Slow-wave sleep: From the cell to the clinic

Accepted Manuscript Slow-Wave Sleep: From the Cell to the Clinic Damien Léger, Eden Debellemaniere, Arnaud Rabat, Virginie Bayon, Karim Benchenane, Mounir Chennaoui PII:

S1087-0792(17)30005-9

DOI:

10.1016/j.smrv.2018.01.008

Reference:

YSMRV 1087

To appear in:

Sleep Medicine Reviews

Received Date: 13 January 2017 Revised Date:

2 January 2018

Accepted Date: 22 January 2018

Please cite this article as: Léger D, Debellemaniere E, Rabat A, Bayon V, Benchenane K, Chennaoui M, Slow-Wave Sleep: From the Cell to the Clinic, Sleep Medicine Reviews (2018), doi: 10.1016/ j.smrv.2018.01.008. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT SLOW-WAVE SLEEP: FROM THE CELL TO THE CLINIC

Damien Léger

a-b

*, Eden Debellemaniere

b-c-d

**, Arnaud Rabat

b-c

, Virginie Bayon

a-b

, Karim

Benchenane e, Mounir Chennaoui b-c

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a. Université Paris Descartes, Sorbonne Paris Cité, APHP, HUPC, Hôtel Dieu, Centre du Sommeil et de la Vigilance, Paris, France

b. Université Paris Descartes, Sorbonne Paris Cité, Équipe d'accueil VIgilance FAtigue SOMmeil (VIFASOM) EA 7330, France

d. Rythm SAS, Paris, France

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c. Institut de Recherche Biomédicale des Armées (IRBA), Brétigny-sur-Orge, France

e. Team Memory, Oscillations and Brain states (MOBs), Brain Plasticity Unit, CNRS, ESPCI Paris, PSL

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Research University, 10 rue Vauquelin, Paris, France

Corresponding authors.

*Damien Léger, Université Paris Descartes, Équipe d'accueil VIgilance FAtigue SOMmeil (VIFASOM) EA 7330, France; Université Paris Descartes, Sorbonne Paris Cité, APHP, Hôtel Dieu, Centre du Sommeil et de la

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Vigilance, Paris, France.383.

** Eden Debellemanière, Université Paris Descartes, Sorbonne Paris Cité, Équipe d'accueil VIgilance FAtigue SOMmeil (VIFASOM) EA 7330, France; Institut de recherche biomédicale des armées (IRBA), Brétigny-sur-

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Orge, France ; Rythm SAS, Paris, France

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E-mail address: [email protected] (D. Léger), [email protected] (E. Debellemaniere)

Acknowledgements:

This work was supported by the French National Agency for Research ANR-12-BSV4-0013-02 (KB), by the CNRS and the ATIP-Avenir program 2014 (KB), and by the Grant Emergence of the city of Paris 2014 (KB). This work also received support under the program Investissements d’Avenir launched by the French Government and implemented by the ANR, with the references: ANR-10-LABX-54 MEMO LIFE (KB) and ANR-11-IDEX-0001-02 PSL* Research University (KB). Eden Debellemaniere is supported by a doctoral research grant from the General Directorate for Armament (DGA, Ministry of Defense) and by Rythm SAS. Mounir Chennaoui and Arnaud Rabat were supported by the PDH-1-SMO-2-0510 grant.

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ACCEPTED MANUSCRIPT Conflicts of interest: Damien Léger is or has been consulted as the main investigator in studies sponsored by Actelion, Agence Spatiale Européenne, Ag2R, Bioprojet, CNES, DGA, iSommeil, Jazz, Vanda, Merck, NASA, Philips, Resmed, Sanofi, Rhythm, Vinci Fondation, and Vitalaire in the last 5 years. He declares no COI regarding this

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manuscript. Eden Debellemaniere is supported by a doctoral research grant partially funded by Rythm SAS.

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ACCEPTED MANUSCRIPT SUMMARY In recent decades, increasing evidence has positioned slow-wave sleep (SWS) as a major actor in neurophysiological phenomena such as glucose metabolism, hormone release, immunity and memory. This proposed role for SWS, coupled with observations of impaired SWS in several pathologies as well as in aging, has led some researchers to implement methods that could specifically enhance SWS.

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This review aims to gather the current knowledge extending from the cell to the clinic, in order to construct an overview of what is currently known about so-called SWS. We slowly expand the view from the molecular processes underlying SWS to the cell unit and assembly to cortical manifestations. We then describe its role in physiology and cognition to finally assess its association with clinical aspects. Finally, we address practical considerations for several techniques that could be used to manipulate SWS, in order to improve our

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understanding of SWS and possibly help the development of treatments for SWS clinical disorders.

Abbreviations:

ACTH adrenocorticotropic hormone

ADP

adenosine di-phosphate

AHI

apnea-hypopnea index

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ADHD attention deficit hyperactivity disorder

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Keywords: sleep, SWS, SWA, SO, K-complex, NREM, EEG, deep sleep, memory consolidation

AMPK adenosine mono-phosphate kinase adenosine tri-phosphate

EEG

electroencephalography

BZD

benzodiazepine

CBT

cognitive behavioral therapy

GH

growth hormone

HRV

heart rate variability

IL

inter-leukin

ITI

intra-tone interval

LFP

local field potential

LTD

long-term depotentiation

LTP

long-term potentiation

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ATP

NREM non-rapid eye movement

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ACCEPTED MANUSCRIPT NREM-1 (N1) non-rapid eye movement stage 1 NREM-2 (N2) non-rapid eye movement stage 2 NREM-3 (N3) non-rapid eye movement stage 3 OSA

obstructive sleep apnea

PLM

periodic limb movements

PSG

polysomnography

REM

rapid eye movement

RLS

restless leg syndrome

SHY

synaptic homeostasis hypothesis

SO

slow oscillation

SOL

sleep onset latency

SOtDCS slow oscillation targeted by tDCS

SWR

sharp wave ripples

SWS

slow-wave sleep

TC

thalamo-cortical

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SWA slow-wave activity

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phase-locked-loop

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PLL

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PER 3 PERIOD 3

tDCS transcranial direct current stimulation tumor necrosis factor

TMS

transcranial magnetic stimulation

TRN

reticular nucleus

TST

total sleep time

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TNF

VLPO ventro-lateral preoptic nucleus 5HT

serotonin

Specific Slow wave glossary.

Up/Down state: Periods of alternation of the membrane potential between a UP state (with a membrane potential close to the action potential threshold) and a down state with a membrane potential far from the threshold and associated with an absence of spikes. ON/OFF period: Period of silence of a large population of cortical neurons as assessed by the neuronal firing rate

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ACCEPTED MANUSCRIPT Delta-wave: negative deflection of the LFP in the superficial layer associated with a positive deflection in the deep layers of the cortex. K Complex: Corresponds to a delta-wave with a more prominent positive deflection (that give a bimodal shape) and likely associated with a rebound of excitation. K-complex are often associated with a spindle. Slow oscillation: oscillation visible in the LFP or EEG between 0.5 and 4.5Hz. It is worth pointing out that the

rhythmical occurrence of delta Waves/K complexes.

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values used to define the SO varies from one study to another. The slow oscillation is likely due to the

Slow wave activity: Signal obtained after filtering in the low frequency range followed by a threshold to detect large fluctuation in the LFP/EEG during sleep. In some studies, the same term is used to define the

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slow oscillations and sleep homeostasis or sleep pressure.

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power in the low frequency band (0.5-4.5Hz). These methods are often used to provide a link between sleep

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ACCEPTED MANUSCRIPT Introduction Since the earliest investigations of brain electrical activity, researchers have noticed that changes during the transition to sleep as well as changes in consciousness level are associated with modified electroencephalographic (EEG) patterns, both in animal models [1] and in humans [2]. These researchers rapidly observed periods of sustained oscillatory activity in the EEG and identified distinctive patterns that were primarily classified as spindles, vertex spikes, and K-complex waveforms. Brain oscillations were

theta (4-8 Hz in human; 5-10 Hz in rodents) and gamma (60-120 Hz).

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classified and named according to their frequency range: alpha (10-15 Hz), beta (30-50 Hz), delta (1-4Hz),

Since then, sleep has been identified according to the occurrence of particular EEG events and/or the modification of oscillatory activity. To facilitate the analysis of large amounts of data obtained from night

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recordings, sleep has been divided into five stages (from stage A to stage E), based on the occurrence of different EEG events [3]. More recent sleep scoring methods, such as the Rechtschaffen and Kales [4] or AASM [5] scales, respectively classify sleep into either 4 or 3 stages of non-rapid eye movement (NREM)

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sleep in addition to rapid eye movement (REM) sleep. Based on this scoring, the sleep stages in this review will be referred to as Stage 1 (S1), Stage 2 (S2), Stage 3 (S3), Stage 4 (S4) for the Rechtschaffen and Kales classification, and Non-rapid eye movement stage 1 (N1), Non-rapid eye movement stage 2 (N2), Non-rapid eye movement stage 3 (N3) for the new three-stage AASM classification.

S1/N1 is defined as an intermediate stage between wakefulness and the deeper stages of sleep lasting only a few minutes in normal subjects. In S1/N1, brain electrical activity consists mainly of moderate-amplitude

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theta waves and the disappearance of the alpha rhythm. During this period, slow and mostly horizontal eye movements replace the rapid eye movements seen in wakefulness, and some vertex shapes can be distinguished.

S2/N2 is characterized by the occurrence of spindles and K-complexes. As the sleep state deepens, the number

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of K-complex/delta waves progressively increases, giving rise to a very progressive transition to S3 and S4. The dominant EEG activity in these two stages consists of delta waves, such that the distinction between S3

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and S4 lies only in the abundance of slow waves. For this reason, in 2007, the AASM modified the Rechtschaffen and Kales classification and change the terminology of stages S3 and S4 to N3 and referred S3+S4 and N3 as slow-wave sleep (SWS). SWS is thus defined by electrophysiological characteristics that can be visually assessed and scored based on the frequency and the amplitude of brain oscillations, according to standardized criteria [4,5]. More precisely, SWS in humans is defined by the presence of high-voltage (> 75 µV) synchronized EEG waveforms: delta oscillations (1-4 Hz) and slow oscillations (SO) (< 1 Hz), for which the power densities in the 0.75-4 Hz EEG range are typically referred to as slow-wave activity (SWA). In young adults, SWS accounts for between 10-25% of the daily total sleep time [6]. However, SWS is not equally distributed during the entire sleep period of a healthy adult. In fact, it is predominantly found during early sleep, whereas REM sleep predominates during the second half of the night. Accordingly, slow waves

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ACCEPTED MANUSCRIPT are mostly (but not exclusively) present during N3/S3-S4, since some SWA can be observed during N2/S2 sleep, and to a lesser extent during N1/S1 and REM sleep [7]. As early as the 1960s it was observed that “an important assumption of these classification (sleep stages, EEG events) is that the variables to be measured by the method described are sensitive indices of biological events” [8]. Nevertheless, detecting these events is difficult and often relies on the selection of an arbitrary threshold. Moreover, the analysis of sleep solely based on the occurrence of EEG events or the duration of sleep stages

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may ignore valuable information such as variations in EEG pattern amplitude or synchronization. The analysis of EEG as a succession of sporadic individual events is difficult to combine with more classical spectral analyses of oscillatory activity, which requires a stationary signal. Finally, inconsistent sleep staging terminology across diverse fields can be confusing, since SWS refers to N3/S3-S4 in humans, whereas it

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typically means the whole NREM in rodents [9]. This has caused fundamental misunderstandings and made it difficult to compare conclusions from stage-based analyses of sleep and more temporally precise invasive physiological experiments.

