmes, C.J. and Jones, B.E., Importance of cholinergic, GABAergic, serotonergic and other neurons in the medial medullary reticular formation for sleep-wake states studied by cytotoxic lesions in the cat, Neuroscience, 62 (4), 1179-1200, 1994. 36. Holstege, J.C. and Bongers, C.M., A glycinergic projection from the ventromedial lower brainstem to spinal motoneurons. An ultrastructural double labeling study in rat, Brain Res., 566 (1-2), 308-315, 1991. 37. Li, Y.Q., Takada, M., Kaneko, T., and Mizuno, N., GABAergic and glycinergic neurons projecting to the trigeminal motor nucleus: a double labeling study in the rat, J. Comp. Neurol., 373 (4), 498-510, 1996. 38. Rampon, C., Peyron, C., Petit, J.M., Fort, P., Gervasoni, D., and Luppi, P.H., Origin of the glycinergic innervation of the rat trigeminal motor nucleus, Neuroreport, 7 (18), 3081-3085, 1996.

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57. Steriade, M., Pare, D., Datta, S., Oakson, G., and Curro Dossi, R., Different cellular types in mesopontine cholinergic nuclei related to ponto-geniculo-occipital waves, J. Neurosci., 10 (8), 2560-2579, 1990. 58. Sakai, K., Petitjean, F., and Jouvet, M., Effects of ponto-mesencephalic lesions and electrical stimulation upon PGO waves and EMPs in unanesthetized cats, Electroencephalogr. Clin. Neurophysiol., 41 (1), 49-63, 1976. 59. Pare, D., Smith, Y., Parent, A., and Steriade, M., Projections of brainstem core cholinergic and non-cholinergic neurons of cat to intralaminar and reticular thalamic nuclei, Neuroscience, 25 (1), 69-86, 1988. 60. Sofroniew, M.V., Priestley, J.V., Consolazione, A., Eckenstein, F., and Cuello, A.C., Cholinergic projections from the midbrain and pons to the thalamus in the rat, identified by combined retrograde tracing and choline acetyltransferase immunohistochemistry, Brain Res., 329 (1-2), 213-223, 1985. 61. Koyama, Y. and Sakai, K., Modulation of presumed cholinergic mesopontine tegmental neurons by acetylcholine and monoamines applied iontophoretically in unanesthetized cats, Neuroscience, 96 (4), 723–733, 2000. 62. McCall, R.B. and Aghajanian, G.K., Serotonergic facilitation of facial motoneuron excitation, Brain Res., 169 (1), 11-27, 1979. 63. Morilak, D.A. and Jacobs, B.L., Noradrenergic modulation of sensorimotor processes in intact rats: the masseteric reflex as a model system, J. Neurosci., 5 (5), 1300-1306, 1985. 64. Kubin, L., Davies, R.O., and Pack, A.I., Control of Upper Airway Motoneurons During REM Sleep, News Physiol. Sci., 13, 91-97, 1998. 65. Guyenet, P.G. and Aghajanian, G.K., ACh, substance P and met-enkephalin in the locus coeruleus: pharmacological evidence for independent sites of action, Eur. J. Pharmacol., 53 (4), 319-328, 1979. 66. Koyama, Y. and Kayama, Y., Mutual interactions among cholinergic, noradrenergic and serotonergic neurons studied by ionophoresis of these transmitters in rat brainstem nuclei, Neuroscience, 55 (4), 1117-1126, 1993. 67. Luppi, P.H., Charlety, P.J., Fort, P., Akaoka, H., Chouvet, G., and Jouvet, M., Anatomical and electrophysiological evidence for a glycinergic inhibitory innervation of the rat locus coeruleus, Neurosci. Lett., 128 (1), 33-36, 1991. 68. Jones, B.E., Noradrenergic locus coeruleus neurons: their distant connections and their relationship to neighboring (including cholinergic and GABAergic) neurons of the central gray and reticular formation, Prog. Brain Res., 88, 15-30, 1991. 69. Onoe, H. and Sakai, K., Kainate receptors: a novel mechanism in paradoxical (REM) sleep generation, Neuroreport, 6 (2), 353-356, 1995. 70. Xi, M.C., Morales, F.R., and Chase, M.H., Evidence that wakefulness and REM sleep are controlled by a GABAergic pontine mechanism, J. Neurophysiol., 82 (4), 20152019, 1999. 71. Xi, M.C., Morales, F.R., and Chase, M.H., The motor inhibitory system operating during active sleep is tonically suppressed by GABAergic mechanisms during other states, J. Neurophysiol., 86 (4), 1908-1915, 2001. 72. Xi, M.C., Morales, F.R., and Chase, M.H., Induction of wakefulness and inhibition of active (REM) sleep by GABAergic processes in the nucleus pontis oralis, Arch. Ital. Biol., 139 (1-2), 125-145, 2001. 73. Darracq, L., Gervasoni, D., Souliere, F., Lin, J.S., Fort, P., Chouvet, G., and Luppi, P.H., Effect of strychnine on rat locus coeruleus neurones during sleep and wakefulness, Neuroreport, 8 (1), 351-355, 1996.

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74. Gervasoni, D., Darracq, L., Fort, P., Souliere, F., Chouvet, G., and Luppi, P.H., Electrophysiological evidence that noradrenergic neurons of the rat locus coeruleus are tonically inhibited by GABA during sleep, Eur. J. Neurosci., 10 (3), 964-970, 1998. 75. Gervasoni, D., Peyron, C., Rampon, C., Barbagli, B., Chouvet, G., Urbain, N., Fort, P., and Luppi, P.H., Role and origin of the GABAergic innervation of dorsal raphe serotonergic neurons, J. Neurosci., 20 (11), 4217-4225, 2000. 76. Boissard, R., Gervasoni, D., Schmidt, M.H., Barbagli, B., Fort, P., and Luppi, P.H., The rat ponto-medullary network responsible for paradoxical sleep onset and maintenance: a combined microinjection and functional neuroanatomical study, Eur. J. Neurosci., 16 (10), 1959-1973, 2002. 77. Boissard, R., Fort, P., Gervasoni, D., Barbagli, B., and Luppi, P.H., Localization of the GABAergic and non-GABAergic neurones projecting to the sublaterodorsal nucleus and potentially gating paradoxical sleep onset, Eur. J. Neurosci., 18, 16271639, 2003. 78. Boissard, R., Gervasoni, D., Fort, P., Henninot, V., Barbagli, B., and Luppi, P.H., Neuronal networks responsible for paradoxical sleep onset and maintenance in rats: a new hypothesis, Sleep, 23 Suppl., 107, 2000. 79. Swanson, L.W., Brain Maps: Structure of the Rat Brain: A Laboratory Guide with Printed and Electronic Templates for Data, Models, and Schematics, 2nd rev. ed. Elsevier, New York, 1998. 80. Paxinos, G. and Watson, C., The Rat Brain in Stereotaxic Coordinates, Compact 3rd ed., Academic Press, Sydney, Orlando, 1997. 81. Pollock, M.S. and Mistlberger, R.E., Rapid eye movement sleep induction by microinjection of the GABA-A antagonist bicuculline into the dorsal subcoeruleus area of the rat, Brain Res., 962 (1-2), 68-77, 2003. 82. Koyama, Y., Schmidt, M.S., Takahashi, K., and Kayama, Y., Cholinergic neurones in the laterodorsal tegmental nucleus regulate penile erection during paradoxical sleep, Soc. Neurosci., (Abstr.) 522, 20, 2001. 83. Maloney, K.J., Mainville, L., and Jones, B.E., Differential c-Fos expression in cholinergic, monoaminergic, and GABAergic cell groups of the pontomesencephalic tegmentum after paradoxical sleep deprivation and recovery, J. Neurosci., 19 (8), 3057-3072, 1999. 84. El Mansari, M., Sakai, K., and Jouvet, M., Unitary characteristics of presumptive cholinergic tegmental neurons during the sleep–waking cycle in freely moving cats, Exp. Brain Res., 76 (3), 519-529, 1989. 85. Steriade, M., Datta, S., Pare, D., Oakson, G., and Curro Dossi, R.C., Neuronal activities in brain-stem cholinergic nuclei related to tonic activation processes in thalamocortical systems, J. Neurosci., 10 (8), 2541-2559, 1990. 86. Gnadt, J.W. and Pegram, G.V., Cholinergic brainstem mechanisms of REM sleep in the rat, Brain Res., 384 (1), 29-41, 1986. 87. Shiromani, P.J. and Fishbein, W., Continuous pontine cholinergic microinfusion via mini-pump induces sustained alterations in rapid eye movement (REM) sleep, Pharmacol. Biochem. Behav., 25 (6), 1253-1261, 1986. 88. Velazquez-Moctezuma, J., Gillin, J.C., and Shiromani, P.J., Effect of specific M1, M2 muscarinic receptor agonists on REM sleep generation, Brain Res., 503 (1), 128131, 1989. 89. Bourgin, P., Escourrou, P., Gaultier, C., and Adrien, J., Induction of rapid eye movement sleep by carbachol infusion into the pontine reticular formation in the rat, Neuroreport, 6 (3), 532-536, 1995.

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107. Luppi, P.H., Gervasoni, D., Peyron, C., Barbagli, B., Boissard, R., and Fort, P., Norepinephrine and REM Sleep, in Rapid Eye Movement Sleep, Mallick, B. N. and Inoue, S., Eds., Norosa Publishing Hoouse, New Delhi, 1999, pp. 107-122. 108. Maquet, P., Peters, J., Aerts, J., Delfiore, G., Degueldre, C., Luxen, A., and Franck, G., Functional neuroanatomy of human rapid-eye-movement sleep and dreaming, Nature, 383 (6596), 163-166, 1996. 109. Deboer, T., Sanford, L.D., Ross, R.J., and Morrison, A.R., Effects of electrical stimulation in the amygdala on ponto-geniculo-occipital waves in rats, Brain Res., 793 (1-2), 305-310, 1998. 110. Lin, J.S., Sakai, K., Vanni-Mercier, G., and Jouvet, M., A critical role of the posterior hypothalamus in the mechanisms of wakefulness determined by microinjection of muscimol in freely moving cats, Brain Res., 479 (2), 225-240, 1989. 111. Steininger, T.L., Alam, M.N., Gong, H., Szymusiak, R., and McGinty, D., Sleepwaking discharge of neurons in the posterior lateral hypothalamus of the albino rat, Brain Res., 840 (1-2), 138-147, 1999. 112. Alam, M.N., Gong, H., Alam, T., Jaganath, R., McGinty, D., and Szymusiak, R., Sleep–waking discharge patterns of neurons recorded in the rat perifornical lateral hypothalamic area, J. Physiol., 538 (Pt 2), 619-631, 2002. 113. Peyron, C., Faraco, J., Rogers, W., Ripley, B., Overeem, S., Charnay, Y., Nevsimalova, S., Aldrich, M., Reynolds, D., Albin, R., Li, R., Hungs, M., Pedrazzoli, M., Padigaru, M., Kucherlapati, M., Fan, J., Maki, R., Lammers, G.J., Bouras, C., Kucherlapati, R., Nishino, S., and Mignot, E., A mutation in a case of early onset narcolepsy and a generalized absence of hypocretin peptides in human narcoleptic brains, Nat. Med., 6 (9), 991-997, 2000. 114. Lin, L., Faraco, J., Li, R., Kadotani, H., Rogers, W., Lin, X., Qiu, X., de Jong, P.J., Nishino, S., and Mignot, E., The sleep disorder canine narcolepsy is caused by a mutation in the hypocretin (orexin) receptor 2 gene, Cell, 98 (3), 365-376, 1999. 115. Chemelli, R.M., Willie, J.T., Sinton, C.M., Elmquist, J.K., Scammell, T., Lee, C., Richardson, J.A., Williams, S.C., Xiong, Y., Kisanuki, Y., Fitch, T.E., Nakazato, M., Hammer, R.E., Saper, C.B., and Yanagisawa, M., Narcolepsy in orexin knockout mice: molecular genetics of sleep regulation, Cell, 98 (4), 437-451, 1999. 116. Verret, L., Goutagny, R., Fort, P., Cagnon, L., Salvert, D., Leger, L., Boissard, R., Salin, P., Peyron, C., and Luppi, P.H., A role of melanin-concentrating hormone producing neurons in the central regulation of paradoxical sleep, B.M.C. Neurosci., 4 (1), 19, 2003. 117. Yamanaka, A., Tsujino, N., Funahashi, H., Honda, K., Guan, J.L., Wang, Q.P., Tominaga, M., Goto, K., Shioda, S., and Sakurai, T., Orexins activate histaminergic neurons via the orexin 2 receptor, Biochem. Biophys. Res. Commun., 290 (4), 12371245, 2002. 118. Bittencourt, J.C., Presse, F., Arias, C., Peto, C., Vaughan, J., Nahon, J.L., Vale, W., and Sawchenko, P.E., The melanin-concentrating hormone system of the rat brain: an immuno- and hybridization histochemical characterization, J. Comp. Neurol., 319 (2), 218-245, 1992. 119. Peyron, C., Tighe, D.K., van den Pol, A.N., de Lecea, L., Heller, H.C., Sutcliffe, J. G., and Kilduff, T.S., Neurons containing hypocretin (orexin) project to multiple neuronal systems, J. Neurosci., 18 (23), 9996-10015, 1998. 120. Ennis, M. and Aston-Jones, G., GABA-mediated inhibition of locus coeruleus from the dorsomedial rostral medulla, J. Neurosci., 9 (8), 2973-2981, 1989. 121. Gallager, D.W. and Aghajanian, G.K., Effect of antipsychotic drugs on the firing of dorsal raphe cells. II. Reversal by picrotoxin, Eur. J. Pharmacol., 39 (2), 357-364, 1976.

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6

Reverse Genetics and the Study of Sleep-Wake Cycle: The Hypocretins and Cortistatin Luis de Lecea

CONTENTS Differential Gene Expression as a Tool to Study Sleep The Hypocretins (Orexins): Two Hypothalamus-Specific Peptides The Hypocretins in Normal Sleep-Wake Cycle Cortical Gene Expression: Cortistatin Transgenic Approaches to Study the Role of Neuropeptides on Sleep Neuropeptides in Control of Sleep Acknowledgments References

DIFFERENTIAL GENE EXPRESSION AS A TOOL TO STUDY SLEEP From the vast amount of information provided by the sequencing of vertebrate genomes,1,2 there are several numbers that are striking, regarding the 20–30% of nonannotated sequences that are expressed in the brain. An estimated 10% of the mouse or human brain transcriptome belongs to the low abundance class; that is, it is expressed in fewer than five copies per cell.3–5 Given the extraordinary cellular complexity of the central nervous system, it can be estimated that a few hundred mRNAs are expressed in small populations (less than106 neurons) of cells.4 Expression of these rare mRNAs in small populations of neurons would confer particular physiological properties to the neurons that produce them, thus there are a few hundred populations of neurons that are functionally distinct and whose characterization may provide essential information about the fine mechanisms of brain function and, in particular, sleep-wake regulation. In spite of major advances in our understanding of the neuronal circuits that govern the sleep-wakefulness cycle,6 the cell groups involved in the different stages 0-8493-1519-0/05/$0.00+$1.50 © 2005 by CRC Press LLC

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FIGURE 6.1 Distribution of the peptide precursors described in this chapter. The hypocretins are two peptides derived from the same precursor and share a 7/7 match with the hormone secretin. The hypocretin precursor is restricted to the lateral hypothalamus. Cortistatin is very similar to the neuropeptide somatostatin and is expressed in GABAergic neurons in the cortex hippocampus and amygdala. Both of these peptides precursors were discovered by differential gene expression methods in the brain. (Modified from Sutcliffe, J.G. and De Lecea, L., The hypocretins: setting the arousal threshold, Nat. Rev. Neurosci., 3, 339–349, 2002. With permission.)

of sleep and in the control of the boundaries between sleep states are poorly understood. The development of molecular markers that define neuronal cell groups with distinct physiological properties will enhance our understanding of the regulation of the states of vigilance. The search of molecular markers that define populations of neurons in areas important for arousal is broadly warranted. This chapter describes the isolation, by differential gene expression analysis, of two peptidergic systems that modulate different aspects of the sleep-wakefulness cycle. The success of this strategy demonstrates the need for new markers of neuronal cell types, which may define populations of neurons critical for our understanding of cortical activity and sleep (Figure 6.1).

THE HYPOCRETINS (OREXINS): TWO HYPOTHALAMUS-SPECIFIC PEPTIDES The hypothalamus can be considered a federation of nuclei with distinct functions that include energy homeostasis, circadian rhythms, sex behavior, and arousal. It is thus expected that mRNAs specifically expressed in restricted areas of the hypothal-

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amus will carry selective functions. Analysis of a collection of the most prevalent cDNAs expressed in the hypothalamus revealed that as many as 40% of these sequences encode secreted proteins.7 Further characterization of a cDNA encoding a novel putative secreted protein revealed that it was restricted to the perifornical area of the lateral hypothalamus. The deduced protein sequence contained a putative signal secretory sequence and several pairs of dibasic residues that were possible substrates of prohormone convertases. Cleavage at these sites would generate two putative products of proteolysis had 13 amino acid identities across 19 residues. This region of one of the peptides contained a 7/7 match with secretin, suggesting that the prepropeptide gave rise to two peptide products that were structurally related both to each other and to secretin. These putative peptides were named hypocretin (hcrt)-1 and -2 to reflect their hypothalamic origin and the similarity to the incretin neuropeptide family.8 The peptides showed neuroexcitatory activity in mature, cultured hypothalamic neurons and were localized in large, dense, core vesicles by immuno electron microscopy. Shortly after the peptides were discovered, Sakurai et al. reported the isolation of the orexins, which are identical to the hypocretins, as the endogenous ligands of two orphan G-protein coupled receptors. These authors named the peptides orexins because they showed feed-inducing activity when injected into the brain ventricles. A great deal of interest was sparked by three reports linking the hypocretinergic system with narcolepsy. The discovery that canine narcolepsy is caused by mutations in hypocretin receptor 2, together with the narcolepsy-like phenotype of hypocretin deficient mice, and the practical absence of hypocretin neurons in the hypothalamus of narcoleptic patients has demonstrated that this system is involved in the state boundary control. Comprehensive reviews of the hypocretinergic system are available elsewhere in the literature.9–11

THE HYPOCRETINS IN NORMAL SLEEP-WAKE CYCLE Even though it appears that the main function of the hypocretins involves stability of the boundaries between wakefulness and sleep, the precise role of hcrts in normal sleep is still a matter of debate. The actions of hcrt on sleep may be integrated into the reciprocal interaction model of REM sleep generation by McCarley and Hobson.12 This model considers two populations of neurons: REM-off cells, which are silent during REM sleep, and REM-on neurons, which generate REM sleep bouts. REM-off cells, which include noradrenergic neurons of the LC, serotoninergic neurons of the raphe nucleus, and the histaminergic neurons of the tuberomammilary nucleus (TMN), are highly active during wake and silent during REM sleep. During wakefulness REM-off neurons inhibit REM-on cells, which include cholinergic neurons of the laterodorsal tegmentum and pedunculo pontine nucleus (LDT/PPT). During REM sleep, REM-on cells show a higher activity after the inhibitory action of REM-off cells is removed. Considering the wake-promoting properties of hcrt-1, it has been suggested that hcrt increases arousal and inhibits REM sleep by activating REM-off cells, in particular the noradrenergic ones in the LC which receive the densest hcrt innerva-

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tion.13,14 This hypothesis is in line with in vitro and in vivo experiments that have shown that hcrt-1 excites this cell population.15–17 Further, local administration of hcrt-1 promotes wakefulness and suppresses REM sleep (Bourgin et al., 2000). Several studies have revealed the ability of hcrt to excite other REM-off neurons18–21 as well as REM-on cells in the LDT/PPT22 and cholinergic neurons in the basal forebrain.23,24 Part of the wake-promoting effects of hcrt seem to be mediated by histaminergic neurons in the tuberomammilary nucleus, as histamine H1 receptor knockout mice are impervious to hcrt administration.21 Similar analyses in mutant animals with alterations in specific neurotransmitter systems will lead to a better understanding of the interaction of the hcrts with the sleep-wake circuitry. Based on the framework of the reciprocal interaction model of McCarley and Hobson, two alternative models have been proposed to integrate the activity of hcrt neurons in the reciprocal inhibitory model for REM sleep regulation. Mignot and collaborators11 considered hcrt neurons as wake neurons. During arousal, hcrt neurons are activated by metabolic, circadian, and stress circuits, and stimulate both REM-off and REM-on neurons leading to the awake state. In contrast during REM sleep hcrt neurons exhibit minimal activity, reducing the firing of REM-off neurons and subsequently activating REM-on neurons. Kilduff and Peyron25 proposed that hcrt cells are wake-on and REM-on neurons. According to this model, hcrt neurons drive the tonus of both REM-on and REM-off neurons during wakefulness. This same model postulates that during REM sleep, REM-off cells will be inhibited by GABAergic neurons from the periaqueductal gray and disinhibit REM-on cells (Figure 6.2). Other authors have suggested that hcrt neurons are activated during wakefulness but only when somato-motor activity is present, independently of the state of vigilance.26 The experimental data currently available is compatible with these three previous models that, while they are verified, provided testable working hypotheses. Investigating the activity of hcrt neurons during states of vigilance is essential to understanding the physiological role of hcrt in the regulation of sleep-wakefulness. Several studies correlate hcrt release with the sleep-wakefulness cycle. Prolonged waking produced by pharmacological and instrumental sleep deprivations produces an increase in extracellular hcrt levels or c-Fos/hcrt mRNA positive cells,27–29 which initially may suggest that hcrt is a factor that accumulates during wakefulness. However there is no correlation between hcrt levels and wake or sleep amounts,29 strongly suggesting that hcrt may be primarily related to the regulation of the transitions between states of vigilance, rather than a particular sleep-wake stage. Electrophysiological studies have investigated the activity of neurons in the lateral hypothalamus in parallel with sleep recording in freely moving rats. These studies have identified two cell types in the lateral hypothalamus (LH): wake-on/REM-on neurons and REM-off cells, indicating that LH neurons, which include hcrt neurons, exhibit a discharge pattern that correlates with arousal and sleep.30

CORTICAL GENE EXPRESSION: CORTISTATIN Cortistatin was discovered as a result of the effort to characterize cortex-specific gene expression modulated by synaptic activity and named after its cortical expres-

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FIGURE 6.2 (See color insert following page 108.) Hypocretinergic activity dependent on the states of vigilance. During wakefulness, metabolic, circadian, and behavioral inputs converge on hypocretin neurons, which activate noradrenergic neurons in the locus coeruleus and promote arousal. During non REM sleep, the activity of hypocretin neurons decreases, but the inhibition of REM-off neurons over REM-on cells is still effective. During REM sleep, hypocretin and REM-off cells are silent disinhibiting REM-on cells. (From Sutcliffe, J.G. and De Lecea, L., The hypocretins: setting the arousal threshold, Nat. Rev. Neurosci., 3, 339–349, 2002. With permission.)

sion and sequence homology to somatostatin.31 The characterization of this peptide is yet another example of the use of reverse genetics to study the molecular components of the sleep machinery. Cortistatin is synthesized as a precursor of 116 amino acids, which gives rise to a C-terminal mature peptide, cortistatin-14 (CST-14), that shares 11 of its 14 residues with the neuropeptide somatostatin. However the similarity between cortistatin and

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somatostatin is restricted to the mature peptide and are the products of different genes. CST-14 binds to all five somatostatin receptors in vitro, although several authors suggest that CST-14 exerts its actions in vivo by binding to its own specific receptor.32 Cortistatin expression is restricted to scattered cells in the cerebral cortex and hippocampus. These neurons use GABA as their neurotransmitter and are different from the population of cortical neurons that express somatostatin.33 Networks of GABAergic inhibitory neurons are known to be critical for synchronization of cortical activity and have been proposed to have a major role in the maintenance of slow wave sleep.34–37 Experiments and models have shown how the network frequency depends on excitation of the interneurons and on the parameters of GABAA-mediated IPSCs between the interneurons (conductance and time course).38 The electrophysiological firing properties of interneurons are substantially different from those of pyramidal cells, and they are thought to be based on the expression of particular ionic conductances (e.g., HCN2, KCNQ2-4, Kv3.1, etc). Further proof that cortical GABAergic neurons and these conductances are important for cortical activity and slow-wave sleep are the significant differences in delta power of mice deficient in Kv3.1 channels.39 Intracerebroventricular infusion of CST14 dramatically increases the amount of slow-wave activity in rats, at the expense of wakefulness. The mechanism by which CST-14 enhances cortical synchronization has been established through the interaction of CST-14 with acetylcholine, a neurotransmitter known to be involved in the maintenance of cortical desynchronization. Application of Ach in the anesthetized animal increases fast activity, and this effect is blocked with the simultaneous addition of CST-14. These data suggest that CST-14 increases slow-wave sleep by antagonizing the effects of acetylcholine on cortical excitability. In addition to this mechanism, cortistatin may enhance cortical synchronization by enhancing Ih, a cation conductance shown to be important in thalamocortical synchronization.32 A set of experiments suggests that cortistatin expression correlates with the sleep homeostat. The concentration of cortistatin mRNA oscillates along the light/dark cycle in rats, with maximal levels at the end of the dark (active) period. Further, the steady-state concentration of cortistatin mRNA increases fourfold upon sleep deprivation and returns to normal levels after sleep rebound, indicating that the expression of the peptide is associated with sleep demand.32

TRANSGENIC APPROACHES TO STUDY THE ROLE OF NEUROPEPTIDES ON SLEEP The most challenging aspect of reverse genetics is the characterization of the function of the newly discovered genes. Genetically modified mice, transgenic and knockout animals, give a wealth of information about the phenotypic consequences of the missing gene. Here we are presenting several transgenic and knockout mice affecting the hypocretinergic and cortistatin systems as examples of the possibilities of this technology in the study of sleep circuits. In addition to the study of canine model of narcolepsy,40 which resulted in the discovery of the hypocretinergic system as a main regulator of transition of states

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of vigilance, the development of new mouse models with altered hypocretinergic transmission is increasing our knowledge on this neurotransmitter system. In particular the generation and phenotypic characterization of hypocretin and orexin knockout mice41 has extended the observations and the importance of the peptide transmitters, rather than the receptors, as critical components of the circuits that control the stability of the states of vigilance. To further support the role for hcrt in narcolepsy, hcrt receptor 2 ko mice have been recently generated. These animals present a milder cataplexyphenotype than the ligand knockouts, suggesting hcrtr2-dependent and hcrtr-2-independent mechanisms in the narcolepsy/cataplexy syndrome.42 Likewise the analysis of mice deficient in cortistatin is uncovering additional functions for this peptidergic system. Preliminary data indicate that these mice are hyperexcitable and display enhanced long-term potentiation in the hippocampus. In contrast, transgenic mice overexpressing cortistatin show decreased long-term potentiation and impaired spatial learning. In addition to knockout animals, expression of different transgenes under the control of selective promoters, such as hypocretin or cortistatin, can modify the neuronal system in such a way that we obtain additional information about its physiology and connectivity. For instance, elegant studies in transgenic mice have demonstrated that hypocretin-containing neurons are important components in the control of homeostasis. Selective degeneration of hypocretin expressing cells by the use of the toxic gene ataxin 3 under the control of the hypocretin promoter, caused narcolepsy, hypophagia, and obesity,43 thus demonstrating a pivotal role for the cells expressing hcrt in the coordination and integration of the homeostatic circuitry. Animal models, in which hcrt-expressing cells are constitutively activated (e.g., by the use of cholera toxin under the control of the hcrt promoter) may shed important light to the effect of stimulus-independent hcrt-ergic activation. Cholera toxin A inhibits the inhibition of adenylyl cyclase and, when expressed intracellularly, causes constitutive high levels of cAMP.44 This constitutive activation is equivalent to placing a stimulating electrode in hypocretin neurons. The recent development of genetically encoded fluorescent reporters of cell activity45 opens a unique opportunity to allow monitoring of ensembles of neurons defined by transgenic promoters. The hypocretinergic system is an outstanding candidate for such endeavors because of its restricted localization to a few thousand neurons and relative electrophysiological homogeneity.46 Other transgenic approaches can be used to map the anatomical afferents to sets of neurons defined by a transgenic promoter. Following the strategy described by DeFalco et al.,47 transgenic mice expressing the cre recombinase can be infected with a recombinant pseudorabies virus that depends on cre-mediated recombination for replication. In addition, this modified virus expresses green fluorescent protein following cre recombination. Thus one could map polysynaptic connections to hypocretin neurons expressing cre in transgenic mice. This would allow a cell-type specific mapping of affererents, to better define the neural system. The signals that regulate hypocretin can be functionally investigated using in vitro cell-specific visualization of neuronal activity through the use of cellular tagging by EGFP. Van den Pol et al. have determined that hcrt neurons can be depo-

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larized by hcrt-1 via a glutamatergic interneuron.46 In contrast, hcrt neurons can be hyperpolarized by norepinephrin or serotonin, suggesting an inhibitory feedback loop between these transmitters and hypocretin cells.46 An alternative method to intracellular recordings in fluorescently labeled hypocretin neurons is the cameleon technology.48 The cameleons are recombinant constructs that consist of two fluorescent molecules, a yellow-emitting molecule (YFP) and a cyan-emitting molecule, linked by a calmodulin domain and a peptide (called M13) that binds calmodulin in the presence of calcium. When the concentration of intracellular calcium, which corresponds to increased neuronal activity, reaches a maximum, the calmodulin domain of the cameleon binds to the M13 peptide and brings the blue and yellow emitting proteins together, resulting in an increase in the ratio of blue fluorescence known as FRET (fluorescence resonance emission transfer).48,49 Thus changes in intracellular calcium concentrations in hypocretin neurons can be detected by expressing cameleons in transgenic mice under the control of the hypocretin promoter. This method has the advantage of measuring the activity of the entire hypocretinergic system in response to defined stimuli. Additional important data of neuronal activity related with sleep can be achieved in vitro by the application of voltage sensitive fluorescent dies to cortical slices. This methodology has demonstrated the importance of specific conductances (Ih) on spontaneous neuronal activity in the cortical micro-circuitry.50 When applied to transgenic and knockout mice, this technology can reveal new mechanisms of cortical physiology relevant to sleep processes. Development of magnetic resonance (MR) imaging in rodents will allow in the future the visualization of activity of hypocretin cells in vivo. MR uses radio frequency energy pulses and a strong magnetic field to provide pictures of internal organs and tissues. The images have remarkable detail and can be visualized in each of the three spatial planes (axial, sagittal, and coronal) using data collected from a single imaging procedure. MR imaging has proven to be an invaluable tool for the diagnosis of a wide range of pathologies including cancer, cardiovascular disease, joint and musculoskeletal disorders, and neurological disease. Although the spatial resolution of MR in most magnets available for in vivo applications does not reach the micron level, it is expected that the development of equipment, parallel to that of protein NMR will allow cellular and subcellular resolution in freely behaving animals. Also, recent development of contrasting agents that depend on ion flux, such as DOPTA-Gd for detection of intracellular calcium,51 may allow the monitoring of ensembles of neurons (e.g., defined by the hypocretin promoter) in freely moving mice.

NEUROPEPTIDES IN CONTROL OF SLEEP Here we have described two examples of new neuropeptides that have different activities and regulate different aspects of the sleep-wakefulness cycle. The use of reverse genetics has uncovered basic properties of the peptides (structure, distribution, electrophysiological activity, connectivity). The development of mouse models has allowed a better understanding of their function and their role on sleep regulation. It is not surprising that neuropeptides have a major role in the modulation of sleep circuits. First the time frame and kinetics of peptidergic action (seconds to minutes)

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is consistent with the reversible modification of synaptic transmission that would be required during sleep. Second peptide release is associated with high frequency firing of presynaptic neurons, which provides another code for the stability of neuronal systems. New peptidergic systems with restricted localizations in areas critical for sleep-wakefulness regulation are likely to come to light in the near future.

ACKNOWLEDGMENTS I thank our collaborators for keeping these stories exciting and especially Raphaelle Winsky, Pilar Ruiz-Lozano, and Steven Henriksen for critical reading. This work was supported by grants from NIH.

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17. Bourgin, P., Huitrón-Reséndiz, S., Spier, A., Fabre, V., Morte, B., Criado, J., Sutcliffe, J., Henriksen, S., and de Lecea, L., Hypocretin-1 modulates REM sleep through activation of locus coeruleus neurons, J. Neurosci., 20, 7760–7765, 2000. 18. Bayer, L., Eggermann, E., Serafin, M., Saint-Mleux, B., Machard, D., Jones, B., and Muhlethaler, M., Orexins (hypocretins) directly excite tuberomammillary neurons, Eur. J. Neurosci., 14, 1571–1575, 2001. 19. Brown, R.E., Sergeeva, O.A., Eriksson, K.S., and Haas, S.L., Orexin A excites serotonergic neurons in the dorsal raphe nucleus of the rat, Neuropharmacology, in press, 2001. 20. Eriksson, K.S., Sergeeva, O., Brown, R.E., and Haas, H.L., Orexin/hypocretin excites the histaminergic neurons of the tuberomammillary nucleus, J. Neurosci., 21, 9273–9279, 2001. 21. Huang, Z.L., Qu, W.M., Li, W.D., Mochizuki, T., Eguchi, N., Watanabe, T., Urade, Y., and Hayaishi, O., Arousal effect of orexin A depends on activation of the histaminergic system, Proc. Natl. Acad. Sci. USA, 98, 9965–9970, 2001. 22. Xi, M., Morales, F.R., and Chase, M.H., Effects on sleep and wakefulness of the injection of hypocretin-1 (orexin-A) into the laterodorsal tegmental nucleus of the cat, Brain Res., 901, 259–264, 2001. 23. Espana, R.A., Baldo, B.A., Kelley, A.E., and Berridge, C.W., Wake-promoting and sleep-suppressing actions of hypocretin (orexin): basal forebrain sites of action, Neuroscience, 106, 699–715, 2001. 24. Thakkar, M.M., Ramesh, V., Strecker, R.E., and McCarley, R.W., Microdialysis perfusion of orexin-A in the basal forebrain increases wakefulness in freely behaving rats, Arch. Ital. Biol., 139, 313–328, 2001. 25. Kilduff, T.S. and Peyron, C., The hypocretin/orexin ligand-receptor system: implications for sleep and sleep disorders, Trends Neurosci., 23, 359–365, 2000. 26. Torterolo, P., Yamuy, J., Sampogna, S., Morales, F.R., and Chase, M.H., Hypocretinergic neurons are primarily involved in activation of the somatomotor system, Sleep, 26, 25–28, 2003. 27. Scammell, T.E., Estabrooke, I.V., McCarthy, M.T., Chemelli, R.M., Yanagisawa, M., Miller, M.S., and Saper, C.B., Hypothalamic arousal regions are activated during modafinil-induced wakefulness, J. Neurosci., 20, 8620–8628, 2000. 28. Estabrooke, I.V., McCarthy, M.T., Ko, E., Chou, T.C., Chemelli, R.M., Yanagisawa, M., Saper, C. B., and Scammell, T., Fos expression in orexin neurons varies with behavioral state, J. Neurosci., 21, 1656–1662, 2001. 29. Yoshida, Y., Fujiki, N., Nakajima, T., Ripley, B., Matsumura, H., Yoneda, H., Mignot, E., and Nishino, S., Fluctuation of extracellular hypocretin-1 (orexin A) levels in the rat in relation to the light-dark cycle and sleep-wake activities, Eur. J. Neurosci., 14, 1075–1081, 2001. 30. Alam, M.N., Gong, H., Alam, T., Jaganath, R., McGinty, D., and Szymusiak, R., Sleep-waking discharge patterns of neurons recorded in the rat perifornical lateral hypothalamic area, J. Physiol., 538, 619–631, 2002. 31. de Lecea, L., Criado, J.R., Prospero-Garcia, O., Gautvik, K.M., Schweitzer, P., Danielson, P.E., Dunlop, C.L., Siggins, G.R., Henriksen, S.J., and Sutcliffe, J.G., A cortical neuropeptide with neuronal depressant and sleep-modulating properties, Nature, 381, 242–245, 1996. 32. Spier, A.D. and de Lecea, L., Cortistatin: a member of the somatostatin neuropeptide family with distinct physiological functions, Brain Res. Rev., 33, 228–241, 2000.

