Local Slow Waves in Superficial Layers of Primary Cortical Areas during REM Sleep

Local sleep in songbirds: different simultaneous sleep states across the avian pallium

Wakefulness and sleep have often been treated as distinct and global brain states. However, an emerging body of evidence on the local regulation of sleep stages challenges this conventional view. Apart from unihemispheric sleep, the current data that support local variations of neural oscillations during sleep are focused on the homeostatic regulation of local sleep, i.e., the role preceding awake activity. Here, to examine local differences in brain activity during natural sleep, we recorded the electroencephalogram and the local field potential across multiple sites within the avian pallium of zebra finches without perturbing the previous awake state. We scored the sleep stages independently in each pallial site and found that the sleep stages are not pallium-wide phenomena but rather deviate widely across electrode sites. Importantly, deeper electrode sites had a dominant role in defining the temporal aspects of sleep state congruence. Altogether, these findings show that local regulation of sleep oscillations also occurs in the avian brain without prior awake recruitment of specific pallial circuits and in the absence of mammalian cortical neural architecture.

Neuronal network controlling REM sleep

Rapid eye movement sleep is a state characterized by concomitant occurrence of rapid eye movements, electroencephalographic activation and muscle atonia. In this review, we provide up to date knowledge on the neuronal network controlling its onset and maintenance. It is now accepted that muscle atonia during rapid eye movement sleep is due to activation of glutamatergic neurons localized in the pontine sublaterodorsal tegmental nucleus. These neurons directly project and excite glycinergic/γ-aminobutyric acid-ergic pre-motoneurons localized in the ventromedial medulla. The sublaterodorsal tegmental nucleus rapid eye movement-on neurons are inactivated during wakefulness and non-rapid eye movement by rapid eye movement-off γ-aminobutyric acid-ergic neurons localized in the ventrolateral periaqueductal grey and the adjacent dorsal deep mesencephalic reticular nucleus. Melanin-concentrating hormone and γ-aminobutyric acid-ergic rapid eye movement sleep-on neurons localized in the lateral hypothalamus would inhibit these rapid eye movement sleep-off neurons initiating the state. Finally, the activation of a few limbic cortical structures during rapid eye movement sleep by the claustrum and the supramammillary nucleus as well as that of the basolateral amygdala would be involved in the function(s) of rapid eye movement sleep. In summary, rapid eye movement sleep is generated by a brainstem generator controlled by forebrain structures involved in autonomic control.

Subthalamic γ Oscillation Underlying Rapid Eye Movement Sleep Abnormality in Parkinsonian Patients

BACKGROUND

Abnormal rapid eye movement (REM) sleep, including REM sleep behavior disorder (RBD) and reduced REM sleep, is common in Parkinson's disease (PD), highlighting the importance of further study on REM sleep. However, the biomarkers of REM disturbances remain unknown, leading to the lack of REM-specific neuromodulation interventions.

OBJECTIVE

This study aims to investigate the neurophysiological biomarkers of REM disturbance in parkinsonian patients.

METHODS

Ten PD patients implanted with bilateral subthalamic nucleus-deep brain stimulation (STN-DBS) were included in this study, of whom 4 were diagnosed with RBD. Sleep monitoring was conducted 1 month after surgery. Subthalamic local field potentials (LFP) were recorded through sensing-enabled DBS. The neurophysiological features of subthalamic LFP during phasic and tonic microstates of REM sleep and their correlation with REM sleep fragmentation and RBD were analyzed.

RESULTS

Differences in subthalamic γ oscillation between phasic and tonic REM correlated positively with the severity of REM sleep fragmentation. Patients with RBD also exhibited stronger γ oscillations during REM sleep compared with non-RBD patients, and both increased β and γ were found before the onset of RBD episodes. Stimulation changes in simulated γ-triggered feedback modulation followed more closely with phasic REM density, whereas an opposite trend was found in simulated β-triggered feedback modulation.

CONCLUSION

Excess subthalamic γ oscillations may contribute to REM instability and RBD, suggesting that γ oscillation could serve as a feedback signal for adaptive DBS for REM sleep disorders. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Multi-region processing during sleep for memory and cognition

Over the past decades, the understanding of sleep has evolved to be a fundamental physiological mechanism integral to the processing of different types of memory rather than just being a passive brain state. The cyclic sleep substates, namely, rapid eye movement (REM) sleep and non-REM (NREM) sleep, exhibit distinct yet complementary oscillatory patterns that form inter-regional networks between different brain regions crucial to learning, memory consolidation, and memory retrieval. Technical advancements in imaging and manipulation approaches have provided deeper understanding of memory formation processes on multi-scales including brain-wide, synaptic, and molecular levels. The present review provides a short background and outlines the current state of research and future perspectives in understanding the role of sleep and its substates in memory processing from both humans and rodents, with a focus on cross-regional brain communication, oscillation coupling, offline reactivations, and engram studies. Moreover, we briefly discuss how sleep contributes to other higher-order cognitive functions.