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In this review, we have focused on the slow waves of sleep using a broad definition that includes slow oscillations (SO) and delta waves that occur during N3/S3-S4 and K-complexes/delta waves in N2/S2. NREM sleep will encompass all the classical sleep stages other that REM (S1 to S4 or N1 to N3). Our review seeks to portray SWS in its entirety, from its electrophysiological signature to the clinic, by taking into account the broad diversity of publications on this topic. Thus, our approach expands in scope from the molecular processes underlying SWS, to the cell unit and assembly, to cortical manifestations. Next, the role of SWS in

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physiology and cognition is described in order to assess its association with clinical aspects. Finally, we address practical considerations for several techniques that could be used to manipulate SWS in order to improve our understanding of SWS, which may possibly help in the development of treatments for SWS

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clinical disorders.

1. Cellular aspects of slow-wave sleep

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The classification of sleep into different sleep stages and the identification of unitary EEG events have allowed the investigation of sleep physiology at different time scales ranging from minutes (S1-S4/N1-N3, REM) to sub-seconds (spindles, K-complexes, etc.). However, the abundance of features and their limited characterization can likely explain the poor prediction of sleep functions based on EEG features. In order to circumvent these issues, researchers have attempted to identify the neuronal bases of these EEG events in animal models using sleep, as well as anesthesia or even slice preparations. These methods allow the characterization of ion channels, neuronal cell types and network structures involved in the generation of the principal rhythms observed during SWS: SO, K-complex/delta waves, and spindles.

1.1. Spindles

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ACCEPTED MANUSCRIPT Spindles are one of the most striking events to occur during NREM sleep [2], with 10-15 Hz waxing and waning oscillations that last 0.5-5 s [10]. These sleep patterns have been observed in most mammals [11]. Their occurrence is used to define the N2/S2 stage, and most of the studies showed an increase density in N2/S2 compared to SWS (see for instance [12]. However, the presence of slow waves in SWS is likely to influence the detection of spindles and several studies, especially with newer method of detection, actually didn’t found any difference in the density of spindles between N2/S2 and SWS (in humans, S2: 5.91 ± 3.7,

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S3: 6.46 ± 5.81 and S4: 6.82 ± 6.60 spindles per min [13]; in monkeys, S2: 1.9 ± 1.0/min, S3: 2.0 ± 1.3/min, S4: 1.8 ± 1.5/min [14]).

Studies from several research groups have shown that spindles are generated in the thalamus and involve a highly non-linear conductance. The observation of spindles in thalamo-cortical slices of ferrets was crucial in

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showing that spindles are generated by interactions between thalamic reticular nucleus (TRN) neurons and thalamo-cortical (TC) relay neurons [15]. During wakefulness, the thalamus receives excitatory input from noradrenergic, histaminergic and cholinergic neurons [16]. These excitatory inputs vanish during NREM

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sleep, leading to a hyperpolarization of reticular thalamic neurons that switches from a tonic firing to a rhythmic bursting mode at spindle frequency [17]. These bursts then generate rhythmic inhibition followed by a rebound of activity in thalamo-cortical neurons. The rhythmical activation of this projection then generates excitatory currents in the cortex, leading to cortical spindles that are visible in the EEG [18]. Importantly, whereas spindles per se are generated in the thalamus, the feedback from cortico-thalamic neurons could play a role in their initiation and their termination [19]. Moreover, this cortical feedback is crucial for the

whole cortex [20].

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synchronization of spindles within the thalamus and the subsequent synchronization of spindles over the

While spindles have long been considered as a single phenomenon, several recent studies proposed that spindles do not refer to a homogenous population. For instance, some spindles could be more local instead of

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present in the whole brain [21,22]. Interestingly, the spindles detected in EEG and MEG do not match entirely, and are more local when recorded with MEG [23]. In addition, other recent observations suggest the

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existence of 2 types of spindles (both slow and fast) during NREM sleep, which could differ both in frequency and topography both for individual detected spindles or sigma power [19][20]. According to this view, slow spindles (< 12 Hz) occur more in the frontal cortex, whereas fast spindles (> 12 Hz) have a more widespread distribution over the parietal and central cortices. In addition, a pharmacological study employing Na+ and Ca2+ antagonists has suggested that the generation mechanisms are different for the two types of spindles [26]. While the fast spindles could be consistent with the classical model of spindle generation and are probably triggered by cortical excitation of the thalamus, the slow spindles should not require an active contribution from the thalamus with only a minor contribution of the canonical thalamic low threshold spikes [27]. Nevertheless, the existence of these two types of spindles is still an intense matter of debate as it could be seen as a continuum such as the one observed with time during sleep that is associated by a slow down of spindles frequency [28].

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ACCEPTED MANUSCRIPT One popular hypothesis maintains that spindles protect the brain from external sensory stimulation during sleep [16,29]. Accordingly, the spindle rate in humans correlates with the resilience to wake up during environmental noise [30]. However, some research has found that neither the cortical response nor behavioral detection is impaired during sleep spindles [31] or high voltage spindles [32]. This suggests that the lack of external sensory perception during sleep and SWS is not only due to the perturbation of the thalamo-cortical

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transmission by sleep spindles, but probably also relies on additional mechanisms within the cortex.

1.2. K-complex and delta waves

K-complexes were first defined in human EEGs during slow-wave sleep [3], as a high-voltage biphasic wave often associated with a spindle. Subsequent studies suggested that K-complexes appear to correspond to a

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transient cessation in synaptic and spiking activity of both principal cells and interneurons in all cortical layers, (OFF periods), followed by episodes of sustained activity ON and OFF period [33]. Intracellular

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recordings showed that these two periods correspond to alternation between two stable membranes potential levels called UP states when the membrane potential is close to the action potential threshold and DOWN states with a low membrane potential with almost no spiking activity. The UP and Down states are synchronized in almost all of the cortical neurons leading to the ON/OFF periods detected by looking at spiking activity. This cortical silence (OFF periods or Down states) is associated with a positive wave in the deep layers of the cortex and a corresponding negative wave in superficial LFP or EEG recordings. This event has been called the “delta wave”, since their duration (200-500ms) corresponds to a 2-4Hz rhythm in the delta

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range. The OFF periods/Down states/delta wave thus correspond to the same event but identified with different electrophysiological methods (respectively multi-neurons recordings, intracellular recordings, LFP/EEG recording).

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It is still controversial whether or not K-complexes and delta waves correspond to the exact same event [34– 36]. Nonetheless, it is generally believed that K-complexes display a more biphasic shape than the classical delta waves. This could be due to a more pronounced burst of activity at the down-up transition associated

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with an over-excitation or an increased synchronization of the transition [34,36]. Although the K-complex was initially defined as a biphasic wave followed by a spindle, it is now clear that they can occur in isolation. The frequent co-occurrence of K-complex and spindles is likely due to the excitatory phase of the K-complex associated with a pronounced down-up transition in the cortex and the cortico-thalamic neurons that excite the thalamus and trigger spindle bursts. K-complexes [3] can arise either spontaneously or they can be evoked by stimuli of all modalities, including slight positional changes in bed [37,38]. This is consistent with the observation that sensory stimuli can trigger down-states [39,40]. The fine mechanisms involved in the fluctuations of up- and down-states are still not very clear. However, it is certain that the down-state is not a result of the action of inhibitory interneurons that are silent during this phase. On the contrary, interneurons fire just before the onset of the silent state and could synchronize the whole network to the silent state [41]. The prolonged up-state could be allowed by recurrent activations within

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ACCEPTED MANUSCRIPT the network [42,43]. Both cortex explants and computer simulations have demonstrated that the ability of a network to maintain an up-state depends on the network size and density of recurring connections [42,44]. As soon as there is any activity in the network, recurrent connections incrementally spread excitement throughout the network and allow the network to remain in the up-state. Conversely, the down-state is characterized by a complete silence that is maintained until a burst of activity in the recurrent network induces a transition towards the up-state [43]. The origin of this activity burst is still unknown, but several hypotheses have been

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proposed including spontaneous fusion of synaptic vesicles to the membrane, glutamate release by astrocytes, and the spontaneous activity of some cortical neurons located in the deep layers [45,46]. This triggering excitation could also originate from non-cortical structures, i.e. hippocampal sharp wave ripples or thalamic activity. For example, it has been reported that a burst of action potential in TC neurons due to low threshold

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calcium potential is often followed by the up-state of cortical neurons and the associated delta wave [47]. The transition from the up- to down- state could be related to potassium (K+) leak currents that make the membrane potential more difficult to maintain in up-states, eventually causing the appearance of a down-state

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[48]. In addition to the involvement of these leak currents, some researchers have proposed that strong synaptic bombardment during the up-state is followed by synaptic depression [49,50], which can cause a propagating decrease in neuronal activity or even silence the entire network. As stated above, these up- and down-state alternations are associated with significant fluctuations in the potential of local cortical fields, which are also visible in the EEG and called “delta wave”. Down-states are detectable in local field potentials (LFP)/EEG as a positive wave in the deep layers of the cortex and as a

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negative wave in the surface layers, which signifies the presence of a dipole generator between the two recordings sites [47]. Nevertheless, it is still unclear how a cessation of neuronal activity in the cortex could be linked to a dipole that is visible in the LFP/EEG signal [51].

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1.3. Slow oscillations

NREM sleep, and especially N3/S3-S4, is associated with large low-frequency fluctuations often called slow

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waves. In several articles, the term “slow wave activity” is used and refer as the detection or extrema of the EEG/LFP filtered between 0.5-4.5Hz but the frequency band is not strict and can vary from one study to another. These slow waves have been linked to sleep pressure, since they are observed in abundance at the beginning of sleep and correlate to the duration of the previous wake episode. The occurrence of these slow waves progressively decreases during the sleep cycle. The slow waves mostly correspond to Kcomplexes/delta waves that are either associated or not with spindles. K-complexes/delta waves are infrequent and mostly irregular during N2/S2. Previous work has shown that the periodicity of slow waves ranges from 20 to 30 s, whereas spindle periodicity is about 4 s [53], giving rise respectively to the 0.047 and 0.22 Hz peaks in the power spectrum computed from filtered data in the delta and spindle bands. These values are close but not completely identical to the values obtained from the average EEG spectrum showing a distinct peak at 0.7 Hz during SWS and a weaker peak at ~0.2 Hz in stage N2/S2. It

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ACCEPTED MANUSCRIPT is still difficult to clearly understand the contribution of K-complex/delta waves to the power spectra of full band EEGs. During N3/S3-S4, delta waves can increase in frequency and become so regular that an oscillatory activity can emerge in the delta range (2-4 Hz) [54]. Due to the variability of the sleep EEG signal across the entire night, and the poor definition of SO in the EEG/LFP field, the term “slow oscillation” often refers to different events in the literature, creating some confusion that has been difficult to correct. In addition to the slow oscillations, Steriade and colleagues also described a slower rhythm called ultra-slow

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or infra-slow oscillation which corresponds to a phenomenon below 0.1Hz. In most cases, these terms have been defined based on animal studies (mostly cats) and in many case under anesthesia [33,36,52]. The occurrence of spindles and K-complexes appears to be modulated by these slow and ultra-slow oscillating process, which can be revealed by auto-correlogram analysis or second-order spectral analysis [26] [44].