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33. de Lecea, L., del Rio, J.A., Criado, J.R., Alcantara, S., Morales, M., Henriksen, S.J., Soriano, E., and Sutcliffe, J. G., Cortistatin is expressed in a distinct subset of cortical interneurons, J. Neurosci., 17, 5868–5880, 1997. 34. Whittington, M.A., Traub, R.D., and Jefferys, J.G., Synchronized oscillations in interneuron networks driven by metabotropic glutamate receptor activation, Nature, 373, 612–615, 1995. 35. Traub, R.D., Whittington, M.A., Stanford, I.M., and Jefferys, J.G., A mechanism for generation of long-range synchronous fast oscillations in the cortex, Nature, 383, 621–624, 1996. 36. Traub, R.D., Jefferys, J.G., and Whittington, M.A., Simulation of gamma rhythms in networks of interneurons and pyramidal cells, J. Comput. Neurosci., 4, 141–150, 1997. 37. Jefferys, J.G. and Whittington, M.A., Review of the role of inhibitory neurons in chronic epileptic foci induced by intracerebral tetanus toxin, Epilepsy Res., 26, 59–66, 1996. 38. Amzica, F. and Steriade, M., Electrophysiological correlates of sleep delta waves, Electroencephalogr. Clin. Neurophysiol., 107, 69–83, 1998. 39. Joho, R.H., Ho, C.S., and Marks, G.A., Increased gamma- and Decreased deltaOscillations in a Mouse Deficient for a Potassium Channel Expressed in Fast-Spiking Interneurons, J. Neurophysiol., 82, 1855–1864, 1999. 40. Lin, L., Faraco, J., Li, R., Kadotani, H., Rogers, W., Lin, X., Qiu, X., de Jong, P.J., Nishino, S., and Mignot, E., The sleep disorder canine narcolepsy is caused by a mutation in the hypocretin (orexin) receptor 2 gene, Cell, 98, 365–376, 1999. 41. Chemelli, R.M., Willie, J.T., Sinton, C.M., Elmquist, J.K., Scammell, T., Lee, C., Richardson, J.A., Williams, S.C., Xiong, Y., Kisanuki, Y., Fitch, T.E., Nakazato, M., Hammer, R.E., Saper, C.B., and Yanagisawa, M., Narcolepsy in orexin knockout mice: molecular genetics of sleep regulation, Cell, 98, 437–451, 1999. 42. Willie, J.T., Chemelli, R.M., Sinton, C.M., Tokita, S., Williams, S.C., Kisanuki, Y.Y., Marcus, J.N., Lee, C., Elmquist, J.K., Kohlmeier, K.A., Leonard, C.S., Richardson, J.A., Hammer, R.E., and Yanagisawa, M., Distinct narcolepsy syndromes in Orexin receptor-2 and Orexin null mice: molecular genetic dissection of Non-REM and REM sleep regulatory processes, Neuron, 38, 715–730, 2003. 43. Hara, J., Beuckmann, C.T., Nambu, T., Willie, J.T., Chemelli, R.M., Sinton, C.M., Sugiyama, F., Yagami, K., Goto, K., Yanagisawa, M., and Sakurai, T., Genetic ablation of orexin neurons in mice results in narcolepsy, hypophagia, and obesity, Neuron, 30, 345–354, 2001. 44. Burton, F.H., Hasel, K.W., Bloom, F.E., and Sutcliffe, J. G., Pituitary hyperplasia and gigantism in mice caused by a cholera toxin transgene, Nature, 350, 74–77, 1991. 45. Zhang, J., Campbell, R. E., Ting, A. Y., and Tsien, R. Y., Creating new fluorescent probes for cell biology, Nat. Rev. Mol. Cell Biol., 3, 906–918, 2002. 46. Li, Y., Gao, X.B., Sakurai, T., and van den Pol, A.N., Hypocretin/Orexin excites hypocretin neurons via a local glutamate neuron-A potential mechanism for orchestrating the hypothalamic arousal system, Neuron, 36, 1169–1181, 2002. 47. DeFalco, J., Tomishima, M., Liu, H., Zhao, C., Cai, X., Marth, J.D., Enquist, L., and Friedman, J.M., Virus-assisted mapping of neural inputs to a feeding center in the hypothalamus, Science, 291, 2608–2013., 2001. 48. Miyawaki, A., Llopis, J., Heim, R., McCaffery, J.M., Adams, J.A., Ikura, M., and Tsien, R.Y., Fluorescent indicators for Ca2+ based on green fluorescent proteins and calmodulin, Nature, 388, 882–887, 1997.

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49. Miyawaki, A., Griesbeck, O., Heim, R., and Tsien, R.Y., Dynamic and quantitative Ca2+ measurements using improved cameleons, Proc. Natl. Acad. Sci. USA, 96, 2135–2140, 1999. 50. Mao, B.Q., Hamzei-Sichani, F., Aronov, D., Froemke, R.C., and Yuste, R., Dynamics of spontaneous activity in neocortical slices, Neuron, 32, 883–898, 2001. 51. Li, W.H., Parigi, G., Fragai, M., Luchinat, C., and Meade, T.J., Mechanistic studies of a calcium-dependent MRI contrast agent, Inorg. Chem., 41, 4018–4024, 2002.

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7

Genetic Regulation of Sleep Yves Dauvilliers, Paul Franken, and Mehdi Tafti

CONTENTS Introduction Evidence for a Genetic Contribution to Sleep Candidate Gene Studies Mutagenesis Studies Quantitative Trait Loci (QTL) Studies QTL Analysis of Sleep Duration, Distribution, and Architecture QTL Analysis of the Sleep EEG QTL Analysis of Homeostatic Regulation of Sleep Gene Expression Studies Genetics of Circadian Rhythms Conclusions Acknowledgments References

INTRODUCTION The behavior sleep is conserved across many species including birds and mammals. Sleep-like behavior has also been characterized in invertebrates,1 notably the fruit fly.2,3 Sleep is of vital importance, although the neurobiological substrates of the functions of sleep remain elusive. Substantial progress has been achieved during the last two decades in our understanding of neurobiology underlying the expression and regulation of sleep. Sleep is regulated by two major processes, one circadian that determines its timing and one homeostatic that determines its need.4 The neuronanatomical and neurochemical pathways involved in sleep initiation and maintenance are now well described5 but, in contrast to impressive advances in the molecular genetics of circadian rhythms, little is known about the molecular bases of sleep. We do know that the expression and the regulation of most of sleep components are under genetic control. Mignot and colleagues identified a mutation in the hypocretin receptor 2 gene as the cause of canine form of narcolepsy,6 a sleep disorder found in several species, including humans. Based on this major discovery, the role of the hypocretin system 0-8493-1519-0/05/$0.00+$1.50 © 2005 by CRC Press LLC

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in human narcolepsy is now well established.7,8 This example demonstrated that with a genetic approach to sleep, unexpected molecular pathways remain to be discovered. Another more recent example of a successful forward genetics approach applied to sleep is the discovery that the metabolic fatty acid beta-oxidation pathway is implicated in the regulation of theta oscillations during sleep.9 This pathway has not been previously implicated in sleep and may also play a role in cognitive functions. The genetic dissection of sleep therefore constitutes a promising approach in understanding the molecular basis of sleep physiology and sleep disorders. Hence, the contribution of genetic components to the pathology of sleep disorders is increasingly recognized as of major importance. In this review we will focus on advances toward genetic approaches to sleep in mice, although it is recognized that the fruit fly offers a powerful alternative.

EVIDENCE FOR A GENETIC CONTRIBUTION TO SLEEP For a wide range of phenotypes, considerable variation has been observed among species, strains, and individuals within a species. These phenotypes include many aspects of sleep such as the daily amount of rapid-eye-movement sleep (REMS) and non-REMS (NREMS) and their distribution across the day. These variations may be attributed to environmental factors such as light, temperature, diet, and behavioral conditioning. The role of these factors in sleep is well established, in terms of both physical and behavioral effects. These factors, however, do not fully account for the variability observed, and the differences in sleep-related phenotypes in animals and humans suggest the presence of an important contribution of genetic factors. The implication of genetic factors is strongly indicated by the many interindividual variations in the different aspects of sleep in laboratory animals kept under identical environmental conditions from birth. Significant differences in NREMS and REMS have been observed in inbred strains of rodents, with far greater interstrain than intrastrain variability, suggesting that environmental factors play a less important role.10–17 Furthermore, these differences are highly resistant to prolonged manipulations such as immobilization, forced activity, or sleep deprivation.18,19 Sleep is a complex behavior both in its manifestation and its regulation. The various aspects of sleep differ in their regulation, and each of these aspects is likely to be under genetic control. Each component of sleep needs to be considered as a complex phenotype. A systematic genetic approach is therefore needed for their identification.20,21 Early work on waking EEG recordings by Vogel22,23 had strongly suggested the effect of single genes. Pioneering work by Valatx in inbred mice had also indicated that several aspects of sleep are controlled by genetic factors.17,24 These studies, together with a diallelique sleep experiment in mice performed by Friedmann,12 clearly indicated that although some aspects of sleep may follow a simple segregation, the classical genetic laws cannot predict most others. As a first step to identify some of the underlying genes, most studies have focused on sleep of pure inbred strains, mainly of mice and rats. Mouse models constitute the best animal tool for discovering genes related to complex behavior such as sleep.25 Mice are easy to breed and study, and high-density genetic marker maps and the sequenced mouse genome are now available. Moreover there are numerous

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other mapping panels, such as the recombinant inbred (RI) lines for which genomewide markers have been genotyped.26 A pure strain is established by successive inbred crossing, and the genome is considered as being at homozygous state after 60 generations of brother-sister mating. All the individuals of a specific strain thus have the same genetic combination, unlike outbred lines. To follow the segregation of a phenotype, two inbred mouse strains differing in the trait of interest are crossed, and their F1 offspring are either intercrossed to generate F2 or backcrossed to one of the progenitor strains to generate backcross populations. Further random intercross or backcross generations can be performed to generate advanced inter- and backcross populations. To generate RI sets, F2 mice are brother-sister mated until full homozygosity, thereby fixing a unique set of recombinations in several inbred lines. Heterogenous stocks are generated by intercrossing several inbred mouse strains over many generations and therefore represent higher rates of recombination and polymorphism useful for fine mapping.27 The first step is thus to compare several inbred strains to identify the differences for a given phenotype. It is usually necessary to combine several approaches to localize a gene and determine its function. Genome-wide search for genes affecting a phenotype of interest, through quantitative trait loci-QTL analysis, mutagenesis, molecular genetic, and candidate gene studies are the most commonly used strategies.

CANDIDATE GENE STUDIES Candidate gene studies address whether a gene, which is already known, is also implicated in sleep regulation. The known physiological role of a gene may lead to its suspected implication to account for phenotype variations between different genotypes. A study of the polymorphism of this gene will look for a relationship (association) between an allele and the phenotype. The use of genetically modified mice has provided efficient tools to study how genes are implicated in sleep and appears to have a particularly promising future. This approach consists of constructing lines of transgenic animals whose genetic material has been altered by either adding supplementary copies of a given gene (transgenic), or by changing the gene of interest (knockout, knockin).28,29 These techniques can be used to address the consequences of overexpression, ectopic expression, time- and tissue-specific expression, and gain or loss of function of a candidate gene. Potential advantages and problems have been discussed in several reviews.20,21,30 If the mutant animal is viable, analysis of its phenotype provides information about the normal function of the modified gene. It is then easy to study the sleep of these transgenic mice and draw conclusions about their implication in sleep regulation. Sleep physiology and pharmacology have identified most of the essential systems from which candidate genes can be chosen. The sleep of several transgenic mice has been studied, each of which shows abnormalities in terms of sleep architecture. Studies in mice transgenic for prion protein (PrP) suggest that PrP plays a role in promoting sleep continuity.31 Also, after sleep deprivation slow-wave activity increased, but the changes in EEG power density were more prominent and lasted longer in the PrP knockout mice.31

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Sleep of transgenic mice producing an excess of growth hormone shows an increase in NREMS; in contrast NREMS was significantly suppressed during both the light and the dark period in the transgenic mice with deficiency in the somatotropic axis without any changes in REMS.32,33 Sleep of TNF receptor knockout mice shows an increase in NREM and a decrease in REMS after interleukin-1beta treatment, results providing new evidence that TNF alpha is involved in physiological sleep regulation.34 Respectively, 5-HT1B receptor, GH-IGF-1, and dopamine transporter (DAT) knockout mice demonstrate a reduction in NREMS, thus testifying to their likely implication in the regulation of NREMS.32,35,36 REMS for its part was significantly increased in 5-HT1B-R knockout mice in contrast to the absence of change in GHRH knockout mice. Transgenic mice overexpressing prostaglandin D2 (PGD2) showed a significant increase in NREMS after nociceptive stimulation (pinching the tail) without alterations in REMS.37 These findings appear to be related to a local inflammation, linked to an increase in the level of cerebral PGD2 release. Sleep of albumin-D binding protein (DBP) knockout mice showed a reduction in circadian amplitude of NREMS and alterations in REMS.38 All these studies suggest a potential role in sleep for each of the investigated genes; however, the invariable presence of sleep abnormalities, irrespective of the transgenic model studied, probably indicates a nonspecific effect of the different genetic manipulations or the high sensitivity of sleep to diverse alterations in general physiology. The first sleep-related gene discovery concerns the orexin (hypocretin) system. Orexin-A (or 1) and -B (or 2) are hypothalamic neuropeptides acting on orexin-A and -B receptors and first thought to be involved in feeding behavior.39–41 Mignot’s group identified, through linkage analysis and positional cloning, mutations in the orexin-B receptor as the cause of canine narcolepsy,6 an animal model of the human sleep disorder narcolepsy. Almost simultaneously Yanagisawa’s group, interested in the role of orexins in feeding behavior, discovered in the mouse a phenotype similar to canine and human narcolepsy after a targeted deletion of the prepro-orexin gene.42 The lack of discovery of new genes could be a limitation inherent to the strategy of the candidate gene approach, but studies in clock-gene knockout and mutant flies and mice revealed a potential new pathway of transcriptional regulators in sleep homeostasis. The genome-wide search or mapping experiments make no a priori assumptions on gene systems involved and, although the outcome may favor a known physiological mechanism, unknown and unexpected systems may be discovered. The systematic search for genes affecting a particular phenotype needs to cover the whole genome of an organism. The hypocretin success story in canine narcolepsy is the unique and the best example of this approach in the field of sleep research; therefore genome-wide search constitutes the method of choice if we are to discover new sleep genes. Another approach, the genome-wide mutagenesis, is also of special interest.

MUTAGENESIS STUDIES Mutagenesis is an important strategy to search for new genes implicated in sleep. Its aim is primarily to produce mutants, which present abnormalities in sleep or

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circadian rhythms, then to localize the defective genes and identify them by means of candidate gene analysis or positional cloning.43,44 Ethylnitrosurea (ENU) is the mutagen most commonly used to randomly induce point mutations. Both dominant and recessive mutations can be screened in the same way as a single gene mutation in a pathological condition. This technique has already been used successfully in the field of circadian rhythms and led to the discovery of the gene Clock, one of the key mammalian genes in circadian rhythmicity.45 More recently, with the same technique, Rab3a gene (coding for the most abundant ras-associated binding brain protein) was found to alter both circadian period and homeostatic response to sleep loss in the mouse.46 Nevertheless, the relatively high number of animals, which have to be studied to isolate abnormal sleep phenotypes of interest, limits the feasibility of this approach. Moreover genetic screens by mutagenesis are for fully penetrant dominant and recessive mutations and therefore cannot identify small-effect sequence variations that may turn out to be essential for some aspects of the phenotype.

QUANTITATIVE TRAIT LOCI (QTL) STUDIES A number of limitations inherent to each of the techniques described above complicate the process of identifying new genes implicated in sleep physiology. The Quantitative Trait Loci (QTL) analysis was developed to overcome some of these obstacles, and it consists of identifying all the loci controlling a given quantitative trait and responsible for interindividual differences (even small).29,47 Sleep physiology and regulation are particularly complex and probably involve numerous genes and many interactions among these genes and environmental factors. QTL analysis has been proposed as a powerful approach in the genetic dissection of complex traits.16,20,47–53 The QTL technique is particularly appropriate when the trait is complex and the number of genes is high. The QTL analysis effectively allows numerous loci to be identified, some of which may have a major effect and others a minor effect on the different phenotypes related to sleep. This analysis can be performed in several segregating mouse populations including inter- and backcross, advanced inter- and backcross, RI and heterogeneous stocks. Although RIs are usually not suitable for QTL mapping due to their limited progenitor strains and number, for QTLs of large effects they may provide significant mapping accuracy because of the fourfold increase in recombination as compared to an F2 population.49 Also, for complex phenotypes with high variability, the use of RIs is advantageous because several individuals per strain are tested instead of, for instance, a single F2 or backcross animal. QTL analysis does not map a gene but a genetic effect in a large chromosomal region (usually 20–30 cM). These large regions may contain a single gene or several genes with variable effects. QTLs may interact with each other (epistasis), an effect which is difficult to detect in QTL mapping experiments. The first step is to make sure that the region contains QTLs of large enough effect. When a QTL has been localized in RI strains, segregating F2 mice is the strategy of choice. Another possibility is to transfer the QTL region from one inbred strain background to another inbred background through repeated backcrossing and selection to produce a con-

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genic strain. Sleep amounts available in eight congenic strains generated by transferring histocompatibility genes from the inbred strain BALB/c (C) to the inbred background of C57BL/6 (B) were analyzed.54 In almost all cases, the results indicated that even if the transferred pieces of chromosomes contained a large effect QTL detected in CXB-RIs, a clear effect could not be observed in the resulting congenic strain. The different loci (QTLs) may interact (epistasis), producing a variable QTL effect between genetically different strains.55 Several subsequent crossing experiments are necessary to obtain recombinant animals and thereby reduce the size of the region of interest, and the responsible genes are finally determined by means of either candidate genes or positional cloning techniques.49 Although natural allelic variation of genes with small effect can be mapped through QTL analysis, the final identification of sequence variants (quantitative trait nucleotides or QTNs) in the QTL region with biologically significant effects on the phenotype may represent prohibitive efforts in terms of both phenotyping and genotyping.49,53 The final identification of functional QTNs is the most difficult part since QTNs may be found every few kilo bases. Therefore a combination of several approaches is necessary for mapping and candidate gene analysis. High-resolution QTL mapping in conjunction with the availability of whole genome sequences of several major mouse strains should identify candidate genes to be investigated. Because most QTNs will probably be involved in gene regulation rather than being mutations affecting the protein function, further gene expression profiling with high throughput genomics technologies (e.g., microarray or TaqMan), gene translation, and post-translational protein analyses should be used to uncover the molecular mechanisms involved. An excellent example of how QTL analysis can further our understanding of complex traits was provided by Takahashi’s group for the circadian behavior in mice.55 Although most of the molecular machinery of the circadian timekeeping system has been discovered mainly by direct molecular techniques and mutagenesis, the identified genes do not explain the complexity of the observed circadian behavior. For instance none of the clock genes has been found to be involved in the approximately 1-hour difference in free-running circadian period between BALB/c and C57BL/6 inbred mouse strains.56 Instead QTL analysis in a BALB/c x C57BL/6 intercross revealed several loci with epistatic interaction.55

QTL ANALYSIS OF SLEEP DURATION, DISTRIBUTION, AND ARCHITECTURE The complex nature of sleep is reflected in high intra- and interspecies phenotypic variability. The 24-hour amount of sleep is highly dependent on the environment.57 Hence many animals of the same strain and of different strains need to be recorded in strictly similar experimental conditions (light, temperature, diet) to verify whether interline variability is clearly higher than intraline variability and ultimately the presence of an underlying genetic factor. Pharmacogenetics studies have been first applied in this field; mouse strains LS and SS (long and short sleepers) were initially the most studied models.58 These

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mouse strains were created in the 1970s by selecting mice that were the most or least sensitive to the hypnotic effect of ethanol by testing their righting reflex. After 18 generations of selection, the resulting strains had a mean sleep time of 10 minutes (SS) or 2 hours (LS), respectively, after similar doses of ethanol. Analyses of the sensitivity of these strains to different pharmacological compounds determined to what extent genetic control of the soporific or anesthetic effect of these hypnotics was similar or different.58–61 Also the different pharmacological effects of alcohol (sedation, hypothermia, toxicity) appeared to be controlled by different genes.58,60 QTL analysis in SS X LS hybrids traced intrastrain variations in the differences in alcohol sensitivity to at least seven or eight loci.62,64 More recent studies have confirmed this result with a total of seven QTLs, accounting for 60% of the variation between SS and LS strains.63,64 These pharmacogenetics studies are nevertheless considerably restricted: The relationship between these mouse models and the genetic control of alcohol-induced sleep is uncertain because sleep and circadian rhythms in LS and SS animals have not been studied. Moreover the effects of benzodiazepines and alcohol on sleep appear to be indirect and highly dependent on prior sleep and waking history. The 24-hour amount of sleep shows highly significant differences between inbred mouse strains; for example AKR mice sleep approximately 3 hours more than DBA mice over the 24-hour day.11 Obviously, as for the difference in the period of circadian rhythms, not a single gene may be found to account for this difference but many genes with complex interactions. A series of experiments have been initiated to dissect different phenotypic aspects of sleep in mice through QTL analysis. The distribution of NREM and REM sleep time over 24 hours also varies according to the genetic background. AKR, C57BL/6 and C57BR strains are characterized by long episodes of REMS and C57BL/6, BALB/c and 129/Ola strains had the longest episodes of NREMS.11 At the other end of the spectrum, the DBA/2 strain is characterized by short episodes of NREM and REM sleep resulting in a very fragmented sleep.11 Genetic studies in F1 and F2 mice also indicate the complex nature of the genetic control of these parameters, implicating the presence of several genes. Regulation of NREMS clearly differs from that of REMS; the genetic control of these two types of sleep probably reflects this difference.11,16,20 A first QTL analysis of REMS identified several loci involved in the variability between two inbred mouse strains (BALB/c and C57BL/6).16 The loci were different for the duration of diurnal REMS (chromosome 7), nocturnal REMS (chromosome 5, near the clock gene), and for total REMS time during 24 hours, suggesting that several genes are involved in the expression and regulation of REMS. Another group using the same methodology reported other QTLs (on chromosomes 4, 16, and 17) for differences in the diurnal amount of REMS between the same mouse strains.65 A QTL analysis between two other mouse strains, C57BL/6 and DBA/2, found another QTL on chromosome 1 with a highly significant effect on the amount of REMs in the 12-hour light period.54 Numerous genes are therefore implicated in the regulation of REMS; about 50% of the variance in REMS time is explained by the presence of at least six different loci. It is worth noting that no significant QTL is so far found for the amount of NREMS.16,20,54 One of the genomic regions relevant

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to REMS regulation contains the candidate gene albumin-D binding protein (DBP). This gene is a transcription factor expressed with strong circadian rhythmicity.66 DBP knockout mice are characterized by a reduction of their circadian period and by an overall drop in locomotor activity. The study of their sleep, apart from revealing a total sleep time identical to that of wild-type mice, showed a reduction in circadian amplitude of NREMS, as well as alterations in REMS regulation.38

QTL ANALYSIS OF THE SLEEP EEG The amplitude and frequency of rhythmic EEG activity (delta, theta, and alpha oscillations) are well quantified using spectral analysis algorithms such as the FastFourier Transform. Spectral analysis of EEG activity during sleep has demonstrated significant variations between different mouse strains for both NREM and REM sleep.9,11,67 EEG activity during NREMS is usually characterized by slow waves and sleep spindles associated with high spectral power in the 0.5–4.5 Hz and the 12–15 Hz, respectively. The main EEG activity in REMS, especially in rodents, is in the theta rhythm frequency range (5–9 Hz). The dominant frequency of theta oscillations depends strongly on genetic background. The theta peak frequency (TPF) is particularly slow (5.75–6.25 Hz) in A/J, C3H/HeJ, AKR/J, and BALB/cByJ (C) strains, and fast (6.75–7.75 Hz) in DBA/2J, SPRET/Ei, PL/J, LP/J, C57BL/6J (B), 129/Ola, and C57BR/cdJ strains.9,11,67 The TPF during REMS shows the highest phenotypic difference between inbred strains and almost all of the inter-strain variability can be attributed to genetic effects (F10,66 = 56, p < 2.10–8; heritability = 0.97).9 To follow the segregation of TPF, a slow (C) and a fast (B) TPF strain were reciprocally crossed. TPF in CXB- and BXC-F1 mice was similar to that of B6 and significantly different from C mice, indicating that the C allele was recessive without maternal effect. An F2 population was generated by crossing CXB-F1 mice to map the underlying genes. The distribution of TPF was approximately normal, which prompted us to use this phenotype in a QTL analysis. QTL interval mapping with 89 polymorphic markers identified a single, highly significant location on midchromosome 5 (LOD score = 11.5, p < 4.10–2), explaining over 65% of total variance.9 Accordingly a backcross population was generated by crossing CXB-F1 mice to the progenitor strain C (CXB-BC). Based on the F2 data, CXB-BC mice were classified as slow or fast TPF, if 6.5 Hz or slower, or 6.75 Hz or faster. Polymorphic markers of chromosome 5 were genotyped in these CXBBCs and a strong linkage to D5Mit240 was found (LOD score = 8.8, p <2.10–10). Genotyping of 200 additional CXB-BC mice and selective sleep recording in 31 recombinant mice narrowed the region of interest to a 2.4 cM interval. Candidate genes within the region included the short-chain acyl-coenzyme A dehydrogenase (Acads).68 A spontaneous mutation in Acads appeared in the BALB/cByJ subline with a highly significantly difference in TPF when compared to the BALB/cBy wildtype subline (7.10 ± 0.22 Hz).9 There was no correlation between waking and REMS TPF, and there was no significant linkage between the waking TPF and any markers of chromosome 5, suggesting that the Acads mutation has a highly specific effect on TPF during sleep

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only. Waking TPF showed no significant difference between the mutant and the wild type BALB/cBy (mutant = 7.6 ± 0.27 Hz, wild type = 7.7 ± 0.36 Hz), clearly indicating that the Acads mutation affects theta oscillations only during sleep.9 This unexpected finding indicates a major role for the mitochondrial b-oxidation during sleep, which is fatty acid chain-length specific because long-chain acyl-coenzyme A dehydrogenase (Acadl) deficiency does not affect theta frequency. High-density cDNA microarrays were then used to evaluate changes in the brain gene expression caused by the Acads mutation.9 Glyoxalase I (Gl01) gene, involved in metabolic detoxification, was identified as the only gene up-regulated in the brain of Acads deficient mice. Increased Gl01 expression was evidenced in all inbred strains that display slow theta oscillations during REMS. Both the slow theta and the increased glyoxalase I expression could be partially reversed by acetyl-L-carnitine treatement, probably through detoxification of excess short-chain fatty acids.9 Brain short-chain fatty acid b-oxidation during sleep might represent a previously unrecognized metabolic pathway in the adult brain with potential role in REMS and brain maturation.

QTL ANALYSIS OF HOMEOSTATIC REGULATION OF SLEEP Two main processes regulate sleep: a circadian and a homeostatic process.69 Delta power (activity in the 0.5–4 Hz range) is an EEG measure of NREMS intensity and a reliable quantitative marker of NREMS homeostasis, thus, sleep loss evokes a proportional increase in delta power, and excess sleep a decrease.70–72 The dynamics of this homeostatically regulated process, referred to as Process S,4,69 have been studied extensively, and mathematical simulations that quantify the relationship between the sleep-wake distribution and delta power predicted the time course of delta power remarkably well.73–75 The neurophysiological substrate of the homeostatic process is unknown, however, and genetic studies might help to elucidate the nature of what is depleted during wakefulness and recovered during NREMS. A recent study involving six inbred mouse strains deprived of 6 hours of sleep, in strictly similar circumstances, revealed significant differences in the intensity of the homeostatic rebound of NREMS.74 The time constant for the accumulation of a need for NREMS varied between genotypes but not for its decline during NREMS. To confirm these predictions, a dose-response experiment was performed in which sleep was deprived of for varying durations (i.e., dose) to verify that the delta power increase in subsequent NREMS (i.e., response) depends on both the duration of prior wakefulness and genotype. The segregation of the rebound of delta power after sleep deprivation in 25 BXD recombinant inbred strains by quantitative trait loci (QTL) analysis was performed and established that additive genetic factors accounted for 67% of the total variance (i.e., heritability in inbred strains).74,76 After performing a genome-wide scan (788 MIT-markers), two genomic regions were identified, a significant QTL was found on chromosome 13 (LOD = 3.57, nominal correlation p <0.00005, genome-wide <0.01) and a suggestive QTL on chromosome 2.74 The QTL on chromosome 13 has a large effect, explaining 49%

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of genetic variance in delta power rebound and 33% of total phenotypic variance. These findings were not related to or influenced by differences in REM or NREM sleep time expressed during the period over which delta power was calculated. These results demonstrate that the increase of NREMS need is under a strong genetic control and provide a basis for identifying genes (significant QTLs) underlying NREMS homeostasis. Homeostatic regulation of sleep has also been addressed with reversed genetic studies. Parallel studies of the homeostatic regulation of both REM and NREM sleep have been conducted in transgenic mice. Mice overexpressing growth hormone show more REMS under baseline conditions but show normal recovery pattern after sleep deprivation.33 Clock mutant mice, in addition to demonstrating important changes in circadian sleep architecture, also present an alteration in homeostatic sleep regulation with a decreased rebound in REMS after sleep deprivation.77 DBP knockout mice also showed a decreased REMS rebound after sleep deprivation without any difference in the rebound of NREMS.38 Mice lacking functional genes for the serotonin-2C receptor,78 Cry 1 and 2,79 and Rab3a46 show an altered NREMS rebound after sleep deprivation. In addition, double cry 1 and 2 knockout mice show a higher amount of NREMS compared to wild-type controls79; therefore the loss of circadian genes (at least Cry 1 and 2) does not only affect the circadian rhythms (see section titled Genetics of Circadian Rhythms) but also the sleep homeostatic process. Recently loss-of-function mutations in other circadian genes (Per, Tim, Clock, and Cycle) in fruit flies were also found to be responsible for a higher sleep rebound after sleep deprivation compared to wild-type flies.80 Cycle-mutant fruit flies show a disproportionally larger sleep rebound and die after 10 hours of sleep deprivation, although they are more resistant than other clock mutants to various stressors.80

GENE EXPRESSION STUDIES The first attempt to identify the molecular basis of sleep has been based on the assumption that there must be genes that change their expression as a function of behavioral states (sleep versus wake) and time spent in a particular state. Because sleep need and intensity are homeostatically regulated, sleep deprivation should lead to a change in the expression of those genes, of which the products are necessary for recovery or at least involved in sleep regulation. The genes studied in most detail are the immediate early genes (IEG), generally the proto-oncogene c-Fos. The changes occurring in the IEG expression are interesting in at least two respects: the expression of c-Fos and the other IEG increases with neuronal activity,81 and the level of expression of their proteins or RNA messengers (mRNA) in discreet brain regions can be determined, thus providing a map of their variations as a function of sleep or wakefulness. Moreover as IEG products are themselves transcription factors, they also alter the expression of numerous other genes. During the day cFos expression in the brain correlates directly with the rest/activity cycle. In nocturnal rodents, expression is high at night and low during the day in most regions of the brain.82,83 This effect is reversed when the animals are deprived of sleep during the day, provoking a sleep rebound the following night.83–85 Diurnal rodents present

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the opposite profile of c-Fos expression, with high levels during the day and low levels at night. IEG genes have also been used to dissect the neuroanatomy of sleep and wakefulness more finely. The locus coeruleus, in particular, appears to play an important role, not only because c-Fos expression changes according to states of sleep and wakefulness in this nucleus, but also because the locus coeruleus appears to control a large part of the c-Fos expression in the entire forebrain in the waking state. Unilateral lesions of the locus coeruleus reduce c-Fos level during wakefulness ipsilaterally rather than controlaterally.86,87 The reduced levels of c-Fos as well as Ngfi-A in wakefulness on the lesioned side is comparable to the levels observed during periods of prolonged sleep. Studies have also been conducted during pharmacological REMS, induced by injecting cholinergic agonists into the pontine reticular formation. This manipulation activates the transcription of the c-Fos gene in several nuclei implicated in the regulation of REMS.88,89 Recently an exception to this wakefulness and high c-Fos levels correlation was objectified in certain cells in the ventrolateral preoptic region, the neurons expressing high c-Fos levels during sleep.90 These neurons probably play a key role in initiating NREMS. Despite such correlations, the functional role of IEGs has yet to be established. In fact only two studies suggest a direct c-Fos role in the regulation of sleep.86,89 The former concerns the observation of a reduction in spontaneous sleep and in sleep rebound after deprivation in c-Fos knockout mice. In the second study, c-Fos antisense oligonucleotide injections in the medial preoptic region reduced c-Fos protein levels and increased wakefulness the following day. Other approaches are needed in gene expression during sleep, such as substractive hybridization, PCR differential display (cDNA display), cDNA microarrays (DNA chips), or real-time RT-PCR (TaqMan) methods.29,87 The substractive hybridization method has been used on rats deprived of sleep for 24 hours.91 Four mRNA clones were isolated with lower levels after sleep deprivation and six with higher mRNA levels. An analysis of the structure of two of these clones identified neurogranin and dendrin proteins.92,93 Several laboratories have used molecular biology techniques, sometimes with sleep deprivation, to determine alterations in the transcriptional activity of several genes including growth factors. Thus variations in mRNA levels over 24 hours of GHRH in the hypothalamus94 of BDNF and its receptor in the hippocampus95 were demonstrated. Adding sleep deprivation sometimes increased these variations. For example, mRNA and GHRH levels and those of the adenosine A1 receptor, respectively, increase at paraventricular and basal telencephalic level after sleep deprivation.96,97 Furthermore, mRNA and interleukin 1 beta protein only increased significantly in the hypothalamus and the brainstem after sleep deprivation.98 Other experiments have been carried out with selective REMS deprivation.99–101 Finally, corticostatin and hypocretin proteins, strongly implicated in the control of different states of sleep and wakefulness have been identified using mRNA screening approaches, demonstrating the importance of this field of investigation.39,102 It should be noted that paradoxically, prepro-hypocretin mRNA levels are not modified in the hypothalamus after 6-hour sleep deprivation.103

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The mammalian brain expresses roughly half of the estimated 30,000 genes, so it is likely that many genes change their level of expression during the states of sleep and wakefulness.104 These alterations in gene transcriptional activities might reflect a change in neuronal activity, although the role of these genes in sleep regulation is still difficult to ascertain. The sensitization of these variations by sleep deprivation merely reflects the effects of prolonged wakefulness on the brain. In addition to using sleep deprivation techniques, future studies should systematically take recovery after sleep deprivation into account, considering the marked NREMS rebound, which may also coincide with alterations in the level of gene expression.79,105 In any case, these studies may reveal negative results because alterations in the system may occur at a post-transcriptional level.