Infraslow noradrenergic locus coeruleus activity fluctuations are gatekeepers of the NREM-REM sleep cycle

The noradrenergic locus coeruleus (LC) regulates arousal levels during wakefulness, but its role in sleep remains unclear. Here, we show in mice that fluctuating LC neuronal activity partitions non-rapid-eye-movement sleep (NREMS) into two brain-autonomic states that govern the NREMS-REMS cycle over ~50-s periods; high LC activity induces a subcortical-autonomic arousal state that facilitates cortical microarousals, whereas low LC activity is required for NREMS-to-REMS transitions. This functional alternation regulates the duration of the NREMS-REMS cycle by setting permissive windows for REMS entries during undisturbed sleep while limiting these entries to maximally one per ~50-s period during REMS restriction. A stimulus-enriched, stress-promoting wakefulness was associated with longer and shorter levels of high and low LC activity, respectively, during subsequent NREMS, resulting in more microarousal-induced NREMS fragmentation and delayed REMS onset. We conclude that LC activity fluctuations are gatekeepers of the NREMS-REMS cycle and that this role is influenced by adverse wake experiences.

Effects of Continuous Positive Airway Pressure Treatment on Sawtooth Waves During Rapid Eye Movement Sleep in Obstructive Sleep Apnea Patients

Background

Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder characterized by recurrent upper airway collapse and obstruction, leading to reduced or absent breathing during sleep, especially rapid eye movement (REM) sleep, and continuous positive airway pressure treatment (CPAP) is often used for treatment of OSA. Sawtooth waves (STWs) are a characteristic of REM sleep.

Objective

To examine effects of CPAP treatment on STWs during REM sleep in the OSA patients.

Methods

Polysomnographic recordings were performed on 20 moderate-to-severe OSA patients and 16 normal controls, and comparisons of STWs during REM sleep in the OSA patients with and without CPAP treatment (paired t-test or Wilcoxon signed-rank test wherever appropriate), and between OSA patients and normal controls (Student's t-test or Wilcoxon rank-sum test) were carried out. In addition, linear correlation analyses were used to estimate the relationship of STWs and REM sleep with duration of non-REM (NREM) sleep stage 3 (N3).

Results

The STWs were classified to be apnea/hypopnea associated and not associated (isolated), and the amplitude of the isolated STWs was significantly higher than that of the apnea/hypopnea associated. With CPAP treatment, the percentage of REM sleep with STWs and the amplitude of STWs were significantly increased to the levels, which were not significantly different from those in the normal controls, while the frequency of STWs was not significantly changed. In addition, the total duration of REM sleep and the duration of REM sleep with STWs were both positively correlated with the duration of N3 sleep in the normal controls and the OSA patients with CPAP treatment. Furthermore, CPAP treatment also caused a significant increase in the duration of rapid eye movements in REM sleep.

Conclusion

These findings suggest that there are some interconnections between NREM and REM sleep, and STWs not only represent the quality of REM sleep but also are correlated with N3 sleep.

Neuronal subtype-specific transcriptomic changes in the cerebral neocortex associated with sleep pressure

Sleep is homeostatically regulated by sleep pressure, which increases during wakefulness and dissipates during sleep. Recent studies have suggested that the cerebral neocortex, a six-layered structure composed of various layer- and projection-specific neuronal subtypes, is involved in the representation of sleep pressure governed by transcriptional regulation. Here, we examined the transcriptomic changes in neuronal subtypes in the neocortex upon increased sleep pressure using single-nucleus RNA sequencing datasets and predicted the putative intracellular and intercellular molecules involved in transcriptome alterations. We revealed that sleep deprivation (SD) had the greatest effect on the transcriptome of layer 2 and 3 intratelencephalic (L2/3 IT) neurons among the neocortical glutamatergic neuronal subtypes. The expression of mutant SIK3 (SLP), which is known to increase sleep pressure, also induced profound changes in the transcriptome of L2/3 IT neurons. We identified Junb as a candidate transcription factor involved in the alteration of the L2/3 IT neuronal transcriptome by SD and SIK3 (SLP) expression. Finally, we inferred putative intercellular ligands, including BDNF, LSAMP, and PRNP, which may be involved in SD-induced alteration of the transcriptome of L2/3 IT neurons. We suggest that the transcriptome of L2/3 IT neurons is most impacted by increased sleep pressure among neocortical glutamatergic neuronal subtypes and identify putative molecules involved in such transcriptional alterations.