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It is important to note that the SO per se is difficult to record, since its frequency is often close to the cutoff frequency of the recording devices. Nonetheless, the more or less rhythmical occurrence of delta waves could lead to the definition of the SO as an oscillation, in which its negative phase corresponds to the occurrence of

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the delta waves. Importantly, the bistability of the membrane potential and the alternation between up- and down-states, are not seen during waking or REM sleep, but is commonly found in various forms of anesthesia or in vitro preparation of cortical slices. suggesting that this state could correspond to a “default mode” in the brain.

The cortical SO is supposed to orchestrate neuronal activity across brain structures during sleep [55] with hippocampal sharp wave ripples (SWR), delta waves and consecutively-occurring spindles [56]. This

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coordination between sleep events likely plays a major role in memory consolidation during sleep (see section 3.1.2.), and could also be involved in the neuropsychiatric disorders as suggested by a study on a rodent model

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of schizophrenia [57].

2. Cortical manifestations of slow-wave sleep

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2.1. SWS differences between rodent and humans Aside from some minor differences, brain oscillations and the electrophysiological events observed in humans are extremely well conserved in mammals, especially rodents [58]. In both humans and rodents, sleep is organized into cycles of alternating REM and NREM sleep. The cycle duration is around 90 min in humans and 10 min in rodents [59]. The most striking difference between human and rodent sleep lies in the overall architecture: while sleep occurs in one monophasic block during the night in humans (but see [60] for a description of a more fragmented human sleep in preindustrial societies), the primarily nocturnal rodents display a highly fragmented polyphasic sleep, which can occur both during the day and the night. In addition, NREM sleep sub-stages appear to be much more blurred in rodents [9]. Stages resembling N1/S1 (with lowamplitude fluctuation) are rather short and difficult to dissociate from quite wakefulness. Moreover, it is

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ACCEPTED MANUSCRIPT difficult to clearly distinguish between stages N2/S2 and N3/S3-S4. This explains why many authors use the term SWS in rodents to describe the whole NREM sleep [9]. Brain oscillations are very similar in most mammalian species, including the various rhythms observed during SWS such as spindles or delta waves [58]. In contrast, the frequency of theta rhythms is typically between 4-7 Hz in humans but range between 5-10 Hz in rodents (e.g. close in range to the human alpha rhythm). Even if some studies have mentioned the existence of theta oscillations in the human hippocampus (especially during

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REM sleep), this theta rhythm is very different from the one observed in rodents. Various studies have provided direct comparisons between human and rodent sleep physiology. One such study has shown that spindles tend to be smaller in rodents (with a lower and more variable frequency), while delineated peaks in the power spectrum are rarely observed in rodents [61]. In contrast, delta waves are very similar in both

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species. Importantly, the development of large-scale recordings in rodents has allowed the identification of delta waves without any ambiguity. Recording a large population of neurons in rodents allows the identification of delta wave-associated down-states. Furthermore, recording at different cortical depths can

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disambiguate the delta waves (which are associated with a negative deflection of the LFP between the superficial and deep layers) from random fluctuations. Interestingly, while most sleep studies in rodents use the term ‘delta wave’, very few have used the term ‘K-complex’. One study specifically investigated Kcomplex waveforms based of the shape of the wave [62], although this report stands out as an exception in the rodent literature. Concerning the related SO, the frequency of the slow oscillation is slightly faster in rodents

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than in humans (1.35 Hz vs. 0.8 Hz) [61].

2.2. SWS: A global or a local phenomenon?

In the early sleep studies, most rhythms were considered to be global. However, this view has increasingly

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come under debate, regarding SO and spindles.

The first main challenge to the global sleep view is that slow oscillations have been observed as a propagating wave [63], in which the frontal cortex could be an integrator of sleep needs that initiate the propagation of

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slow oscillations across the rest of the brain. This hypothesis is consistent with several observations. Slow oscillations originate from the transition between the dorsolateral and orbitofrontal cortices, two structures particularly important for sleep. Electrical stimulation of the frontal cortex causes an outbreak of EEG slow waves and induces behavioral sleep [64]. Moreover, these structures display low cerebral blood flow values during NREM sleep [65,66], and are associated with an increase in slow wave activity after sleep deprivation [67]. Finally, total sleep deprivation some signs and symptoms that share some similarity with the ones observed after orbitofrontal lesions [68]. The second issue is that pure local events have been observed both in humans [21]) and rodents [69]. Largescale neuronal recordings in sleeping rats have shown that delta waves and down-states can occur locally [69,70]. Similar observations have been made in human intracranial recordings, leading some researchers to state that most spindles and slow waves are in fact local [21,22]. However, these observations are strongly

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ACCEPTED MANUSCRIPT affected by the manner in which EEG events are identified. As stated above, the delta wave corresponds to a negative deflection of the EEG in the LFP (when recorded in the superficial layers) and is associated with a silence in neuronal activity in cortical recordings (down-states). Their identification is thus extremely sensitive to the arbitrary threshold on the amplitude for detecting delta waves, as well as the number of spikes for detecting down-states. Local delta waves have smaller amplitudes and are associated with a more attenuated decrease in firing rate [21]. Depending on the threshold, some researchers may or may not refer to

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these events as (local) delta waves.

Even if the relative occurrence of local versus global events is still debated, their existence is well acknowledged. Several lines of evidence in humans suggest that SO may be locally modified relative to previous experiences. Specifically, the amplitude of the slow oscillation may be locally modulated according

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to previous waking activity [71]. Similarly, a local increase in the number of spindles relative to previous waking activity can be modulated via a brain-computer interface [72]. Similar observations have been performed in rodents as well. Indeed, recent work in awake, sleep-deprived rats has demonstrated that local

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slow waves similar to NREM sleep activity associated with a transient silencing of neurons could occur, and that these are associated to attention deficits [69]. This local sleep has typically been linked to the synaptic homeostasis hypothesis [73], which posits that long-term potentiation of synapses associated with learning during wakefulness increases neuronal synchronization, leading to enhanced slow oscillations during the subsequent sleep period. This could explain why brain structures involved in a given learning task during

2.3. SWS regulation

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wakefulness display enhanced local slow oscillations during subsequent sleep.

The importance of sleep requires a tight regulation in order to maintain a constant amount per day. According to the two-process model of sleep regulation, the timing and structure of sleep are determined by the

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interaction of homeostatic and circadian processes. In this model, the homeostatic process (Process S) is due to the accumulation of hypnogenic substances in the brain (although not clearly identified), which then

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generate a homeostatic sleep drive, while circadian rhythm (Process C) is governed by the internal biological and circadian clocks. Both processes are influenced to some extent by external factors (i.e. light, exercise, stress, etc.) but also by the genes of the individual [74–76]. Contrary to such features as core body temperature, plasma melatonin, cortisol and REM sleep, SWS and SWA do not appear to be influenced or regulated by the circadian clock [7,77], although they do seem to have a tight relationship with homeostatic regulation. Indeed, numerous previous experiments in humans and rodents have shown that SWS amounts and SWA are determined by the time spent awake [78]. Other studies conducted in healthy young subjects have clearly demonstrated that SWA increases with deeper sleep and then decreases during the night [7,79–81]. Moreover, this rise and fall in SWA also decreases across consecutive NREM episodes during one sleep night [7,79–81]. Other studies using a nap protocol in healthy individuals have confirmed this hypothesis [82,83] and have demonstrated that SWA increases with waking

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ACCEPTED MANUSCRIPT duration [7,79]. Following a prolonged wakefulness period (> 24 h of sleep deprivation), SWA occurring during SWS epochs in the next nocturnal sleep period are significantly enhanced in both humans [78] and rats [84]. This increase in SWA after a total sleep deprivation period is not homogeneously distributed across the scalp but is more localized to several brain areas in both humans [71,85,86] and rodents [87,88]. These are mostly the frontal brain areas [67,85] and areas previously engaged in a task such as the somatosensory cortex [86,89], suggesting that SWS is regulated in response to local activation of brain areas during the previous

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wakeful period [7].

Finally, the individual differences in SWS and SWA parameters in healthy young humans [7] should not be overlooked. Indeed, individual differences and polymorphism in a number of genes, including those that encode the PERIOD3 (PER3) protein [90] and adenosine deaminase enzyme [91], have been reported to

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predict individual differences in SWS.

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2.4. Ontogeny of slow-wave sleep and its modification with age

In many species, the structure, total duration and temporal organization of sleep exhibit profound changes across an individual’s lifespan [92], including in humans [6]. Sleep is polyphasic during early human life, only to become monophasic during childhood [93]. Sleep duration has been found to decrease in later life until the age of 60, at which point it either levels off or increases [6,93]. These changes are accompanied by modifications in sleep macro- and micro-structure, with the greatest changes occurring in SWS [6,94]. At

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birth, sleep is almost entirely composed of REM sleep, and NREM sleep progressively merges in the first years of life. SWS quantities reach a peak during pre-puberty [95] and then decline with age [94,96,97]. Sleep is less consolidated in elderly people without sleep complaints or sleep disorders, and thus there is a significant increase in the number of awakenings, particularly during NREM sleep [6,93]. The reduced number of slow waves (which may result in a very small number of epochs scored as “N3” in the elderly) is

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thought to primarily reflect a decrease in the amplitude of delta wave activity, rather than the absence of slow frequency activity, since the traditional scoring of SWS requires that the amplitude of the delta waves and SO

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are greater than 75 µV, in addition to the presence of at least 20% delta wave activity per epoch [4]. More broadly, it is the whole delta power, which reflects amplitude and incidence of delta waves, that decreases with aging [98,99]. Furthermore, there is also a decrease in the incidence of spontaneous K-complexes during N2 [100], as well as their incidence and amplitude [101,102]. The origin of this decrease in slow waves is still unknown. The neuronal loss or decrease in synaptic strength from puberty to advanced age may explain these changes regarding SWS. Indeed, synchronized EEG waveforms, such as the delta waves, SO and K-complexes that are present during SWS require large numbers of healthy neurons, which become less and less abundant with age. The decrease of SWS amounts and SWA after puberty seems to be related to a decrease of synaptic density in cortical layers [103]. However, this hypothesis is still debated [104]. Indeed, others studies suggest a strong relationship between sleep EEG (especially SWA) and cortical maturation during adolescence [105] and a causal link between structural

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ACCEPTED MANUSCRIPT degradation with age of cortical regions implicated in SWS generation and propagation and age-related changes in SWS [106]. Results from longitudinal studies of normal aging demonstrate a significant loss in cortical gray matter volume, in which regional analyses revealed the greatest loss in the prefrontal cortex, where SO are believed to be triggered [107,108]. As stated above in section 1.2, up-states are maintained by network activity in a recurrent network. Any decrease in the number of synapses could lead to a decrease in the recurrent network activity, which could explain the decrease in slow waves with age. In addition to grey

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matter, glial cells have been proposed to play a role in producing the slow frequency oscillations involved in

may be partly responsible for the SWS decrease.

3. Roles of slow-wave sleep 3.1. Cognitive functions of slow-wave sleep

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3.1.1. Attention and executive processes

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the generation of K-complexes and delta waves [109]. This suggests that age-related changes in glial function

Numerous experiments have shown that sleep loss, whether induced by a total sleep deprivation protocol or by chronic sleep restriction, leads to dysfunctions in cognitive and behavioral performance [110,111]. However, there is no clear evidence that SWS is directly implicated in such cognitive reductions or recovery. Indeed, SWS quantity is either slightly reduced or not at all under sleep restriction, whereas cognitive processes (i.e. sustained attention, inhibition and working memory) are continuously reduced [112–114].

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Furthermore, it has been shown that two nights of SWS disruption (acoustic stimulation) primarily leads to an increase in sleepiness and mood decrements with minor effects on sustained attention and no effect on other cognitive processes [115].