GENETICS OF CIRCADIAN RHYTHMS Circadian rhythmicity is a virtually universal property in all unicellular organisms, plants, and animals.106 The alterations in circadian rhythms related to variations in our environment are now well established, with temperature and light able to affect our internal biological clock. However a number of genetic factors have also been implicated in regulating these rhythms. Behavioral genetic research on circadian rhythms is one of the most advanced in biology. This is greatly facilitated by the fact that circadian rhythms in mammals are generated or synchronized by a discrete region of the hypothalamus, the suprachiasmatic nuclei (SCN).107 Lesioning of the SCN abolishes all rhythmicity, and circadian fluctuations are restored by transplanting fetal hypothalamic tissue to lesioned animals.108 This furthers the idea that the SCN is necessary and sufficient for the generation of behavioral rhythms. It can thus be demonstrated that the neuronal, metabolic, and neurochemical activity of the SCN themselves vary in a circadian fashion even when the tissue is isolated in vitro.107 When the activity of several of these neurons is recorded simultaneously, each cell presents a different circadian rhythmicity (period or phase) with no apparent synchronized activity.109,110 More recently the cloning and characterization of mammalian clock genes have revealed that they are expressed in a circadian manner throughout the body and even in cultured cells and organs.111–113 It is now generally accepted that peripheral cells contain a circadian clock, which is similar to the one present in SCN neurons, except that the latter seems to be selfsustained. It is still unclear how the central SCN clock synchronizes these peripheral clocks, albeit humoral signals appear to be crucial. Unlike the rhythms entrained by the pacemaker SCN, some rhythms of peripheral origin may be entrained by our eating habits (dominant Zeitgeber for peripheral circadian oscillators).113 The phase of peripheral clocks can be completely uncoupled from the SCN by restricted feeding. The glucocorticoid hormones seem to inhibit the uncoupling of peripheral and central circadian oscillators by altered feeding time.113 Genetic research in this field has already led to the isolation of several genes, whose mutations in the fruit fly Drosophila melanogaster can considerably alter the period of circadian rhythms or completely abolish rhythmicity as a function of the implicated allele.114–118 In mammals, notably in mice, considerable variations exist in circadian period length among inbred lines: Some are particularly long, others

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particularly short117,119; i.e., C57BL and BALB/c mice have a 1-hour period difference.55,56 Numerous genetic factors are likely to be involved to account for these different phenotypes. Mutations responsible for altering circadian rhythmicity have been reported in mammals.106,117,119,121 Two genes that are essential for the production of behavioral rhythmicity, Clock (Circadian Locomotor Output Cycles Kaput) and Wheel, have indeed been isolated, both resulting from a mutagenesis screens in mice using the ENU mutagen.117,121 The mutant Wheel gene (chromosome 4 of the mouse) exerts a dominant effect and causes complex neurological disruption associating hyperactivity, rotating behavior, and circadian rhythmicity.121 The mutant Clock gene (chromosome 5 of the mouse) exerts a semidominant effect, responsible only for altering the circadian period, which becomes abnormally long.117 These mutant mice are capable of following a rhythm entrained by alternating light/dark but loose endogenous circadian rhythm in constant environment (dark/dark). The Clock gene was functionally identified using a combination of techniques including positional cloning45 and a transgenic rescue approach.122 The CLOCK protein has sequence motifs with direct DNA binding properties (the basic Helix Loop Helix or bHLH domain), enhancing its implication in regulating the transcription of several genes. In another rodent species, the golden hamster, a spontaneous semidominant mutation of the Tau gene has been responsible for the isolated alteration of the circadian period.119 This mutation in the homozygous state exclusively induces a shorter circadian period (approximately 20 hours) resistant to variations in alternating light and dark. This gene codes for a protein belonging to the casein kinase Ie family; the mutation may be responsible for deactivating the protein via its inability to fix and phosphorylize PER protein.123 After cloning the gene Clock, three homologues of the Drosophila Per gene, coding for PERIOD protein, were isolated in the mouse and in humans, mPer1, mPer2, and mPer3.124–127 These Per genes are expressed in several cerebral regions, but significant rhythmic daily fluctuations are only found in the SCN, indicating their implication in generating circadian rhythms.124,127,128 Only mPer1 and mPer2 appear to be strongly implicated because their knockout mice develop abnormal circadian rhythmicity. mPer3 has only added effects. Two homologues of the Drosophila Cry gene, mCry1 and mCry2, coding for photoreceptive flavoproteins Cryptochromes, have been isolated in mammals with demonstrated oscillatory activity,129,130 moreover these genes appear to have stronger rhythmic activity in mice than in Drosophila, with highly altered circadian periodicity in knockout mice. Proteins mCRY1 and -2 are transcription inhibitors of their own genes, but also of mPER1, 2, and 3 via the protein complex CLOCK-BMAL1.125 More precisely mCRY1 and mCRY2 are nuclear proteins that interact with each of the mPer proteins, translocate each mPER protein from cytoplasm to nucleus, and are rhythmically expressed in the SCN. The mPER and mCRY proteins appear to inhibit the transcriptional complex differentially. Analysis of Cryptochrome inhibition of CLOCK-BMAL1 mediated transcription shows that the inhibition is through direct protein-to-protein interactions, independent of the PERIOD and TIMELESS proteins. PER2 is a positive regulator of the BMAL1 loop; and Cryptochromes are the negative regulators of the PER and CRY cycles involved in the negative limb of the feedback loop. Hence two other genes affecting circadian rhythms have been

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localized in mammals, Bmal1 (homologue of cyc gene in Drosophila) coding for Bmal1 protein, which also belongs to the bHLH family131,132 and mTim gene. A single mTim gene has been isolated in humans and mice, but its function, unlike its homologue tim in Drosophila, is still unknown.133 Because knockout mice for mTim die at the embryonic stage, it has not been possible to distinguish any transcriptional rhythmic activity, as light does not appear to exert any effect on protein mTim, and the nuclear translocation of mPer would depend on heterodimerization between proteins mPer and mCry and not mTim.130 The role of Drosophila protein TIM seems to be equivalent to that of CRY in mammals. Finally the two proteins CLOCK and BMAL1, which are activators of mPer transcription and coded respectively by mClock and Bmal1, also have strong homology with Drosophila genes dClock and Cyc; however the activity of these genes differs between the two species. In Drosophila the expression of dClock follows a circadian rhythmicity with oscillation regulated by the protein complex PER-TIM-CLOCK inhibiting its own mRNA synthesis. In mice the transcriptional activity of Clock is not rhythmical, contrary to the Bmal1 gene whose regulation is thought to be carried out by mPer2. Recently the orphan nuclear receptor REV-ERB alpha (transcription factor) was found to be a major regulator of cyclic Bmal1 transcription.134 The circadian REV-ERB alpha expression is controlled by components of the general feedback loop in which BMAL1 and CLOCK, players of the positive limb, activate transcription of the cryptochrome and period genes, components of the negative limb. Thus REV-ERB alpha constitutes a molecular link through which components of the negative limb drive antiphasic expression of components of the positive limb. REV-ERB alpha influences the period length and affects the phaseshifting properties of the clock, but it is not required for circadian rhythm generation. On the whole, and irrespective of the species studied, Drosophila or mouse, Per and Clock genes are central to the cellular machinery for circadian regulation. It is thus likely that PER and CLOCK proteins work together in producing 24-hour rhythmicity in the SCN. The effect of the environment and, more precisely, the alternation between light and darkness in regulating circadian rhythmicity is well known, and its mechanism of action is beginning to be understood. In the mammalian retina, besides the conventional rod-cone system, a melanopsin-associated photoreceptive system exists that conveys photic information for accessory visual functions such as circadian photoentrainment.135 Melanopsin is expressed in retinal ganglion cells (RGCs) which are intrinsically photosensitive. Melanopsin knockout mice entrained to a light and dark cycle, phase-shifted after a light pulse and increased circadian period when light intensity increased; however the magnitude of these behavioral responses in knockout mice was 40% lower than in wild-type mice.136 Although melanopsin is not essential for the circadian clock to receive photic input, it contributes significantly to the magnitude of photic responses. The effect of the alternation between light and darkness also involves the transcription regulation of several genes. It seems that light has a direct influence on the TIM protein in Drosophila and CRY in mammals via phosphorylation, ubiquitination, and finally degradation.129 The transcriptional activity of Per and Tim genes is not altered by light, however the formation of the protein complex PER-TIM and

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its nuclear translocation are clearly affected by light-dark alternation. But the intermittent presence of light is not essential for the generation of rhythmic activity. The duration of formation of the PER-TIM complex once in the nucleus appears to be determined by two different endogenous processes: a self-regulation phenomenon and the presence of the Double-Time (DBT) protein. DBT is capable of binding to PER in vitro and in Drosophila cells, suggesting that a physical association of PER and DBT regulates PER phosphorylation and accumulation in vivo.137 DBT belongs to the same family as TAU protein, the casein kinase Ie family.123,138 The function of these two proteins, in Drosophila and in mammals, appears to be relatively well conserved; this kinase protein allows PER phosphorylation, the inhibition of its translocation to the nucleus, and the reduction of its stability. The expression of genes implicated in circadian regulation also occurs outside the central nervous system, in Drosophila as in mammals.113,139 However, although these genes function independently of each other, they remain photosensitive in Drosophila, contrary to the case for mammals. An accumulation of the neuropeptide vasopressin relies on the presence of other proteins CLOCK and CYC, and establishes the circadian phase via coordination of the rhythmic activity of different neurons.112,140 In mammals numerous studies have pointed to the presence of soluble factors diffusing from the SCN to other cerebral regions, thus entraining sleep and wake rhythms and locomotor activity. These factors are in the process of being identified. One of the factors, TGF alpha, was recently isolated in relation to a yeast secretion tap system.141 This peptide appears to play an inhibiting role in locomotion by acting on the subparaventricular zone. Lastly it appears that certain peripheral circadian oscillators depend on food intake through a hormonal glucocorticoid signal.113

CONCLUSIONS Sleep is a complex behavior both in its manifestation and regulation, which can be studied at many different levels. The complexity of sleep-wake regulation, in addition to the many environmental influences, implies a predisposing genetic determinism that is beginning to be understood. Most of the current progress in the study of sleep genetics comes from animal (mainly mice and Drosophila) studies. Multiple approaches using both animal models and genetic techniques are needed to determine new sleep genes and molecular bases of sleep. Over the past few years, a revolution in the understanding of the molecular basis of circadian rhythm generation has led to the identification of a number of core clock genes and the development of feedback models that explain how these core components interact to generate a circadian rhythm. Recent progress in molecular genetics and the development of detailed human genomic map have already led to the identification of genetic factors in the contribution of the pathology of sleep disorders. At least eight human orthologs of mouse core clock genes have been identified, and a mutation in hPer2 is responsible for the autosomal-dominant familial advanced-sleep-phase syndrome in humans. In addition, the successful identification of a mutation in the hypocretin-2 receptor underlying canine narcolepsy, leading to the discovery of the hypothalamic hypo-

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cretin (orexin) neurotransmitter system as a key target for human narcolepsy, is one of the best examples of how a genetic approach can not only further our understanding of the pathophysiology of sleep disorders but also bring new insights into sleep physiology.

ACKNOWLEDGMENTS Y.D. is supported by the “Association pour l’Etude du Sommeil,” MontpellierFrance, M.T. is supported by The Swiss National Science Foundation and the Geneva University Hospitals, P.F. is supported by The NIH Heart, Lung, and Blood Institute.

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134. Preitner, N. et al., The orphan nuclear receptor REVERBalpha controls circadian transcription within the positive limb of the mammalian circadian oscillator, Cell, 110, 251, 2002. 135. Berson, D.M. Strange vision, ganglion cells as circadian photoreceptors, Trends. Neurosci., 26, 314, 2003 136. Ruby, N.F. et al., Role of melanopsin in circadian responses to light, Science, 298, 2211, 2002. 137. Kloss, B. et al., The Drosophila clock gene doubletime encodes a protein closely related to human casein kinase, I epsilon, Cell, 94, 97, 1998. 138. Keesler, G.A. et al., Phosphorylation and destabilization of human period I clock protein by human casein kinase I epsilon, Neuroreport, 11, 951, 2000. 139. Silver, R. et al., A diffusible coupling signal from the transplanted suprachiasmatic nucleus controlling circadian locomotor rhythms, Nature, 382, 810, 1996. 140. Park, J.H. et al., Differential regulation of circadian pacemaker output by separate clock genes in Drosophila, Proc. Natl. Acad. Sci. USA, 97, 3608, 2000. 141. Kramer, A. et al., Regulation of daily locomotor activity and sleep by hypothalamic EGF receptor signaling, Science, 294, 2511, 2001.

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8

Searching for Sleep Mutants of Drosophila Melanogaster Chiara Cirelli and Giulio Tononi

CONTENTS Introduction Forward Genetics to Understand Sleep Regulation and Functions Fly Sleep Shares Many Features with Mammalian Sleep Complex Behaviors and Single-Gene Mutations The Genetics of Sleep Short Sleepers, Sleep Deprivation, and Sleep Restriction The Sleep Phenotype in Wild-Type Drosophila Lines The Sleep Phenotype in Drosophila Mutant Lines Conclusions References

INTRODUCTION Sleep is present in all species where it has been studied, but its functions remain unknown. A sufficient amount of sleep constitutes a fundamental biological need. Curtailing the amount of sleep in normal sleepers affects performance, vigilance, memory, and health. Like all complex behaviors, sleep is both environmentally modulated and genetically determined; however the responsible genes have not been discovered. To identify them we have initiated a genetic screening for short sleepers in the fruit fly Drosophila melanogaster. Mutagenesis screening in drosophila has helped unravel cellular mechanisms that are highly conserved across species; e.g., those controlling development, aging, stress, memory, and circadian rhythms. For the past few years, our laboratory and others have shown that fly sleep shares many key features with mammalian sleep. As in mammals, sleep in drosophila is characterized by increased arousal thresholds and by changes in brain electrical activity. Fly sleep is regulated independent of the circadian clock, modulated by stimulants and hypnotics, affected by age, and is associated with changes in brain gene expression similar to those observed in mammals.

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In the past 2 years, our laboratory has screened ~7000 mutant drosophila lines, most of which were carrying single-gene mutations. We found that the amount and regulation of sleep are highly conserved: Almost all flies sleep 400–800 min in 24 hours and show increased sleep duration and continuity after sleep deprivation. We have also identified several short sleeper lines that sleep <280 min in 24 hours. The short sleep mutation is often due to the genomic insertion of a P element whose mobilization reverts the flies to normal sleep, suggesting a single gene effect. The current work is aimed at characterizing these mutant lines genetically, molecularly, and behaviorally to identify the genes responsible for the short sleep phenotype and investigate the molecular pathways controlled by these genes. This research will help to identify the molecular mechanisms regulating the need for sleep and provide novel clues to its functions.

FORWARD GENETICS TO UNDERSTAND SLEEP REGULATION AND FUNCTIONS The importance of sleep is strongly suggested by the time spent in this state and by its ubiquitous occurrence in all animal species studied so far (Rattenborg et al., 2000; Tobler, 2000). In both humans and animals, sleep need is tightly regulated and sleep pressure becomes overwhelming after just a few hours without sleep (Borbély and Achermann, 1999). Total sleep deprivation and chronic sleep restriction cause significant cognitive deficits, including decrease in attention and short-term memory, speech impairment, and inflexible thinking (Horne, 1988; Forest and Godbout, 2000; Belenky et al., 2003; Van Dongen et al., 2003). Sleep loss also affects host defense systems and can lead to a decrease in glucose tolerance and an increase in peripheral metabolic rate and in cortisol level (Spiegel et al., 1999; Rogers et al., 2001). If sustained for several days, sleep deprivation is fatal in rats and other mammals (Rechtschaffen et al., 1989; Rechtschaffen and Bergmann, 2002). Like hunger or thirst, the drive for sleep appears to satisfy an elementary need, but unlike eating and drinking, the purpose of sleep remains obscure: Sleep is the one major biological process whose functions have not yet been identified (Rechtschaffen, 1998). It is generally thought that sleep is “by the brain and for the brain” (Horne 1988; Hobson 1989). Some evidence indicates that sleep may represent a favorable time for brain protein synthesis (Ramm and Smith, 1990; Nakanishi et al., 1997). Another possibility, suggested by behavioral studies, is that sleep may promote memory consolidation (Stickgold et al., 2001; Walker et al., 2002). It is also widely thought that the functions of sleep may ultimately relate to cellular and molecular aspects of neural function (Moruzzi, 1972; Rechtschaffen, 1998; Cirelli and Tononi, 2000; Steriade and Timofeev, 2003). In line with this assumption, we have used mRNA differential display and, more recently, high-density microarrays to perform a genome-wide expression profiling to identify brain transcripts whose expression changes as a function of sleep and wakefulness (Cirelli and Tononi, 2000; Cirelli et al., 2004). We found that the expression of hundreds of genes is modulated in the brain as a function of behavioral state and independently of circadian time, supporting the notion that wakefulness and sleep differ not only at the behavioral, electrophysiological, and metabolic level, but also at the molecular level.

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Gene expression studies can only provide correlative evidence, and any link between a putative sleep function and a specific gene (or gene category) needs to be supported by causal experimental approaches. Forward genetic approaches consist in mutating (ideally) all the genes expressed in the brain and in studying how each single mutation affects sleep, its regulatory mechanisms, and its functional consequences. A systematic mutagenesis screening of sleep mutants in mammals remains a daunting task (Tafti and Franken, 2002; Dugovic et al., 2003), but it has recently been shown that drosophila sleep shares many features with mammalian sleep (Hendricks et al., 2000; Shaw et al., 2000). This finding has advanced our knowledge of the phylogeny of sleep, supporting the notion that sleep fulfills at least one fundamental function in many divergent animal species. Moreover D. melanogaster may now be used as a powerful tool for the genetic dissection of sleep with forward genetics, an approach that has greatly benefited research on circadian rhythms. In forward genetics either a P element insertion or a chemical such as N-ethyl-Nnitrosurea (ENU, in mice) or ethyl methanesulfonate (EMS, in flies) is used to mutate at random the whole genome; this is followed by a high throughput screening of all mutant offspring to detect major effects on the phenotype of interest. The power of forward genetics is that mutant screens make no assumption concerning the mechanisms underlying a behavior and require only a clear phenotype to be expressed. For the past 2 years, our laboratory has embarked on a large-scale mutagenesis screening in search for flies that need little sleep or show abnormal homeostatic response after sleep deprivation (Cirelli et al., 2003). The final goal is to screen as many mutant fly lines as there are fly genes. So far we have screened ~7000 mutant lines and shown that sleep amount and response to sleep deprivation are highly conserved phenotypes in wild-type flies as well as in mutant lines. Most importantly this work has demonstrated that sleep mutants can be isolated and that the identification of the corresponding genes is feasible.

FLY SLEEP SHARES MANY FEATURES WITH MAMMALIAN SLEEP Sleep is a complex integrative phenomenon that needs to be defined using multiple criteria. As mentioned above, drosophila had been extensively used in circadian research long before it became of interest to sleep researchers (Konopka and Benzer, 1971). Circadian studies had shown that fruit flies are active and move around during the day and much less so during the night, but until 3 years ago it was not known whether the sustained periods of immobility during the night represented a sleeplike state or just quiet wakefulness; i.e., a state of behavioral inactivity in which the ability to respond to the environment is preserved. The demonstration that flies sleep, much as other animals and humans do, was achieved using behavioral, pharmacological, molecular, and genetic techniques (Hendricks et al., 2000; Shaw et al., 2000). More recently classical electrophysiological methods have proven that EEG correlates on sleep and wakefulness are also present in the fruit fly (Nitz et al., 2002). Fly behavior was monitored using visual observation, an ultrasound activity monitoring system, and an automatic infrared system (Drosophila Activity Monitoring System, DAMS; Trikinetics, Waltham, MA). The ultrasound method (Shaw et

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al., 2000) allows a continuous, high-resolution measurement of the behavior of a single fly housed inside an ultrasound standing wave chamber. Whenever the fly moves its head, wings, or limbs, a perturbation of the standing wave is produced and is counted as a movement. Although very precise, this method is impractical for evaluating sleep-waking parameters in a large-scale project. The DAMS is instead designed to monitor hundreds or thousands of flies simultaneously. One DAMS monitor contains 32 glass tubes, each housing a single fly and enough food for 1week recording (Figure 8.1 A). As each fly moves back and forth in its tube, it interrupts an infrared light beam that bisects the tube. Each crossing is counted as a movement and the number of movements every minute are summed up and expressed as an activity index. Both the ultrasound and the infrared system had been validated by visual observation and give similar results: Flies are mostly active and moving around during the day, and during the night they show long periods of immobility that can last several hours. Immobility qualifies as sleep only if it is accompanied by a reversible increase in arousal threshold. Arousal threshold in flies has been measured using vibratory, visual, auditory (Shaw et al., 2000; Nitz et al., 2002), and more recently, thermal stimuli (Huber et al., 2004). In all cases it was found that flies that had been behaviorally awake immediately before the stimulus readily responded to low and medium stimulus intensities. By contrast flies that had been behaviorally quiescent for 5 min or more rarely showed a motor response, although they quickly responded when the stimulus intensity was increased. Thus sleep can be operatively defined in FIGURE 8.1 (See facing page.) Analysis of locomotor activity and sleep in fruit flies. (A) A Drosophila Activity Monitoring System (DAMS) monitor containing thirty-two 6.5-mm (5 mm I.D.) glass tubes, each housing a single fly. (B) Typical pattern of sleep in a population of 96 female wild-type Canton-S flies as measured in a DAMS monitor. DAMS measures activity as counts (number of crossings) per minute. Wakefulness is defined as any period of at least one minute characterized by activity (one or more counts per minute). Based on arousal threshold data, sleep is defined as any period of uninterrupted behavioral quiescence (no counts/min) lasting for at least 5 min. Mean values of the amount of sleep are calculated on consecutive 30-min time intervals, and the time course is graphically shown over the entire day. In female flies most of the sleep occurs at night. (C) Increase in sleep duration following 6,12, and 24 hours of sleep deprivation (SD) in female Canton-S flies (n = 20–40 for each experiment). Each diagram shows the daily amount of sleep for baseline day (blue line), SD day (red line), and the first recovery day after SD (green line). Time and duration of SD are indicated by the red bars below the x axis. An increase in sleep duration is present after all 3 periods of SD, and occurs mainly during the first 6 hours following the end of SD. Flies were maintained in a 12:12 light dark cycle (light on at 8:00 A.M.). (D) To measure sleep fragmentation, a sleep continuity score is calculated, which increases during continuous epochs with no locomotor activity and decreases during epochs with one or more counts of activity. The sleep continuity score is high if sleep is continuous and undisturbed, and low if sleep is fragmented. Blue lines in the upper diagram represents sleep scores for 16 female Canton-S flies during baseline. Green lines in the lower diagram show the sleep score for the same flies the day following 24 hours SD. Note the significant increase in the sleep score immediately after the end of SD. In several flies this increase persists during the following night.

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flies as any period of behavioral quiescence (no counts detected by the DAMS) lasting longer than 5 min (Figure 8.1 B). All animals studied so far show a homeostatic regulation of sleep (Tobler, 1995, 2000). Flies, as well as other invertebrates such as cockroaches (Tobler, 1983; Tobler and Neuner-Jehle, 1992), scorpions (Tobler and Stalder, 1988), and honey bees (Kaiser and Steiner-Kaiser, 1983; Sauer et al., 1999) are no exception. Sleep deprivation can be performed by gentle tapping on the glass tube whenever the fly stops moving for more than 5 min, or automatically. Currently in our laboratory wakefulness is enforced by placing the DAMS monitors vertically within a framed box able to rotate along its major axis under the control of a motor. The box can rotate 180∞ clockwise or counter-clockwise (2–3 revolutions per min). At the nadir of each rotation, the monitors are dropped 1 cm. This causes the flies to fall from their current position to the bottom of the tube. This method can effectively sleep-deprive thousands of flies simultaneously for one or more days.

FIGURE 8.1 (See color insert following page 108.)

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Wild-type flies sleep longer after being sleep-deprived (Figure 8.1 C). As in mammals, this sleep rebound occurs mainly immediately after the end of the sleep deprivation period, is more pronounced after longer (12–24 hours) than after shorter (6 hours) periods of sleep loss, and the recovered sleep only represents a fraction of what was lost (Figure 8.1 C). There is no increase in sleep duration when female flies are subjected to 12 hours of the same stimulation during the day (when they are normally awake), ruling out aspecific effects (Shaw et al., 2000). In mammals sleep after sleep deprivation is also qualitatively different; i.e., is richer in slow-wave activity, a well-characterized EEG marker of sleep intensity and sleep pressure, and less fragmented (there are fewer periods of brief awakenings during sleep) (Borbély and Achermann, 1999; Huber et al., 2000). New evidence from our laboratory shows that in flies sleep continuity is increased and the number of brief awakenings is reduced after sleep deprivation (Biesiadecki et al., 2003; Huber et al., 2004; Figure 8.1 D). The homeostatic regulation of sleep in mammals can be dissociated in part from circadian factors. A similar dissociation between circadian and homeostatic regulation of sleep can be seen in flies in which the central circadian clock has been genetically destroyed by a mutation in one canonical circadian gene; e.g., cycle, period, or Clock. These mutant flies sleep across the entire 24-hour period rather than just at night; however, after 24 hours of sleep deprivation, they still show a sleep rebound (Shaw et al., 2000, 2002). Fly sleep seems to be sensitive to at least some of the same stimulants and hypnotics that modulate behavioral states in mammals. For example, when given caffeine (Hendricks et al., 2000; Shaw et al., 2000) or modafinil (Hendricks et al., 2003), flies stay awake longer. By contrast, when fed with antihistamines, they go to sleep earlier (Shaw et al., 2000). As mentioned above, hundreds of genes change their expression in the rat brain between sleep and wakefulness, suggesting that in mammals sleep and wakefulness differ significantly at the molecular level (Cirelli et al., 2004). A first systematic screening of state-dependent gene expression in drosophila using mRNA differential display suggested that this might also be the case in fruit flies. We identified (Shaw et al., 2000; Cirelli and Tononi, 2001) several wakefulness-related genes in the fly that corresponded to wakefulness-related genes in the rat, including, for instance, those coding for the mitochondrial enzyme cytochrome oxidase C (subunit I), the endoplasmic reticulum chaperone BiP, and the transcription factor Stripe A (homologue to the rat immediate early gene NGFI-A). Whether molecular similarities between flies and rats extend to sleep-related genes is now been tested using high-density cDNA microarrays. Recently Nitz et al. (2002) were able to obtain prolonged recordings of local field potentials (LFPs) from the medial part of the fly brain between the mushroom bodies. They found that LFPs from awake, moving fruit flies are dominated by spikelike potentials and that these spikes largely disappear during the quiescent state when arousal thresholds are increased. Targeted genetic manipulations demonstrated that LFPs had their origin in brain activity and were not merely an artifact of movement or electromyographic activity. Thus as in mammals, wakefulness and sleep in fruit flies are accompanied by different patterns of brain electrical activity. Sleep in mammals is prominent in the very young, stabilizes during adolescence and adulthood, and declines during old age. Sleep in drosophila follows a similar

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pattern (Shaw et al., 2000). On the first full day after eclosion, the amount of sleep is high but declines steadily until day 3, when it reaches an adult pattern. As the flies ages, the amount of sleep during the night declines and by 33 days of age is significantly below that found in young adults (Shaw et al., 2000). Thus, as for mammalian sleep, sleep in drosophila is characterized by increased arousal threshold, changes in brain electrical activity, and is homeostatically regulated independent of the circadian clock. As in mammals, sleep is abundant in young flies and it is reduced in older flies, and it is modulated by stimulants and hypnotics. Several molecular markers modulated by sleep and wakefulness in mammals are also modulated by behavioral state in drosophila.

COMPLEX BEHAVIORS AND SINGLE-GENE MUTATIONS Complex biological processes such as development, learning and memory, and aging are both environmentally modulated and genetically determined. It is often assumed that complex behaviors are under polygenic control. It is also widely assumed that the mechanisms underlying complex behaviors may differ in simple organisms, such as invertebrates, compared to mammals in general and humans in particular; however several recent examples demonstrate that complex behaviors can be strongly regulated by single genes and that the cellular and molecular mechanisms underlying complex biological processes are often shared between invertebrates and humans (Sokolowski, 2001). The completion of the sequencing and annotation of the fly, mouse, and human genomes has made these findings less surprising. All the major gene families are present in both mammals and nonmammalian species and the total number of genes is much more similar between species than previously thought: The human genome contains ~40000 genes, compared to ~30000 and 14000 genes in the mouse and fly genome, respectively. The majority of fly genes are shared with humans. It is becoming increasingly apparent that the vertebrate genome arose from the amplification of a core set of genes not much larger than that of the fly; for instance, the sequence diversity between the various potassium channels is greater within either drosophila or mouse than the divergence of a particular channel between drosophila and mouse (Miklos and Maleszka, 2000). The majority (77%) of the genes involved in human diseases have fly counterparts (Reiter et al., 2001), and the expression of human genes into flies very often results in phenotypes that mimic human diseases (e.g., human a-synuclein in drosophila causes a phenotype that resembles human Parkinson’s disease; Auluck et al., 2002). Moreover single-gene mutations identified in drosophila have been instrumental in understanding complex biological processes in mammals, including humans (Benzer, 1971). Compelling examples include aging, memory, and the circadian clock. In drosophila, a mutation in the gene methuselah or in the gene Indy (for I’m not dead yet) results in a significant (up to twofold) increase in the average adult life span (Lin et al., 1998; Rogina et al., 2000). Indy is involved in intermediary metabolism (Rogina et al., 2000), and caloric restriction is the only intervention that extends life span in mammals, suggesting that flies and mammals share at least one molecular mechanism to extend life span. Forward genetics in drosophila has

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identified several single-gene mutations of the cAMP and CREB pathways that affect learning and memory (e.g., Waddell and Quinn, 2001; Sanyal et al., 2002). Related studies in mice have shown that cAMP and CREB are also crucial for memory formation in mammals (e.g., Barco et al., 2002; Kida et al., 2002). Finally, Konopka and Benzer in 1971 showed that a single-gene mutation of the period locus can abolish a complex behavior such as locomotor and eclosion rhythms. Since then mutagenesis screening in both flies and mice have identified all the currently known canonical circadian genes and have demonstrated that all the major components of the molecular clock are shared between drosophila and mammals (e.g., Blau, 2003).

THE GENETICS OF SLEEP Several sleep disorders, from narcolepsy and somnambulism to REM sleep behavior disorder and fatal familial insomnia, are under strong genetic control (O’Hara and Mignot, 2000; Toth, 2001; Franken and Tafti, 2003). The circadian regulation of sleep is also partly genetically determined; e.g., morningness-eveningness tendencies in humans show substantial heritability (Katzenberg et al., 1998). Familial advanced sleep phase syndrome (FASPS), in which the sleep cycle occurs 4 hours earlier than normally, is a highly penetrant autosomal dominant circadian rhythm variant due a point mutation in a casein kinase-epsilon phosphorylation site of the circadian gene Per2 (Toh et al., 2001). By contrast the delayed sleep phase syndrome has been associated with a structural (Ebisawa et al., 2001) and a length (Archer et al., 2003) polymorphism in the human Per3 gene. Several aspects of normal human sleep, from EEG pattern to the duration of total sleep and of REM sleep, are also strongly genetically determined, suggesting that allelic variants or gene mutations must be responsible for these variations. There is long-standing and compelling evidence (reviewed in Franken and Tafti, 2003) that sleep duration and EEG patterns of monozygotic twins are more similar than those of dizygotic twins or unrelated subjects, confirming that these complex traits are controlled by genes more than by environmental factors. The genetic control of sleep has been confirmed in mice by QTL analysis that has shown that the amount of total sleep, NREM sleep, and REM sleep are under genetic control (Friedmann, 1974; Valatx et al., 1972, 1974; Tafti et al., 1997, 1999; Franken et al., 1998; Toth and Williams, 1999). The increase in sleep pressure during sleep deprivation, as measured by an increase in slow wave activity in the 0.75–4Hz range, is also controlled by genetic factors in mice (Franken et al., 2001). Finally, it has been shown that a mutation in the gene coding for short-chain acylcoenzyme A dehydrogenase (Acads), an enzyme involved in brain fatty acid beta oxidation, affects theta oscillations during sleep, but not during wakefulness (Tafti et al., 2003). Thus classical genetic sleep in humans and QTL analysis in mice have shown that there is a strong genetic control for sleep phenotypes such as amount of sleep, EEG patterns, and the response to sleep deprivation. However, with the exception of Acads, these approaches have yet to identify the underlying mechanisms; i.e., the genes involved and their downstream targets.

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SHORT SLEEPERS, SLEEP DEPRIVATION, AND SLEEP RESTRICTION A sufficient amount of sleep constitutes a fundamental biological need, but such a need may be fulfilled by different amounts of sleep in different people. Most people require more than 6 hours of sleep to feel well rested, but there is a group of people who only need between 3 and 6 hours of sleep. There are a few reported cases of extreme short sleepers belonging to the same family (Jones and Oswald, 1968). Natural short and long sleepers have similar amounts of stage 3 and 4 sleep, but long sleepers have more stage 2 and REM sleep (Webb and Friel, 1971; Hartmann and Brewer, 1976; see also refs in Horne, 1988). Several studies have focused on differences in personality and life styles between long and short sleepers (e.g., Hartmann and Brewer, 1976), but very little is known relative to cognitive and physiological functions in short sleepers relative to normal and long sleepers. It is well established now that sleep deprivation and sleep restriction can impair cognitive performance when sustained for more than a few hours or a few days, respectively (see Van Dongen et al., 2003 and refs therein), but these studies were performed in normal sleepers, thus it is not known whether sleep loss will equally affect daytime performance in short and normal sleepers. A few studies used slow wave activity as a measure of sleep pressure and showed that, although the homeostatic sleep regulatory mechanisms do not differ between short and normal sleepers, short sleepers may tolerate a higher sleep pressure than long sleepers (Aeschbach et al., 1996, 2001). Whether living under a higher sleep pressure has behavioral consequence on daytime performance, vigilance, and memory, remains unknown. The elucidation of the genes and pathways that regulate sleep need in drosophila is significant for several reasons. Judging from the remarkable similarities between drosophila and mammalian circadian control, an understanding of how sleep need is controlled in drosophila should open the way to dissecting similar mechanisms in mammals. Knowledge of the genes and molecular pathways that control the amount of sleep will also shed light on how sleep is reduced or increased in pathological conditions. Such knowledge will permit the delineation of crucial molecular targets and the development of appropriate pharmacological interventions. Understanding the differences between normal and short sleeper lines at the molecular level will indicate through which mechanisms the latter can afford to sleep much less while preserving normal levels of performance. We expect that these molecular differences will be closely related to the biological functions fulfilled by sleep and thereby provide a mechanistic approach toward unraveling such functions.