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3.1.2 Learning and memory

The role of sleep in memory processes has been examined since the very first experimental studies on

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learning. The protection from interference mechanism was one of the first mechanisms proposed to explain the beneficial role of sleep on memory [116]. However, this mechanism alone cannot explain all effects of sleep on memory. Indeed, the beneficial effect of sleep is stronger when it occurs shortly after learning, as opposed to at a later time [117]. This time-dependent effect suggests an additional role of sleep beyond the simple reduction of interference, and is likely to be related to the consolidation of memory. Although it is now accepted that SWS plays an important role in memory processes, the fine mechanisms involved remain strongly debated. Moreover, it is still not clear which memory task benefits the most from sleep.

Dual processes hypothesis

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ACCEPTED MANUSCRIPT The first evidence of a role for SWS in memory consolidation came from a split-night design. This paradigm essentially compares retention performance across periods of equal length that cover either the early (rich in SWS) or late half (rich in REM) of nocturnal sleep, which avoids possible confounding effects from stressful repeated awakenings that accompany the standard procedures of selective sleep deprivation [118,119]. When learning is followed by a 4 h retention interval placed in the early half of nocturnal sleep (characterized by extended epochs of SWS), the subsequent recall of word pairs is markedly superior to recall after a 4 h

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retention interval placed in the late part of sleep (when REM sleep is dominant) [119,120]. Similarly, one study has demonstrated a better retention of declarative memories after SWS than after a control interval filled with wakefulness [121]. Conversely, several other studies report that late REM-rich retention sleep selectively improves procedural and implicit learning [120,122].

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Based on the studies summarized above, a dual processes hypothesis has been proposed, which states that SWS promotes declarative memory, while REM sleep promotes non-declarative memory [120,122]. Although this is supported by several studies, a large body of evidence precludes the overall generalization of this

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hypothesis. Indeed, this hypothesis has been challenged by reports stating that procedural tasks like visiomotor adaptation and visual texture discrimination can also benefit from SWS. However, training such skills is not completely independent from declarative memory mechanisms, especially at the initial stage of training. Furthermore, performance in non-declarative memory tasks is enhanced after sleep, whereas performance in non-declarative memory tasks is often decreased less than during comparable awake periods [123]. Therefore,

Sequential hypothesis

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it seems unlikely that a unified theory on the effect of sleep for all types of memories could ever be proposed.

In contrast to the view of SWS and REM as two separate systems devoted to different types of memory, it has been suggested that the two sleep periods could have a complementary contribution to memory consolidation.

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Indeed, improvement in a procedural memory task correlates with the amount of SWS in the first quarter of the night and the amount of REM sleep in the fourth quarter [124]. Taking these two observations together, it

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has been hypothesized that relevant information is replayed and tagged during NREM [125], in which neurons are selected that will undergo molecular modification in subsequent REM sleep episodes [125,126]. These hypotheses are based on observation and correlative studies but do not propose any mechanisms at the neuronal level. Two other theories have been developed that attempt to propose neuronal mechanisms: the Synaptic Homeostasis hypothesis (SHY), which relies on the reduction of synaptic weight during sleep; and the “reactivation theory” or “active system consolidation”, which posits that the information encoded during wakefulness is replayed during sleep.

Sleep homeostasis hypothesis: Synaptic downscaling The SHY hypothesis, as proposed by Giullio Tononi and Chiara Cirelli, posits that sleep should downscale synaptic weights that are potentiated during wakefulness and that should thus be intimately linked to sleep

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ACCEPTED MANUSCRIPT homeostasis. According to this theory, memory is assumed to be formed in the brain by the modification of the weight of synapses that allows neurons to communicate with each other, in a process known as long-term potentiation (LTP) or long-term depression (LTD). During learning, the weight of significant synapses is increased by LTP while other synapses remain unchanged or are even decreased through LTD. Synaptic communication consumes a large part of the energy required for the brain to operate during learning. After awake learning, the role of subsequent sleep should thus be to decrease the weight of synapses, thereby

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achieving energy savings and promoting a return to the basal energy rate. Whereas SHY does not accord any importance to sleep reactivations, a newer version of the hypothesis proposes that all synapses decrease their weight during sleep except those involved in reactivation. According to this view, sleep reactivations should protect certain synapses from depression so that relevant memories are not erased: “synapses that are

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reactivated most strongly and consistently during sleep would be protected and survive mostly unscathed, whereas synapses that are comparatively less activated would be depressed” [127].

Regarding SWS, the SHY hypothesis suggests that synaptic downscaling should be orchestrated by the slow

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waves of NREM. In this case, the increase in slow waves would result from synaptic potentiation during wakefulness associated with learning processes. Indeed, strengthening synapses may result in increased neuronal synchronization. In turn, this synchronization should enhance slow oscillations at the onset of sleep. To decrease synaptic weights and thus save energy, the system must undergo some form of LTD. The frequency of the slow oscillations (close to 1 Hz) roughly corresponds to the optimal frequency to induce LTD. As a result, slow waves should consist of an autonegative feedback loop that downscales the

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physiological processes underlying their generation. Active system consolidation hypothesis

A final hypothesis posits that the information encoded during wakefulness is replayed during sleep [55,116,128–130]. In 1989, Gyorgy Buzsaki proposed a two-stage model of memory consolidation, in which

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encoding occurs concomitantly with theta oscillation during wakefulness, whereas consolidation occurs during NREM sleep via reactivation at the time of SWR. This prediction is experimentally supported by a

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number of ‘sleep replay’ studies, wherein the same patterns of hippocampal place-selective neurons that are active during the waking experience are replayed during sleep SWR events. This hypothesis assumes that reactivation functions just like repetition in classical learning and allows the gradual integration of novel information into preexisting long-term memory traces. Therefore, sleep reactivations should allow the transfer of the declarative memory trace from the hippocampus, where it is initially encoded, to the neocortex for long-term storage (i.e. system consolidation). In this theoretical framework, the cortical slow waves play an important role in orchestrating the dialog between the hippocampus and the neocortex [70]. Indeed, hippocampal SWRs occurs exclusively during NREM sleep at a particular phase of the slow oscillation, so that they are often followed by a delta wave and finally by spindles [48,121,122]. The importance of reactivation and sleep rhythms in memory consolidation has received strong support in numerous studies, both in rodents and humans. Indeed, the selective suppression of sleep SWRs impairs memory consolidation in

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ACCEPTED MANUSCRIPT rats. Moreover, the spontaneous sleep reactivation of a given place cell can be used to create an artificial memory in mice, leading to a place preference towards the location encoded by the place cell after waking up [133]. Indeed, enhancing this sequence with the use of a brain-computer interface is reported to enhance memory consolidation and improve the performance of a spatial memory task in rats [134]. Finally, reactivation of rule learning-related cell assemblies in the rat prefrontal cortex occurred at the time of hippocampal SWR, consistent with the transfer of the memory trace from the hippocampus to the prefrontal

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cortex, further supporting the role of sleep SWRs in system consolidation [135]. Reactivation has been observed in humans as well. A positron emission tomography study has shown that the hippocampal areas activated during the learning phase of a virtual game were subsequently reactivated during SWS [136]. In this experiment, the navigation performance after sleep was predicted by the hippocampal reactivation. Similarly,

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when subjects implicitly learned to adapt their movements to a rotated display, a local increase in SWA was observed during sleep in the very same brain areas that had previously been activated during the task [71].

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This improved performance in the rotation adaptation task after sleep was predicted by the increase in SWA. In addition to a correlate between memory performance and SWA, it appears that the different parameters that characterize SWS, and more precisely the SO per se, are involved in memory consolidation. Indeed, the slow wave synchronization (<1 Hz) following intense declarative learning is increased; furthermore, the increase in coherence is mostly observed at a phase of the SO corresponding to cortical up-states [131], an effect almost identical in humans and rodents [61]. A separate study has found that the length of the up-state estimated by

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the positive wave of the slow oscillations directly relates to overnight memory improvements in a declarative memory task [137]. Altogether, these results show that SWS plays a major role in memory processes, even if the SHY and reactivation hypotheses propose different mechanistic explanations for these effects. The importance of SWS in memory is consistent with observations that diminished amounts of SWS in

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patients with primary insomnia [138] as well as in the elderly [139,140] correlate with a reduced benefit from overnight effects. This suggests that sleep manipulation and especially SWS enhancement could be an

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attractive strategy to improve memory in clinical practice.

Improving memory by increasing reactivation of cue-associated information The enhancement of memory reactivation during sleep in order to enhance memory consolidation has been extensively studied lately, using “targeted memory reactivation” (TMR). Indeed, specific auditory cues associated with picture-locations foster memory reactivation if presented during SWS, increasing subsequent recall performance [141–143]. Similarly, a study implicating a hippocampus-dependent task involving learning spatial locations in the presence of an odor has shown that re-exposure to this odor during SWS (but not REM sleep) enhances spatial memories and induces stronger hippocampal activation (assessed by fMRI) than during wakefulness [128]. This suggests that hippocampal networks are particularly sensitive to inputs that can reactivate memories during SWS. In rodents, it has been shown that a sound associated during

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ACCEPTED MANUSCRIPT wakefulness to a particular location and to the hippocampal place-selective neurons (called place cells) increases the reactivation of the same place cells when presented during sleep. This suggests that cortical activity during sleep could bias the hippocampal replays and that this process could be used to artificially enhance the reactivation of specific information during sleep [144]. Interestingly, auditory cues during SWS can also enhance the retention of non-declarative tasks by using a melody for cueing a sequence of button presses in a motor skill task [145]. This effect was found to be highly

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specific, in that participants only perform better on the part of the sequence that has been cued during sleep, while performance of the un-cued part is unchanged [146]. Finally, it has been demonstrated that the strengthening effect of reactivation cues for memory is specific to SWS, since presenting the same cues

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during wakefulness can disturb the memory trace [147].

Improving memory with SWS manipulation

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An increasing number of studies that have enhanced SWS have also succeeded in improving memory consolidation (cf. section 5 for more details). Indeed, transcranial application of oscillatory potentials (0.75 Hz) during the night increase SWS and enhance the retention of a hippocampus-dependent finger tapping memory task in both young [148] and old subjects [149], as well as in rats [150]. Similar results were found by replacing electrical stimulations by auditory closed-loop stimulations delivered during the up-phase of the SO. These techniques were able to enhance both SO and performance in a word-pair memory task, overnight

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[151] and during naps [152], in young subjects. The memory benefits were predicted by the phase at the time of stimulation, supporting the view that reactivation most likely occurs during cortical up-states [153]. It is important to note however that several studies were unable to replicate the enhancing effects of tDCS

3.2.

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stimulation on memory in young [154] and elderly subjects [155].

Physiological functions of slow-wave sleep

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3.2.1. Energy saving

One function of sleep is to refuel the energy consumed during the day. Several studies have investigated the energetic consumption during sleep, using either direct or indirect methods. As described above in section 3.1.2, the SHY hypothesis postulates that energy saving could be achieved during sleep by SO through the depression of synaptic weight. Nevertheless, sleep may have a more direct action on energy stores. For instance, it was recently reported that SWS may have an active role in the reuptake of adenosine tri-phosphate (ATP), the energy currency of brain cells. Accordingly, an ATP surge has been demonstrated in the first hours of spontaneous sleep in wake-active brain regions of rats that positively correlates with delta activity (0.5–4.5 Hz) [156]. The infusion of adenosine into the basal forebrain to induce delta activity during the normally active dark period also increased ATP. Moreover, the authors observed reciprocal changes in phosphorylated adenosine mono-phosphate kinase (AMPK), a main regulator of

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ACCEPTED MANUSCRIPT anabolic and catabolic pathways. Therefore, the authors hypothesized the existence of an increase in deltaATP coupling. This hypothesis was examined in a second experiment, in which rats were anesthetized with ketamine-xylazine to induce a uniform state of pure delta waves [157].Delta oscillations induced by ketamine increase energy levels in sleep-wake related brain regions; both ATP and ADP were also positively correlated with delta waves. Moreover, ATP-consuming Na+/K+-ATPase mRNA levels were significantly decreased under ketamine-xylazine treatment. The authors therefore concluded that reduced neuronal activity (such as

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cortical delta oscillations) could restore the energy consumed during the day.