THE SLEEP PHENOTYPE IN WILD-TYPE DROSOPHILA LINES The full characterization of the fly sleep phenotype discussed above was mainly performed in one wild-type strain, the Canton-S strain, which has been maintained

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in the laboratory for several decades. To establish whether the sleep phenotype is stable among other wild-type strains, we examined sleep patterns and the response to sleep deprivation in 123 lines derived from single female flies (isofemale lines) collected in the wild between 1994 and 2002. We found that the amount of sleep over the 24-hour period and the homeostatic response to sleep deprivation are wellconserved phenotypes across wild-type strains: All flies tested so far are diurnal, most flies sleep between 400 and 800 min/day, and sleep deprivation for 24 hours is in all cases followed by an increase in sleep duration and in sleep continuity as measured by the sleep continuity score (Holladay et al., 2003; Huber et al., 2004). The analysis of wild-type strains has also confirmed a significant difference between male and female flies: Female flies sleep almost exclusively during the night, while males show also a long period of siesta in the middle of the day (Figure 8.2 A). The daily amount of sleep in 123 isofemale lines is shown in Figure 8.2 B. For both female and male flies, mean values are similar to those of the originally described Canton-S flies (Shaw et al., 2000).

THE SLEEP PHENOTYPE IN DROSOPHILA MUTANT LINES The ~7000 mutant lines tested so far include deficiency lines, lines obtained through insertional mutagenesis with transposable elements (P and EP elements), and lines chemically mutagenized with EMS. The collection of deficiencies included ~150 lines, each carrying a deletion of a relatively large portion of the fly genome. The advantage of this collection is that as a whole it covers ~80% of the fly genome, thus allowing a quick and comprehensive screening of most of the fly genome. The main disadvantage is that the identification of the gene of interest may be difficult because each deletion includes many different genes, sometimes more than 100. The insertional lines tested so far include the ~1000 lines of the Berkeley Drosophila Genome Project primary collection (Spradling et al., 1999) and the ~2300 EP lines of the Rörth collection (Rörth et al., 1998). They include both loss-offunction mutations and gain-of-function mutations. The former are often due to the insertion of a transposon inside a transcription unit, the latter to gene overexpression following the transposon insertion upstream of the transcription start site. Insertional mutagenesis usually permits the rapid identification of the mutated gene by sequencing the flanking sequences from one or both ends of the transposon insertion. Moreover the mobilization of the inserted element can generate new alleles, and expression patterns can be characterized by lacZ staining of tissues. A limitation of this approach, however, is that transposons do not insert at random into the genome but have preferred hot spots (Liao et al., 2000). For this reason we are also using chemical mutagenesis with EMS, which is known to randomly induce small (point) mutation over the entire genome at a reasonable rate. EMS has been the most frequently used chemical mutagen in drosophila over the last 25 years, such as in the search for learning mutants, circadian mutants, and paralytic mutants (Roberts, 1998). The disadvantage of chemical mutagenesis is that the molecular characterization of the gene of interest may be not as straightforward as with insertional mutagenesis.

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FIGURE 8.2 (See color insert.) Sleep pattern and sleep amount in 123 wild-type lines. (A) Daily amount of sleep in wild-type Canton-S female (blue line, n = 14) and male (red line, n = 15) flies. (B) Daily amount of sleep in 123 isofemale lines derived from single wild-type female flies. Most female flies (blue line) sleep between 500 and 800 min/day, with a mean of 650 ± 126 (mean ± SD; median = 670, min = 289, max = 935; female Canton-S flies = 664 ± 137). Male flies of the same isofemale lines (red line) sleep between 600 and 1000 min/day with a mean of 786 ± 170 (mean ± SD; median = 799, min = 109, max = 1106; Canton-S male flies = 864 ± 137).

We are currently screening 50–100 mutant lines every week. Flies are continuously recorded in a DAMS monitor for one week, including 2–3 baseline days, 24 hours of sleep deprivation, and 1–3 days of recovery after sleep deprivation. Ten to sixteen flies (4–7 days old at the beginning of the experiment) are tested for each line. In agreement with the results obtained with isofemale lines, we have found

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FIGURE 8.3 (See color insert.) Daily sleep amount in 1547 mutant fly lines. Mean ± SD is 616 ± 169 (min 131, max 1155). Shaded areas show one (dark red) and two (light red) standard deviations from the mean.

that daily sleep amount and response to sleep deprivation are highly conserved phenotypes in most mutant lines. Figure 8.3 shows the daily sleep amount in female flies of 1547 insertional lines. The amount of sleep in 24 hours is normally distributed, with a mean of 616 ± 169 (mean ± SD; min 131, max 1155). As shown in Figure 8.3, few fly lines qualify as short sleepers, defined here as those in which sleep amount is less than 280 min in 24 hours for female flies, and less than 450 min in 24 hours in male flies (i.e. less than two standard deviations from the mean of all fly mutant lines screened so far). Of the 7000 lines screened so far, only 15 lines qualify as short sleeper lines. As observed with Canton-S flies, the great majority of the isofemale lines and of the mutant lines tested so far showed a sleep rebound after 24 hours of sleep deprivation. As in wild-type flies (Figure 8.1 C), a 24-hour sleep deprivation was followed by an increase in sleep duration that was most pronounced during the first 4–6 hours immediately after the end of the sleep deprivation period and was over in most cases by the end of the second day of recovery. Moreover, similar to wildtype flies, most mutant lines also showed an increase in sleep continuity after sleep deprivation. Finally, like wild-type lines, mutant fly lines only recovered a fraction (10–40%) of the sleep lost during the sleep deprivation period. Three short sleeper lines are shown in Figure 8.4. These are some of the lines that we are currently characterizing. Each of these lines is homozygous for a single P element insertion, and our initial revertant analysis indicates that the mutation

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FIGURE 8.4 (See color insert.) Daily amount of sleep in 3 short sleeper lines. Red lines in each panel represents daily amount of sleep during baseline (min/24 H; mean ± SD) of 16 female (upper panel) and male (lower panel) flies of a short sleeper line. For comparison the blue line in each panel represents the daily amount of sleep in wild-type flies (n = 16/panel). Daily amount of sleep (mean ± SD, n = 40–80 flies, at least 3 independent experiments) is as follows: ss1 flies = 190 ± 40, 200 ± 50 (females and males, respectively); ss2 flies = 210 ± 30 and 280 ± 90; ss3 flies = 230 ± 40, 270 ± 70.

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caused by the transposon insertion is solely responsible for the short sleeper phenotype, pointing to a single-gene effect. It should be mentioned that the overall baseline performance of these mutant flies, assessed by measuring levels of locomotor activity, sensitivity to anesthetics, geotaxic response, sensitivity to heat, and vigilance tests, is normal, ruling out major aspecific abnormalities as responsible for the short sleeper phenotype.

CONCLUSIONS Work in other laboratories and ours for the past 5 years has demonstrated that fly sleep shares many fundamental features of mammalian sleep. We have shown that sleep phenotypes in flies are well defined and stable, and can thus be employed to screen for mutations affecting sleep amount, sleep quality and the homeostatic regulation of sleep. Our ongoing, high-throughput screening of >7000 drosophila lines (~1/3 of the fly genome) has led to the identification of several mutations that significantly shorten daily sleep amount, indicating that key features of sleep are under genetic control. The three lines shown in Figure 8.4 are all insertional lines for which the location of the transposon insertion is known, thus it is possible to identify and characterize the genes responsible for the short sleep phenotype. The demonstration that the short sleeper phenotype can be reverted in these lines by transposon jumping indicates that a single gene is likely to be involved. By characterizing the molecular pathways involved in producing the short sleeper phenotype, we should be able to determine whether the three genes act through the same pathway or whether each of them has specific downstream targets. It should be mentioned here that our screening also identified a few long sleeper lines. The reason why we do not focus our efforts currently on some of these lines is largely practical. Behavioral characterization of long sleepers may be challenging and time consuming because any mutation affecting the general health of the fly (e.g., paralytic mutations) is likely to result in a decrease in locomotor activity and therefore can affect our calculation of sleep amount. It should also be mentioned that so far our screening has not identified any mutation that can produce a no-sleep fly. This finding per se is yet another proof that sleep must serve a very important function and that wakefulness cannot substitute for sleep. In line with this conclusion, the fly mutagenesis screening discussed here shows that while the daily sleep quota differs among mutant lines, very few mutations can shorten sleep time to less than 300 min per day. Whatever the function of sleep might be, this seems to be the minimum time required in flies to carry out that function (interestingly, 2–3 hours is also the limit for human short sleepers). It could be argued that the identification of a no-sleep mutant is only a question of time; however compelling evidence from sleep deprivation experiments suggests that this may not be the case. Sleep deprivation is fatal in rats if prolonged for several days (Rechtschaffen et al., 1989; Rechtschaffen and Bergmann, 2002), and a recent study has shown that flies also die when kept awake for more than 60 hours (Shaw et al., 2002), thus the no-sleep phenotype might be missed altogether by mutagenesis screenings performed at a late developmental stage or in adulthood. It cannot be excluded, however, that such phenotype could be identified in the future by screens

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performed at an earlier developmental stage should such studies become possible in flies and other invertebrates.

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Franken P. and Tafti M., Genetics of sleep and sleep disorders, Front. Biosci., 8, E381, 2003. Friedmann J.K., A diallel analysis of the genetic underpinnings of mouse sleep, Physiol. Behav., 12, 169, 1974. Hartmann E. and Brewer V., When is more or less sleep required? A study of variable sleepers, Comp. Psychiatry, 17, 275, 1976. Hendricks J.C., Finn S.M., Panckeri K.A., Chavkin J., Williams J.A., Sehgal A., and Pack A.I., Rest in Drosophila is a sleep-like state, Neuron, 25, 129, 2000. Hendricks J.C., Kirk D., Pancheri K., Miller M.S., and Pack A.I. Modafinil maintains waking in the fruit fly Drosophila melanogaster, Sleep, 26, 139, 2003. Hobson J.A., Sleep, Scientific American Library HPHLP, New York, 1989. Holladay C., Huber R., Biesiadecki M., Martinez-Gonzalez D., Hill S., Kreber R., Ganetzky B., Tononi G., and Cirelli C., Natural variation in the sleep phenotype in Drosophila melanogaster, Sleep, 26S, A23, 2003. Horne J.A., Why we sleep: The functions of sleep in humans and other mammals, Oxford University Press, Oxford, 1988. Huber R., Deboer T., and Tobler I., Effects of sleep deprivation on sleep and sleep EEG in three mouse strains: empirical data and simulations, Brain Res., 857, 8, 2000. Huber R., Hill S., Holladay C., Biesiadecki M., Tononi G., and Cirelli C., Sleep homeostasis in drosophila melanogaster, Sleep, 27, 628–639, 2004. Jones H.S. and Oswald I., Two cases of healthy insomnia, Electroencephalogr. Clin. Neurophysiol., 24, 378, 1968. Kaiser W., Steiner-Kaiser J., Neural correlates of sleep, wakefulness, and arousal in a diurnal insect, Nature, 301, 707, 1983. Katzenberg D., Young T., Finn L., Lin L., King D.P., Takahashi J.S., Mignot E., A CLOCK polymorphism associated with human diurnal preference, Sleep, 21, 569, 1998. Kida S., Josselyn S.A., de Ortiz S.P., Kogan J.H., Chevere I., Masushige S., Silva A.J., CREB required for the stability of new and reactivated fear memories, Nat. Neurosci., 5, 348, 2002. Konopka R.J., Benzer S., Clock mutants of Drosophila melanogaster, Proc. Natl. Acad. Sci. USA, 68, 2112, 1971. Liao G.C., Rehm E.J., Rubin G.M., Insertion site preferences of the P transposable element in Drosophila melanogaster, Proc. Natl. Acad. Sci. USA, 97, 3347, 2000. Lin Y.J., Seroude L., and Benzer S., Extended life-span and stress resistance in the Drosophila mutant methuselah, Science, 282, 943, 1998. Miklos G.L. and Maleszka R., Deus ex genomix, Nature Neuroscience, 3, 424, 2000. Moruzzi G., The sleep-waking cycle, Ergeb. Physiol., 64, 1, 1972. Nakanishi H. et al., Positive correlations between cerebral protein synthesis rates and deep sleep in Macaca mulatta, Eur. J. Neurosci., 9, 271, 1979. Nitz D.A., van Swinderen B., Tononi G., and Greenspan R.J., Electrophysiological Correlates of Rest and Activity in Drosophila melanogaster, Curr. Biol., 12, 1934, 2002. O’Hara B.F. and Mignot E., Genetics of sleep and its disorders, in Genetic Influences on Neural and Behavioral Functions, Pfaff D.W., Berrettini W.H., Joh T.H., and Maxon S.C., Eds., CRC Press, Boca Raton, FL, 2000. Ramm P. and Smith C.T., Rates of cerebral protein synthesis are linked to slow wave sleep in the rat, Physiol. Behav., 48, 749, 1990. Rattenborg N.C., Amlaner C.J., and Lima S.L., Behavioral, neurophysiological and volutionary perspectives on unihemispheric sleep, Neurosci. Biobehav. Rev., 24, 817, 2000. Rechtschaffen, A., Current perspectives on the function of sleep, Perspect. Biol. Med., 41, 359, 1998.

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Rechtschaffen A., Bergmann B.M., Everson C.A., Kushida C.A., Gilliland M.A., Sleep deprivation in the rat: X. Integration and discussion of the findings, Sleep, 12, 68, 1989. Rechtschaffen A. and Bergmann B.M., Sleep deprivation in the rat: an update of the 1989 paper, Sleep, 25, 18–24, 2002. Reiter L.T., Potocki L., Chien S., Gribskov M., and Bier E., A systematic analysis of human disease-associated gene sequences in Drosophila melanogaster, Genome Res., 11, 1114, 2001. Roberts D.B. (Ed)., Drosophila, A Practical Approach, 2nd ed., Oxford University Press, 1998. Rogers N.L., Szuba M.P., Staab J.P., Evans D.L., Dinges D.F., Rogers N.L., Szuba M.P., Staab J.P., Evans D.L., and Dinges D.F., Neuroimmunologic aspects of sleep and sleep loss, Semin. Clin. Neuropsychiatry, 6, 295, 2001. Rogina B., Reenan R.A., and Nilsen S.P., Helfand S.L., Extended life-span conferred by cotransporter gene mutations in Drosophila, Science, 290, 2137, 2000. Rörth P., Szabo K., Bailey A., Laverty T., Rehm J., Rubin G.M., Weigmann K., Milan M., Benes V., Ansorge W., and Cohen S.M., Systematic gain-of-function genetics in Drosophila. Development, 125, 1049, 1998. Sanyal S., Sandstrom D.J., Hoeffer C.A., and Ramaswami M., AP functions upstream of CREB to control synaptic plasticity in Drosophila, Nature, 416, 870, 2002. Sauer S., Herrmann E. and Kaiser W., The effect of forced activity on a behavioral sleep sign in honey bees, Sleep Research Online, 2 (S1), 217, 1999. Shaw P.J., Cirelli C., Greenspan R.J., and Tononi G., Correlates of sleep and waking in Drosophila melanogaster, Science, 287, 1834, 2000. Shaw P.J., Tononi G., Greenspan R.J., and Robinson D.F., Stress response genes protect against lethal effects of sleep deprivation in Drosophila, Nature, 417, 287, 2002. Sokolowski M.B., Drosophila: genetics meets behavior, Nature Reviews, 2, 879, 2001. Spiegel K., Leproult R., and Van Cauter E., Impact of sleep debt on metabolic and endocrine functions, Lancet, 354, 1435, 1999. Spradling A.C., Stern D., Beaton A., Rhem E.J., Laverty T., Mozden N., Misra S., and Rubin G.M., The Berkeley Drosophila Genome Project gene disruption project: Single Pelement insertions mutating 25% of vital Drosophila genes, Genetics, 153, 135, 1999. Steriade M. and Timofeev I., Neuronal plasticity in thalamocortical networks during sleep and waking oscillations, Neuron, 37, 563, 2003. Stickgold R., Hobson J.A., Fosse R., and Fosse M., Sleep, learning, and dreams: off-line memory reprocessing, Science, 294, 1052, 2001. Tafti M., Franken P., Kitahama K., Malafosse A., Jouvet M., and Valatx J.L., Localization of candidate genomic regions influencing paradoxical sleep in mice, Neuroreport, 8, 3755, 1997. Tafti M., Chollet D., Valatx J.L., Franken P., Quantitative trait loci approach to the genetics of sleep in recombinant inbred mice, J. Sleep Res., 8 (S1), 37, 1999. Tafti M. and Franken P., Genetic dissection of sleep, J. Appl. Physiol., 92, 1339, 2002. Tafti M., Petit B., Chollet D., Neidhart E., de Bilbao F., Kiss J.Z., Wood P.A., and Franken P., Deficiency in short-chain fatty acid beta-oxidation affects theta oscillations during sleep, Nat. Genet., 34, 320, 2003. Tobler I., Effect of forced locomotion on the rest-acticity cycle of the cockroach, Behavior Brain Res., 8, 351, 1983. Tobler I., Is sleep fundamentally different between mammalian species?, Behavior Brain Res., 69, 35, 1995. Tobler I., Phylogeny of sleep regulation, in Principles and Practice of Sleep Medicine, M.H. Kryger, T. Roth, and W.C. Dement, Eds., W.B. Saunders, Philadelphia, 2000, p. 72.

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Tobler I. and Neuner-Jehle M., 24-H variation of vigilance in the cockroach Blaberus giganteus, J. Sleep Res., 1, 231–239, 1992. Tobler I. and Stalder J., Rest in the scorpion — a sleep-like state?, J. Comp. Physiol., A163, 227, 1988. Toh K.L., Jones C.R., He Y., Eide E.J., Hinz W.A., Virshup D.M., Ptacek L.J., and Fu Y.H., Per2 phosphorylation site mutation in familial advanced sleep phase syndrome, Science, 291, 1040, 2001. Toth L.A. and Williams R.W., A quantitative genetic analysis of slow-wave sleep and rapideye movement sleep in CXB recombinant inbred mice, Behav. Genet., 29, 329, 1999. Toth L.A., Identifying genetic influences on sleep: an approach to discovering the mechanisms of sleep regulation, Behav. Genet., 31, 39, 2001. Valatx J.L., Bugat R., and Jouvet M. Genetic studies of sleep in mice, Nature, 238, 226, 1972. Valatx J.L., Bugat R., Genetic factors as determinants of the waking-sleep cycle in the mouse, Brain Res., 69, 315, 1974. Van Dongen H.P.A., Maislin G., Mullington J.M., Dinges D.F., The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation, Sleep, 26, 117, 2003. Waddell S. and Quinn W.G., Flies, genes, and learning, Ann. Rev. Neurosci., 24, 1283, 2001. Walker M.P., Brakefield T., Morgan A., Hobson J.A., and Stickgold R., Practice with sleep makes perfect: sleep-dependent motor skill learning, Neuron, 35, 205, 2002. Webb W.B. and Friel J., Sleep stage and personality characteristics of “natural” long and short sleepers, Science, 171, 587, 1971.

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9

Sleep Phylogeny: Clues to the Evolution and Function of Sleep Jerome M. Siegel

CONTENTS Introduction Terrestrial Mammals Aquatic Mammals Reptiles Conclusions References

INTRODUCTION A persuasive argument for the importance of sleep rests on its ubiquity among animals. All mammals sleep.1 Reptiles appear to sleep, although by some measures they may not.2–9 It has not been conclusively demonstrated that fish sleep, although some species show marked circadian rhythms of activity.10,11 To meet the accepted definition of sleep, animals must show periods of inactivity with raised arousal thresholds and must show sleep debt when deprived, leading to rebound sleep when deprivation is ended. Fruit flies (Drosophila melanogaster) show periods of inactivity with raised arousal thresholds and sleep rebound after deprivation.12–14 If such periods are homologous to sleep in vertebrates, one must consider any reported absence of sleep in higher vertebrates as an error due to inadequate assessment or to be an evolved adaptation to particular ecological niches that has done away with a sleep state present in ancestral animals. This chapter discusses the special situation of marine mammals, which appear to have evolved adaptations that at the very least mask some aspects of sleep and certainly dispense with the need for immobility during what otherwise appears to be sleep. A further issue is the nature of sleep. Most mammals1,15 and birds16 show evidence of REM sleep — also known as paradoxical sleep (PS) — although this state may not exist in certain marine mammals.17 Amounts of sleep differ substantially between species, with some sleeping as little as 2 h per day and others as much as 20 h.1,15 Surely these enormous variations

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offer some insight into the physiological needs responsible for sleep. Although animals in different ecological niches might most adaptively have evolved differing durations of activity and inactivity, it is unlikely that no animals would have evolved a complete or nearly complete absence of sleep unless it served some vital function. The cost of sleep in terms of vulnerability, loss of time to eat, procreate, and gain an edge in competition with other animals is considerable. Certainly contemporary humans make major efforts to reduce sleep time to achieve their goals. Differences in sleep amounts seem to be systematically related to certain constitutional variables, suggesting that underlying physiological factors, rather than ecological niche, determine sleep need. The study of sleep phylogeny can help explain the essence of sleep debt; i.e., which physiological, neurochemical, and genetic events are conserved across sleep in differing animals.

TERRESTRIAL MAMMALS Although there are approximately 4,000 mammalian species, fewer than 100 have been studied under laboratory conditions. Most of these have been observed in only a single study. Perhaps an additional 100 have been observed in zoos. Certainly there is no need to study sleep in all mammalian species; however it is likely that a thorough examination of sleep physiology, exploring the genetic variations and adaptations that have occurred over more than 100,000,000 years of mammalian evolution, may reveal aspects of sleep not seen in the four or five laboratory species that have been most thoroughly studied. For example, humans and rats have been shown to have a clear link between REM sleep and penile erections.16,19 A recent study of sleep in the armadillo revealed that penile erections occur in non-REM sleep, but not in REM sleep in this species.20 Such observations are not merely a curiosity but speak to the issue of which aspects of sleep are core phenomena and which are perhaps epiphenomena not linked to particular sleep-waking states. In this case, the findings suggest that certain aspects of sympathetic and parasympathetic control during sleep differ across species. One may speculate that other aspects of standard sleep signs in rats, cats, and humans, such as high voltage electroencephalogram (EEG) during nonREM sleep, low voltage EEG during REM sleep or high voltage EEG occurring simultaneously in both hemispheres, may not be essential for sleep. Many of the largest mammals such as elephants, and giraffes have only been studied by visual observation. Understanding sleep in these animals is crucial, because the extreme points in any cross species comparison can be most informative as to the underlying variables that determine sleep amounts and physiology. Perhaps the most surprising conclusion from studies of mammalian sleep is that knowing the order to which an animal belongs tells you very little about the amount of total sleep or REM sleep they have.1,15 In other words, as a group rodents do not have characteristic sleep patterns that differentiate them from carnivores, primates, artiodactyls, insectivores, and so on. Each of these groups shows a wide and overlapping range of total and REM sleep amounts. Each order is characterized by a common genetic inheritance that produces characteristic behaviors, brain and body anatomy, intelligence, diet, and reproductive physiology that tends to differentiate it from other orders. Yet their sleep is not characteristic of the group, suggesting that

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TABLE 9.1 Correlations between Sleep Parameters and Constitutional Variables

Body weight Brain weight Metabolic rate Encephalization quotient a b

Total Daily Sleep Time

Quiet Sleep Time

REM Sleep Time

REM Sleep%

Sleep Cycle Length

–0.53a –0.55a 0.33b –0.17

–0.53a –0.48a 0.30b –0.10

–0.45a –0.52a 0.13 –0.20b

–0.12 –0.25 –0.09 –0.30b

0.83a 0.89a 0.82 0.52b

P<0.001. P£0.05.

these variables do not determine sleep amount. A comprehensive analysis of the determinants of sleep time looking at body weight, metabolic rate, brain weight, encephalization quotient (brain-to-body weight ratio), body temperature, neonatal brain weight as a percentage of adult weight, gestation period, and litter size concluded that total sleep time was most closely correlated with body weight15 (Table 9.1). This was a negative correlation; i.e., big animals sleep less. Body weight is inversely related to metabolic rate, so we can say that animals with higher metabolic rate have more total sleep time. The implications of this is discussed in the Conclusions section. Among terrestrial mammals REM sleep amounts are positively correlated with total sleep amounts; however this explains only a small amount of the total variance in REM sleep time. It has been noted that predator animals and animals with safe sleeping sites have relatively larger amounts of REM sleep.21 This makes some sense, because arousal thresholds are elevated in some animals in REM sleep, so it might be dangerous for prey animals to have large amounts of REM sleep., It is not true, however, that REM sleep is deep sleep in all animals.22 In humans, for example, arousal from REM sleep is more rapid than from non-REM sleep, and there is evidence that in general animals aroused from REM sleep function better than those aroused from non-REM sleep.23 It is difficult to quantify safety of sleep site by measures such as frequency of death during sleep. Sites that seem exposed may in fact be safe, and there is little evidence that animals are disproportionately hunted during sleep. Therefore, while there is little doubt that certain predator animals have large amounts of REM sleep, this relation does not appear to adequately explain REM sleep time. An alternate correlate of REM sleep time is how immature animals are at birth. At birth all mammals so far examined have their maximal amounts of REM sleep. Amounts diminish with age to adult levels.24 Animals such as rats or cats that are born relatively immature have a greater elevation in REM sleep at birth. Animals such as horses or guinea pigs that are born relatively mature have little elevation of REM sleep at birth. This suggests that REM sleep may have some role in brain or body development or in the protection of small animals without substantial thermoregulatory capacity from hypo- or hyperthermia. This role has not been identified. Furthermore, even though animals that are immature at birth have

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TABLE 9.2 Correlations of REM Sleep Parameters with Measures of Neonatal Maturity and Reproductive Variables

Altricial-precocial rating Neonatal brain weight (% adult) Litter size a b

REM Sleep Time

REM Sleep% of Total Sleep Time

–0.66a –0.61a 0.51a

–0.45b –0.55b 0.41b

P<0.001. P£0.05.

decreasing REM sleep amounts as they age, they continue to have higher REM sleep amounts when they reach adulthood (Table 9.224). No theory has been offered as to why this is; however, from a statistical standpoint, the correlation between immaturity at birth and REM sleep time in adulthood accounts for a large amount of the interspecies variability in REM sleep time between mammals. Figure 9.125 shows some mammals with relatively high and low amounts of REM sleep. It is important to note that humans do not have unusual amounts of REM sleep either in terms of the number of hours per day or the percent of sleep time devoted to REM sleep (Figure 9.1); rather the amount of REM sleep time shown by humans is in line with our intermediate state of maturity at birth. This is obviously a problem for any theory hypothesizing that REM sleep amount is linked to intellectual capacity or any other characteristic in which humans are believed to be at an extreme within the animal kingdom.25 The monotremes are one of the three branches of the mammalian line, the other two being the placentals and the marsupials.1 The extant monotremes are the shortand long-nosed echidna and the platypus. The monotremes are egg-laying mammals that have relatively low but regulated body temperature (approximately 32∞C). They nurse their young from milk-secreting patches, rather than nipples and have thick fur. Their bone structure contains some reptilian characteristics, and genetic analysis indicates that they are more similar to reptiles and birds than other mammals. The platypus has a bill that responds to electric fields and a poison spur, characteristics typically seen in reptiles or fish but not in mammals. Despite the origin of monotremes early in the mammalian line, relatively little speciation has occurred, with only five monotreme species known to have evolved, presumably because their geographic isolation from other species reduced evolutionary pressure.1 Thus the physiology of monotremes is likely to more closely resemble that of the first mammals than any other mammals, and an early report that echidnas did not have REM sleep generated considerable interest.26,27 It suggested that REM sleep was a more recently evolved state with some higher cognitive function. Because of the possibility that a REM sleep-like state might be missed in the echidna, we reexamined this issue. In addition to recording electroencephalograms and electromyograms, we monitored brainstem neuronal activity.28 We know that

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FIGURE 9.1 Total sleep amounts and REM sleep amounts. Humans are not unusual either in terms of their total sleep or REM sleep amounts. (From Siegel J.M., The REM sleep-memory consolidation hypothesis, Science, 294, 1058–1063, 2001. With permission.)

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brainstem neuronal activity generates REM sleep29; therefore it might be possible to detect a REM sleep-like state in the echidna even if the forebrain EEG did not resemble that of REM sleep in placental mammals. We found that brainstem activity during sleep in the echidna did not resemble the activity seen in other mammals during non-REM sleep. Rather it resembled that seen in REM sleep (Figure 9.2). We concluded that echidnas did have a REM sleep-like state, but one that was not accompanied by low voltage cortical EEG as is seen in adult mammals. In this respect the REM sleep-like state resembled that of many neonatal animals, which have high voltage activity during periods of REM sleep. It has also been reported that a state looking like REM sleep, with low voltage EEG, may occur in echidnas.30 We saw no such state in our studies, and the Nicol et al. study did not demonstrate that the state they were observing was a sleep state, rather than a quiet waking state, so further investigation of this issue may be warranted. Both studies agree, however, that the echidna has a REM sleep-like state, in contrast to the earlier work. Because we saw a state that resembled REM sleep in the echidna, we next studied sleep in the platypus, which is considered the most primitive of the mammals.31 This raised special problems, because these animals are very delicate, are dangerous to handle because of their poison spurs, and are partially aquatic and cannot be housed in conventional cages. The platypus requires special animal husbandry procedures. Water pumps, needed to circulate water in their pool, generate large electrical fields, which stress and thereby can cause the death of the animals. Shielding procedures have to be used to minimize this stimulus. Telemetry, both on land and under water, is necessary to allow continuous recording. When we succeeded in recording from the platypus, we found that it has a particularly vigorous motor activation during sleep, equal to or greater than that of other mammals in REM sleep (Figure 9.3). A video of this can be viewed at our web site http://www. npi.ucla.edu/sleepresearch. When we calculated the amount of REM sleep, we were surprised to discover that the platypus had more REM sleep than any other animal, up to 8 hours per day. Both the echidna and platypus are extremely immature at birth, resembling a worm. They crawl out of the birth canal and into a pouch where they are protected, warmed, and nourished for several months. The high levels of REM sleep in monotremes strengthen the relation between immaturity at birth and REM sleep amounts in adulthood.

AQUATIC MAMMALS Aquatic mammals have sleep patterns that are quite different from those in terrestrial mammals, so investigation of sleep in marine mammals may be instructive in understanding sleep as a whole, as well as the role of REM versus non-REM sleep. Under some conditions dolphins swim 24 hours a day for long periods. During their swimming they breathe regularly and are able to avoid the sides of the pool. Lilly first noticed that dolphins often close one of their eyes but rarely close both. The significance of this was discovered by Lev Mukhametov and colleagues, who developed reversible, relatively noninvasive techniques for recording EEG during swimming. They found that dolphins generated the high voltage EEG typical of nonREM sleep in either the right or left side of their cortex, but never in both sides.32

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FIGURE 9.2 Instantaneous rate plots of medial reticular neurons during sleep and waking states. In the echidna, rate varies in a way resembling that seen in placental mammals during REM sleep, rather than that in the regular manner of non-REM sleep. (From Siegel J.M., Manger P., Nienhuis R., Fahringer H.M., Pettigrew J., The echidna Tachyglossus aculeatus combines REM and non-REM aspects in a single sleep state: implications for the evolution of sleep, J. Neuroscience, 16, 3500–3506, 1996. With permission.)

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FIGURE 9.3 Sleep states in the platypus. The platypus has periods of rapid eye movements during a state characterized by high voltage EEG. (From Siegel J.M., Manger P.R., Nienhuis R., Fahringer H.M., Shalita T., Pettigrew J.D., Sleep in the platypus, Neuroscience 91, 391–400, 1999. With permission.)

The same unihemispheric sleep has now been seen in several cetacean species.33 Figure 9.4 shows an EEG recording we carried out in a beluga whale. USWS appears to be at least partially an adaptation to the complex brain activity required for breathing in the dolphin. In contrast to other animals that breathe automatically during sleep, dolphins and other cetaceans need to be at the surface to breathe, need to sense wave action, and minimize water ingestion during breathing movements. Administration of light doses of barbiturates to dolphins will stop breathing (long before it produces effective analgesia). This is quite different from terrestrial mammals that breathe and regulate blood gasses effectively even when deeply anesthetized. In the dolphin the optic chiasm is completely crossed so that all visual input to each hemisphere comes from the opposite eye. Because visual input can block certain

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EEG rhythms,34 it is necessary to determine if any EEG change observed contralateral to a closed eye is secondary to the reduction of visual input. Generally the eye contralateral to the hemisphere that was sleeping was closed, as one would expect, because it is unlikely that visual input could be effectively processed in the hemisphere with EEG synchronization; however we also demonstrated that eye closure was not the cause of the high voltage EEG Closure or covering of one eye did not necessarily produce high-voltage activity in the opposite hemisphere. Conversely we noted that the eye opposite the sleeping hemisphere could open without blocking the high voltage EEG33; therefore the EEG-defined sleep state is not simply a consequence of reduced visual input to the contralateral eye. A similar control for vision-related EEG changes has not been done to support claims of USWS in birds, who in any case show relatively subtle differences between the EEG in the two hemispheres, consistently detectable only with power spectral analysis,35 in contrast to the USWS visible in cetaceans. If one hemisphere is prevented from showing high-voltage EEG by gently stimulating dolphins, a rebound of sleep in the deprived hemisphere is seen when deprivation stops,36 important evidence that sleep debt can be localized to one hemisphere. Despite many studies, no convincing evidence of REM sleep in dolphins or any other EEG instrumented cetacean has been produced. The absence of such evidence may result from the conditions under which these observations have been made. Often the animals are restrained during recordings. In those cases where they have been unrestrained, the stimulation caused by the recording cable may have prevented appearance of REM sleep but, considering the strong REM sleep pressure shown by all terrestrial mammals that have been deprived and that allows high levels of REM sleep even under uncomfortable sleeping conditions, one would expect that some unequivocal REM sleep would have been seen even under less than natural conditions. One can conclude that if REM sleep exists in the dolphin, REM sleep amounts are among the smallest of any mammal or are uniquely sensitive to disturbance. A more subtle issue is whether REM sleep may take some novel form in dolphins that has escaped detection. An alternate hypothesis is that unihemispheric slow-wave sleep may eliminate the need for REM sleep; for example, if REM sleep has evolved to stimulate brainstem areas after non-REM sleep to allow optimal functioning in subsequent waking, the presence of virtually continuous brainstem activity required for the continuous movement and breathing shown by dolphins may make REM sleep unnecessary.

REPTILES The presence of REM sleep in large amounts in the most primitive mammals and in birds suggests that it may have been present in a common ancestor of these two classes of animals. That would indicate that at least some reptiles have REM sleep. The alternate theory, that REM sleep evolved twice, once in mammals and once in birds, suggests that a REM sleep precursor state must have existed in pre-avian, premammalian reptiles. According to both hypotheses, examination of state organization in reptiles would provide an insight into the primitive aspects of REM sleep. The key challenge is devising a method that would be effective in detecting such a

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state. To do this we decided to follow the approach we used in the echidna. Because we knew what the pattern of brainstem neuronal activity is in the mammalian REM sleep state, and because midbrain and pontine brainstem regions are both necessary and sufficient for generating the major neurological changes seen in REM sleep,29 we decided to conduct the first investigations searching for aspects of REM sleep at the neuronal level in reptiles.37 We chose the turtle as a representative reptile because excellent prior behavioral studies had been conducted on these animals7 and because they adapted well to the laboratory.

FIGURE 9.4

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At the neuronal level, there are two consistent brainstem activity changes underlying REM sleep. One is the burst-pause discharge pattern that gives rise to the rapid eye movements and twitching characteristic of REM sleep. This pattern is present in most medial reticular neurons and therefore should be relatively easy to detect. The second is the cessation of release of norepinephrine, serotonin and histamine during REM sleep. This would be much more difficult to detect, because these monoaminergic cell groups are intermingled with other cell types and there is no easy way to determine the transmitter phenotype of any recorded cell. Our immunohistochemical analyses showed, however, that these cell groups were present in the turtle, so we focused our effort on recording neuronal activity from medial reticular cells during quiescent states, expecting to record from non-monoaminergic and possibly monoaminergic cells. Our results were clear. We saw no acceleration of discharge and no burst-pause pattern of discharge analogous to that seen in mammalian reticular cells during sleep. It appears that this aspect of REM sleep is not present in any form in turtles. We do not know the pattern of discharge of monoamine cells in the sleep of the turtle, and it is possible that a cessation of discharge occurs during behavioral immobility. However, we did not see cells that had the tonic waking discharge with cessation of discharge within the sleep period even though some of the neurons we recorded were within the serotonergic raphe region. Further investigations are necessary to test for this possibility. What we can conclude is that the periodic occurrence of brainstem activation that is so characteristic of REM sleep in terrestrial mammals is absent in the turtle. REM sleep precursor states may be present in the reptilian species that gave rise to mammals and birds but not in modern day turtles. Alternatively the phasic motor activation seen in REM sleep may have evolved rapidly at the onset of the avian and mammalian lines, perhaps in relation to homeothermy.

FIGURE 9.4 (See facing page.) Relationship between EEG and the state of eyes in a beluga whale. (A) The state of eyelids and EEG spectral power (1–3 Hz; 5-sec epochs) from the two hemispheres (R, right; L, left) in a white whale recorded over a 3-h period. EEG power was normalized as a percentage of the maximal power in each hemisphere during this period. The state of each eye (R, right; L, left) was scored in real time (O, open; I, intermediate; or C, closed) and then categorized for 5-sec epochs as described. Compressed figure does not show short-lasting changes in eye state. (B) Expansion of the two 2.5-min recordings of the EEG and the state of both eyes. The examples show the EEG asynchrony and parallel changes in eye state recorded in this whale at the times marked as 1 and 2 in Figure 4 A. Note that the EEG does not change immediately with changes in eye position. The right eye did not close during episode 1, and the left eye did not close during episode 2. (C) The average EEG spectral power in the two hemispheres during episodes with unilateral eye opening (LO/RC, left open and right closed; LC/RO, left closed and right open). EEG power was normalized as a percentage of the average 1–3 Hz power recorded in each hemisphere during SWS with the contralateral eye closure. Reported values are the means S.E. (LO/RC, n = 238 epochs; LC/RO, n = 441 epochs). (From Lyamin, O.I., Mukhametov, L.M., Siegel, J.M., Nazarenko, E.A., Polyakova, I.G., and Shpak, O.V., Unihemispheric slow wave sleep and the state of the eyes in a white whale, Behavioral Brain Res., 129, 125, 2002. With permission.)

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CONCLUSIONS The ultimate question for sleep researchers is the function of REM and non-REM sleep. Phylogenetic evidence constrains any theory attempting to answer these questions. We know that sleep amounts vary by more than an order of magnitude across mammalian species. Either the amount of time spent sleeping has no relation to underlying function, which would distinguish sleep from many other homeostatically regulated processes, or sleep need varies considerably across species. The correlates of this variation should provide some insight into sleep functions. A survey of the available data indicates that phylogenetic order does not explain much of the variation of sleep time across species. There is extensive overlap of both REM and non-REM sleep time between orders, despite the genetic, anatomical, physiological, and behavioral commonalities within order. Prior data and new data on primitive mammals and cetaceans indicate a strong negative correlation between total sleep time and weight. Because metabolic rate is strongly and negatively correlated with body mass, this is also a positive correlation between metabolic rate and sleep time. Some evidence suggests that brain regions with high metabolic rate have higher levels of sleep deprivation-induced damage. We hypothesized that sleep serves to repair damage caused by oxidative stress.38,39 REM sleep amounts are positively correlated with non-REM sleep amounts, suggesting that REM sleep may work in concert with non-REM sleep. One persistent hypothesis that has been raised in several forms is that REM sleep serves to stimulate the brain to prepare for waking after a period of non-REM sleep.23,40,41 Most of the variation in REM sleep amounts is independent of non-REM sleep duration. The phylogenetic data indicate that animals born in a relatively immature state have more REM sleep early in development. One may hypothesize that in these immature animals REM sleep’s activation of the brain facilitates development. In animals that are more mature at birth, this process may have occurred in utero and continued postnatally in their direct interactions with the environment in waking. Immature animals are obviously not able to interact with the environment in the same way. A major mystery that remains is why immaturity at birth should be correlated with REM sleep time in adulthood. Marine mammals have sleep patterns that differ greatly from those seen in other animals. They show unihemispheric sleep, with both hemispheres never being in deep sleep at the same time. They can sleep while swimming, apparently controlling muscles bilaterally. Finally, they appear to have little or no REM sleep. Understanding the mechanisms and functional relations underlying these unusual sleep adaptations of marine mammals can offer a major insight into the function and mechanisms of sleep.

REFERENCES 1. Siegel, J.M., The evolution of REM sleep, in Lydic, R., Baghdoyan, H.A., Eds., Handbook of Behavioral State Control, CRC Press, Boca Raton, FL, 1999, p. 87. 2. Flanigan, W.F., Sleep and wakefulness in iguanid lizards, Ctenosaura pectinata, and Iguana iguana, Brain Behav. Evol., 8, 401, 1973.

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3. Huntley, A.C., Electrophysiological and behavioral correlates of sleep in the desert iguana, Dipsosaurus dorsalis hallowell, Comp. Biochem. Physiol., 86A, 325, 1987. 4. Ayala-Guerrero, F., Huitron-Resendiz, S., and Mancilla, R., Characterization of the raphe nuclei of the reptile Ctenosaura pectinata, Physiol. Behav., 50, 717, 1991. 5. DeVera, L., Gonzalez, J., and Rial, R.V., Reptilean waking EEG: slow waves, spindles, and evoked potentials, Electroenceph. Clin. Neurophysiol., 90, 298, 1994. 6. Flanigan, W.F., Sleep and Wakefulness in Chelonian Reptiles II. The Red-Footed Tortoise, Gechelone Carbonaria, Arch. Ital. Biol., 112, 253, 1974. 7. Flanigan, W.F., Knight, C., Hartse, and K., Rechtschaffen, A., Sleep and wakefulness in chelonian reptiles. 1. The box turtle, Terrapene carolina, Arch. Ital. Biol., 112, 227, 1974. 8. Flanigan, W.F., Knight, C.P., Hartse, and K.M., Rechtschaffen, A., Sleep and wakefulness in chelonian reptiles I. — the box turtle, Terrapene carolina, Arch. Ital. Biol., 112, 227, 1974. 9. Peyrethon, J. and Dusan-Peyrethon, D., Etude polygraphique de cycle veille-sommeil chez trois genres de reptiles, Seanc. Soc. Biol., 162, 181, 1968. 10. Shapiro, C.M. and Hepburn, H.R., Sleep in a Schooling Fish, Tilapia, mossambica, Physiology and Behavior, 16, 613, 1976. 11. Tobler, I. and Borbely, A.A., Effect of rest deprivation on motor activity of fish, J. Comp. Physiol. A., 157, 817, 1985. 12. Shaw, P.J., Tononi, G., Greenspan, R.J., and Robinson, D.F., Stress response genes protect against lethal effects of sleep deprivation in Drosophila, Nature, 417, 287, 2002. 13. Shaw, P.J., Cirelli, C., Greenspan, R.J., and Tononi, G., Correlates of sleep and waking in Drosophila melanogaster, Science, 287, 1834, 2000. 14. Hendricks, J.C., Finn, S.M., Panckeri, K.A., Chavkin, J., Williams, J.A., Sehgal, A., Pack, A.I., Rest in Drosophila is a sleep-like state, Neuron, 25, 129, 2000. 15. Zepelin, H., Mammalian sleep, in Kryger, M.H., Roth, T., and Dement, W.C., Eds., Principles and Practice of Sleep Medicine, W.B. Saunders, Philadelphia, 2000, p. 82. 16. Amlaner, C.J. and Ball, N.J., Avian sleep, in Kryger, M.H., Roth, T., Dement, W.C., Eds., Principles and Practice of Sleep Medicine, W.B. Saunders Company, Philadelphia, 1994, p. 81. 17. Mukhametov, L.M., Supin, A.Y., and Polyakova, I.G., Interhemispheric asymmetry of the electroencephalographic sleep patterns in dolphins, Brain Res., 134, 581, 1977. 18. Schmidt, M.H., Valatx, J.L., Sakai, K., Fort, P., and Jouvet, M., Role of the lateral preoptic area in sleep-related erectile mechanisms and sleep generation in the rat, J. Neurosci., 20, 6640, 2000. 19. Moore, C.A., Fishman, I.J., and Hirshkowitz, M., Evaluation of erectile dysfunction and sleep-related erections, J. Psychosom. Res., 42, 531, 1997. 20. Affanni, J.M., Cervino, C.O., and Marcos, H.J., Absence of penile erections during paradoxical sleep, Peculiar penile events during wakefulness and slow wave sleep in the armadillo, J. Sleep Res., 10, 219, 2001. 21. Allison, T. and Cicchetti, D.V., Sleep in mammals: Ecological and constitutional correlates, Science, 194, 732, 1976. 22. Rechtschaffen, A., Siegel, J.M., Sleep and Dreaming, in Kandel, E.R., Schwartz, J.H., and Jessel, T.M., Eds., Principles of Neuroscience, McGraw Hill, New York, 2000, p. 936. 23. Snyder, F., Toward an evolutionary theory of dreaming, Amer. J. Psychiat., 123, 121, 1966. 24. Jouvet-Mounier, D., Astic, L., and Lacote, D., Ontogenesis of the states of sleep in rat, cat, and guinea pig during the first postnatal month, Dev. Psychobiol., 2, 216, 1970.

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25. Siegel, J.M., The REM sleep-memory consolidation hypothesis, Science, 294, 1058, 2001. 26. Allison, T., Van Twyver, H., and Goff, W.R., Electrophysiological studies of the echidna, Tachyglossus aculeatus, I. Waking and sleep, Arch. Ital. Biol., 110, 145, 1972. 27. Allison, T. and Van Twyver, H., Electrophysiological Studies of the Echidna, Tachyglossus aculeatus, II. Dormancy and Hibernation, Arch. Ital. Biol., 110, 185, 1972. 28. Siegel, J.M., Manger, P., Nienhuis, R., Fahringer, H.M., and Pettigrew, J., The echidna Tachyglossus aculeatus combines REM and non-REM aspects in a single sleep state: implications for the evolution of sleep, J. Neuroscience, 16, 3500, 1996. 29. Siegel, J.M., Brainstem mechanisms generating REM sleep. In, Kryger, M.H., Roth, T., Dement, and W.C., Eds., Principles and Practice of Sleep Medicine, W.B. Saunders Company, 2000, p. 112. 30. Nicol, S.C., Andersen, N.A., Phillips, N.H., and Berger, R.J., The echidna manifests typical characteristics of rapid eye movement sleep, Neurosci. Lett. 2000, Mar. 31; 28, 3 (1): 49–52, 283, 49, 2000. 31. Siegel, J.M., Manger, P.R., Nienhuis, R., Fahringer, H.M., Shalita, T., and Pettigrew, J.D., Sleep in the platypus, Neuroscience, 91, 391, 1999. 32. Mukhametov, L.A., Supin, A.Y., and Polyakova, I.G., Interhemispheric asymmetry of the electroencephalographic sleep patterns in dolphins, Brain Res., 134, 581, 1977. 33. Lyamin, O.I., Mukhametov, L.M., Siegel, J.M., Nazarenko, E.A., Polyakova, I.G., and Shpak, O.V., Unihemispheric slow wave sleep and the state of the eyes in a white whale, Behavioral Brain Res., 129, 125, 2002. 34. Pfurtscheller, G., Neuper, C., and Mohl, W., Event-related desynchronization (ERD) during visual processing, Int. J. Psychophysiol., 16, 147, 1994. 35. Rattenborg, N.C., Amlaner, C.J., and Lima, S.L., Unilateral eye closure and interhemispheric EEG asymmetry during sleep in the pigeon (Columba livia), Brain Behav. Evol., 58, 323, 2002. 36. Oleksenko, A.I., Mukhametov, L.M., Polykova, I.G., Supin, A.Y., and Kovalzon, V.M., Unihemispheric sleep deprivation in bottlenose dolphins, J. Sleep Res., 1, 40, 1992. 37. Eiland, M.M., Lyamin, O.I., and Siegel, J.M., State-related discharge of neurons in the brainstem of freely moving box turtles, Terrapene carolina major, Arch. Ital. Biol., 139, 23, 2001. 38. Eiland, M.M., Ramanathan, L., Gulyani, S., Gilliland, M., Bergmann, B.M., Rechtschaffen, A., and Siegel, J.M., Increases in amino-cupric-silver staining of the supraoptic nucleus after sleep deprivation, Brain Res., 945, 1, 2002. 39. Ramanathan, L., Gulyani, S., Nienhuis, R., and Siegel, J.M., Sleep deprivation decreases superoxide dismutase activity in rat hippocampus and brainstem, Neuroreport, 13, 1387, 2002. 40. Ephron, H.S. and Carrington, P., Rapid eye movement sleep and cortical homeostasis, Psychol. Rev., 73, 500, 1966. 41. Vertes, R.P., A life-sustaining function for REM sleep: a theory, Neurosci. Biobehav. Rev., 10, 371, 1986.

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10

Sleep, Synaptic Plasticity, and the Developing Brain Marcos Gabriel Frank

CONTENTS Introduction Ontogenesis of Mammalian Sleep and Sleep Regulation Dissociation Concordance Maturation Experimental Approaches to Neonatal Sleep Function Correlation- and Association-Based Studies and Pharmacological Sleep Suppression Sleep and Visual System Development Sleep and the Developing Lateral Geniculate Nucleus (LGN) Sleep and Developmentally Regulated Cortical Plasticity Further Considerations Theories of Sleep Function in Developing Animals The Ontogenetic Hypothesis Sleep and the Consolidation of Experience Summary References

INTRODUCTION In a variety of mammalian species, sleep amounts are highest during neonatal periods of rapid brain development and synaptic plasticity than at any other time of life;24,39,64 therefore if sleep contributes to synaptic plasticity, one would expect this to be especially true in developing animals. This chapter reviews evidence in support of this hypothesis. It begins with an overview of several landmark events in the ontogenesis of sleep and sleep regulation to provide context to the more function-based discussions that follow. It then discusses the results of several studies that provide indirect or suggestive evidence of a role for sleep in general brain maturation. This is followed by a review of findings in the developing visual system that more specifically address a possible role for sleep in developmental synaptic plasticity.

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The chapter concludes with a discussion of several theories regarding sleep function in developing animals.

ONTOGENESIS OF MAMMALIAN SLEEP AND SLEEP REGULATION The ontogenesis of mammalian sleep can be broadly divided into three general stages, here referred to as the dissociation, concordance, and maturation stages.14 The dissociation stage of sleep ontogeny is characterized by the absence of clear polysomnographic features of REM and non-REM sleep. The concordance stage represents the period of time when distinct, immature forms of REM and non-REM sleep are first detected. The further maturation of these immature sleep states and the emergence of regulatory sleep mechanisms occur in the third developmental stage.14

DISSOCIATION Recordings of electrographic and autonomic activities in very young, developing mammals do not reveal clear signs of REM and non-REM sleep, reflecting the extreme immaturity of the nervous system at this time.14,26 Although distinct couplings of autonomic and brain activities typical of adult sleep are not observed, independent oscillations in these systems can occur. In precocial species, such as the guinea pig, dissociation is present in the fetal period. In altricial species, which complete a larger portion of their neural development ex utero, this stage appears to extend into the postnatal period.14,26

CONCORDANCE During the concordance stage of sleep ontogeny, independent oscillations in autonomic function and brain activity begin to coalesce into discrete episodes that appear to be immature forms of REM and non-REM sleep. In precocial species and humans, this concordance begins in utero, whereas in altricial species this begins ex utero, generally in the second postnatal week. The precise timing of this event in altricial species is not known, with some investigators reporting the presence of pre-EEG ‘precursor’ states several days before the appearance of EEG defined vigilance states.14,23 The nature of these putative precursor states is controversial. According to some investigators, the precursor states known as active sleep and quiet sleep are homologous to REM and non-REM sleep; however it is also possible that they are more closely related to the spontaneous cyclic activity typical of the immature nervous system.14,23

MATURATION In the third stage of sleep ontogeny, the now polysomnographically identified states of REM and non-REM sleep rapidly develop and begin to more closely resemble adult forms of sleep. There are rapid increases in the amplitude of the EEG in both non-REM and REM sleep, and stereotyped patterns of neuronal activity, such as

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pontine-geniculate-occipital (PGO) waves in REM sleep are first observed.14 In addition, distinct ultradian, homeostatic, and circadian regulatory mechanisms begin to organize sleep into patterns similar to those found in adult mammals. In the rat homeostatic regulation of non-REM sleep is present soon after the appearance of EEG-defined states (postnatal day [P]12). Sleep deprivation in preweanling neonatal rats results in robust compensatory changes in non-REM sleep amounts and consolidation, followed in the fourth postnatal week by increases in non-REM slowwave activity.28 Conversely homeostatic regulation of REM sleep has a much more protracted development. Total and selective REM sleep deprivation fails to induce compensatory REM rebounds until the third postnatal week in the rat.21,28 Much less is known regarding ultradian and circadian organization of neonatal sleep states; e.g., in the developing cat, ultradian periodicities in REM and non-REM sleep are generally not observed until the third or fourth postnatal week.36 Circadian regulation of sleep and wake may begin as early as the second postnatal week in the rat but is not consistently reported until the fourth-fifth postnatal week.23 In addition to the initial absence of regulatory mechanisms and the presence of rudimentary forms of neurophysiologic activity, neonatal sleep during this stage differs from adult sleep in several important ways: • • •

The amount of REM sleep is greatly elevated, declining to adult levels during the course of postnatal maturation.24,39,64 Latencies to REM sleep are shortened, and sleep-onset REM periods (SOREMs) are quite frequent.30,39,48 Sleep is generally more fragmented, possibly reflecting the absence of strong circadian and ultradian mechanisms at these ages.14

EXPERIMENTAL APPROACHES TO NEONATAL SLEEP FUNCTION CORRELATION- AND ASSOCIATION-BASED STUDIES AND PHARMACOLOGICAL SLEEP SUPPRESSION This section reviews findings from two classes of experiments that provide evidence that sleep has a generally permissive effect on brain development. The first class of experiments has shown associations or correlations between the amount of sleep, or sleep phasic activity, and certain indices of brain development. Mirmiran and colleagues reported that placing juvenile rats in enriched environments resulted in increased brain weight and increased amounts of REM sleep.53 Similar enhancements of REM sleep are also reported in adult animals during learning tasks and may be necessary for the consolidation of the learned material.5 Associations were also reported between the frequency of REMs and subsequent eye-opening in the rat, suggesting that the former events represent preparatory activation of visual motor circuits.78 Further suggestive evidence for a role for sleep in brain development comes from a study in the ovine fetus. In agreement with findings in adult animals,54,61 Czikk and colleagues found that cerebral protein synthesis (as measured by 14[C]L

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eucine uptake) was elevated during fetal non-REM sleep, suggesting that this sleep state may promote morphological or structural changes in the developing brain.10 The second class of experiments employ REM sleep deprivation (RSD) in the postnatal period followed by behavioral, neurological, and biochemical assessments in adulthood. Because sleep pressure is very high in developing animals, the majority of these experiments have used pharmacological means of RSD (anti-depressant medications, or related REM sleep-inhibiting compounds). Pharmacological RSD in the neonatal period is reported to induce a number of neurochemical and behavioral changes in adult rats, including changes in REM sleep architecture,8,51,52,80 circadian rhythms,17,41,83 anxiety, and sexual behavior,32–34,79 and alterations in neurotransmission in cholinergic and monoaminergic sytems.31,34,59,60 However many of the behavioral effects are not uniform across studies (even within the same laboratory) and vary depending on the drug used in a given experiment.22,25 The interpretation of these results is further complicated by the fact that it is unknown if the observed deficits are caused by REM sleep loss or non-specific teratogenetic effects induced by these compounds. There are additional reasons to doubt the claim that neonatal REM sleep suppression is an important factor in the reported results following pharmacological RSD. Gentle forms of mechanical RSD do not produce the suite of deficits reported after neonatal clomipramine exposure.50 More vigorous mechanical RSD is reported to produce some effects similar to drug-induced behavioral changes,20 but regrettably the technique employed (periodic shaking of the rat pup) replaces one confounding variable (nonspecific teratogenicity) with another (neonatal stress). Many deficits reported after pharmacological REM sleep deprivation are more easily explained by persistent alterations in monoaminergic function. For example, the changes in adult sleep architecture ascribed to pharmacological RSD are only observed following neonatal treatments with agents that alter serotonergic neurotransmission (e.g., serotonin uptake inhibitors). Other REM-sleep inhibiting compounds delivered neonatally have no effect on subsequent adult sleep patterns.25 Likewise the changes in anxiety and sexual behavior reported after neonatal REM sleep deprivation are more likely due to changes in serotonergic neurotransmission than pharmacological RSD; for example, compounds that reduce serotonin and REM sleep in neonatal rats decrease anxiety and increase sexual behavior in adulthood, effects that are precisely opposite to those reported after neonatal clomipramine administration.2,19,82 Because both compounds decrease REM sleep but have opposite effects on serotonin levels, it is unlikely that RSD contributes in a significant way to the behavioral changes noted in adult rats following neonatal antidepressant treatment.

SLEEP

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VISUAL SYSTEM DEVELOPMENT

Sleep and the Developing Lateral Geniculate Nucleus (LGN) More persuasive evidence for a role for sleep in developmental plasticity comes from experiments that combine sleep manipulation with assays of visual system development. In the developing visual system, endogenous activity in retinal and thalamocortical circuits helps establish initial patterns of synaptic circuitry that are

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elaborated and sculpted by experience during subsequent critical periods of postnatal development.71,76 The initial development of the central visual pathways and their subsequent sculpting by experience occur at ages when sleep amounts are very high or during dramatic changes in sleep expression.14 The following sections consider the evidence that sleep contributes to both of these processes in the development of two important components of the central visual pathway, the lateral geniculate nucleus (LGN) and primary visual cortex (V1). A role for REM sleep in brain maturation has been examined by studying the effects of RSD (using mechanical techniques), or the elimination of REM sleep PGO waves (RSPD), on subsequent visual system development. Davenne and Adrien examined changes in neuronal morphology in the LGN in kittens after lesioning PGO generating centers in the brainstem.12 Bilateral electrolytic lesions in the rostral pontine tegmentum abolished PGO waves in the neonatal cat, resulting in smaller LGN volumes and reduced LGN soma sizes. These findings were extended in a second study, which showed that PGO wave elimination in kittens produced much slower LGN responses to stimulation of the optic chiasm (compared to sham or unilaterally lesioned control cats) and more LGN cells with mixed On-Off responses (as opposed to pure On or Off responses to an annulus of light centered in the receptive field) and fewer X cell responses (relative to On-Off responses).13 These morphological and functional changes in LGN cells are consistent with delayed development in the LGN11,81 and suggest that REM sleep neuronal activity may be necessary for normal LGN development. Sleep may also be important for the later occurring, critical periods of visual development. The critical period has been traditionally investigated by surgically closing an eye (monocular deprivation [MD]), which rapidly alters responsiveness and morphology in both subcortical and cortical neurons.67,73 Pompeiano et al (1995) reported that total sleep deprivation combined with MD increased the effects of MD on LGN cell morphology.57 Unfortunately, this study is difficult to interpret because the amount of visual experience was not controlled and very little quantitative data on sleep architecture were presented. Stronger evidence for a role for sleep in subcortical development is reported when different forms of selective RSD or RSDP are combined with MD. Oksenberg et al. found that 1 week of RSD in kittens (using the pedestal technique) augmented the effects of MD on cell morphology in the binocular segment of the LGN.56 LGN cells innervated by the occluded eye were smaller in kittens deprived of REM sleep and vision in one eye, resulting in a greater difference in the size of LGN cells activated by the open and deprived eyes. A comparable increase in LGN cell size disparity was found when MD was combined with brainstem lesions that remove PGO waves. In this case LGN cells receiving input from the open eye appeared to increase in size.69 An additional finding is that RSD combined with MD also reduces cell sizes in the monocular segment of the LGN, which does not depend upon competitive interactions between the two eyes.68 Work from this laboratory has also shown that RSD for one week reduces immunoreactivity for the calcium binding protein parvalbumin in GABAergic interneurons in the developing LGN.35 These latter findings are particularly interesting because parvalbumin may play a role in certain forms of synaptic plasticity.7 In sum,

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these results suggest that RSD or RSDP may influence LGN maturation during critical periods of visual system development. Sleep and Developmentally Regulated Cortical Plasticity Sleep may also play an important role in developmentally regulated forms of cortical plasticity. REM sleep, for example, appears to influence a form of long-term potentiation (LTP) elicited during the critical period for visual system development.70 In this type of LTP, high-frequency white-matter stimulation in cortical slices prepared P28–P30 rats produces synaptic potentiation in cortical layers II and III. This form of LTP decreases with age (P35+) and is not observed in cortical slices from adult rats.40 Using a less stressful version of the pedestal technique of RSD (multiple small-platforms), Shaffery et al. measured the effects of 1 week of RSD on this form of LTP in rat visual cortex.70 The authors reported that 1 week of RSD prolonged the critical period for the developmentally regulated form of LTP; LTP was evoked from slices of visual cortex from RSD rats at ages when this type of LTP is not normally observed (P34–P40). A similar extension of the critical period was not seen in cortical slices from control rats that were left in their nests, or from rats placed on larger platforms (largeplatform control) that presumably permitted REM sleep. Conversely, RSD had no effect on a nondevelopmentally regulated form of LTP evoked by layer IV stimulation. The extension of the critical period by RSD was similar to effects produced by dark rearing, which also prolongs the period of induction of this form of LTP.70 These findings suggest a maturational delay in visual cortex, and are in general agreement with previous findings from the same group suggesting that RSD impairs normal brain maturation. Evidence that sleep contributes to developmental cortical plasticity has also been demonstrated in vivo. We investigated the role of sleep in cortical plasticity by combining MD with periods of sleep or sleep deprivation.27 Kittens at the height of the critical period were divided into four experimental groups, all of which had one eye closed and were kept awake in a lighted environment for 6 hours. This MD period provided a common stimulus for the synaptic remodeling in all groups. The four groups differed in their experience thereafter. Cats in the baseline group (MD6) were quickly anesthetized for physiological measurement of ocular dominance in primary visual cortex using optical imaging of intrinsic cortical signals and extracellular unit recording. Cats in a second group (MDS) were allowed to sleep for an additional 6 hours in complete darkness before making optical and unit recordings. The third group of kittens (MDSD) were treated identically to those in the MDS group except that they were kept awake during the 6 hours in complete darkness before the recordings. The fourth group (MD12) was also kept awake for 6 additional hours but remained in a lighted environment, effectively giving them 6 additional hours of monocular deprivation before the recordings. These experiments determined whether: •

The effects of MD were enhanced by subsequent sleep (MD6 compared to MDS).

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•

•

The enhancement of plasticity observed in group MDS required sleep or simply a period of time following the inducing stimulus (MDS compared to MDSD). The mechanical sleep deprivation indirectly impeded ocular dominance plasticity (MD12 compared to MDSD).

Optical imaging of intrinsic cortical signals and extracellular unit recording showed that sleep nearly doubled the effects of MD on visual cortical responses, and wakefulness in complete darkness tended to erase the effects of the preceding monocular visual experience. No brain state other than sleep is known to have such augmenting effects on ocular dominance plasticity because anesthetic states and cortical inactivation suppress ocular dominance plasticity.29,37,62,63 The enhancement of plasticity by sleep was similar to that produced by an equal amount of additional MD. Although the precise contribution of REM and non-REM sleep to this process is still unknown, we did find that the enhancement of cortical plasticity was highly correlated with non-REM sleep time, suggesting an important role for non-REM sleep in the rapid cortical synaptic remodeling elicited by MD.27 Further Considerations The findings discussed above support a role for sleep in visual system development, but a number of caveats should be kept in mind. One must consider potential side effects of the experimental manipulation used in each study; for example, sleep deprivation indirectly influences behavior and neurochemistry in ways that may influence the results of an experiment irrespective of sleep changes.5,72 Moreover manipulations performed in one state may influence neural processes in other vigilance states as well, making it difficult to determine which vigilance state is responsible for the observed effects. In all of the experiments reviewed above, experimental changes in sleep structure, or lesions that damage parts of the brain active in sleep, were used to test the role of sleep in visual development. Many of these manipulations are likely to have complicated effects on neural development and behavior in addition to their effects on sleep. In studies using brainstem lesions, it is not clear if the reported deficits are due to PGO reduction, or the elimination of ascending innervation to target LGN neurons from cholinergic and monoaminergic brainstem projections.16,75 These afferents not only provide tonic excitatory input to the LGN, they may also promote neural growth and maturation.43,44 It is therefore possible that bilateral removal of this input, rather than the elimination of REM sleep PGO waves, may partially account for the results reported in the Davenne studies (though this an unlikely factor in the Shaffery study since cell sizes increased following PGO elimination). The role of stress should also be considered in studies using sleep deprivation. RSD using the pedestal technique is stressful because the animal periodically contacts water and in some cases is unable to properly groom itself. Repeated stress can increase neuronal degeneration1,66,84 and modifies synaptic plasticity in complex ways.15 Thus although the enhancement of the anatomical effects of MD in the LGN by RSD is consistent with a maturational delay induced by RSD, it may also reflect

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the influence of stress hormones on degenerative processes triggered by sensory deprivation. This seems an especially important consideration, given that the anatomical effects of RSD were reported in the monocular segment of the LGN, an area not especially influenced by developmental critical periods. Stress, however, likely plays a minor role in studies using sleep deprivation combined with in situ LTP, immunohistochemical assays of calcium binding proteins (parvalbumin), or cortical plasticity in vivo. In the Shaffery et al. investigation, the chronic stress hormone release induced by long-term RSD should impair, not extend, susceptibility to LTP.15 The RSD induced down-regulation of parvalbumin in the LGN is also unlikely due to stress. Increases in stress hormones have no effect on parvalbumin concentrations in the hippocampus-a brain region sensitive to circulating glucocorticoid levels.42,66 Moreover, the acute release of stress hormones elicited by very short periods of TSD tends to enhance, not impair, synaptic plasticity15 and thus is not a factor in the results reported in Frank et al. (2001).27 A second issue that complicates the interpretation of some studies occurs when the experimental manipulation alters vigilance states in ways likely to influence neuronal activity during wake as well as subsequent processing during REM and non-REM sleep; for example, RSD increases noradrenergic activity in the CNS,38,58 which enhances signal detection in sensory neurons during wake.3,74 Even when total non-REM sleep amounts are preserved, RSD frequently alters non-REM sleep architecture (fragmentation, loss of deeper stages of non-REM sleep).4,18 Thus in experimental designs that employ prolonged RSD combined with periods of sensory input, the observed changes in neuronal morphology and plasticity may result from many factors including RSD, changes in wake neural processing, and subtle changes in non-REM sleep. Determining the cause of developmental effects is also difficult in studies using brainstem lesions, because they interrupt ascending pathways that profoundly influence neuronal processing in sleep and wake.

THEORIES OF SLEEP FUNCTION IN DEVELOPING ANIMALS THE ONTOGENETIC HYPOTHESIS In their classic study in human infants, Roffwarg and colleagues proposed that the large amounts of REM sleep in early infancy provide an important source of endogenous neural activity necessary for brain maturation.64 In the more recent formulations of the Ontogenetic Hypothesis, it is suggested that REM sleep not only promotes normal brain development but also insulates the brain from excessive experience-dependent plasticity.56,65 Both functions are thought to be mediated by the PGO waves, or heightened release of acetylcholine during REM sleep. The Ontogenetic Hypothesis and its variants,47,49 is intuitively appealing since REM sleep amounts are unusually high in infants, and decrease as the brain develops. It is supported by findings that indicate that RSD or RSDP can modify morphological and electrophysiological development of the LGN, and developmentally regulated cortical synaptic plasticity in situ (see previous discussion). The theory that REM

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sleep offsets waking experience in infants has less direct support, but is suggested by three findings: •

• •

REM sleep PGO waves in adult cats activate all LGN lamina simultaneously, indicating that this activity, in contrast to visual experience, is not eye-specific.46 Theoretically such nonspecific activation of neural circuits, if present in developing animals, could counter-balance the more specific, experience-dependent activation of neural circuitry present in wake. In contrast to normal adult mammals, latencies to REM sleep in infants are very short, and sleep-onset REMs (SOREMs) frequently occur.39,48 In our study cortical plasticity was negatively correlated with REM sleep amounts, suggesting that REM sleep inhibits experience-dependent plasticity.27

Thus it is possible that the short latencies to REM sleep and SOREMs in neonates interfere with the consolidation of experience-dependent changes in neural circuitry. Despite the appeal of the Ontogenetic Hypothesis, several issues remain to be resolved. As discussed previously, potential side effects of the procedures used in RSD and RSDP stress complicate the experimental support for the Ontogenetic Hypothesis. A second issue is that sleep in newborns may not be identical or homologous to adult REM sleep,26 and even when unambiguous periods of REM sleep are observed, the neurophysiological phenomena typical of adult REM sleep (e.g., PGO waves) are not always present; for example, REM sleep PGO waves in the kitten are not reported at ages when REM sleep is maximally expressed.6 It is also unknown if other aspects of REM sleep, such as heightened cholinergic activity, are present in newborn animals. Considering the slow maturation of cholinergic systems,9,45,55 and the late appearance of other REM sleep phenomena,85 this seems rather unlikely. Indeed, the majority of studies suggesting a developmental role for REM sleep have been performed at ages when REM sleep has already declined to near adult levels.35,56,68–70 A fourth and final point is that the Ontogenetic Hypothesis does not consider the potential role of non-REM sleep in brain development. This may be a historical oversight, considering the emphasis REM sleep has received in the past, but there are now several findings linking non-REM sleep to synaptic plasticity and neuronal development.5 In summary, although there are data to support predictions of the Ontogenetic Hypothesis, they are limited to a narrow, developmental period and are restricted to REM sleep. In addition their interpretation must await further experiments that determine the homology between infant and adult sleep states, and more carefully control for indirect effects induced by RSD and RSDP.

SLEEP

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CONSOLIDATION

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EXPERIENCE

The correlation between non-REM sleep amounts and the effects of MD on cortical plasticity suggests that non-REM sleep may also play an important role in brain development.27 The augmentation of experience-dependent synaptic plasticity is consistent with findings in adult animals, where non-REM sleep has been linked to

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learning and memory consolidation, and neuronal events that contribute to synaptic remodeling (reviewed in Reference 27). A role for non-REM sleep in developmental cortical plasticity is further suggested by ontogenetic changes in non-REM sleep that coincide with periods of heightened cortical plasticity. In the cat there is a rapid decline in REM sleep and a corresponding increase in non-REM sleep amounts near the beginning of the critical period for visual development.39 In rats the beginning of the critical period for visual development coincides with the development of non-REM sleep homeostasis. Sleep deprivation does not increase non-REM sleep EEG activity until the fourth postnatal week, indicating that the regulatory relationship between wake and non-REM sleep develops in parallel with periods of heightened cortical plasticity.28 These findings suggest that non-REM sleep may consolidate waking experience; a process that begins during critical periods of brain development when the animal is most sensitive to waking experience, but is retained throughout life. While it appears that one function of non-REM sleep may be to consolidate waking experience, it is likely that this sleep stage has other functions in the developing brain as well. In altricial species such as the rat and cat, the most rapid increase in non-REM sleep occurs several weeks before the critical period for visual development.24,39 In precocial species such as the sheep, non-REM sleep amounts are near adult levels in utero, a time when exogenous visual experience is minimal.77 Given that the appearance of non-REM sleep coincides with periods of extensive neocortical development in all mammalian species, and that it is homeostatically regulated within 24 to 48 hours of its electrographic appearance,14 it is possible that non-REM sleep promotes the formation of rudimentary circuitry that is subsequently shaped by experience.