One possible role for SWS in glucose resolution has been suggested by indirect evidence showing that SWS initiation coincides with hormonal changes that affect glucose regulation. Numerous studies have investigated the role of glucocorticoids in sleep, and glucocorticoid administration in humans and animal models appears

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to promote waking EEG activity, resulting in decreased REM sleep and SWS promotion [158]. Recently, more direct evidence has been highlighted by selective SWS deprivation, without any change in total sleep time (TST) [159]. The authors of this study observed a marked decrease in insulin sensitivity without an

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adequate compensatory increase in insulin release, resulting in a reduced glucose tolerance and increased diabetes risk. Importantly, the magnitude of the decrease in insulin sensitivity was strongly correlated with the magnitude of the reduction in SWS [159].

Finally, energy stores appear to be important to maintain the neuronal excitation required to trigger and maintain SWS. Indeed, ventrolateral preoptic nucleus (VLPO) neurons play a key role in promoting and maintaining slow-wave sleep (SWS) [160–162]. One recent study has demonstrated that the infusion of

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glucose into the VLPO of mice promotes SWS. The authors observed that glucose specifically increases the excitability of neurons that exhibit the properties of sleep promoting neurons, an effect that requires the intraneuronal metabolization of glucose to ATP [163]. Interestingly, it was shown in vitro that the number of slow waves could be modulated by potassium channels that are sensitive to the energetic status and to the

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intracellular concentration of ATP [164]. This suggests that glucose and the amount of ATP could have a

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direct influence on the generation of slow waves.

3.2.2. Hormone release and regulation During sleep, the human hypothalamo-pituitary-adrenal and SNS axes are down-regulated. As a result, there is a decrease in plasma cortisol, epinephrine and norepinephrine levels during the night, accompanied by a marked increase in growth hormone (GH), prolactin, and melatonin [165,166]. A rapid increase in plasma thyroid stimulating hormone is also observed in the early evening [167]. SWS plays a crucial role during endocrine release by exerting important modulatory effects. For instance, SWS has been linked to growth hormone (GH) release. One study has specifically shown that GH release is sleep-dependent, and that selective SWS deprivation results in the diminished and delayed secretion of GH [168]. By contrast, these authors observed that pharmacologically enriching SWS with

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ACCEPTED MANUSCRIPT ritanserin led to an increase in delta power and GH secretory rates during the first 3 h of sleep. Separately, the amount of GH secreted during significant GH pulses has been correlated with the amount of concomitant delta wave activity [169]. Along these lines, one study performed in young men showed that the SWS stimulant gamma-hydroxybutyrate can double GH secretion, resulting from an increase in the amplitude and duration of the first GH pulse after sleep onset [170]. Moreover, the administration of ghrelin (an endogenous ligand of

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GH) can stimulate GH release and increase the total amount of SWS [171]. Subsequently, it was shown that the secretory profile of prolactin, like GH, exhibits a peak during the early hours of sleep [172]. Since high serum levels of prolactin in breast-feeding mothers is associated with an increase in SWA [173], it has been proposed that prolactin release is linked to SWS. However, animal studies with prolactin administration and studies in prolactin knockout mice demonstrate that this hormone does not

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induce any consistent change in SWS [174,175]. These varied results indicate that SWS and prolactin may be implicated in joint processes without showing any causal relationship between them.

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Sleep is also characterized by important changes in heart rate and blood pressure induced by a decline in sympathetic activity and an increase in parasympathetic activity throughout the night, in particular during SWS. Many studies have assessed the changes in autonomic nervous system activity by using heart rate variability (HRV), which is frequently applied to understand autonomic changes during different sleep stages as well as in pathologies during sleep and wakefulness [176]. Sleep disorders and sleep loss induce a wellknown, constant sympathetic over-activity associated with increases in heart rate and blood pressure, in

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addition to the increased risk of cardiovascular and metabolic diseases [176–178]. Accordingly, patients with chronic insomnia display an increase in heart rate and a decrease in HRV before sleep onset and during N2 sleep. However, no differences during SWS in comparison to controls have been reported. 3.2.3 Immune system

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A bi-directional communication has been reported between sleep and the immune system [179]. Investigations of the normal sleep-wake cycle indicate that immune cells (natural killer cells) and the production of anti-

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inflammatory cytokines (IL-10) increase during daytime, whereas immune parameters (numbers of undifferentiated naive T cells) and the production of pro-inflammatory cytokines (IL-1β, IL-6 TNFα, and IL12), which are mainly produced by brain microglia and macrophages, increase during early nocturnal sleep.

Injection of either TNFα or IL-1β enhances SWS [180] as do all pro-inflammatory cytokines (IL-1β, IL-2, IL6, IL-18, and TNFα), whereas anti-inflammatory cytokines (IL-4, IL-10, and IL-13) inhibit SWS [181]. In normal humans and in multiple disease states, plasma levels of TNFα covary with SWA and sleep propensity. Unilateral application of IL-1β or TNFα induces the ipsilateral enhancement of state-dependent EEG SWA, suggesting greater regional sleep intensity. Interventions such as unilateral somatosensory stimulation enhance localized sleep EEG SWA, blood flow, and somatosensory cortical expression of IL1β and TNFα [182]. In humans, the administration of the synthetic IL-1 receptor antagonist anakinra (IL-1ra) strengthens SWS by

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ACCEPTED MANUSCRIPT enhancing EEG SWA, whereas sleep-associated memory consolidation remains unchanged [183]. Moreover, IL-1ra slightly increases prolactin and reduces cortisol levels during sleep. These effects very likely reflect central nervous actions of IL-1ra involving both direct actions on neuronal IL-1 receptors as well as a decrease in brain-borne IL-1. These findings lead to the view that SWS induces an endocrine milieu that can strongly support the initiation

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of an adaptive immune response. However, reported findings in humans contrast with those from animal models, as some pro-inflammatory cytokines that promote NREM sleep in animals appear to suppress NREM sleep or sleep depth in humans [184]. Indeed, the acute administration of either IL-6 or IFN-α can suppress SWS during the early part of the night and reduce amounts of REM sleep [185], whereas an acute dose of the T cell IL-2 does not show any effect on sleep amounts or measures of sleep architecture. One study has

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reported that the chronic exposure of IFN-α leads to a similar suppression of SWS along with disturbances in sleep maintenance [186].

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3.2.4. Cleaning of metabolites

SWS may also have a restorative role by taking part in the cleaning of metabolites. Indeed, during wakefulness, potentially neurotoxic waste products such as β-amyloid accumulate in the cerebral fluid. Recently, it was shown using two-photon microscopy in mice that natural sleep and slow wave oscillations induced by anesthesia are associated with a 60% increase in the interstitial space, resulting in a striking increase in convective exchange of cerebrospinal fluid with interstitial fluid [187]. In humans, it was shown

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that the cerebro-spinal fluid (CSF) of Aβ42 was inversely correlated with SWS duration as well as frontal SWA whereas TST, N1, N2 and REM did not show any trend [188]. Cortical β-amyloid was also shown to be associated with impaired generation of SWS [140]. Moreover, the specific SWS deprivation in humans led to a significant increase of amyloid- β40[189]. Although these findings involved correlations and no direct proof

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of the role of SWS for metabolites cleaning, it still opens a hypothesis role of SWS for cleaning metabolites

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and thus delaying the onset of dementia in the case of β-amyloid flushing.”

4. Clinical aspects of slow-wave sleep Independently of the important amount of knowledge that has been developed in the previous lines on its role on cognitive processes and physiological functions, SWS is poorly or not considered and taken into account in the clinical approach of sleep disorders. Insomnia definitions do not include items on SWS. Except for parasomnias that have been clearly separated into REM and NREM parasomnias, the clinical approach of other major sleep disorders (such as OSA or RLS) do not usually consider the interaction between respiratory or movement events and SWS. This is why we want to specifically present in this part how sleep pathologies and several chronic diseases may be better approached and treated with a better consideration of SWS.

4.1. Sleep pathologies of slow-wave sleep

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ACCEPTED MANUSCRIPT 4.1.1. Sleepwalking International classifications (i.e. ICDS-3 and DSM-V) clearly separate NREM sleep parasomnias (i.e. sleepwalking or somnambulism, confusional arousals, sleep terrors, and sleep-related eating disorder) from REM sleep parasomnia (i.e. REM sleep behavior disorder, recurrent isolated sleep paralysis, and nightmare disorder). NREM parasomnias are predominant during childhood and significantly decrease with the onset of

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puberty. NREM parasomnias usually take place in the first third of the night when SWS is predominant. Somnambulism is a common NREM parasomnia affecting up to 4% of the adult population [190]. It is considered to be an “arousal disorder” and is characterized by behavioral manifestations that mostly occur during SWS.

Episodes of somnambulism are facilitated by sleep deprivation, stressful events, drugs, and auditory stimuli

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during SWS. Polysomnographic characteristics emphasize abnormal deep sleep associated with arousal and slow-wave sleep fragmentation [191]. Sleepwalkers are vulnerable to increased homeostatic sleep pressure

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following sleep deprivation when sleep is initiated at a circadian time of increasing wake propensity. Moreover, sleep deprivation significantly increases the number of SWS awakenings in sleepwalkers, which may trigger these episodes [192].

The presence of specific EEG parameters during the immediate period preceding the episode onset has been thoroughly discussed in the literature. Several studies examining the delta activity prior to the sleepwalking episode have either not found any distinguishable pattern [193] or deny any specificity for the diagnosis of

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NREM parasomnias [194], whereas one study comparing sleepwalkers and controls described an increase in the delta spectral power during the 4-16 s preceding episode onset [195]. Others researchers have shown that the episodes are not preceded by a gradual increase in delta spectral power, but rather by a significant change in the density of slow-wave oscillations, as well as in very slow oscillations with significant increases occurring during the final 20 s immediately preceding episode onset [196]. Moreover, a recent study showed

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that SWA and slow oscillation density are significantly greater prior to patients’ somnambulistic episodes, in comparison to non-behavioral awakenings. However, there was no evidence for a gradual increase over the

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3 minutes preceding the episodes [197]. The authors conclude that increased SWA and slow oscillation density appear to be specific to sleepwalking episodes, rather than a feature that can be generalized to all sleep-wake transitions in sleepwalkers.

4.1.2. Insomnia

Very few research has considered SWS as major criteria for the diagnosis and even for the treatment of insomnia. SWS and insomnia diagnosis Insomnia is the most frequent sleep disorder, affecting one fifth of the general adult population (of which 10% are severely affected) [198,199]. Whether the predominant complaint is dissatisfaction with sleep quantity or

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ACCEPTED MANUSCRIPT quality, insomnia is consensually defined as reporting at least one of the following symptoms: difficulty in initiating sleep, difficulty in maintaining sleep characterized by frequent awakenings or trouble to return to sleep after awakenings, early morning awakening with instability in returning to sleep, and non-restorative sleep. These sleep complaints are accompanied by great distress or impairment in daytime functioning (i.e. fatigue, low energy, daytime sleepiness, cognitive impairments, mood disturbance, etc.). Furthermore, sleep difficulty occurs at least three nights per week for at least three months, and occurs despite adequate

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opportunity for sleep (ICSD-3).

However, none of these definitions mention the disturbance of SWS as a criterion for insomnia. One possible explanation is that sleep architecture is typically not studied in insomniac patients. Indeed, polysomnography (PSG) is the gold standard for sleep assessment but is not recommended in clinical practice for the diagnosis

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and evaluation of insomnia, except when other sleep disorders are suspected (ICSD-3). Several studies have attempted to focus on the sleep macro or micro architecture in several subtypes of insomnia, compared to good sleepers. Bastien et al, have initially compared 15 subjects with chronic insomnia to 16 good sleepers

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and found a significant different rate of S3-4: (4.29 vs. 8.52 % of TST)[200]. They also observed signs of greater cortical arousal in insomnia individuals. Wu et al have investigated how hyperarousal may be observed in the waking electroencephalogram of 52 subjects with primary insomnia compared to 32 good sleepers[201]. They did not find significant differences between group in waking or NREM EEG power. StJean et al have observed REM and NREM power spectral analysis of 26 subjects with psychophysiological insomnia, compared to the one of 20 subjects with paradoxical insomnia and 21 good sleepers[202]. They

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found significantly more SWS in those with paradoxical insomnia, combined with a relative cortical activation during REM. Normand et al focused on sleep misperception comparing there groups of subjects with paradoxical insomnia, psychophysiological insomnia and good sleepers. The proportion of SWS was low (with a high intra-individual variability), but not significantly different in the three groups (4.87 (0.23-14.51)

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for good sleepers, 4.78 (0.99-17.49) for paradoxical insomnia, 2.93 (0.00-21.82) and was not calculated as predictive of sleep misperception)[203].. Another report, a vast epidemiological study that investigated the objective prevalence of insomnia by PSG, did not identify any difference in the percentage of SWS between

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good sleepers and individuals with insomnia symptoms [204]. Nevertheless, some differences in the macrostructure of sleep have been traditionally observed between insomnia patients that issue complaints and those that do not. Objective measures from patients that issue complaints reveal that they tend to spend more time in stage 1 but less time in SWS, and that they display more frequent stage shifts throughout the night [205,206]. Compared to good sleepers, sleep EEG analysis of patients with primary insomnia reveals a deficiency in SWS, in addition to excessive hyper-arousal [207]. An elevated power of higher frequencies (i.e. 13–40 Hz) has also been found [208–211]. In another study, higher frequencies were only found in frontal areas, although they were associated with a decrease in central ultra-slow power (0.3-0.79 Hz) [212]. A sleep homeostasis dysfunction hypothesis has been proposed to explain the SWS deficit of insomniac patients [213]. This is based on the observation of a diminished increase or rebound in SWS (as compared to

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ACCEPTED MANUSCRIPT healthy subjects after sleep deprivation), reduced SWS pressure, reduced delta power without reduced SWS duration, longer SWS latencies, normal or decreased objective daytime sleepiness (as measured by multiple sleep latency tests), less sleepiness than controls after sleep deprivation, and differences in sleep stage proportion increases during recovery after total sleep deprivation [213]. However this hypothesis is controversial as sleep restriction therapy in subjects with insomnia did not systematically improve the percentage of SWS. Vallieres et al have analyzed the PSG of 5 subjects with insomnia on 10 nights following

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different steps of sleep restriction therapy. They did not found a significant increase of SWS after treatment. However sleep restriction contributed in this study to a decrease of waking periods and a significant increase of stage 2 [214]. Other authors have hypothesized that SWS is inhibited by an increase in adrenocorticotropic hormone (ACTH) and cortisol secretion. For instance, one study reported that sleep and particularly SWS

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have an inhibitory influence on the hypothalamic-pituitary-adrenergic axis and cortisol secretion [215]. In normal adults, SWS is associated with declining plasma cortisol levels [216], and induced sleep disruption is associated with a significant increase in plasma cortisol levels [217]. A subsequent study that explored the

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association between chronic insomnia and the activity of the stress system in fifteen young adults complaining of insomnia revealed that norepinephrine tends to correlate positively with the percentage of N1 and wakefulness after sleep onset, whereas it correlates negatively with the percentage of SWS [218]. In another study, 24 h ACTH and cortisol plasma concentrations were found to be significantly higher in insomniacs than in matched normal controls [219].

Sex and age, two important factors that contribute to insomnia, could also influence sleep architecture. Several

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epidemiological studies have demonstrated that insomnia complaints increase with age and are twice as prevalent in women as in men [220,221]. In addition, some large PSG studies have found significant differences in SWS between men and women [222]. Specifically, in a one-night study of the general population in a sleep laboratory, women were found to display a significantly higher percentage of sleep time,

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a lower percentage of S1, and a higher percentage of SWS than men (n = 1,324 subjects). A high rate of comorbidity exists between chronic insomnia and other medical and psychiatric disorders.

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Architectural abnormalities of sleep have been described in cases of major depression, including abnormal REM latency, a prolonged first REM period, increased REM density and REM percentage, and diminished SWS [223,224]. Depression is very often accompanied by insomnia: 50 to 95% of depressed patients suffer from some sleep disorder, with insomnia being the most frequent. Altered intra-night distribution of REM sleep with increased early REM sleep and reduced REM sleep latency are the most specific pathophysiological changes in such depressive disorders. However, reduced REM sleep latency has been attributed to the decrease in SWS latency, following sleep onset [225]. A recent meta-analysis of polysomnographic sleep in several mental disorders demonstrated that sleep continuity disturbances were present in all psychiatric disorders, with the exception of seasonal affective disorder, panic disorder and ADHD [224]. Furthermore, this study showed that sleep depth was altered in affective disorders, anxiety, and schizophrenia. Patients with affective disorders differed from healthy controls

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ACCEPTED MANUSCRIPT in all sleep variables (total sleep time, sleep onset latency, sleep efficiency index, SWS duration, and REM sleep).

SWS and insomnia treatments Although PSG is not recommended to evaluate and diagnose insomnia, it is considered to be an essential tool

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in insomnia treatment efficacy studies (EMEA criteria, NIH). A wide variety of treatment interventions target insomnia. For example, the 2005 NIH state-of-the-science conference on insomnia concluded that only 2 treatment modalities had adequate evidence to support their use in the management of chronic insomnia: Cognitive Behavioral Therapy (CBT), and benzodiazepine receptor agonists.

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The CBT treatment of insomnia is a brief, sleep-focused, multimodal intervention that includes psychological and behavioral procedures such as sleep restriction, stimulus control, relaxation, cognitive strategies, and

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sleep hygiene education. Sleep restriction is a method that limits the time spent in bed as close as possible to the actual sleep time, thereby producing a mild sleep debt, which results in a more consolidated sleep. The sleep window is gradually increased throughout several days or weeks until optimum sleep duration is achieved. All available reviews and meta-analyses have confirmed that CBT improves insomnia [226]. However, objective studies regularly use sleep onset latency, wake after sleep onset, and sleep efficiency (but not SWS) to measure improvement (145).

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The physiological mechanisms of action of CBT on SWS and PSA (power spectral analysis of EEG bands) have been however investigated by several controversial studies. Cervena et al observed the EEG changes following a 8 weeks CBT PSG in 9 patients with insomnia [227]. They found a significant increase of the SWS average duration (86.5 (+/-23.8) vs. 62.6 (+/-33.3) minutes; p=0.01), but no significant change of SLW

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percentages (21.2 (+/-7.1) vs. 19.1 (+/-9.2)%; p=0.2). Regarding PSA, in NREM sleep SWA was significantly increased both in absolute and relative power after CBT in comparison with the period before CBT. Theta and alpha bands were significantly increased in absolute power but significantly decreased in relative power after

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CBT. Krystal and Edinger also tried to assess the EEG impact of CBT in 30 patients assigned to CBT or to placebo intervention with two ambulatory PSG before and after the treatment [228]. Conversely they found no significant CBT-versus-PC differences in NREM EEG SPA for any frequency band or for all-night averaged NREM EEG absolute spectral power indices. They also found no change in the percentages or duration of SWS (Stage 3 and 4). Interestingly, Vallieres et al, tried to assess the impact of sleep restriction as a single factor of CBT on different factors including SWS and PSA SWA[214]. They followed five patients with insomnia registered five times two nights at different steps of the process. Surprisingly percentages of SLW (Stage 3 and 4) decreased beneath the baseline level for the subjects (3 on 5) who responded positively to the CBT. It decreased also drastically in those (2 on 5) who did not respond. PSA indicated a decrease in the beta1 and 2 beginning with the second treatment night. It was therefore not possible to conclude based on these last studies on the effect of CBT on SWS. It would be interesting in the future to identify how sleep restriction

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ACCEPTED MANUSCRIPT or stimulus controlled may be powerful in enhancing SWS in larger group of patients compared with good sleepers.

Regarding drugs, benzodiazepines (BZD) and benzodiazepine-like (BZD-like; zolpidem and zopiclone) hypnotics are considered the preferred drugs to treat insomnia. However, while these sleep aids improve sleep

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initiation and increase the total sleep time, they do not improve daytime functioning, probably as a result of SWA suppression [229]. Indeed, BZD drugs induce changes in sleep architecture of insomniac patients [230]. Moreover, these drugs increase stage 2 NREM sleep and reduce SWS and REM sleep and induced a reduction in the power density in the delta and theta frequencies [231]. In contrast, BZD-like drugs do not appear to significantly change sleep macro-architecture [232].

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Antidepressants with sedative effects are also frequently used in the treatment of primary insomnia [233]. In general, antidepressants improve sleep continuity and increase total sleep time and NREM sleep (see section

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5.1 for more details).

Although the effectiveness of these treatments has been clearly demonstrated, a substantial proportion of patients are either treatment-resistant or display limited gains. The question of long-term pharmaceutical treatment of chronic insomnia is thus unresolved and awaits continuing investigations.

4.2. S Slow-wave sleep in other sleep disorders

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It is usually well admitted that SWS is decreased in Restless legs syndrome (RLS) with or without periodic limb movements (PLM) during sleep and in patients with obstructive sleep apnea (OSA). However the rate of SWS is not generally used as a criterion of severity in both disorders according to the international classification of sleep disorders 4th ed. (ICSD-4).Increased sleep latency and decreased sleep efficiency are

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observed in patients with severe RLS symptoms [234]. Most PLM occur during NREM sleep and the wakesleep transition period such that patients with RLS display a higher NREM sleep instability, with approximately 75% of this sleep stage occupied by cyclic alternating pattern sequences [235]. During sleep,

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RLS-associated PLM predominate during the first cycle, mostly in SWS, only to disappear in the second half of the night [236].

OSA is typically accompanied by intermittent hypoxemia and recurrent arousals, producing a fragmented sleep in which SWS and REM sleep are diminished. Although OSA is generally worse in REM sleep due to the desaturation and duration of apnea, the NREM Apnea-Hypopnea Index (AHI) is higher than the REM AHI in up to one half of individuals [237,238].