SUMMARY The abundance of sleep during periods of rapid brain maturation and synaptic plasticity suggest a role for sleep in brain development. The strongest experimental support for this view has come from studies in the visual system, where it has been shown that sleep and sleep loss modify developmental processes in the LGN and in primary visual cortex. In particular RSD and RSDP trigger several morphological and electrophysiological changes in the LGN, and modify cortical plasticity in situ. Non-REM sleep appears to be necessary for the consolidation of visual experience during critical periods of experience-dependent cortical plasticity in vivo. These findings indicate that both sleep states could be important for neuronal development and plasticity, although the contribution of each state might be quite different. Although the precise role of each sleep state in brain development is still unknown, current findings suggest that the relative amounts of REM and non-REM sleep during infancy may critically influence brain maturation. REM sleep is maximally expressed when endogenous neuronal activity is critical for the establishment of rudimentary neural circuitry in the visual system. Although non-REM sleep might also be important for this latter process, it seems to be more strongly associated with synaptic changes elicited by experience, because it rapidly matures after eye

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opening24,30,39 and becomes homeostatically regulated by wake in a manner similar to adult non-REM sleep during critical periods of experience-dependent synaptic plasticity.28 Thus it seems plausible that although both REM and non-REM sleep promote the formation of rudimentary circuits, non-REM sleep additionally consolidates changes in neural circuitry elicited by waking experience. In conclusion, many phenomena strongly suggest a role for sleep in brain development and plasticity, but definitive evidence is still lacking. Basic questions about sleep in developing animals are still unanswered, and we currently know little about the cellular and molecular mechanisms by which sleep might modify synaptic plasticity. The opportunities afforded by advances in genetic and pharmacological manipulation of specific molecules and signaling pathways, and by chronic recording and manipulation of neural activity, raise the hope that these questions may soon be answered.

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13. Davenne D., Fregnac Y., Imbert M., and Adrien J., Lesion of the PGO pathways in the kitten, II. Impairment of physiological and morphological maturation of the lateral geniculate nucleus, Brain Res., 485, 267, 1989. 14. Davis F.C., Frank M.G., and Heller H.C., Ontogeny of sleep and circadian rhythms, in Regulation of Sleep and Circadian Rhythms, Zee P.C. and Turek F.W., Eds., Marcel Dekker, New York, 1999, p. 19. 15. De Kloet E.R., Oitzl M.S., and Joels M., Stress and cognition: are corticosteroids good or bad guys?, TINS, 22, 422, 1999. 16. Derrington A., The lateral geniculate nucleus, Current Biol., 11, R635, 2001. 17. Dwyer S.M. and Rosenwasser A.M., Neonatal clomipramine treatment, alcohol intake and circadian rhythms in rats, Psychopharmacology, 138, 176, 1998. 18. Endo T., Schwierin B., Borbely A.A., and Tobler I., Selective and total sleep deprivation: effect on the sleep EEG in the rat, Psychiatry Res., 66, 97, 1997. 19. Farabollini F., Hole D.R., and Wilson C.A., Behavioral effects in adulthood of serotonin depletion by p-chorophenylalanine given neonatally to male rats, Int. J. Neurosci., 41, 187, 1988. 20. Feng P., Postnatal REM sleep deprivation and depression: New findings and hypothesis, Actas de Fisiologica, 7, 141, 2001. 21. Feng P., Ma Y., and Vogel G.W., Ontogeny of REM rebound in postnatal rats, Sleep, 24, 645, 2001. 22. File S.E. and Tucker J.C., Neonatal clomipramine treatment in the rat does not affect social, sexual and exploratory behaviors in adulthood, Neurobehav. Toxicol. Teratol., 5, 3, 1983. 23. Frank M.G. and Heller H.C., Development of diurnal organization of EEG slow-wave activity and slow-wave sleep in the rat, Am. J. Physiol., 273, R472, 1997. 24. Frank M.G. and Heller H.C., Development of REM and slow-wave sleep in the rat, Am. J. Physiol., 272, R1792, 1997. 25. Frank M.G. and Heller H.C., Neonatal treatments with the serotonin uptake inhibitors clomipramine and zimelidine, but not the noradrenaline uptake inhibitor desipramine, disrupt sleep patterns in adult rats, Brain Res., 768, 287, 1997. 26. Frank M.G. and Heller H.C., The ontogeny of mammalian sleep: a reappraisal of alternative hypotheses, J. Sleep Res., 12, 25, 2003. 27. Frank M.G., Issa N.P., and Stryker M.P., Sleep enhances plasticity in the developing visual cortex, Neuron, 30, 275, 2001. 28. Frank M.G., Morrissette R., and Heller H.C., Effects of sleep deprivation in neonatal rats, Am. J. Physiol., 275, R148, 1998. 29. Freeman R.D. Effects of brief uniocular patching on kitten visual cortex, Trans. Opthal. Soc. U.K., 99, 382, 1979. 30. Gramsbergen A., The development of the EEG in the rat, Developmental Psychobiology, 9, 501, 1976. 31. Henderson M.G., McConnaughey M.M., and McMillen B.A., Long-term consequences of prenatal exposure to cocaine or related drugs: Effects on rat brain monoaminergic receptors, Brain Res. Bull., 26, 941, 1991. 32. Hilakivi L. and Sinclair J.D., Effect of neonatal clomipramine treatment on adult alcohol drinking in the AA and ANA rat lines, Pharmacol. Biochem. Behav., 24, 1451, 1986. 33. Hilakivi L.A. and Hilakivi I., Increased adult behavioral “despair” in rats neonatally exposed to desipramine or zimeldine: an animal model of depression?, Pharmacol. Biochem. Behav., 28, 367, 1987.

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34. Hilakivi L.A., Hilakivi I., Ahtee L., Haikala H., and Attila M., Effect of neonatal nomifensine exposure on adult behavior and brain monoamines in rats, J. Neural. Transm., 70, 99, 1987. 35. Hogan D., Roffwarg H.P., and Shaffery J.P., The effects of 1 week of REM sleep deprivation on parvalbumin and calbindin immunoreactive neurons in central visual pathways of kittens, J. Sleep Res., 10(4), 285–296, 2001. 36. Hoppenbrouwers T. and Sterman M.B., Development of sleep-state patterns in the kitten, Experimental Neurology, 49, 822, 1975. 37. Imamura K. and Kasamatsu T., Ocular dominance plasticity restored by NA infusion to aplastic visual cortex of anesthetized and paralyzed kittens, Exp. Brain Res., 87, 309, 1991. 38. Irwin M., Thompson J., Miller C., Gillin J.C., and Ziegler M., Effects of sleep and sleep deprivation on catecholamine and interleukin-2 levels in humans: clinical implications, J. Clinical Endocrinology and Metabolism, 84, 1979, 1999. 39. Jouvet-Mounier D., Astic L., and Lacote D., Ontogenesis of the states of sleep in rat, cat and guinea pig during the first postnatal month, Developmental Psychobiology, 2, 216, 1970. 40. Kirkwood A., Lee H.K., and Bear M.F., Co-regulation of long-term potentiation and experience-dependent synaptic plasticity in visual cortex, Nature, 375, 328, 1995. 41. Klemfuss H. and Gillin C.J., Neonatal scopolamine or antidepressant treatment: effect on development of hamster circadian rhythms, Pharmacol. Biochem. Behav., 59, 369, 1997. 42. Krugers H.J., Koolhaas J.M., Medema R.M., and Korf J., Prolonged subordination stress increases Calbindin-D28k immunoreactivity in the rat hippocampal CA1 area, Brain Res., 729, 289, 1996. 43. Lauder J.M., Hormonal and humoral influence on brain development, Psychoneuroendocrinology, 8, 121, 1983. 44. Lauder J.M. and Schambra U.B., Morphogenetic roles of acetylcholine, Environ. Health Pespect., 107, 65, 1999. 45. Lee W., Nicklaus K.J., Manning D.R., and Wolfe B.B., Ontogeny of cortical muscarinic receptor subtypes and muscarinic receptor-mediated responses in rat, J. Pharmacol. Exp. Ther., 252, 284, 1990. 46. Marks G.A., Roffwarg H.P., and Shaffery J.P., Neuronal activity in the lateral geniculate nucleus associated with ponto-geniculate-occipital waves lacks lamina specificity, Brain Res., 815, 21, 1999. 47. Marks G.A., Shaffery J.P., Oksenberg A., Speciale S.G., and Roffwarg H.P., A functional role for REM sleep in brain maturation, Behavioral Brain Res., 69, 1, 1995. 48. McGinty R.J., Stevenson M., Hoppenbrouwers T., Harper R.M., Sterman M.B., and Hodgman J., Polygraphic studies of kitten development: sleep state patterns, Developmental Psychobiology, 10, 455, 1977. 49. Mirmiran M. and Maas Y.G.H., The function of fetal/neonatal REM sleep, in Rapid Eye Movement Sleep, Mallick B.N. and Inoue S., Eds., Narosa Publishing House, New Delhi, 1999, p. 326. 50. Mirmiran M., Scholtens J., van de Poll N.E., Uylings H.B., van der Gugten J., and Boer G.J., Effects of experimental suppression of active (REM) sleep during early development upon adult brain and behavior in the rat, Brain Res., 283, 277, 1983. 51. Mirmiran M., Scholtens J., van de Poll N.E., Uylings H.B., van der Gugten J., and Boer G.J., Effects of experimental suppression of active (REM) sleep during early development upon adult brain and behavior in the rat, Brain Res., 283, 277, 1983.

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52. Mirmiran M., van de Poll N.E., Corner M.A., van Oyen H.G., and Bour H.L., Suppression of active sleep by chronic treatment with chlorimipramine during early postnatal development: effects upon adult sleep and behavior in the rat, Brain Res., 204, 129, 1981. 53. Mirmiran M., van den Dungen, H., and Uylings H.B., Sleep patterns during rearing under different environmental conditions in juvenile rats, Brain Res., 233, 287, 1982. 54. Nakanishi H., Sun Y., Nakamura R.K., Mori K., Ito M., Suda S., Namba H., Storch F.I., Dang T.P., and Mendelson W., Positive correlations between cerebral protein synthesis rates and deep sleep in Macaca mulatta, Eur. J. Neurosci., 9, 271, 1997. 55. Ninomiya Y., Koyama Y., and Kayama Y., Postnatal development of choline acetyltransferase activity in the rat laterodorsal tegmental nucleus, Neuroscience Lett., 308, 138, 2001. 56. Oksenberg A., Shaffery J.P., Marks G.A., Speciale S.G., Mihailoff G., and Roffwarg H.P., Rapid eye movement sleep deprivation in kittens amplifies LGN cell-size disparity induced by monocular deprivation, Developmental Brain Res., 97, 51, 1996. 57. Pompeiano O., Pompeiano M., and Corvaja N., Effects of sleep deprivation on the postnatal development of visual-deprived cells in the cat’s lateral geniculate nucleus, Archives Italiennes de Biologie, 134, 121, 1995. 58. Porrka-Heiskanen T., Smith S.E., Taira T., Urban J.H., Levine J.E., Turek F.W., and Stenberg D., Noradrenergic activity in rat brain during rapid eye movement sleep deprivation and rebound sleep, Am. J. Physiol. (Regulatory Integrative Comp. Physiol.), 268, R1456, 1995. 59. Prathiba J., Kumar K.B., and Karanth K.S., Effects of REM sleep deprivation on cholinergic receptor sensitivity and passive avoidance behavior in clomipramine model of depression, Brain Research, 867, 243, 2000. 60. Prathiba J., Kumar K.B., and Karanth K.S., Hyperactivity of hypothalamic pituitary axis in neonatal clomipramine model of depression, J. Neural Trans., 105, 1335, 1998. 61. Ramm P. and Smith C.T., Rates of cerebral protein synthesis are linked to slow-wave sleep in the rat, Physiol. Behav., 48, 749, 1990. 62. Rauschecker J.P. and Hahn S., Ketamine-Xylazine anesthesia blocks consolidation of ocular dominance changes in kitten visual cortex, Nature, 326, 183, 1987. 63. Reiter H.O., Waitzman D.M., and Stryker M.P., Cortical activity blockade prevents ocular dominance plasticity in the kitten visual cortex, Exp. Brain Res., 65, 182, 1986. 64. Roffwarg H.P., Muzio J.N., and Dement W.C., Ontogenetic development of the human sleep-dream cycle, Science, 604, 1966. 65. Roffwarg H.P. and Shaffery J.P., The ontogenetic hypothesis of REM sleep function: Its history, current status and prospects for confirmation, Sleep Research Online, 2, 714, 1999. 66. Sapolsky R.M. Stress, glucocorticoids, and damage to the nervous system: The current state of confusion, Stress, 1, 1, 1996. 67. Sengpiel F., Godecke I., Stawinski P., Hubener M., Lowel S., and Bonhoffer T., Intrinsic and environmental factors in the development of functional maps in cat visual cortex, Neuropharmacology, 37, 607, 1998. 68. Shaffery J.P., Oksenberg A., Marks G.A., Speciale S.G., Mihailoff G., and Roffwarg H.P., REM sleep deprivation in monocularly occluded kittens reduces the size of cells in LGN monocular segment, Sleep, 21, 837, 1998. 69. Shaffery J.P., Roffwarg H.P., Speciale S.G., and Marks G.A., Ponto-geniculo-occipital wave suppression amplifies lateral geniculate nuclues cell-size changes in monocularly deprived kittens, Developmental Brain Res., 114, 109, 1999.

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70. Shaffery J.P., Sinton C.M., Bissette G., Roffwarg H.P., and Marks G.A., Rapid eye movement sleep deprivation modifies expression of long-term potentiation in visual cortex of immature rats, Neuroscience, 110, 431, 2002. 71. Shatz C.J., Emergence of order in visual system development, PNAS, 93, 602, 1996. 72. Siegel J.M., The REM Sleep-Memory Consolidation Hypothesis, Science, 294, 1058, 2001. 73. Singer W., Neuronal mechanisms in experience dependent modification of visual cortex function, in Development and Chemical Sensitivity of Neurons, Cuenod M., Kreutzberg G.W., and Bloom F.E., Eds., Elsevier/North-Holland Biomedical Press, Amsterdam, 1979, p. 457. 74. Smiley J.F., Monoamines and acetylcholine in primate cerebral cortex: what anatomy tells us about function, Rev. Bras. Biol., 56, 153, 1996. 75. Steriade M., Arousal: revisiting the reticular activating system, Science, 272, 225, 1996. 76. Sur M. and Leamey C.A., Development and plasticity of cortical areas and networks, Nat. Rev. Neurosci., 2, 251, 2001. 77. Szeto H. and Hinman D.J., Prenatal development of sleep-wake patterns in sheep, Sleep, 347, 1985. 78. Van Someren E.J., Mirmiran M., Bos N.P., Lamur A., Kumar A., and Molenaar P.C., Quantitative analysis of eye movements during REM-sleep in developing rats, Dev. Psychobiol., 23, 55, 1990. 79. Vogel G., Neill D., Hagler M., and Kors D., A new animal model of endogenous depression: a summary of present findings, Neurosci. Biobehav. Rev., 14, 85, 1990. 80. Vogel G., Neill D., Kors D., and Hagler M., REM sleep abnormalities in a new animal model of endogenous depression, Neurosci. Biobehav. Rev., 14, 77, 1990. 81. Williams A.L. and Jeffery G., Growth dynamics of the developing lateral geniculate nucleus, J. Comparative Neurology, 430, 332, 2001. 82. Wilson C.A., Pearson J.R., Hunter A.J., Tuohy P.A., and Payne A.P., The effect of neonatal manipulation of hypothalamic serotonin levels on sexual activity in the adult rat, Pharmacol. Biochem. Behav., 24, 1175, 1986. 83. Yannielli P.C., Cutrera R.A., Cardinali D.P., and Golombek D.A., Neonatal clomipramine treatment of Syrian hamsters: effect on the circadian system, Eur. J. Pharmacol., 349, 143, 1998. 84. Yusim A., Ajilore O., Bliss T., and Sapolsky R.U., Glucocorticoids exacerbate insultinduced declines in metabolism in selectively vulnerable hippocampal cell fields, Brain Res., 870, 109, 2000. 85. Chase M.H., Brain stem somatic reflex activity in neonatal kittens during sleep and wakefulness, Physiol. Behav., 7, 165–172, 1971.

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11

Changes in Brain Gene Expression between Sleep and Wakefulness Giulio Tononi and Chiara Cirelli

CONTENTS Introduction References

INTRODUCTION In order to determine the molecular changes that occur in the brain during the sleepwaking cycle and after sleep deprivation, we have performed a systematic screening of brain gene expression in sleeping, spontaneously awake, and sleep-deprived rats. The data summarized here refer to the completed analysis of >20,000 transcripts expressed in the cerebral cortex. The expression of the majority (~95%) of these genes does not change between sleep and wakefulness or after sleep deprivation, even when forced wakefulness is prolonged for several days. A few hours of wakefulness, either spontaneous or due to sleep deprivation, increase the expression of several transcripts involved in energy metabolism, excitatory neurotransmission, transcriptional activation, memory acquisition, and cellular stress. The ~100 genes whose expression increases during sleep, on the other hand, provide molecular support for the proposed involvement of sleep in protein synthesis and neural plasticity, and point to a novel role for sleep in membrane trafficking and maintenance. The pattern of changes in gene expression after long periods of sleep deprivation is unique and does not resemble that of short-term sleep deprivation or spontaneous wakefulness. A notable exception is represented, however, by the enzyme arylsulfotransferase, whose induction appears to be related to the duration of previous wakefulness. In rodents this enzyme plays a major role in the catabolism of catecholamines, suggesting that an important role for sleep may be that of interrupting the continuous activity during wakefulness of brain catecholaminergic systems. The issue whether changes in gene expression occur in relation to sleep, wakefulness, and sleep deprivation is an old one. Early experiments did not focus on specific genes but examined overall changes in RNA content1 or synthesis2,3 as well

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as global changes in protein synthesis.4–7 Giuditta et al.3 injected [3H]-orotate intraventricularly and measured its incorporation into newly synthesized RNA during the following hour. In a fraction of neuronal perikarya in the cerebral cortex, the relative content of radioactive RNA was increased in sleep with respect to waking in the nuclear but not in the cytoplasmic compartment. Panov1 found variations in protein and RNA content in individual neurons and glial cells of some brainstem nuclei after 1–4 days of total or selective REM sleep deprivation. Bobillier et al.4 reported a generalized decrease of [3H]-amino acid incorporation into the proteins of telencephalon and brainstem after 3 h of total sleep deprivation in rats. Conversely a striking increase of labeled proteins was found in rats that were allowed to sleep for 1.5 h after 1.5 h of total sleep deprivation. Ramm and Smith6 found that in the rat higher rates of cerebral protein synthesis were associated with a higher slow-wave sleep score. In rhesus monkeys Nakanishi et al.7 found that protein synthesis, as measured by L-[1-14C]-leucine incorporation, was positively correlated with deep sleep in most brain regions. Later studies focused instead on specific genes, the so-called immediate early genes (IEGs), such as c-fos, NGFI-A, c-jun and junB. IEGs are among the first genes to be turned on or off in the cascade of molecular events that leads to changes in the expression of other genes. Their protein products have specific DNA binding domains by which they act as nuclear transcription factors.8 It was found9 that the expression of c-fos, NGFI-A, and other IEGs is powerfully modulated by behavioral state, their expression being low or absent in most brain regions if the animals had spent most of the previous 3–8 h asleep, and high if the animals had been either spontaneously awake or sleep deprived for a few hours before sacrifice.10–12 Most IEGs code for transcription factors, and therefore can regulate the expression of many other genes. Thus an important implication of these experiments was that the strong state-dependent modulation of IEGs expression could signal widespread transcriptional changes across behavioral states. Over the last several years our laboratory has employed mRNA differential display (mRNA DD), nylon membrane arrays, and, more recently, GeneChip technology to systematically establish the differences in gene expression that occur across behavioral states.13–16 The main goal was to determine whether there are genes whose expression is specifically increased during sleep and the identity of these genes. Brain gene expression was compared between rats that had been asleep for the first 3 or 8 h of the light period, in rats that had been spontaneously awake for the first 3 or 8 h of the dark period, and in rats that had been sleep deprived during the light period for 3 or 8 h. This experimental paradigm allowed us to distinguish between changes in gene expression related to sleep and waking per se as opposed to circadian time or to the sleep deprivation procedure. This is an important issue, because several laboratories have recently performed genome-wide expression analyses to isolate genes regulated by the circadian clock. These studies have identified hundreds of transcripts cycling in the brain and in peripheral tissues of mice17–20 and flies21–25 as a function of circadian time. Cycling genes have been involved in extremely diverse biological functions, from protein synthesis and immune response to metabolism, and may thus play a role in biological processes that change between day and night, including wakefulness and sleep.

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FIGURE 11.1 Biological functions associated with transcripts with higher expression in wakefulness (left box) and sleep (right box). The tree on the left (dots and connecting paths) represents biological processes annotations according to the gene ontology hierarchy.

However these studies did not control for behavioral state and therefore could not determine to what extent changes in gene expression between day and night depend on circadian time or on sleep and wakefulness. We have also examined gene expression in the brain of rats chronically deprived of sleep for long periods of time, ranging from 4 to 14 days.26 Prolonged sleep loss was enforced using the disk-over-water method,27 the best controlled method of long-term sleep deprivation. The main target of our analysis has been the cerebral cortex because it generates the characteristic electrical rhythms of sleep;28 responds to prolonged wakefulness with increasing sleep pressure;29 is responsible for the cognitive defects observed after sleep deprivation;30–31 and is at the center of most hypotheses concerning the functions of sleep.28,32–35 The studies performed so far have allowed the screening of more than 20,000 transcripts, including an estimated ~15,000 transcripts using Affymetrix GeneChip technology (GeneChips RGU34A, B, C; [16]) and ~10,000 transcripts using mRNA DD and nylon membrane arrays.13–15 Since the number of genes expressed in the rat cerebral cortex is likely to range between 15,000 and 30,000,36–37 this screening represents the most extensive (yet probably still not exhaustive) analysis of statedependent changes in gene expression performed so far. The following conclusions were derived from this study.

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First, up to ~5% of the transcribed sequences tested in the cerebral cortex (~800 out of 15,000) were found to be up- or down-regulated in rats that had slept for 8 h relative to rats that had been spontaneously awake or sleep deprived for a similar period of time. These sequences included both known (annotated) transcripts as well as expressed sequence tags (ESTs). In the cerebral cortex of the same animals, a similar number of transcribed sequences were found to change their expression because of time of day, rather than because of behavioral state. Day- or nighttime and sleep or wakefulness appear to influence gene expression in the cerebral cortex to a similar extent. A direct implication of these results is that changes in behavioral state should be taken into account in all gene expression studies. Second, the number of known transcripts upregulated during wakefulness (wakerelated genes) was similar (~100) to the number of transcripts upregulated during sleep (sleep-related genes). Thus, although sleep is a state of behavioral inactivity, it is associated with the increased expression of many genes in the brain. Moreover, the increased expression in the brain during sleep was found to be specific, because transcripts that were sleep-related in the brain were not sleep-related in other tissues such as liver and skeletal muscle.16 Third, ~40% of the genes wake-related in the cerebral cortex were also wakerelated in the cerebellum. Similarly, 50% of the cortical sleep-related genes were also sleep-related in the cerebellum. The finding that molecular correlates of sleep and wakefulness are found in the cerebellum indicates that cellular processes associated with sleep may occur in brain structures that are not known for generating sleep rhythms. This suggests that, at the cellular level, functions associated with sleep may take place whether or not electrographic signs of sleep can be recorded. Finally and most importantly, a functional analysis of transcripts modulated by behavioral state suggests that sleep and wakefulness may favor different cellular processes. Several transcripts involved in energy metabolism (mitochondrial genes, GLUT1), excitatory neurotransmission (Narp, Vesl/Homer), transcriptional activation (Per2, NGFI-A, NGFI-B, CHOP), memory acquisition (Arc, NGFI-A, BDNF), and cellular stress (HSPs, Bip) were wakefulness-related. Among sleep-related transcripts was Dbp, which in other tissues is regulated by the circadian clock. Sleeprelated transcripts also included a two-pore domain potassium channel controlling resting membrane potential (TREK-1); key components of the translational machinery (translation elongation factor 2, initiation factor 4AII); and genes involved in depotentiation, depression, as well as in the consolidation of long-term memory (e.g., calcineurin, calmodulin-dependent protein kinase IV). A large number of sleeprelated transcripts are involved in membrane trafficking and maintenance, including synaptic vesicle turnover (Rab genes, NSF; ARF1, ARF3) glia/myelin function (MOBP, MAG, plasmolipin carbonic anhydrase II), and synthesis and transport of glia-derived cholesterol (e.g., HMG-CoA synthase, squalene synthase), the limiting factor for synapse formation and maintenance. Wakefulness-related transcripts may help the brain to face high energy demand, high synaptic excitatory transmission, high transcriptional activity, the need for synaptic potentiation in the acquisition of new information, and the cellular stress that may derive from one or more of these processes. An analysis of brain sleeprelated transcripts supports an involvement of sleep in protein synthesis and in

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complementary aspects of neural plasticity such as synaptic depression35 and suggests for the first time that sleep may play a significant role in membrane trafficking and maintenance. Our findings, in line with intracellular recording studies,28 suggest that sleep, far from being a quiescent state of global inactivity, may actively favor specific cellular functions. Many transcripts upregulated during wakefulness are induced diffusely in the cerebral cortex and in many other brain regions. We hypothesized that a key factor responsible for their induction might be the level of activity of neuromodulatory systems, such as the noradrenergic and the serotonergic systems. These systems project diffusely to most of the brain, and their activity is strictly state-dependent. Noradrenergic neurons of the locus coeruleus fire regularly at very low rates during sleep, whereas during wakefulness they fire at higher rates and emit phasic, short bursts of action potentials in response to salient events.38 Norepinephrine enhances brain information transmission, promotes attentive processes, and can enable various forms of activity-dependent synaptic plasticity by stimulating gene transcription.15 To assess the role of the noradrenergic system in the induction of gene expression during wakefulness, we studied behavior, brain electrical activity, and mRNA levels of several genes in normal rats and in rats in which the central noradrenergic system had been lesioned either bilaterally or unilaterally.15,39 We found that after the lesion of the locus coeruleus waking behavior associated with a normal low-voltage fast activity EEG was not accompanied by the induction of molecular markers of plasticity such as c-fos, NGFI-A, P-CREB, Arc, and BDNF. These results indicate that the activation of the EEG can be dissociated from the activation of gene expression and that the noradrenergic system plays a major role in the induction of plasticity-related genes during wakefulness. Whether such a role extends to other functional classes of state-dependent genes is the subject of ongoing research in our laboratory. Nevertheless the available findings suggest that the reduced expression of plasticity-related genes due to the reduced firing of locus coeruleus neurons may be a key factor that determines why the ability to learn new material is impaired during sleep. Like locus coeruleus cells, serotonergic neurons of the dorsal raphe also fire at higher levels during wakefulness and decrease their firing during sleep.40 However in sharp contrast to noradrenergic neurons, dorsal raphe cells are activated during repetitive motor activity such as locomoting, grooming, or feeding and are inactivated during orientation to salient stimuli.41 Lesions of the dorsal raphe nucleus were unable to affect the expression of c-fos, NGFI-A, PCREB, Arc, and BDNF, either during wakefulness or during sleep.42 Thus the noradrenergic, but not the serotonergic, system plays a crucial role in state-dependent brain gene expression. Most of the waking-related and sleep-related genes discussed above did not change their expression if sleep loss was prolonged. One important exception is represented by the enzyme arylsulfotransferase (AST), which is induced more markedly after several days than after several hours of sleep deprivation.26 The progressively stronger induction of AST is the first demonstration of a molecular response in the brain that is related to the duration of sleep loss. AST is responsible for the sulfonation of norepinephrine, dopamine, and to a lesser extent, serotonin. AST induction during sleep deprivation may therefore constitute a homeostatic response

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to the uninterrupted activity of the central noradrenergic system during wakefulness. This could indicate that at least some of the detrimental effects of sleep loss may be dependent on the continuous activation of the noradrenergic system and that an important function of sleep is that of counteracting the effects of continued monoaminergic discharge.

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36. Milner F.D. and Sutcliffe J.G., Gene expression in rat brain, Nucleic Acid Res., 11, 5497–5520, 1983. 37. Velculescu V.E., Madden S.L., Zhang L, Lash A.E., Yu J., Rago C., Lal A., Wang C.J., Beaudry G.A., Ciriello K.M., Cook B.P., Dufault M.R., Ferguson A.T., Gao Y., He T.C., Hermeking H., Hiraldo S.K., Hwang P. M., Lopez M.A., Luderer H.F., Mathews B., Petroziello J.M., Polyak K., Zawel L., Kinzler K.W. et al., Analysis of human transcriptomes, Nat. Genet., 23, 387–388, 1999. 38. Aston-Jones G. and Bloom F.E., Activity of norepinephrine-containing locus coeruleus neurons in behaving rats anticipates fluctuations in the sleep-waking cycle, J. Neurosci., 1, 876–886, 1981. 39. Cirelli C., Pompeiano M., and Tononi G., Neuronal gene expression in the waking state: a role for the locus coeruleus, Science, 274, 1211–1215, 1996. 40. McGinty D.J. and Harper R.M., Dorsal raphe neurons: depression of firing during sleep in cats, Brain Res., 101, 569–575, 1976. 41. Jacobs B.L. and Fornal C.A., Activity of serotonergic neurons in behaving animals, Neuropsychopharmacology, 21, 9S–15S, 1999. 42. Tononi G., Cirelli C., and Shaw P.J., The Molecular correlates of sleep, waking, and sleep deprivation, in Human Frontier Workshop VIII, The Regulation of Sleep, Borbély A., Hayaishi O., Sejnowski T.J., and Altman J.S., Eds., Human Frontier Scientific Press, Strasbourg, PA, 2000, pp.155–167.

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Neuronal Reverberation and the Consolidation of New Memories across the Wake-Sleep Cycle Sidarta Ribeiro, Damien Gervasoni, and Miguel A.L. Nicolelis

CONTENTS Introduction Searching for Neuronal Reverberation after Novel Sensory Stimulation Novelty-Induced Neuronal Reverberation Is Sustained and Long-Lasting Novelty-Induced Neuronal Reverberation Occurs in Multiple Forebrain Areas Novelty-Induced Neuronal Reverberation Is Context-Dependent Neuronal Reverberation Is State-Dependent and Peaks during Slow-Wave Sleep Forebrain Reverberation Consists of Low-Fidelity Replay at Physiological Speeds A Model for the Complementary Roles of SW and REM Sleep in Memory Consolidation Acknowledgments References

INTRODUCTION In mammals and birds, long episodes of nondreaming sleep (slow-wave sleep, SW) are followed by short episodes of dreaming sleep (rapid-eye-movement sleep, REM).1–9 Despite early insight10 it was not until the 1970s that science began to recognize the key role of sleep in memory consolidation. The main findings supporting this view are the detrimental effects of sleep deprivation on learning,11–26 the improved memory retention in rats when REM sleep is enhanced,27 the increase in sleep amounts following memory acquisition,28–35 and the fact that theta rhythm, a learning-related36–42 hippocampal oscillation typical of high arousal,43–49 also char0-8493-1519-0/05/$0.00+$1.50 © 2005 by CRC Press LLC

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acterizes REM sleep.50–53 Given the involvement of the hippocampus in memory acquisition,54–66 these results indicated that sleep is a privileged offline window for the processing of novel and ecologically relevant information.67–69 What are the mechanisms underlying the beneficial role of sleep for learning? Donald Hebb was perhaps the first to point out that the neuronal activity present during memory encoding must linger in the brain until structural cellular changes have time to occur, thus transforming a short-lived reverberatory trace in a longlasting memory.70 The chase for the neural mechanisms underlying the mnemonic role of sleep had a major breakthrough with a pioneering investigation of the poststimulus activity of hippocampal neurons in rats.71 This elegant study took advantage of the fact that pyramidal hippocampal neurons fire very selectively according to the spatial position of the animal in a given environment.72,73 By way of chronic electrode implants in the hippocampus, Pavlides and Winson first identified and recorded neuronal pairs with nonoverlapping place fields. Then rats were restricted from entering the place field of either cell overnight. Finally animals were confined for 10–15 minutes within the place field of one of the cells to strongly stimulate one neuron while suppressing the activity of the yoked one. By quantifying the neuronal firing rates during and after spatial confinement across the wake-sleep cycle, Pavlides and Winson found that the firing rates observed during waking (WK) experience recur in the hippocampus during ensuing SW and REM sleep71 (Figure 12.1 A). These results indicated that sleep harbors the first mechanism postulated by Donald Hebb to be necessary for learning, namely the post-acquisition neuronal reverberation of memory traces.70 Subsequent exploration was prolific: Post-acquisition neuronal reverberation during sleep or quiet WK was found to preserve the temporal firing relationships of alert, exploratory WK in the hippocampus74–80 and the cerebral cortex,81,82 causing a correlated replay of activity patterns across two-74 or many-neuron ensembles.79 To date, experience-dependent brain reactivation during sleep has been observed in rodents,71,74–76,78–81 nonhuman primates,82 humans,83 and even songbirds (Figure 12.1 B),84 pointing to a very general biological phenomenon. Furthermore experiencedependent brain expression of the plasticity-related gene zif-268 was observed during REM sleep (Figure 12.1 C),85,86 providing a compelling tie between neuronal reactivation during sleep and cellular plasticity able to consolidate memories.87–101 Finally and most importantly, post-acquisition brain reactivation during sleep has been shown to be proportional to memory acquisition in rats102 and humans (Figure 12.1 D),103 and to quantitatively predict learning.25,104 In spite of the positive evidence, the neural reverberation hypothesis for memory consolidation during sleep faces several objections: •

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The neocortical reverberation detected to this date is extremely subtle and decays rapidly within less than 1 H of memory trace formation.81,82 Such short-lived reverberation falls short of explaining the disruption of memory traces by sleep deprivation several hours and even days after initial acquisition.11–23,25,26 Strictu sensu neuronal reverberation during sleep in mammals has only been investigated in the hippocampo-cortical loop,71,74–76,78–82 making it

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FIGURE 12.1 (See color insert following page 108.) (A) Hippocampal place cells are reactivated during SW and REM sleep after WK exposure to their place fields. Asterisk for p<0.05. (Modified from Pavlides, C. and Winson, J., J. Neurosci., 9, 2907, 1989. With permission.) (B) Premotor neurons in nucleus RA of a zebra finch accurately replay singingspecific activity during sleep. Shown are raw traces of neuronal activity (900 ms) recorded during singing (premotor activity) and sleep (spontaneous activity). A color spectrograph of the song that the bird sang is shown on top, time-aligned to the premotor activity, with horizontal bars indicating different song syllables. (Modified from Dave, A.S. and Margoliash, D., Science, 290, 812, 2000. With permission.) (C) The plasticity-related gene zif-268 is reinduced during REM sleep in an experience-dependent manner. Autoradiograms of brain sections hybridized with zif-268 radioactive riboprobes. In controls kept in a familiar environment, zif-268 expression decreased from WK to SW and REM sleep. In animals exposed to a novel enriched environment for 3 H before the experiment, zif-268 levels decreased from WK to SW sleep but increased from the latter to REM. This effect was particularly noticeable in the cerebral cortex and the hippocampus. (Modified from Ribeiro, S. et al., Learn. Mem., 6, 500, 1999.) (D) Learning levels attained prior to sleep modulate regional cerebral blood flow (CBF) during REM sleep in humans, as measured by positron emission tomography. (Modified from Peigneux, P. et al., Neuroimage, 20, 125, 2003.)103

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•

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difficult to determine whether the phenomenon is particular to this neural circuit or whether it represents global experience-dependent changes in the brain. Neuronal reverberation has mostly been observed in highly-trained animal subjects,74–76,79–82,84 raising skepticism about its relevance for the acquisition and consolidation of novel information.105 Experience-dependent neuronal reverberation has been reported to occur in all behavioral states,71,74,75,79–81 including WK.76,78,82

Although the first finding in this regard has hinted at a possible predominance of reverberation during SW sleep (Figure 12.1 A),71 a comprehensive comparison of the relative contributions of WK, SW, and REM sleep for neuronal reverberation is still missing. To further complicate the issue, recent studies have raised the possibility that neuronal processing may occur at either slower or faster speed than normal physiological rates during REM79 and SW,76,80 respectively, thus it is uncertain at the moment how neuronal reverberation relates to different behavioral states.

SEARCHING FOR NEURONAL REVERBERATION AFTER NOVEL SENSORY STIMULATION In order to address these objections, we set out to investigate the effects of a transient novel tactile experience on the long-term evolution of ongoing brain activity across the major behavioral states of the rat.106 In each of the 5 animals studied, the extracellular activity of up to 159 neurons per animal and local field potentials (LFP) representing larger-scale neural rhythms were simultaneously recorded from four different brain regions: hippocampus (HP), primary somatosensory barrel-field cortex (CX), ventral posteromedial thalamic nucleus (TH), and putamen (PU) (Figure 12.2 A). These brain regions were chosen because they comprise three major forebrain circuit loops essential for rodent species-specific behaviors. Rats are nocturnal gatherers that exhibit a variety of exploratory behaviors during the night, sleeping intermittently and mostly during the day51 (top panel, Figure 12.2 B). In the wild, rats rely on spatial navigation and exquisite whisker-based tactile discrimination to explore new territories in search of food.107 The cortico-thalamic, cortico-hippocampal, and cortico-striatal loops probed in this study have been implicated in tactile information processing,108,109 spatial navigation, and memory formation,59,72 and the execution of complex motor sequences.110,111 In our study106 neural signals were continuously recorded across the natural sleep-wake cycle for 48–96 H, with a single 1-H exposure to four complex objects placed in the four corners of the recording box (Figure 12.2 B). All objects were strictly novel to the subjects and were designed to maximize shape, texture, and behavioral value differences (Figure 12.2 C). Objects were presented halfway through the recording time around midnight, when lights were off and WK reached a peak (Figure 12.2 B), to maximize the drive for whisker-based tactile exploration of the environment. As expected, this paradigm strongly increased WK relative to sleep during the exposure time (Figure 12.2 D), leading to robust novel sensory stimulation. Other than novel stimulation and the periodic removal of waste and

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introduction of food pellets and water, animals were kept undisturbed in the same environment throughout the recordings. Our paradigm produced marked and acute exploratory behavior without disrupting the large-scale sleep-wake structure across the many hours of recording (Figure 12.2 B, top panel). The experiment, therefore, consisted of a naturalistic behavioral paradigm involving multiple novel sensory and spatial cues, and was designed to maximize novelty-induced neuronal changes as opposed to changes caused by behavioral over-training. In order to investigate the long-term effects of novel stimulation on the spatiotemporal evolution of ongoing neuronal activity, we took advantage of a neuronal ensemble correlation method previously shown to detect experience-dependent reactivation of rodent hippocampal ensembles during SW and REM sleep.79 This method generalizes the concept of pairwise neuronal correlations74,81,82 to an arbitrarily large number of neurons, quantifying the degree of similarity between spatiotemporal patterns of neuronal activity by way of a firing-rate-normalized template-matching algorithm (Figure 12.2 E). Templates of alert WK neuronal ensemble activity were selected from moments when animals made whisker contact with the novel objects (n = 5 templates per animal). Control templates were selected from epochs of alert WK 24 H (three rats) or 48 H (two rats) before novel stimulation (n = 5 templates per animal), during which familiar tactile stimulation was produced by the contact of whiskers with the smooth walls of the recording box to which animals were habituated. Templates were matched against the entire record of neuronal activity using the neuronal ensemble correlation method (Figure 12.2 F). The resulting correlation temporal profiles were averaged for each template set, aligned with reference to the light-darkness cycle to control for possible circadian effects, and compared.

NOVELTY-INDUCED NEURONAL REVERBERATION IS SUSTAINED AND LONG-LASTING First we tested whether the neuronal ensemble correlation method could detect in our dataset any trace of increased neuronal reverberation after exposure to the novel stimuli. For this we examined correlation profiles obtained for all recorded neurons (3–4 brain areas pooled together) in each animal. As shown for 2 different animals in Figure 12.3 A, post-novelty average correlation distributions were significantly right-shifted relative to pre-novelty distributions (ANOVA of mean pre- and postnovelty correlations over 24 or 48 H, n = 5 animals, F = 9.5, d.f. 1, P = 0.016). This indicated that the neuronal firing patterns concomitant with novel stimulation persisted significantly more during the ensuing time than patterns sampled 24 or 48 H before novel stimulation, when animals were in the same behavioral state (alert WK), but without novel objects to explore. The effect was independently observed, to a variable degree, in all the five animals studied (Bonferroni, P <0.01), corroborating the efficacy of the neuronal ensemble correlation method for the detection of experience-dependent neuronal reverberation.79 Next we assessed whether the neuronal ensemble correlation method could detect neuronal reverberation lasting at least more than 1 H after exposure to novel stimulation. Figure 12.3 B shows the temporal evolution of neuronal ensemble

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correlations for 2 different animals. Despite the marked inter-animal variability in the shapes and magnitudes of these profiles, a significant and sustained increase of neuronal ensemble correlations after exposure to novel stimulation was observed in all animals. Importantly, these increases lasted well above 1 H. Indeed, post-novelty increased neuronal ensemble correlations decayed slowly over time, and persisted

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above the pre-novelty baseline after 48 H as revealed by the temporal evolution of associated Bonferroni P values.106 These results indicate that post-stimulus neuronal reverberation occurs in rats that are completely naïve with respect to the reference stimuli. Such novelty-induced neuronal reverberation is, at least in principle, capable of implementing the mnemonic function anticipated by Hebb70 and directly contradicts the notion that only the performance of highly trained behaviors is followed by neuronal reverberation.105 The new data also demonstrate that sustained experience-dependent neuronal reverberation can be detected in the forebrain up to 48 H after exposure to novel stimulation. As mentioned above, previous measurements of persistent changes in neuronal firing rates71 or pairwise neuronal correlations74,75,81,82,105 had not been able to track the reverberation of neuronal activity produced by WK experience beyond a mere hour after the reference stimulus. Our results indicate that the neuronal ensemble correlation method79 is more robust in that respect, detecting experiencedependent changes that persist for several hours after the initial encoding. Thus the use of the neuronal ensemble correlation method for the analysis of very long neural records provides evidence for the first time of reverberatory processes that are compatible with memory impairment effects of sleep deprivation applied hours or days after training.13,15,18,19,21,23,112

FIGURE 12.2 (See facing page.) (A) Neuroanatomical location of multielectrode implants, shown on a schematic parasaggital section based on.141 Indicated are the cerebral cortex (CX), the hippocampus (HP), the thalamus (TH), and the putamen (PU). (B) Experimental design. Top panel shows a representative example of the strong circadian dynamics of the rat sleepwake cycle (rat #5). Grey bands indicate lights-off, white bands indicate lights-on; notice the fixed 12-h periods of darkness and light. Bottom panels: Animals continuously recorded for up to 96 hours were kept undisturbed except for a 1-h period of novel sensory stimulation (white segment) produced by the tactile exploration of 4 distinct novel objects placed at the corners of the recording box. Neural data from pre- and post-novelty periods (middle panel, black and red segments, respectively) were compared. (C) Four different objects were used to produce novel complex stimulation of different shapes and textures: a food cache filled with Fruit Loops, a shoe brush, a golf ball mounted in a spring, and a spiky object made of metal pins attached to a wooden axis. (D) Data for rat #3. All animals were highly habituated to the recording box, so exposure to novel complex objects caused a general increase in time spent in WK with respect to SW and REM sleep during the exploration of the objects, as compared to adjacent pre- and post-periods of equal length (60 min). (E) Neuronal ensemble correlation method. Neuronal activity templates (red boxes) were compared with extensive recordings of neuronal action potentials (top panel, green ticks) by way of an off-line templatematching algorithm79 that generalizes the notion of pair-wise correlations to neuronal ensembles of any size. Templates and targets (white boxes) were binned, firing-rate normalized, and correlated (middle panel). This procedure yields a time series of neuronal ensemble correlations for each template-target sliding match (bottom panel). (F) Templates of interest (9 second-long red boxes) were sampled around the origin of pre- and post-novelty periods during alert WK and slid against their corresponding neuronal targets so as to sample neuronal correlations every 30 sec for up to 48 H.

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FIGURE 12.3 (A) Post-novelty neuronal correlations were significantly larger (right-shifted) than pre-novelty correlations in all animals studied. (B) Temporal profiles of multiple-area neuronal ensemble correlations reveal long-lasting reverberation. Grey bands indicate lightsoff; white bands indicate lights-on. (C) Long-lasting neuronal reverberation occurs in the cerebral cortex, hippocampus, putamen, and thalamus. Shown are temporal profiles of neuronal ensemble correlations calculated for single areas (all panels correspond to rat #1 except the putamen, which corresponds to rat #3). (D) Neural activity sampled when animals were aroused but not touching the objects yielded enhanced neuronal reverberation that was nearly identical to that obtained when animals made sensory contact with the objects. This indicates that neuronal reverberation reflects the novelty context rather than stimulus complexity.

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NOVELTY-INDUCED NEURONAL REVERBERATION OCCURS IN MULTIPLE FOREBRAIN AREAS In order to assess the anatomical distribution of experience-dependent neuronal reverberation, we performed the neuronal ensemble correlation analysis for each area separately. Despite considerable interanimal and interarea variation in the magnitude and shape of correlation profiles, significant changes between pre- and postnovelty correlations were observed in all areas studied (CX 5/5, HP 3/4, PU 4/4, and TH 4/5 rats; Bonferroni, P <0.05) (Figure 12.3 C). The temporal evolution of P values (Bonferroni, P <0.05) associated with single-area correlation profiles confirmed that significant reverberation was present in 16 of 18 recording sites for up to 48 H after exposure to novel stimulation.106 At first glance this indicates that all forebrain areas studied are equally capable of reverberating neuronal patterns of activity after novel stimulation. Indeed, experience-dependent changes were not statistically different across different forebrain areas (ANOVA, F = 0.24, d.f. 3, P = 0.86). Within the five animals studied, no sign of forebrain neuroanatomical specificity was found in the correlations measured, and in particular no significant differences between hippocampal and extra-hippocampal areas could be detected. However these results may be related to the high variability of pre- and post-novelty correlation profiles in all animals, which typically showed predominant effects in a different subset of areas for each animal. For example, rat #4 showed marked reverberation in the HP but small changes in the CX, and rat #5 showed just the opposite. The most widespread reverberation was seen in rat #1, which showed sustained reverberation in the CX and decaying reverberation in the HP and TH (Figure 12.3 C). A somewhat similar pattern was seen in rat #5, but rat #3 showed strong reverberation only in the PU (Figure 12.3 C), and rat #4 in the HP and TH. Rat #2 showed the least reverberation of all, with somewhat stronger effects in the PU.106 At present it is unclear whether such differences reflect real interanimal differences in the neuroanatomical distribution and dynamics of neuronal reverberation, or rather a variation from animal to animal due to possible differences in chronic electrode implants or other experimental variables. Taken together our results indicate that the tactile, gustatory, olfactory, spatial, and motor activity produced by the free exploration of the experimental objects engaged multiple forebrain structures in widespread neuronal reverberation. Our results also suggest that enhanced neuronal reverberation (post- >precorrelations) is not the only kind of experience-dependent change possible.106 Although postnovelty traces (red) run above pre-novelty traces (black) for the majority of the recorded sites, some animals showed significant long-lasting anti-reverberation (pre>post-correlations); i.e., patterns of activity that were statistically more dissimilar from novel stimulation templates than expected by chance. Anti-reverberation occurred in the HP (1 of 4 rats), PU (2 of 4 rats) and TH (1 of 5 rats) but not in the CX. In principle the novelty-induced reverberation and anti-reverberation of neuronal firing patterns could play balancing roles in the delineation of new memory traces, embossing high- and low relieves in the vast synaptic landscape where memories reside.

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NOVELTY-INDUCED NEURONAL REVERBERATION IS CONTEXT-DEPENDENT Our results indicate that large-scale neuronal firing patterns generated during the exploration of the experimental objects can recur for several hours after the reference experience throughout the forebrain, but firing patterns associated with the walls of the recording box are substantially less detectable over time. The sensory stimulation provided by the free exploration of the experimental objects is at once novel and complex. In contrast, exploration of the smooth walls of the recording box to which animals were habituated elicited stimulation that was both familiar and simple. Therefore the enhanced neuronal reverberation detected after object exploration could in principle be related not to object novelty but rather to object complexity. In order to disambiguate these effects, we scrutinized the neuronal reverberation associated with templates of neuronal activity sampled during alert WK within the novel stimulation 1-h period but excluding moments of contact between whiskers and objects. Surprisingly no-contact templates yielded correlation profiles that were almost indistinguishable from those obtained when animals had tactile contact with the novel objects (Figure 12.3 D). Equivalent results were obtained for CX, HP, TH, and PU.106 The exploration of a novel environment enhanced the reverberation of all the neuronal activity patterns concomitant with that experience and not just of those corresponding to moments in which animals received tactile inputs from the objects. This rules out the possibility that stimulus complexity, rather than novelty, was the underlying cause of the enhanced neuronal reverberation observed after exploration of the objects. It also indicates that the kind of experience-dependent neuronal reverberation detected by the neuronal ensemble correlation method79 does not reflect the specific features of the stimuli but is related to the overall behavioral salience of the novel stimulation period; i.e., context- rather than stimulus-specific. In principle these results are compatible with a slow and progressive process of memory consolidation,67 proportional to the novelty of the experience, and able to bind together a multitude of contextual cues related to its core sensory elements.113

NEURONAL REVERBERATION IS STATE-DEPENDENT AND PEAKS DURING SLOW-WAVE SLEEP Results observed in single forebrain areas indicated that neuronal ensemble correlations peak during discrete epochs that may last a few hours, causing marked oscillations of the correlation trace. These observations suggested that some underlying biological process, with slow evolution but with sharp phase transitions, governs the long-term reverberation of neuronal firing patterns. To test whether transitions in the wake-sleep cycle could amount for these effects, we investigated how experience-dependent changes in neuronal ensemble correlations varied across the three major rat behavioral states, which were coded as WK, SW, and REM sleep based on a spectral analysis of LFP and visual inspection of videotaped behaviors, according to previously described criteria.85

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A comparison across states of post- and pre-correlations ratios calculated from averages of entire recordings indicated a significant state-specific effect (ANOVA, F = 9.289, d.f. 2, P = 0.0004), with SW ratios being significantly higher than those of both WK (Bonferroni, P <0.05) and REM (Bonferroni, P <0.003). Indeed, significant state-specific differences in post-/pre-correlation ratios were individually detected in 4 out of 5 animals (ANOVAs, d.f. 2: rat #2, PU, F = 4.13, P = 0.039; rat #3, CX, F = 6.45, P = 0.026; rat #4, HP, F = 3.99, P = 0.029; rat #5, CX, F = 13.81, P <0.0001). The mean correlation values found in those recording sites for the three behavioral states revealed that SW correlations were systematically larger than WK correlations; several other recorded sites displayed similar but nonsignificant trends.106 Meanwhile the REM correlations measured were variable and could not be consistently ranked in relation to WK and SW sleep. Comparable neuronal reverberation between SW and REM sleep was observed in only one animal (rat #5 CX). A major effect of SW sleep on neuronal reverberation was corroborated by the temporal evolution of successive state-specific Bonferroni P-values calculated for pre- and post-novelty 4-h average correlations across all animals and brain areas studied.106 Altogether our results indicate that neuronal reverberation was consistently stronger during SW sleep and decreasing during WK. This is remarkably wellillustrated by a superimposition of behavioral state classification and neuronal ensemble correlations (Figure 12.4 A), which reveals an exquisite long-term temporal match between SW episodes (red) and epochs of increased neuronal ensemble correlations in the CX. Likewise neuronal ensemble correlation troughs show a tight correspondence with WK episodes (blue). This characteristic state-dependency persisted throughout the 45 h of post-novelty recording (Figure 12.4 B). Notice that REM sleep only showed SW-like results in one out of five animals (Rat #5 CX, depicted in Figure 12.4). In the remaining animals, REM correlations were either closer to WK levels than to SW levels or in between.106 Given this marked variability and the very short duration of total REM sleep in comparison with total SW sleep (WK 52%, SW 40%, and REM 8% of total recording time for five animals), one must conclude that REM sleep plays a minor role in neuronal reverberation.106 Therefore the function of experience-dependent brain reactivation during REM sleep71,79,83,103 remains to be explained. One attractive possibility yet to be tested is that neuronal reverberation during REM sleep, being noisier than that of SW sleep, may facilitate memory-trace restructuring and the generation of insights.114 Interestingly a comparison of the correlation temporal profile (Figure 12.4 C, top panel) with the concurrent neuronal firing record (Figure 12.4 C, bottom panel) reveals that SW correlation peaks correspond to periods of decreased firing rate, and WK correlation troughs match epochs of increased neuronal activity. Figure 12.4 C depicts data segments approximately 2 h long comprising the three major behavioral states studied. The first segment (*) corresponds to the 60¢ of novel stimulation and the immediately ensuing sleep-wake cycles, and the second segment (**) illustrates sleep-wake episodes occurring ~15 h after the original experience. Although novel stimulation templates of neuronal activity were selected from WK episodes characterized by high firing rates, ensuing reverberation of these neuronal firing patterns was most pronounced during SW sleep under lower firing rates. Neuronal ensemble reverberation decreased but did not disappear during WK (Figure 12.4 D), in agree-

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ment with the original findings of post-stimulus changes in hippocampal firing rates (Figure 12.1 A),71 and a more recent investigation of the same issue.78 Taken together these data indicate that novel experience causes sustained neuronal reverberation70 rather than discrete reactivation,74,105 in the sense that traces of a given salient experience are continuously detectable during subsequent periods across all behavioral states in a state-dependent manner.

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The inverse correlation between neuronal ensemble correlations and concurrent firing rates suggests that reverberating patterns of neuronal activity associated with past novel experience are largely — but not completely — masked during WK by incoming sensory inputs unrelated to the reference experience. By the same token, peak neuronal ensemble correlations arise during SW sleep, when sensory interference ceases. These observations corroborate the notion that the importance of sleep for memory consolidation stems from the off-line processing of memory traces; i.e., from the absence of sensory interference.10,69,115 The consistent increase in neuronal reverberation during SW sleep, the high interanimal variability of neuronal reverberation during REM sleep, and the small contribution of REM sleep to total sleep time suggest a major role for SW sleep in the post-acquisition recall of new memory traces.

FOREBRAIN REVERBERATION CONSISTS OF LOW-FIDELITY REPLAY AT PHYSIOLOGICAL SPEEDS One interesting aspect of the long-term assessment of forebrain neuronal ensemble correlations is that the measurements obtained were typically small (on the order of 0.1 to 0.3), in complete agreement with values previously reported for pairwise74,75,81,82 or many-neuron correlations.79 Qualitatively similar results were observed for bins ranging from 5 ms to 1000 msec, with larger bin sizes corresponding to higher correlation values, due to an averaging effect.106 This suggests that neurons of multiple forebrain areas, once exposed to novel experience, do not accurately replay prior WK activity patterns longer than 5 msec but show instead a mild but long-lasting bias toward (or against) the reference activity patterns. Highfidelity replay of neuronal firing patterns was not observed when single areas were considered: Not a single template-to-target match (out of 979,200 matches sampled) yielded correlation values higher than 0.45, indicating that novelty-induced neuronal reverberation occurs at low-fidelity.

FIGURE 12.4 (See facing page.) (A) Neuronal reverberation is strongest during SW sleep. The superimposition of successive neuronal ensemble correlations and concurrent behavioral states for the CX neurons of rat #5 dramatically illustrate the state-dependency of neuronal ensemble correlations, which are strongly increased by SW sleep but readily decreased by WK. Nearly all correlation peaks correspond to SW episodes, and almost all troughs match WK epochs. (B) State-dependent neuronal reverberation was sustained throughout the recording period, as shown by segments representing the beginning (3200–3300¢), middle (4700–4800¢) and end (5200–5250¢) of the experimental record. On the left panel, notice the progressive increase of neuronal correlations across a single SW sleep episode (white arrows), suggesting a progressive amplification of the memory trace. (C) Blow-up of two selected data segments indicated by asterisks in (A). Despite having being sampled from moments of high neuronal firing rates,* novel stimulation templates reverberate most strongly during SW sleep when firing rates are low.*,** The high firing rates that characterize WK correspond to decreased neuronal reverberation, probably due to sensory interference. (D) Post-novelty neuronal ensemble correlations decrease during WK but do not reach pre-novelty levels, indicating the occurrence of post-stimulus neuronal reverberation, and not reactivation.

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Nonetheless studies of hippocampal place cells have proposed that a high-fidelity replay of neuronal firing patterns during sleep may be achieved, assuming that replayed patterns can undergo time compression and expansion76,79,80; i.e., that the experience-dependent reverberation of neuronal firing patterns during sleep can be slower (REM)79 or faster (SW)76,80 than during WK. To investigate this possibility, we obtained template-to-target matches at different speed factors by comparing 250 msec-binned templates with targets binned within a range of different bin sizes (12.5 msec to 500 msec), temporally compressing and expanding target spike records before matching them to templates. This procedure allowed us to determine the magnitude of neuronal ensemble correlations for speed factors ranging from 0.5 to 20 times the physiological WK processing speed, which covers the reported optimum speed ranges for SW76,80 and REM sleep.79 We found a predominance of neuronal reverberation during SW sleep for all speed factors, as indicated by Bonferroni comparisons.106 However, no significant differences were seen when post- and pre-correlations ratios (calculated from averages of entire recordings) were compared across different speed factors (ANOVA, F = 1.496, d.f. 5, P = 0.19). Within any given state or area, neuronal reverberation did not vary systematically with speed factor, and the temporal distribution of neuronal ensemble correlations was largely insensitive to speed factor.106 We found no evidence that forebrain neuronal reverberation can be optimized, assuming replay speeds different from the WK normal rate. A subtle but consistent decrease of Pvalues can be observed for speed factors 10¥ and 20¥ faster than normal WK rates, while speed factors near the physiological range (2¥ to 0.5¥) show stronger and similar effects.106 This was the case even in the HP, in contrast with previous findings in hippocampal place cells recorded in over-trained animals performing a spatial navigation task.76,79,80 At present it is unclear whether this discrepancy reflects differences in stimulus familiarity (novel versus habitual), stimulation modality (tactile exploration versus spatial navigation), the very low representation of place cells in our hippocampal samples (<5%), or possible analytical artifacts in previous studies based on the statistical boot-strapping of relatively small datasets.76,79,80 In the face of consistently low neuronal ensemble correlation values under a broad range of putative processing speeds, we conclude that for most of the rat forebrain the high-fidelity replay hypothesis should be rejected, at least for time periods longer than 5 msec. This is at variance with the compelling example of accurate neuronal replay found in the motor nucleus RA of zebra finches84 (Figure 12.1 B). Such remarkable case of high-fidelity replay likely derives from the very specialized neural processing carried out by the nucleus RA,116–119 namely the descending motor generation of a single, unique, and invariant sequential object: the bird’s own song. In contrast our experiment involved the free exploration of four novel and complex objects placed in four well-separated places and including the presence of novel food. Exposure to these objects is not at all stimulus-invariant because the animals recursively explore the different objects at different angles and moments. Compared to the performance of birdsong, our paradigm is much more representative of the sensory stimulation encountered by higher vertebrates in their routine interactions with the natural environment. It involves a plethora of tactile, gustatory, olfactory, spatial, and motor cues, likely producing distributed and partially over-

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lapping memory traces that may directly account for the fact that neuronal replay occurs at low fidelity. An equivalent argument can be made for the performance of spatial and sensory-motor tasks previously used in rats74–76,79–81 and nonhuman primates,82 suggesting that the low-fidelity replay of neuronal firing patterns is the rule, not the exception, in the brains of higher vertebrates.

A MODEL FOR THE COMPLEMENTARY ROLES OF SW AND REM SLEEP IN MEMORY CONSOLIDATION The results discussed in this chapter indicate that novelty-induced neuronal reverberation consists of a low-fidelity recurrence of previously salient neuronal firing patterns replayed at physiological speeds. Such recurrence is sustained and long lasting, occurs in multiple forebrain areas in a context-dependent manner, and peaks during SW sleep in inverse correlation with firing rates. Neuronal reverberation is sustained for long epochs during SW sleep, which suggests that unconsolidated synaptic changes may not only be recalled but also amplified over time during SW sleep. Indeed a progressive increase of neuronal ensemble correlations across single SW sleep episodes was often observed (Figure 12.4 B, white arrows). We have proposed in this regard106 that intrinsic brain activation during SW sleep, free of sensory interference, would be biased toward previously potentiated synapses; hence neuronal firing patterns originally produced during novel WK experience would reverberate during SW sleep significantly above chance levels. The periodic activation of calcium-dependent second-messenger cascades120–124 by the large-amplitude SW oscillations125,126 would then result in the progressive amplification of the synaptic changes that encode the novel memory trace.106 Our view resonates to a degree with a previous suggestion that the neuronal reverberation of newly acquired synaptic changes during SW sleep may lead to the recall and storage of new memories by way of “calcium-mediated intracellular cascades” capable of opening the “molecular gates to plasticity.”127 This hypothesis is partially contradicted by evidence that calcium-dependent gene expression related to synaptic plasticity is shut down during SW sleep.85,86,128 Based on the current literature, we have proposed instead106 that SW and REM sleep play distinct and complementary roles on memory consolidation: Initial neuronal reverberation depends mainly on SW sleep episodes,71,74,106 but transcriptional events able to effect durable neuronal plasticity129 are only triggered during ensuing REM sleep.85,86 This functional dissociation with regard to memory consolidation implies that the two main sleep phases separately satisfy Hebb’s two learning postulates,70 with memory recall occurring during SW sleep and memory storage taking place during REM sleep (Figure 12.5). According to this view, the deleterious effects of sleep deprivation on memory consolidation would be a consequence of the disruption of the underlying neuronal reverberation and gene expression during SW and REM sleep, respectively. Such a scheme fulfills earlier conceptual notions of a two-step process for memory consolidation during sleep130–132 and is in line with evidence that SW and REM sleep have synergistic effects on human procedural learning.26,133 The postsynaptic nature of the zif-268 response,98,134 its putative consequences on synaptic strengthening,101,135 and the hippocampofugal pattern of zif-268 expres-

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Labile

Consolidated

Memory Trace Strength

WK

SWS

REM WK

SWS

REM

WK

* * * GE

Encoding

GE

GE

Time

FIGURE 12.5 We have proposed106 that SW and REM sleep play distinct and complementary roles in the processing of new memory traces, with memory recall (reverberation) occurring during SW sleep and memory storage (plasticity-related gene expression, GE) taking place during REM sleep. Such functional dissociation implies that memory processing progresses in cycles of pretranscriptional amplification of labile traces during SW sleep and transcriptional trace consolidation triggered by REM sleep. According to this scheme, the combined action of SW and REM sleep would cause a progressive increase in the strength and consolidation level of memories produced over several hours via plasticity-related protein synthesis. The model also predicts that after some time in WK, such effects would be completed and memory strength would then start to decrease, due to sensory interference (indicated by asterisks). The recurrence of sleep would therefore prevent sensory interference from further degrading the strength of recently acquired memory traces.

sion during REM sleep86 led us to propose that the cyclical reiteration of trace amplification during SW sleep and trace storage during REM sleep promotes a postsynaptic propagation of memory traces.106,136 Potentially this propagation could cause memory traces to progressively reach farther and farther away from the original synaptic trajectory activated at initial encoding. Over time this sleep-dependent propagation could lead to deeper memory encoding within the cerebral cortex,137–139 cumulative learning after memory trace acquisition,133 and progressive hippocampal disengagement.54,57,59–61,63,65,66,140 In conclusion sustained neuronal reverberation during SW sleep, immediately followed by plasticity-related gene expression during REM sleep, may be sufficient to explain the beneficial role of sleep on the consolidation of new memories.

ACKNOWLEDGMENTS We thank Jonathan Winson, Robert Stickgold, Ivan de Araújo, and David Schwartz for fruitful discussions of the views exposed here, and Susan Halkiotis for secretarial

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help. This work was supported by a fellowship from the Pew Latin American Program in Biomedical Sciences to SR, by an INSERM fellowship to DG, and by NIH 5 R01, DE11451, and 5 R01 DE13810 grants to MALN.

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101. Jones, M.W., Errington, M.L., French, P.J., Fine, A., Bliss, T.V., Garel, S., Charnay, P., Bozon, B., Laroche, S., and Davis, S., A requirement for the immediate early gene Zif268 in the expression of late LTP and long-term memories, Nat. Neurosci., 4, 289–296, 2001. 102. Gerrard, J.L., Reactivation of Hippocampal Ensemble Activity Patterns in the Aging Rat: Insights into Memory Consolidation within the Aged Brain, University of Arizona, 2002. 103. Peigneux, P., Laureys, S., Fuchs, S., Destrebecqz, A., Collette, F., Delbeuck, X., Phillips, C., Aerts, J., Del Fiore, G., Degueldre, C., Luxen, A., Cleeremans, A., and Maquet, P., Learned material content and acquisition level modulate cerebral reactivation during posttraining rapid-eye-movements sleep., Neuroimage, 20, 125–134, 2003. 104. Datta, S., Avoidance task training potentiates phasic pontine-wave density in the rat: A mechanism for sleep-dependent plasticity, J. Neurosci., 20, 8607–8613, 2000. 105. Kudrimoti, H.S., Barnes, C.A., and McNaughton, B.L., Reactivation of hippocampal cell assemblies: effects of behavioral state, experience, and EEG dynamics, J. Neurosci., 19, 4090–4101, 1999. 106. Ribeiro, S., Gervasoni, D., Soares, E.S., Zhou, Y., Lin, S.C., Pantoja, P., Lavine, M., and Nicolelis, M., Long-lasting novelty-induced neuronal reverberation during slowwave sleep in multiple forebrain areas, PLoS Biology, 2, 126–137, 2004. 107. Nowak, R.M., Walker’s Mammals of the World, 6th ed., Johns Hopkins University Press, Baltimore, MD, 1999. 108. Simons, D.J., Response properties of vibrissa units in rat SI somatosensory neocortex, J. Neurophysiol., 41, 798–820, 1978. 109. Ghazanfar, A.A., Stambaugh, C.R., and Nicolelis, M.A., Encoding of tactile stimulus location by somatosensory thalamocortical ensembles, J. Neurosci., 20, 3761–3775, 2000. 110. Dieckmann, G. and Hasser, R., Stimulation experiments on the function of putamen in the cat, J. Hirnforsch., 10, 187–225, 1968. 111. Jog, M.S., Kubota, Y., Connolly, C.I., Hillegaart, V., and Graybiel, A.M., Building neural representations of habits, Science, 286, 1745–1749, 1999. 112. Fenn, K.M., Nusbaum, H.C., and Margoliash, D., Consolidation during sleep of perceptual learning of spoken language, Nature, 425, 614–616, 2003. 113. Kohler, W., Gestalt Psychology: An Introduction to New Concepts in Modern Psychology, Reissue ed., Liveright, New York, 1947. 114. Wagner, U., Gais, S., Haider, H., Verleger, R., and Born, J., Sleep inspires insight, Nature, 427, 352–355, 2004. 115. Melton, A.W. and Irwin, J.M., The influence of degree of interpolated learning on retroactive inhibition and the overt transfer of specific responses., Am. J. Psychol., 53, 173–203, 1940. 116. Nottebohm, F., Stokes, T.M., and Leonard, C.M., Central control of song in the canary, Serinus canarius, J. Comp. Neurol., 165, 457–486, 1976. 117. Doupe, A.J. and Konishi, M., Song-selective auditory circuits in the vocal control system of the zebra finch, Proc. Natl. Acad. Sci. USA, 88, 11339–11343, 1991. 118. Vicario, D.S. and Yohay, K.H., Song-selective auditory input to a forebrain vocal control nucleus in the zebra finch, J. Neurobiol. 24, 488–505, 1993. 119. Margoliash, D., Functional organization of forebrain pathways for song production and perception, J. Neurobiol., 33, 671–693, 1997. 120. Dingledine, R., N-methyl-D-aspartate activates voltage-dependent calcium conductance in rat hippocampal pyramidal cells, J. Physiol., 343, 385–405, 1983.