4.2.3

Dementia

A growing body of evidence suggests that poor sleep (in terms of quantity and/or quality) proceeds the development of dementia by years. The contribution of sleep microarchitecture to neurodegenerative disorder

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ACCEPTED MANUSCRIPT is still unclear, although SWS appears to be disturbed in patients with dementia. Accordingly, one study has found a loss of delta band power dynamics through the night in patients with dementia as compared to matched controls, as well as a different distribution of delta waves during SWS [239]. Another study has reported reduced SWA in a group of patients with amnestic mild cognitive impairment as compared to agematched controls [240]. The relationship between SWS and protein deposits, and the causal role between them, has long been

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overlooked. Recent studies of Alzheimer’s disease suggest that β-amyloid deposits should impair neural communication in such a way as to disturb SWS. Indeed, one study found that β-amyloid stress in the medial prefrontal cortex was significantly correlated with the severity of impairment in NREM SWA generation [140]. Reduced NREM SWA generation was further associated with impaired overnight memory

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consolidation. In mice, the overexpression of β-amyloid protein was found to lead to shorter NREM sleep duration and greater NREM sleep fragmentation [241]. The disturbance of SWS should in turn exacerbate the neurodegenerative process, since the clearance of toxic metabolites promoted by sleep should be damaged

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[187].

4.2.4. Fibromyalgia

Fibromyalgia is characterized by chronic widespread pain and a range of symptoms including: fatigue, lack of refreshing sleep, cognitive dysfunction, depression, and gastrointestinal symptoms. Sleep disturbance is

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frequent in patients with fibromyalgia (up to 90%), and several prospective studies have found correlations between poor sleep quality and worsening fibromyalgia symptoms [242]. Fibromyalgia patients show a decrease in SWS as compared to healthy individuals [243]. Since extended wakefulness increases SWS whereas daytime napping shortens SWS during the following night, an impairment of the homeostatic drive has been suggested to explain the SWS decrease in fibromyalgia [7]. In these patients, an alpha-intrusion

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during NREM sleep is frequently observed [244]. An alpha intrusion superimposed upon the delta slow wave of SWS has been referred to as alpha-delta-sleep [245]. One study that assessed the potential role of sleep

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disruption in fibromyalgia demonstrated that interrupting SWS with auditory stimuli induces fibromyalgialike myalgia in healthy subjects [246]. Therefore, sleep and particularly SWS may play a role in the regulation of pain. Indeed, sleep disruption with reduced SWS might impair pain inhibition, leading to an increase in pain [247].

4.2.5. Attention Deficit Hyperactivity Disorder Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting 5-10% of schoolaged children. Psychiatric comorbid disorders [248] as well as sleep disturbances (sleep onset insomnia, circadian rhythm sleep-wake disorders, hypersomnia, sleep-related breathing disorders, and sleep-related movement disorders (RLS and PLMS)) are very frequent, and their co-association with ADHD is unclear and probably multifactorial [249]. One review of the literature indicates that children with ADHD have higher

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ACCEPTED MANUSCRIPT daytime sleepiness, more movements during sleep, and higher apnea-hypopnea indexes in comparison to controls [250]. Regarding sleep architecture, few differences are reported in the sleep of children with ADHD as compared to controls [251], with no significant differences in sleep architecture parameters (i.e. number of stage changes; percentages of S1, 2, SWS, and REM sleep; REM latency and sleep efficiency) [250]. However, one recent study reported that SWA is enhanced over central brain positions but attenuated over frontal positions [252]. Moreover, patients with ADHD display deficits with respect to the sleep-dependent

[253,254].

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5. Moving toward an improvement of slow-wave sleep

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consolidation of declarative memory associated with dysfunctional SO activity during early NREM sleep

Numerous methods, mostly using pharmacological agents, have been used to promote or restore sleep. The

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evaluation of treatment effects depends on several endpoints specified by the European Medicines Agency (EMEA 2009). Whereas sleep onset latency (SOL), sleep continuity, TST, the feeling of restorative sleep, sleep quality, daytime functioning, and quality of life are all acknowledged by this agency, SWS does not appear as a restorative criterion. However, given the evidences for the implication of SWS in both cognitive and physiological aspects as well as the implication of impaired SWS in clinic, studies have been initiated and resulted in efforts to increase sleep efficacy by enhancing SWS, and more precisely by potentiating SWA.

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These methods range from invasive methods (drugs) to transmagnetic/electrical stimulations, auditory stimulations and hypnosis. Their benefits and limits in both healthy subjects and patients will be presented in the following paragraph.

Pharmacological methods

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5.1.

5.1.1. Existing methods in healthy subjects

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A number of drugs have been shown to increase SWS by enhancing GABAergic transmission, as reviewed by Bellesi et al. [255]. Specifically, various clinical investigations have shown that tiagabine, mirtazapine, trazodone and gaboxadol increase SWS duration after sleep restriction [256,257]. Tiagabine also improved performance on cognitive tasks that evaluate executive functions, and reduced the negative effects of sleep restriction on alertness [257]. Finally, the orexin antagonist almorexant has been shown to provoke a shorter latency to SWS [258].

5.1.2. Existing methods in patients As described in section 4.1, both SWS and sleep maintenance are impaired in insomnia as well as in depression. For this reason, methods that specifically increase SWS and SWA in insomniac patients have been investigated.

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ACCEPTED MANUSCRIPT In general, antidepressants improve sleep continuity and increase both SWS and TST. The effects of antidepressants on SWS are quite diverse. Antidepressants with significant 5-HT2A/2C receptor antagonist properties can increase SWS, whereas other drugs, such as selective 5-hydroxytryptamine reuptake inhibitors or monoamine oxidase inhibitors, either lower SWS or do not produce any change [259]. Trazodone is particularly known for its ability to increase SWS [260]. Nortriptyline, a tricyclic antidepressant, has been tested in trials that measured sleep by EEG in elderly patients with recurrent major depression [261]. The

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authors found that nortriptyline administration led to prolongation of the first NREM sleep episode. Nortriptyline was associated with more delta wave production and higher delta wave density in the first NREM period relative to the second period, and nortriptyline levels were positively related to all-night delta wave production during maintenance [261]. The authors concluded that effective long-term pharmacotherapy

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of recurrent major depression was associated with an enhanced rate of delta wave production in the first NREM period.

The effects of mirtazapine, an antagonist of presynaptic alpha 2-adrenergic autoreceptors and heteroreceptors,

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has been examined on both norepinephrine and serotonin (5-HT) presynaptic axons during polysomnographic sleep in patients with major depressive disorder. Mirtazepine administration (30 mg) increased total SWS, as well as SWS in the first sleep cycle (but not in the second sleep cycle); furthermore, it reversed sleep markers of depression and reduced depressive symptoms [262].

Agomelatine is a high-affinity agonist of MT1 and MT2 as well as a 5HT2B receptor antagonist for use in treating anxiety and depression. This molecule has been shown to improve both subjective and objective

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parameters of sleep in patients with insomnia associated with major depression and anxiety disorders [263]. In this study, agomelatine improved sleep efficiency, SWS, and the distribution of delta activity throughout the night and did not induce any change in the amount or latency of REM sleep. Moreover, SWS was resynchronized to the first sleep cycle of the night.

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In poor sleepers and patients with chronic primary insomnia, anxiety, or mood disorders, ritanserin has been shown to induce an increase in SWS [264] and to increase GH release [169].

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Several investigations of tiagabine and gaboxadol use in patients with primary or transient insomnia have been published recently [257,265]. These two molecules involved in enhancing GABAergic transmission consistently increased SWS, but generally appear to have an inconsistent and less robust effect on traditional hypnotic efficacy measures than benzodiazepine receptor agonists, mostly due to their short treatment duration. These molecules also do not improve any of the endpoints specified by the European Medicine Agency. Moreover, SWS enhancement by tiagabine did not improve memory performance, as it may be expected [266]. However, one could speculate that SWS enhancement may benefit insomniac patients in ways that are not necessarily related to standard efficacy measures. Indeed, a leading hypothesis related to the pathophysiology of insomnia postulates that general CNS hyperarousal is involved [267]. Since cortisol levels in patients with chronic insomnia are positively correlated with total wake time and negatively correlated with

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ACCEPTED MANUSCRIPT SWS [218], increasing SWS may have a beneficial effect on these arousal systems. Therefore, these physiological parameters should be investigated in future studies.

5.1.3. Limits Although the results from the studies reviewed in the previous section are promising, pharmacological

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approaches to enhance SWS often raise issues related to dependence and tolerance, and are commonly

investigated.

5.2.

Transcranial Direct Current Stimulation

5.2.1. Existing methods in healthy subjects

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associated with residual daytime side effects. To overcome these limitations, other strategies must be

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As a way to boost SWS without using medications, one alternative methodology is to use brain stimulation to directly influence the activity of the neuronal population.

This method relies on the use of intermittent transcranial direct stimulation (tDCS), a non-invasive, painless brain stimulation treatment that uses direct electrical currents to stimulate specific parts of the brain. The underlying principle is that constant low intensity current passing through two electrodes placed over the head can generate an electrical field to modulate the activity of neurons. Several studies have shown that neuronal

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activity can indeed be manipulated and that it is possible to increase the modulation of neurons by rhythmical stimulations. The first study to investigate the possibility of enhancing slow oscillations during sleep with tDCS was conducted by Lisa Marshall in Ian Born’s research group. In this study, a 0.75 Hz current was applied for 5-min intervals separated by 1 min off periods after SWS. As expected, the authors found an

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increase in the EEG power in the slow oscillation band (<1 Hz) during the stimulation-free intervals. Importantly, the increase in slow oscillations was associated with an increase in fast spindle power, supporting

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the link between the two rhythms. The acceleration of the SWA homeostatic decay in subjects stimulated by tDCS at the beginning of SWS was reported using a similar paradigm, further suggesting that SO generated by tDCS share the same physiological properties as spontaneous SO. Similar results have been observed in rats. For example, intracranial electrical stimulation in the rat cortex during sleep triggered LFP potentials that were visually indistinguishable from naturally occurring slow waves. These waves propagated from the area of stimulation through the neocortex and were followed by spindling activity. As in humans, the properties of stimulation-induced SO were sensitive to sleep pressure: SO were larger, and had steeper slopes and fewer multipeaks during early sleep as compared to SO evoked during later sleep [269]. In another study, Massimini et al. (2007) demonstrated that slow waves could be triggered by directly perturbing the cortex during NREM sleep using transcranial magnetic stimulation (TMS), a noninvasive technology that delivers a magnetic pulse to a localized region of the brain. TMS pulses of 5 Hz applied to the

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ACCEPTED MANUSCRIPT cortex during wakefulness have been shown to induce a localized increase in SWA during the subsequent sleep episode [270], while administering a < 1 Hz TMS pulse during sleep evoked a high-amplitude slow wave and increased SWA [271]. The possibility of specifically enhancing SO is an attractive way to investigate the causal role of slow waves in memory consolidation. In the study by Marshall and colleagues, an increase in slow oscillations was associated with enhanced retention of hippocampal-dependent declarative memories, providing the first direct

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causal evidence of slow waves in memory processes [272]. Using the same methods, SWA and memory performances were successfully enhanced in the elderly, both at night [149] and during naps [273]. These same effects have been observed in rats. The artificial enhancement of SO by tDCS applied over the frontal cortex during SWS leads to the transient enhancement of endogenous SO activity, and is associated with an

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improvement in memory [150,274].

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5.2.2. Existing methods in patients

Given the evidence that electric and magnetic stimulation can increase SWA in healthy subjects and that this change in EEG pattern can influence relevant functional brain networks, the efficacy of these methods in improving the disturbed SWS in patients has been investigated.