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121. MacDermott, A.B., Meyer, M. L., Westbrook, G. L., Smith, J., and Baker, J. L., NMDA-receptor activation increases cytoplasmic calcium concentration in cultured spinal cord neurones, Nature, 321, 519–522, 1986. 122. Fields, R.D., Yu, C., and Nelson, P.G., Calcium, network activity, and the role of NMDA channels in synaptic plasticity invitro, J. Neurosci., 11, 134–146, 1991. 123. Collingridge, G.L., Randall, A.D., Davies, C.H., and Alford, S., The synaptic activation of NMDA receptors and Ca2+ signalling in neurons, Ciba Found. Symp., 164, 162–171, 1992. 124. Augustine, G. J. and Neher, E., Neuronal Ca2+ signalling takes the local route, Curr. Opin. Neurobiol., 2, 302–307, 1992. 125. Steriade, M. and McCarley, R. W., Brainstem Control of Wakefulness and Sleep, Plenum Press, New York, 1990. 126. Steriade, M., McCormick, D. A., and Sejnowski, T. J., Thalamocortical oscillations in the sleeping and aroused brain, Science, 262, 679–685, 1993. 127. Sejnowski, T. J. and Destexhe, A., Why do we sleep?, Brain Res., 886, 208–223, 2000. 128. Pompeiano, M., Cirelli, C., and Tononi, G., Immediate-early genes in spontaneous wakefulness and sleep: expression of c-fos and NGIF-A mRNA protein, J., Sleep Res., 3, 80–96, 1994. 129. Gutwein, B.M., Shiromani, P.J., and Fishbein, W., Paradoxical sleep and memory: long-term disruptive effects of Anisomycin, Pharmacol. Biochem. Behav., 12, 377–384, 1980. 130. Giuditta, A., A sequential hypothesis for the function of sleep, in Sleep in ’84, Koella, W.P., Ruther, E., and Schultz, H., Eds., Fisher-Verlag, Stuttgart, 1985, pp. 222–224. 131. Giuditta, A., Ambrosini, M.V., Montagnese, P., Mandile, P., Cotugno, M., Zucconi, G.G., and Vescia, S., The sequential hypothesis of the function of sleep, Behav. Brain Res., 69, 157–166, 1995. 132. Stickgold, R., Sleep: off-line memory reprocessing, Trends Cogn. Sci., 2, 484–492, 1998. 133. Stickgold, R., Whidbee, D., Schirmer, B., Patel, V., and Hobson, J. A., Visual discrimination task improvement: A multi-step process occurring during sleep, J. Cogn. Neurosci., 12, 246–254, 2000. 134. Lemaire, P., Vesque, C., Schmitt, J., Stunnenberg, H., Frank, R., and Charnay, P., The serum-inducible mouse gene Krox-24 encodes a sequence-specific transcriptional activator, Mol. Cell Biol., 10, 3456–3467, 1990. 135. Bozon, B., Kelly, A., Josselyn, S.A., Silva, A.J., Davis, S., and Laroche, S., MAPK, CREB, and zif268 are all required for the consolidation of recognition memory, Philos. Trans. R. Soc. Lond. B. Biol. Sci., 358, 805–814, 2003. 136. Pavlides, C. and Ribeiro, S., Recent evidence of memory processing in sleep, in Sleep and Brain Plasticity, Maquet, P., Smith, C., and Stickgold, R., Eds., Oxford University Press, Oxford, 2003, pp. 327–362. 137. Hebb, D.O., The effects of early and late brain injury upon test scores, and the nature of normal adult intelligence, Proc. Am. Philosophical Soc., 85, 275–292, 1942. 138. Craik, F. and Lockhart, R., Levels of processing: A framework for memory research., J. Verb. Learn. Verb. Behav., 11, 671–684, 1972. 139. Cermak, L. and Craik, F., Levels of Processing in Human Memory, John Wiley & Sons, New York, 1979. 140. Frankland, P.W., O’Brien, C., Ohno, M., Kirkwood, A., and Silva, A.J., AlphaCaMKII-dependent plasticity in the cortex is required for permanent memory, Nature, 411, 309–313, 2001. 141. Paxinos, G. and Watson, C., The Rat Brain in Stereotaxic Coordinates, Compact 3rd ed., Academic Press, San Diego, 1997.

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13

Cerebral Functional Segregation and Integration during Human Sleep Pierre Maquet, Fabien Perrin, Steven Laureys, Tahn Dang-Vu, Martin Desseilles, Mélanie Boly, and Philippe Peigneux

CONTENTS Introduction Two Sleep Types, Two Different Distributions of Regional Brain Activity NREM Sleep REM Sleep Brain Responses to External Stimuli during Sleep Brain Responses to Internal Stimuli during Sleep: Does the PGO Activity Exist in Humans? Experience-Dependent Changes in Functional Connectivity during Post-Training Sleep Conclusion Acknowledgments References

INTRODUCTION A comprehensive understanding of human brain function requires the characterization of both cerebral segregation and connectivity.1 Functional segregation pertains to the involvement of certain cerebral areas and networks in specific cerebral functions. For instance, Broca’s and Wernicke’s areas are known to participate in language. On the other hand, functional integration reflects how different regions interact to mediate a specific function. At the level of macroscopic systems, functional neuroimaging using positron emission tomography (PET) or functional magnetic resonance imaging (fMRI) can probe in vivo the segregation and integration of the human brain function during sleep and wakefulness. 0-8493-1519-0/05/$0.00+$1.50 © 2005 by CRC Press LLC

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Early studies described the functional anatomy of normal human sleep. They showed that the distribution of brain activity was specific for each type of sleep and differed from the waking pattern of brain activity. While the activity of subcortical structures was easily explained by the mechanisms that generate rapid eye movement (REM) sleep and non-REM (NREM) sleep in animals, the distribution of the activity within the cortex was harder to explain and its origin remains speculative. In order to better understand how cortical function is organized during sleep, regional cerebral responses have been explored in three different situations: 1. In response to external auditory stimulations in NREM sleep 2. In response to the internal activation due to putative PGO activity in humans during REM sleep 3. In relation to previous waking experience This chapter reviews these three issues after a short account of the functional neuroanatomy of NREM and REM sleep.

TWO SLEEP TYPES, TWO DIFFERENT DISTRIBUTIONS OF REGIONAL BRAIN ACTIVITY NREM SLEEP In mammals the neuronal activity observed during NREM sleep oscillations (spindles and slow rhythms) is characterized by bursting patterns that alternate short bursts of firing with long periods of hyperpolarization.2 The latter have a major impact on the regional blood flow, which on the average decreases in the areas where these oscillations are expressed. Accordingly the average cerebral metabolism and blood flow begin to decrease in light (stage 2) NREM sleep,3,4 and their nadir is observed in deep (stage 3 and 4) NREM sleep or slow-wave sleep (SWS).5,6 The cascade of events that underpin the NREM sleep oscillations in the thalamoneocortical networks is conditional upon a decreased firing in the activating structures of the brainstem tegmentum. In humans the brainstem blood flow is decreased during light NREM sleep7 as during SWS.7–9 In light NREM sleep, the pontine tegmentum is specifically deactivated, whereas the mesencephalon seems to retain an activity that is not significantly different from wakefulness.7 In SWS both pontine and mesencephalic tegmenta are deactivated. The thalamus plays a central role in the generation of NREM sleep rhythms due to the intrinsic properties of its neurones and to the intrathalamic and thalamocortico-thalamic connectivity. In humans the thalamus is deactivated during both light and deep NREM sleep7–9 in proportion to the power density in the spindle and delta frequency range,10 respectively. The role of the cortex in the generation of NREM sleep oscillations is equally important and begins to be better understood,2 but the respective contribution of the different parts of the neocortex in NREM sleep rhythms generation is still unknown at the microscopic level. In humans the deactivation of the cortex is not homogeneous. The most deactivated areas are located in associative cortices of the frontal,

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FIGURE 13.1 (See color insert following page 108.) Functional neuroimaging of REM sleep. Schematic representation of the relative increases and decreases in neural activity associated with REM sleep. Left panel: lateral view; middle panel: ventral view; right panel: medial view. A, H = amygdala and hypothalamus; B = basal forebrain; Ca = anterior cingulate gyrus; Cp = posterior cingulate gyrus and precuneus; F = prefrontal cortex; M = motor cortex; P = parietal supramarginal cortex; PH = parahippocampic gyrus; PT = pontine tegmentum; O = occipital-lateral cortex; Th = thalamus; T-O = temporo-occipital extrastriate cortex.(Adapted from Schwartz, S. and Maquet, P., Sleep imaging and the neuro-psychological assessment of dreams, Trends Cogn. Sci., 6, 23, 2002.)

parietal — And to a lesser extent temporal and insular lobes7–9,11 — while the primary cortices are the least deactivated. This observation suggests that the first cortical relay areas for exteroceptive stimuli remain relatively active during SWS. Although attractive, this hypothesis is challenged by another interpretation of the data. It should be emphasized that polymodal association cortices are the most active cerebral areas during wakefulness. Because of this high waking activity, they might be more profoundly influenced by SWS rhythms than primary cortices.12 This suggestion supports the view that sleep intensity is targeted disproportionately to areas of the brain intensely used during prior waking.13 Accordingly, in cats involved for some time in an active visual task, neurones in the associative visual cortex can adopt a bursting pattern typical for the sleeping cortex and become less responsive to visual stimulation, while the primary visual areas maintain a normal response to visual inputs.14

REM SLEEP In mammals neuronal populations in the mesopontine tegmentum are the source of a major activating input to the thalamic nuclei during REM sleep.15 The thalamus forwards this activation to the entire forebrain. In humans the activation of mesopontine tegmentum and thalamic nuclei has been systematically reported during REM sleep8,16,17 (Figure 13.1). In the forebrain PET data showed that limbic and paralimbic areas (amygdala, hippocampal formation, anterior cingulate, orbito-frontal, and insular cortices) were among the most active areas in REM sleep. Temporal and occipital cortices were also shown to be very active,8 although this result is less reproducible.16 The functional integration is modified during human REM sleep. The functional relationship between striate and extrastriate cortices, usually excitatory, is inverted during REM sleep.18 Likewise, the functional relationship between the amygdala

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and the temporal and occipital cortices is different during REM sleep than during wakefulness or SWS.19 The reasons for these changes in the cerebral activity patterns remain unclear. One possibility is that the brainstem structures influence the forebrain activity in a regionally specific way through aminergic modulation or direct excitatory activities such as pontine waves.

BRAIN RESPONSES TO EXTERNAL STIMULI DURING SLEEP Sleep is not a state of complete unresponsiveness to external stimuli. Although animal studies have suggested a decreased processing of sensory information during sleep,20 human behavioral and physiological studies have shown that stimuli can be integrated even into the sleeper’s mental or oneiric activity.21 External stimuli can also induce an autonomic or electrophysiological response, in particular after a relevant stimulus presentation.22 Event-related potentials (ERPs) studies have demonstrated that external information is efficiently processed during sleep. The brainstem auditory-evoked potentials are not modulated by the vigilance state but rather by the circadian variations of the body temperature, whereas the middle latencyevoked potentials are found to be reduced during deep sleep.23 Long-latency components are also observed during sleep but are modulated by the sleep stage. During NREM sleep (and especially in stage 2 sleep) ERPs correspond to K-complexes, which are differently affected by the characteristics of the stimulus, the early ones being more connected to the stimulus physical attributes and the latter ones to its intrinsic significance.24 In contrast during REM sleep, the morphology of long-latency components was very comparable to that observed in wakefulness. Notably it has been shown that N100, mismatch negativity (MMN), P300, and N400 could be recorded during this sleep stage. This implies that during PS, subjects may automatically detect stimulus occurrence and discernible changes in environment,25 may discriminate a deviant tones26,27 as well as her or his own first name28 and may detect the presence of a linguistic incongruence.29,30 As indicated earlier, the preserved capacity to evaluate salient stimulus features during SWS might be related to the relative preservation of cerebral activity in unimodal sensory cortical regions.7,8 Accordingly the presentation of auditory stimuli activates bilaterally the thalamus and the auditory cortex during NREM sleep as well as during wakefulness;31 however hearing one’s own name (as compared to hearing a neutral pure tone) additionally activates the left amygdala and prefrontal (associative) cortex. These results suggest that the processing of external stimuli can go beyond the primary cortices during NREM sleep. The mechanisms by which salient stimuli can recruit associative cerebral areas during sleep remain unclear.

BRAIN RESPONSES TO INTERNAL STIMULI DURING SLEEP: DOES THE PGO ACTIVITY EXIST IN HUMANS? Ponto-geniculo-occipital (PGO) waves are prominent phasic bioelectrical potentials that occur in isolation or in bursts just before and during REM sleep.32 In several

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mammal species, including nonhuman primates, PGO waves seem to represent a fundamental process of REM sleep, at least in its phasic aspects.33 PGO waves are closely related to the generation of ocular saccades,34 therefore during REM sleep saccades might be also generated in humans by mechanisms similar to PGO waves in cats.35–37 This hypothesis implies that the neural activity of the brain regions from which PGO are the most easily recorded in cats (i.e., the mesopontine tegmentum,38 the lateral geniculate bodies,39 and the occipital cortex32) should be more closely related to spontaneous ocular movements during REM sleep than during wakefulness. According to this prediction, regional blood flow changes in the lateral geniculate bodies and in the striate cortex are significantly more correlated to ocular movement density during REM sleep than during wakefulness (Figure 13.2).40 Hence cerebral mechanisms for spontaneous ocular movement generation differ during REM sleep and during wakefulness in humans, and brain regions known to be involved in the generation of PGO waves in animals are involved in this phenomenon. This finding is potentially important because PGO waves have been implicated in various nonexclusive processes, such as the alerting reaction to external stimuli or internal signals,41 sensorimotor integration through the transmission of an efferent copy of ocular movements to the visual system,33 and facilitation of brain plasticity.42

EXPERIENCE-DEPENDENT CHANGES IN FUNCTIONAL CONNECTIVITY DURING POST-TRAINING SLEEP Sleep is believed to participate in the consolidation of memory traces.43,44 Although the processes of this consolidation remain unknown, the reactivation during sleep of neuronal ensembles activated during learning appears as a possible mechanism for the off-line memory processing. Such a reactivation has been reported in at least two experimental situations: in the rat hippocampus45–50 and in the song area of young zebra finches.51 This suggests the generality of the reactivation in the processing of memory traces during sleep. In order to observe the reactivation of brain areas during post-training sleep in humans, we designed a multi-group experiment.52 A first group of subjects (group 1) were trained on a probabilistic serial reaction time (SRT) task* in the afternoon, * In this task six permanent position markers are displayed on a computer screen above six spatially compatible response keys. On each trial a black circle appears below one of the position markers, and the task consists of pressing as fast and as accurately as possible on the corresponding key. The next stimulus is displayed at another location after a 200-ms response-stimulus interval. Unknown to the subjects, the sequential structure of the material is manipulated by generating series of stimuli based on a probabilistic finite-state grammar that defines legal transitions between successive trials. To assess learning of the probabilistic rules of the grammar, there is a 15% chance on each trial that the stimulus generated based on the grammar (grammatical stimuli; G) is replaced by a nongrammatical (NG), random stimulus. Assuming that response preparation is facilitated by high predictability, predictable G stimuli should thus elicit faster responses than NG stimuli, but only if the context in which stimuli may occur has been encoded by participants. In this task contextual sensitivity emerges through practice as a gradually increasing difference between the reaction times (RTs) elicited by G and NG stimuli occurring in specific contexts set by 2 to 3 previous trials at most.53

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FIGURE 13.2 (See color insert.) Cerebral areas more active responding proportionally more in relation to saccades during REM sleep than during wakefulness. Upper panel: transverse sections from –4 mm to 0 mm from the bi-commissural plane. The functional data are displayed at p < 0.001 uncorrected, superimposed on the average MRI of the subjects, coregistered to the same reference space. Bottom panel: plot of the regional adjusted CBF (arbitrary units) in the right geniculate body in relation to the rapid eye movement (REMs) counts. The geniculate CBF is correlated to the rapid eye movement counts more during REM sleep (in red) than during wakefulness (in green). (Adapted from Peigneux, P. et al., Generation of rapid eye movements during paradoxical sleep in humans, Neuroimage, 14, 701, 2001.)

then scanned during the post-training night, both during waking and in various sleep stages (i.e., SWS, stage 2, and REM sleep). A postsleep training session verified that learning had occurred overnight. The analysis of PET data identified the brain areas more active in REM sleep than during resting wakefulness. To ensure that the post-training REM sleep rCBF distribution differed from the pattern of typical REM sleep, a second group of subjects (group 2), not trained to

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the task, were similarly scanned at night, both awake and during sleep. The analysis was aimed at detecting the brain areas that would be more active in trained than in nontrained subjects and during REM sleep as compared to resting wakefulness. And finally, to formally test that these brain regions, possibly reactivated during REM sleep, would be among the structures that had been engaged by executing and learning the task, a third group of subjects (group 3) were scanned during wakefulness both while they were performing the SRT task and at rest. The comparison described the brain areas that are activated during the execution of the SRT task. A conjunction analysis identified the regions that would be both more active during REM sleep in the trained subjects (group 1) compared to the nontrained subjects (group 2) and activated during the execution of the task during waking (group 3); i.e., the regions reactivated in post-training REM sleep. Our results (Figure 13.3) showed that the bilateral cuneus and the adjacent striate cortex, the mesencephalon, and the left premotor cortex were both activated during the practice of the SRT task and during post-training REM sleep in subjects previously trained on the task, significantly more than in control subjects without prior training, suggesting a reactivation process that may have contributed to overnight performance improvement in the SRT task. In addition we reasoned that if the reactivated regions participate in the processing of memory traces during REM sleep, they should establish or reinforce functional connections between parts of the network activated during the task. Consequently such connections should be stronger, and the synaptic trafficking between network components more intense, during post-training REM sleep than during the typical REM sleep of nontrained subjects. Accordingly we found that among the reactivated regions, the rCBF in the left premotor cortex was significantly more correlated with the activity of the pre-SMA and posterior parietal cortex during post-training REM sleep than during REM sleep in subjects without any prior experience with the task54 (Figure 13.3). The demonstration of a differential functional connectivity during REM sleep between remote brain areas engaged in the practice of a previously experienced visuo-motor task gave further support to the hypothesis that memory traces are replayed in the cortical network and contributes to the optimization of the performance. It should be stressed that in this first experiment our conclusions were limited by the fact that we could not specify whether the experience-dependent reactivation during REM sleep was related to the simple optimization of a visuo-motor skill or to the high-order acquisition of the probabilistic structure of the learned material, or both. To test the hypothesis that the cerebral reactivation during post-training REM sleep reflects the reprocessing of high-order information about the sequential structure of the material to be learned, a new group of subjects (group 4) was scanned during sleep after practice on the same SRT task but using a completely random sequence.55 The experimental protocol was identical in all respects to the trained group in our original study,52 except for the absence of sequential rules. Therefore post-training regional cerebral blood flow differences during REM sleep between the subjects trained respectively to the probabilistic SRT or to its random version should be related specifically to the reprocessing of the high-order sequential information.

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FIGURE 13.3 (See color insert.) Experience-dependent reactivations during human REM sleep. (A) Brain regions that are both activated in subjects scanned while performing the task during wakefulness and more active in trained than in nontrained subjects scanned during REM sleep. The SPM is displayed thresholded at P < 0.001 (uncorrected). (Data from Maquet, P. et al., Experience-dependent changes in cerebral activation during human REM sleep, Nat. Neurosci., 3, 831, 2000. Reproduced with permission from Nature Neuroscience.) (B) Significant group (trained versus nontrained) by left premotor rCBF interaction in the posterior parietal cortex (upper image) and the supplementary motor area (lower image). The red arrow in panel A indicates the left premotor cortex. The SPM is displayed at P < 0.001 (uncorrected). On the right-hand side, plots of the adjusted and centered rCBF of the left premotor cortex (abscissa) and, respectively, the posterior parietal cortex and the supplementary motor area (ordinate). The functional relationships between these two areas are significantly different in trained subjects (red) than in nontrained subjects (green). (Adapted from Laureys, S, et al., Experience-dependent changes in cerebral functional connectivity during human rapid eye movement sleep, Neuroscience, 105, 521, 2001.)

During post-training REM sleep, blood flow in left and right cuneus increased more in subjects previously trained to a probabilistic sequence of stimuli than to a random one (Figure 13.3 B). Because both groups were exposed prior to sleep to identical SRT practice that differed only in the sequential structure of the stimuli, our result suggests that reactivation of neural activity in the cuneus during posttraining REM sleep is not merely due to the acquisition of basic visuo-motor skills,

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but rather it corresponds to the reprocessing of elaborated information about the sequential contingencies contained in the learned material. If the material does not contain any structure, as it is the case in the random SRT task, post-training REM sleep reactivation does not occur, or it occurs at a significantly lesser extent. These results are reminiscent of previous experiments. At the behavioral level, increase in REM sleep duration was observed in rats following aversive conditioning in which a tone is paired with a foot-shock, but not after pseudo-conditioning in which the tone and the foot-shock were not paired.56 Using a similar procedure at the systems level, tone-evoked responses were obtained in the medial geniculate nucleus57 during REM sleep after a conditioning procedure initiated at wake, but not after pseudo-conditioning. Likewise in humans REM sleep percentage increased after learning textbook passages, but only when they were meaningful.58 A similar situation occurred when the material to learn was so complex that its underlying structure could not be extracted through practice. Consequently, during REM sleep, functional connections should be reinforced between the reactivated areas and cerebral structures specifically involved in sequence learning only after the practice of the probabilistic version of the task. As compared to the practice of the random sequence, we observed that the cuneus establishes or reinforces functional connections with the caudate nucleus during REM sleep following probabilistic SRT practice (Figure 13.3 C). The cuneus, which participates in the processing of the probabilistic sequence both during SRT practice and post-training REM sleep, has been shown to be activated during sequential information processing in the waking state.59 On the other hand, the striatum is known to play a main role in implicit sequence learning60 and specifically in the encoding of the temporal context set by the previous stimulus in the probabilistic SRT task.61 The finding that the strength of the functional connections between cuneus and striatum is increased during post-training REM sleep suggests the involvement of the basal ganglia (Figure 13.3 D) in the off-line reprocessing of implicitly acquired high-order sequential information. Finally, a direct relationship between the presleep learning performance and regional blood flow was found in the cuneus. In this region the regional blood flow during post-training REM sleep is modulated by the level of high-order, but not loworder, learning attained prior to sleep (Figure 13.3 E). In other words, the neural activity recorded during REM sleep in brain areas already engaged in the learning process during wakefulness is related to the amount of high-order learning achieved prior to sleep. This latter result further supports the hypothesis that sleep is actively involved in the processing of recent memory traces.

CONCLUSION As compared to wakefulness, segregated patterns of regional CBF activity are observed during NREM and REM sleep in humans. The cortical activity is not only influenced by the processes that lead to the generation of specific sleep patterns but remains responsive to external stimuli. Moreover the neural populations recently challenged by a new experience are reactivated and increase their functional con-

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nectivity during the post-training sleep episodes, suggesting the off-line processing of recent memory traces in sleep.

ACKNOWLEDGMENTS The work summarized in this paper was supported by the Fonds National de la Recherche Scientifique – Belgique (FNRS), the Fondation Médicale Reine Elisabeth, the Research Fund of ULg, PAI/IAP Interuniversity Pole of Attraction P4/22, and the Welcome Trust. PM and SL are supported by the FNRS.

FIGURE 13.4 (See color insert.)

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REFERENCES 1. Friston, K.J., Imaging neuroscience: principles or maps? Proc. Natl. Acad. Sci. USA, 95, 796, 1998. 2. Steriade, M. and Amzica, F., Coalescence of sleep rhythms and their chronology in corticothalamic networks, Sleep Research Online, 1, 1, 1998. 3. Madsen, P.L. et al., Cerebral oxygen metabolism and cerebral blood flow in man during light sleep (stage 2), Brain Res., 557, 217, 1991. 4. Maquet, P. et al., Cerebral glucose utilization during stage 2 sleep in man, Brain Res., 571, 149, 1992. 5. Madsen, P.L. et al., Cerebral O2 metabolism and cerebral blood flow in humans during deep and rapid-eye-movement sleep, J. Appl. Physiol., 70, 2597, 1991. 6. Maquet, P. et al., Cerebral glucose utilization during sleep-wake cycle in man determined by positron emission tomography and [18F]2-fluoro-2-deoxy-D-glucose method, Brain Res., 513, 136, 1990. 7. Kajimura, N. et al., Activity of midbrain reticular formation and neocortex during the progression of human non-rapid eye movement sleep, J. Neurosci., 19, 10065, 1999. 8. Braun, A.R. et al., Regional cerebral blood flow throughout the sleep-wake cycle. An H2(15)O PET study, Brain, 120, 1173, 1997. 9. Maquet, P. et al., Functional neuroanatomy of human slow wave sleep, J, Neurosci., 17, 2807, 1997.

FIGURE 13.4 (See facing page.) Probabilistic versus random serial reaction time task. Data from Peigneux, P. et al., Learned material content and acquisition level modulate cerebral reactivations during post-training REM sleep (submitted). (A) Average reaction times (and standard errors) per block for grammatical (G; red lines) and nongrammatical (NG; blue lines) stimuli during pre- and postsleep sessions in Probabilistic (left-hand side) and Random (righthand side) groups. Subjects in the Random Group were exposed to the random sequence in presleep sessions and to the probabilistic sequence in blocks 1–20 of postsleep sessions. In contrast to the subjects in group 1 (trained to the probabilistic sequence, left panel), reaction times for G and NG stimuli do not differ during the presleep training session for the subjects of group 4 (trained to the random sequence, right panel). (B) Statistical parametric maps of the brain regions that both activated during SRT practice (versus rest) and activated more during REM sleep (versus wakefulness) in Probabilistic rather than Random group, superimposed on the coronal section of a subject’s normalized MRI at 68 and 70 mm behind the anterior commissure. The SPM is displayed at p < 0.001, uncorrected. (C) The right caudate nucleus, with which the right cuneus has a tighter functional connection in subjects trained to the probabilistic SRT task than in subjects trained to the random SRT task. A similar regression is observed between cuneus and caudate nucleus in the left hemisphere. The SPM is displayed at p < 0.001, uncorrected. (D) Plot of the regression of centred CBF in the right cuneus (32, –68, 12 mm) and right caudate nucleus (18, –12, 20 mm) during post-training REM sleep in subjects trained to the probabilistic SRT task (red circles) and subjects trained to the random SRT task (blue stars). (E) Regression of presleep high-order performance on post-training REM sleep CBF (centred) in the right parieto-occipital fissure (coordinates 26, –70, 24 mm in standard anatomical space), in Probabilistic SRT (circles) and Random SRT (stars) subjects.

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10. Hofle, N. et al., Regional cerebral blood flow changes as a function of delta and spindle activity during slow wave sleep in humans, J. Neurosci., 17, 4800, 1997. 11. Andersson, J.L. et al., Brain networks affected by synchronized sleep visualized by positron emission tomography, J. Cereb. Blood. Flow. Metab., 18, 701, 1998. 12. Maquet, P., Functional neuroimaging of normal human sleep by positron emission tomography, J. Sleep. Res., 9, 207, 2000. 13. Krueger, J. et al., Sleep Modulation of the Expression of Plasticity Markers, in Sleep and Brain Plasticity, Maquet, P., C., Smith, and Stickgold, R., Eds., 2003, Oxford University Press, Oxford, (in press). 14. Pigarev, I.N., Nothdurft, H.C., and S. Kastner, Evidence for asynchronous development of sleep in cortical areas, Neuroreport, 8, 2557, 1997. 15. Steriade, M. and McCarley, R.W., Brainstem Control of Wakefulness and Sleep, 1990, Plenum Press, New York, p. 499. 16. Maquet, P. et al., Functional neuroanatomy of human rapid-eye-movement sleep and dreaming, Nature, 383, 163, 1996. 17. Nofzinger, E.A. et al., Forebrain activation in REM sleep: an FDG PET study, Brain Res., 770, 192, 1997. 18. Braun, A.R. et al., Dissociated pattern of activity in visual cortices and their projections during human rapid eye movement sleep, Science, 279, 291, 1998. 19. Maquet, P. and Phillips, C., Functional brain imaging of human sleep, J. Sleep Res., 7, 42, 1998. 20. Pompeiano, O., Mechanisms of sensory-motor integration during sleep, Progr. Physiol. Psychol., 3, 1, 1970. 21. Burton, S.A., Harsh, J.R., and Badia, P., Cognitive activity in sleep and responsiveness to external stimuli, Sleep, 11, 61, 1988. 22. Bonnet, M., Performance during sleep, in Biological Rythms, Sleep, and Performance, W. Webb, Ed., 1982, John Wiley & Sons, Chichester, p. 205. 23. Bastuji, H. and García-Larrea, L., Evoked potentials as a tool for the investigation of human sleep, Sleep Medicine Rev., 3, 23, 1999. 24. Perrin, F. et al., Functional dissociation of the early and late portions of human Kcomplexes, Neuroreport, 11, 1637, 2000. 25. Atienza, M., Cantero, J.L., and Escera, C., Auditory information processing during human sleep as revealed by event-related brain potentials, Clin. Neurophysiol., 112, 2031, 2001. 26. Bastuji, H. et al., Brain processing of stimulus deviance during slow-wave and paradoxical sleep: a study of human auditory evoked responses using the oddball paradigm, J. Clin. Neurophysiol., 12, 155, 1995. 27. Niiyama, Y. et al., Endogenous components of event-related potential appearing during NREM stage 1 and REM sleep in man, Intl. J. Psychophysiol., 17, 165, 1994. 28. Perrin, F. et al., A differential brain response to the subject’s own name persists during sleep, Clin. Neurophysiol., 110, 2153, 1999. 29. Brualla, J. et al., Auditory event-related potentials to semantic priming during sleep, Electroencephalogr. Clin. Neurophysiol., 108, 283, 1998. 30. Perrin, F., Bastuji, H., and Garcia-Larrea, L., Detection of verbal discordances during sleep, Neuroreport, 13, 1345, 2002. 31. Portas, C.M. et al., Auditory processing across the sleep-wake cycle: simultaneous EEG and fMRI monitoring in humans, Neuron, 28, 991, 2000. 32. Mouret, J., Jeannerod, M., and Jouvet, M., L’activité électrique du système visuel au cours de la phase paradoxale du sommeil chez le chat, J. Physiol. (Paris), 55, 305, 1963.

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33. Callaway, C.W. et al., Pontogeniculooccipital waves: spontaneous visual system activity during rapid eye movement sleep, Cell Mol. Neurobiol., 7, 105, 1987. 34. Datta, S., PGO wave generation: mechanism and functional significance, in Rapid Eye Movement Sleep, Mallick, B.N. and Inoue, S., Eds., 1999, Narosa Publishing House, New Delhi, p. 91. 35. Inoué, S., Saha, U.K., and Musha, T., Spatio-temporal ristribution of neuronal activities and REM sleep, in Rapid-Eye-Movement Sleep, B.N. Mallick and S. Inoue, Eds., 1999, Narosa Publishing, New Delhi, p. 214. 36. McCarley, R.W., Winkelman, J.W., and Duffy, F.H., Human cerebral potentials associated with REM sleep rapid eye movements: links to PGO waves and waking potentials, Brain Res., 274, 359, 1983. 37. Salzarulo, P. et al., Direct depth recording of the striate cortex during REM sleep in man: are there PGO potentials? EEG Clin. Neurophysiol., 38, 199, 1975. 38. Jouvet, M. and Michel, F., Corrélations électromyographiques du sommeil chez le Chat décortiqué et mésencéphalique chronique, C.R. Soc. Biol. (Paris), 153, 422, 1959. 39. Mikiten, T.H., Niebyl, P.H., and Hendley, C.D., EEG desynchronization during behavioral sleep associated with spike discharges from the thalamus of the cat, Fed. Proc., 20, 327, 1961. 40. Peigneux, P. et al., Generation of rapid eye movements during paradoxical sleep in humans, Neuroimage, 14, 701, 2001. 41. Bowker, R.M. and Morrison, A.R., The startle relfex and PGO spikes, Brain Res., 102, 185, 1976. 42. Datta, S., A physiological substrate for sleep dependent memory processing, Sleep Research Online, 2, 23, 1999. 43. Peigneux, P. et al., Sleeping brain, learning brain, the role of sleep for memory systems, Neuroreport, 12, A111, 2001. 44. Maquet, P., The role of sleep in learning and memory, Science, 294, 1048, 2001. 45. Kudrimoti, H.S., C.A., Barnes, and McNaughton, B.L., Reactivation of hippocampal cell assemblies: effects of behavioral state, experience, and EEG dynamics, J. Neurosci., 19, 4090, 1999. 46. Lee, A.K. and Wilson, M.A., Memory of sequential experience in the hippocampus during slow wave sleep, Neuron, 36, 1183, 2002. 47. Louie, K. and Wilson, M.A., Temporally structured replay of awake hippocampal ensemble activity during rapid eye movement sleep, Neuron, 29, 145, 2001. 48. Nadasdy, Z. et al., Replay and time compression of recurring spike sequences in the hippocampus, J. Neurosci., 19, 9497, 1999. 49. Pavlides, C. and Winson, J., Influences of hippocampal place cell firing in the awake state on the activity of these cells during subsequent sleep episodes, J. Neurosci., 9, 2907, 1989. 50. Wilson, M.A. and McNaughton, B.L., Reactivation of hippocampal ensemble memories during sleep, Science, 265, 676, 1994. 51. Dave, A.S. and Margoliash, D., Song replay during sleep and computational rules for sensorimotor vocal learning, Science, 290, 812, 2000. 52. Maquet, P. et al., Experience-dependent changes in cerebral activation during human REM sleep, Nat. Neurosci., 3, 831, 2000. 53. Cleeremans, A. and McClelland, J.L., Learning the structure of event sequences, J. Exp. Psychol. General, 120, 235, 1991. 54. Laureys, S, et al., Experience-dependent changes in cerebral functional connectivity during human rapid eye movement sleep, Neuroscience, 105, 521, 2001.

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55. Peigneux, P. et al., Learned material content and acquisition level modulate cerebral reactivations during post-training REM sleep (submitted). 56. Hennevin, E. and Leconte, P., The function of paradoxical sleep: facts and theories, Annee. Psychol., 71, 489–519, 1971. 57. Hennevin, E. et al., Learning-induced plasticity in the medial geniculate nucleus is expressed during paradoxical sleep, Behav. Neurosci., 107, 1018, 1993. 58. Verschoor, G. and Verschoor, H.T.L., REM bursts and REM sleep following visual and auditory learning, S. Afr. J. Psychol., 14, 69, 1984. 59. Schubotz, R.I. and von Cramon, D.Y., Interval and ordinal properties of sequences are associated with distinct premotor areas, Cerebral Cortex, 11, 210, 2001. 60. Rauch, S. et al., A PET investigation of implicit and explicit sequence learning, Human Brain Mapping, 3, 271, 1995. 61. Peigneux, P. et al., Striatum forever, despite sequence learning variability: a random effect analysis of PET data, Human Brain Mapping, 10, 179, 2000. 62. Schwartz, S. and Maquet, P., Sleep imaging and the neuro-psychological assessment of dreams, Trends Cogn. Sci., 6, 23, 2002.

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COLOR FIGURE 3.6 W/SWS

PS

PeF, HLA Hcrt Pef/HLA

MCH

MCH

Thalamus EEG activation Glu

Thalamus EEG activation Glu

SLD PAG, DPMe, PRN

SLD

LC/DRN PAG, MRN, PRN

NA/5-HT

Glu

Glu

Glu

DPMe, PRN, SLD D

Ach

LC/DRN NA/5-HT

Glu

GABA

LDT/PPT

PeF, HLA Hcrt

Pef/HLA

DPMe, PRN, SLD GABA

LDT/PPT Ach

Mc

Mc

Muscle atonia Gly

Muscle atonia Gly vlPAG/DPGi

vlPAG/DPGi

GABA

PS-on

COLOR FIGURE 5.5

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GABA

PS-off

PS-on

PS-off

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COLOR FIGURE 8.1

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COLOR FIGURE 8.2

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COLOR FIGURE 8.4

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A

0.60

Post-Nonexposure Post-Exposure

SPIKES/SEC

0.50

B

* 0.40 0.30

SONG

*

SLEEP

0.20 0.10

100ms 0.00

WK SWS REM

C

WK

SWS

REM

D

Enriched Environment

Control

Brain activation during REM sleep (adjusted CBF)

4 3 2 1 0

-1 -2 -3

Low

High

Zif-268 Expression

COLOR FIGURE 12.1

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-20

0

20

40

60

80

100

Learning level attained prior to sleep

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Neuronal Correlations

A

*

**

Time (minutes)

Neuronal Correlations

B

Time (minutes)

C

D

WK Pre Post

Neuronal Correlations

Cortical Neurons Neuronal Correlations

0.3

0.2

0.1

0.0

EXP

COLOR FIGURE 12.4

COLOR FIGURE 13.1

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-0.1

0

12 24 36 Time (hours)

48

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COLOR FIGURE 13.2

COLOR FIGURE 13.3

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COLOR FIGURE 13.4

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