Notably, it has been hypothesized that 0.75-4 Hz stimulation by tDCS on SO (SOtDCS) may be helpful for insomniacs who suffer from lack of SWA. One study showed that SOtDCS applied during N2 increased the

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duration of N3, the sleep efficiency, and the probability of transition from N2 to N3, whereas SOtDCS decreased N1 and wake time after sleep onset as compared to a sham [275]. Overall, the study highlighted a sleep-stabilizing role for this intervention in insomniacs, which may mimic the effect of sleep slow waveenhancing drugs. Altogether, the effect of this intervention suggests that this strategy can elicit very promising

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results in the short term.

In addition to insomnia, these methods have been investigated in ADHD children. This is based on the hypothesis that since children suffering from ADHD are suspected to display less frontal slow-wave activity

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during sleep [252], ADHD should be frequently accompanied by memory deficits; additionally, since SOtDCS appears to enhance both SO and memory performance, triggering SO may be a putative way to restore SWS as well as memory performance. This stimulation enhanced the slow oscillation power in children with ADHD and boosted memory performance to the same levels as in healthy children. Therefore, the authors hypothesized that the external induction of frontal SO by SOtDCS might have been superimposed over the SO deficiency, thereby normalizing sleep-dependent memory consolidation in children with ADHD [276].

5.2.3. Limits

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ACCEPTED MANUSCRIPT In spite of the hopeful results reviewed in the previous section, no beneficial effect on memory in any tDCS study conducted with human participants or rats has ever been successfully replicated [154,155]. Although some encouraging results for the architecture of sleep in insomniacs have been found [275], their effects on the subjective rating of sleep quality and next-day functioning have not been conclusive. Moreover, one recent review with a quantitative approach has obscured these results, since the authors did not find any evidence for cognitive effects in healthy populations from single-session tDCS [277].

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These conflicting accounts illustrate that the effectiveness of these methods is now strongly debated. Indeed, it has been difficult to evaluate the impact of these methods for several reasons. First, the various experiments have all used a very small sample size. Another factor is that EEGs recorded during stimulations are strongly affected by electrical artifacts, preventing a detailed EEG analysis. Furthermore, although tDCS generates

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sustained and widespread changes in regional neuronal activity, it also produces a complex pattern of activated and deactivated brain areas, making it difficult to predict the impact on slow waves [278]. Finally,

the skull possibly acting as a shunt [279].

5.3.

Hypnosis

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using a cadaver, one recent study has argued that up to 90% of electrical inputs do not reach the brain, with

Drugs that induce a greater amount of SWS share the drawbacks of other types of medications: they typically

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lose their efficacy during long-term treatment; they have adverse side effects; and they are often associated with a high risk of addiction. As the long-term effects from repeated exposure to tDCS and TMS are still unknown, there is a great need to develop effective and risk-free approaches to improve sleep, and particularly SWS.

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One such alternative is hypnosis, which was recently used in both young and elderly healthy subjects with the aim of deepening their sleep. In the first study, the benefits of hypnotic suggestion were investigated in healthy females in their early twenties [280]. The authors compared, in a 90 min nap, the effects of hypnotic

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suggestion to sleep more deeply (with a metaphor of a fish swimming deeper and deeper) against a control nap with verbal information about minerals, in both highly and poorly suggestible participants. As a control, they repeated this experiment by replacing the hypnotic suggestion to sleep more deeply with the hypnotic suggestion to sleep less deeply, and added an experiment in which participants were simply informed that listening to verbal information before sleep increases subsequent SWS as the brain tries to consolidate the learned information. The audio recording tape about mineral deposits (used as a control tape in all other experiments) was used to “induce” SWS in this experiment, and an incomprehensible version of the text was used as the control condition. Consequently, the authors demonstrated that the hypnotic suggestion to sleep more deeply enhanced SWA and the time spent in SWS, in highly suggestible women. This deepening of sleep was predicted by the theta activity during the session. No such result was found in the poorly suggestible group or in the group receiving the hypnotic suggestion to sleep less deeply. This result indicates that the type

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ACCEPTED MANUSCRIPT of hypnotic suggestion should have been critical for the beneficial effect on SWS and should exclude alternative explanations like general relaxation and demand characteristics. In order to confirm whether their results were generalizable, the authors proposed a second study on elderly subjects in their late sixties [281]. As in the first study, highly suggestible females who listened to a hypnotic suggestion “to sleep deeper” during a midday nap significantly extended their amounts of SWS. In addition, left prefrontal SWA was significantly increased during sleep after listening to the hypnosis, and performance in a prefrontal-dependent

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verbal fluency task was significantly improved. The benefits of hypnosis on sleep in elderly suggestible females were highly comparable to the effects of hypnotic suggestion on sleep in younger suggestible females, as reported previously.

In summary, hypnotic suggestion appears to be an efficient non-pharmacological tool to extend SWS and

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SWA in healthy females. Although these results should not be extended to males, they do provide an important basis for future studies examining the benefits of hypnotic suggestions in patients with sleep

5.4.

Sound stimulations

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disturbances.

It is well-established that sounds can influence EEG activity by producing K-complexes, which are similar in structure to slow waves (in addition to acting as slow wave precursors) [36]. Given this evidence and a previous report showing that low-frequency stimulations can enhance SWA [148], one recent study has investigated an innovative and non-invasive method to enhance SO using acoustic stimulations [255]. One of

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the first studies that investigated sound stimulation as a way to enhance SWA used 15-s blocks of consecutive 50-ms pips that were applied using a 1 Hz fixed intra-tone interval (ITI). These “ON” blocks were compared to adjacent “OFF” blocks with no tone. Within these ON blocks, slow waves appeared remarkably larger and numerous, with an overall 6-27% increase in the SWA as compared to the OFF blocks. A separate report also

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determined that the fixed ITI acoustic stimulation method enhanced slow waves in healthy young subjects [282]. Indeed, stimulations at 0.8 Hz (beginning 2 min before sleep) that lasted 90 min were able to induce an

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increase in slow oscillation activity (0.5–1 Hz) during the rhythmic stimulation, as compared to a sham condition with no stimulation [282]. Additional stimulation studies have shown that the response might change as a function of the stimulus timing relative to the phase of the slow EEG rhythms [30,151,283]. In this way, more complex stimulation approaches have been adopted to take into consideration these phasedependent responses to stimulation. First, stimuli were applied in a time-locked manner to the SO up-phase [151]. This method confirmed the effectiveness of time-acoustic stimulation in enhancing slow-wave oscillations as well as phase-coupled spindle activity. The stimulation also improved declarative memory performance in comparison to control nights in which either the stimulation was delivered out of phase (during the down-states) or no stimulation was provided. Subsequently, another algorithm to predict SO in real-time was introduced [284], confirming the importance of the phase. Recently, a phase-locked-loop (PLL) method was developed that provides more precise real-time estimates of the ongoing phase [152], enabling

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ACCEPTED MANUSCRIPT PLL to rapidly adjust to EEG changes. This method, used in a nap protocol, was able to enhance SWA in both N3 and N2, by stimulating K-complexes. Thus, among the different sensory modalities, acoustic stimulation appears to be one of the safest and most effective methods to increase the magnitude of slow waves. Although the underlying mechanism is unclear, it has been hypothesized that sub-arousal threshold stimuli should be able to synchronize the cortical activity of large populations of neurons through the activation of neurons that project diffusely over the cerebral cortex

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[255]. This phenomenon is possible due to the bistable behavior of the thalamo-cortical system during NREM sleep, in which rapid and synchronous neuronal depolarization is followed by a massive hyperpolarization. In addition to optimizing the efficacy of the algorithm in terms of its accuracy to detect the phase and delay to stimulate with the right timing, several other features could be considered in order to have the greatest effect

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on SO. Accordingly, one recent review selected such main features as intensity, sound frequency, timing, and entrainment [255]. Specifically, the authors indicated that stimulation intensity could be tuned according to sleep depth, based on a threshold below which the stimulation intensity is effective in enhancing slow waves,

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and above which the stimulation intensity causes arousal. Changing the sound frequency could also counteract a possible habituation effect and thus maximize the enhancing effect. The phase of the sound hitting also needs to be further investigated in different populations (i.e. the elderly). Repetitive stimulation patterns might entrain endogenous brain rhythms, leading to waves that are increasingly organized around the frequency of stimulation. Finally, the interaction of these factors may be different in specific subsets of the population, for instance in subjects who are easily aroused during sleep by environmental noise, or in elderly people who

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typically exhibit reduced amounts of SWA.

Conclusions and future directions

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SWS as a homeostatic process is precisely regulated and compensated for. The dominance of SWS in frontal areas associated with higher brain function as well as specific studies linking SWS to memory and attention emphasize the significant role of SWS in cognition. Moreover, the physiological evidence regarding SWS in

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terms of metabolism regulation, hormone release, immunity, and cleaning of metabolites highlight the possibly important role of this sleep stage. In the face of this vast amount of information regarding SWS, it is surprising to observe that it is rarely retained as a sufficiently crucial criterion for sleep quality and efficiency, especially in clinical settings such as insomnia. This review has described several novel techniques that can enhance SWS and boost SO, from pharmacological treatment to acoustic stimulation to hypnosis. Although the clinical use of these methods appears promising, a restorative function from generating SO must still be demonstrated. In order to address this question, we propose boosting SWS chronically in clinical trials for patients with either natural SWS impairment (i.e. the elderly) or pathological SWS impairment (patients with insomnia), or evaluating the recovery of patients with injuries sustained under SWS boosting.

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ACCEPTED MANUSCRIPT Practical points

-

The definition of SWS usually differs between animals and humans. SWS refers to N3 in humans, whereas it encompasses all of NREM sleep in animal models. SWS includes both local and global slow oscillations, which are tightly linked to spindles and SWR.

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SWS is believed to have a major role in sleep cognition, and more precisely in declarative memory

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-

consolidation. -

SWS could be critical for energy restoration, immunity, hormone release, and cleaning of metabolites.

-

SWS could be a crucial endpoint to observe in sleep disorders such as insomnia and obstructive sleep

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apnea, as well as in comorbidities associated with poor sleep such as fibromyalgia, dementia, and ADHD. Improving SWA in such disorders may significantly decrease fatigue and the non-restorative

-

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complaints associated with these disorders.

Pharmaceutical drugs such as agomelatine, gaboxadol, mirtazapine, nortriptyline, ritanserin, tiabagine and trazodone as well as non-pharmaceutical treatments such as CBT, tDCS, sound stimulation, and hypnosis are proven to benefit SWS.

Research agenda

SWS may be a crucial marker to observe in insomnia. However, few studies have been conducted

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-

with SWS as a primary endpoint, or with cognitive function as a potentially associated clinical endpoint. Therefore, tools should be developed to better assess the role of SWS in such pathologies. -

An increasing number of methods have concentrated on boosting SO. However, whether these

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oscillations correspond to “true SO” with all of the putative benefits of SO (i.e. energy, immunity, etc.) must still be demonstrated. -

The chronic impact of enhancing SWS must be investigated to better understand whether it may have

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an effect on chronic sleep pathologies and comorbidities associated with poor SWS.

References [1]

Caton R. The Electric Currents of the Brain. Brit Med J 1875;2:278.

[2]

Berger H. Das Elektrenkephalogramm des Menschen. Naturwissenschaften 1935;23:121–4.

[3]

Alfred L. Loomis, E. Newton Harvey GAHI. Distribution of disturbance-patterns in the human electroencephalogram with special reference to sleep. J Neurophysiol 1938;1:413–30.

[4]

Rechtschaffen A, Kales A. A manual of standardized techniques and scoring system for sleep stages of human subjects. Washington, DC US Gov Print Off 1968;NIH Public.

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