A nonoscillatory, millisecond-scale embedding of brain state provides insight into behavior

The most robust and reliable signatures of brain states are enriched in rhythms between 0.1 and 20 Hz. Here we address the possibility that the fundamental unit of brain state could be at the scale of milliseconds and micrometers. By analyzing high-resolution neural activity recorded in ten mouse brain regions over 24 h, we reveal that brain states are reliably identifiable (embedded) in fast, nonoscillatory activity. Sleep and wake states could be classified from 100 to 101 ms of neuronal activity sampled from 100 µm of brain tissue. In contrast to canonical rhythms, this embedding persists above 1,000 Hz. This high-frequency embedding is robust to substates, sharp-wave ripples and cortical on/off states. Individual regions intermittently switched states independently of the rest of the brain, and such brief state discontinuities coincided with brief behavioral discontinuities. Our results suggest that the fundamental unit of state in the brain is consistent with the spatial and temporal scale of neuronal computation.

Sleep-like cortical dynamics during wakefulness and their network effects following brain injury

By connecting old and recent notions, different spatial scales, and research domains, we introduce a novel framework on the consequences of brain injury focusing on a key role of slow waves. We argue that the long-standing finding of EEG slow waves after brain injury reflects the intrusion of sleep-like cortical dynamics during wakefulness; we illustrate how these dynamics are generated and how they can lead to functional network disruption and behavioral impairment. Finally, we outline a scenario whereby post-injury slow waves can be modulated to reawaken parts of the brain that have fallen asleep to optimize rehabilitation strategies and promote recovery.

Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SOs, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the mechanisms by which global and local SOs arise from micro-scale neuronal dynamics and network connectivity remain poorly understood. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and SWS, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. An increase in the overall synaptic strength led to synchronized global SO, while a decrease in synaptic connectivity produced only local slow-waves that would not propagate beyond local areas. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

Thalamocortical Dynamics during Rapid Eye Movement Sleep in the Mouse Somatosensory Pathway

Rapid eye movement (REM) sleep, also referred to as paradoxical sleep for the striking resemblance of its electroencephalogram (EEG) to the one observed in wakefulness, is characterized by the occurrence of transient events such as limb twitches or facial and rapid eye movements. Here, we investigated the local activity of the primary somatosensory or barrel cortex (S1) in naturally sleeping head-fixed male mice during REM. Through local field potential recordings, we uncovered local appearances of spindle waves in the barrel cortex during REM concomitant with strong delta power, challenging the view of a wakefulness-like activity in REM. We further performed extra- and intracellular recordings of thalamic cells in head-fixed mice. Our data show high-frequency thalamic bursts of spikes and subthreshold spindle oscillations in approximately half of the neurons of the ventral posterior medial nucleus which further confirmed the thalamic origin of local cortical spindles in S1 in REM. Cortical spindle oscillations were suppressed, while thalamus spike firing increased, associated with rapid mouse whisker movements and S1 cortical activity transitioned to an activated state. During REM, the sensory thalamus and barrel cortex therefore alternate between high (wake-like) and low (non-REM sleep-like) activation states, potentially providing a neuronal substrate for mnemonic processes occurring during this paradoxical sleep stage.

Spatial-Temporal Analysis of Neural Desynchronization in Sleeplike States Reveals Critical Dynamics

Sleep is characterized by nonrapid eye movement sleep, originating from widespread neuronal synchrony, and rapid eye movement sleep, with neuronal desynchronization akin to waking behavior. While these were thought to be global brain states, recent research suggests otherwise. Using time-frequency analysis of mesoscopic voltage-sensitive dye recordings of mice in a urethane-anesthetized model of sleep, we find transient neural desynchronization occurring heterogeneously across the cortex within a background of synchronized neural activity, in a manner reminiscent of a critical spreading process and indicative of an "edge-of-synchronization" phase transition.

Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and slow-wave sleep, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. Increase of the overall synaptic strength led to synchronized global SO, while decrease of synaptic connectivity produced only local slow-waves that would not propagate beyond local area. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

Consciousness and sleep

Sleep is a universal, essential biological process. It is also an invaluable window on consciousness. It tells us that consciousness can be lost but also that it can be regained, in all its richness, when we are disconnected from the environment and unable to reflect. By considering the neurophysiological differences between dreaming and dreamless sleep, we can learn about the substrate of consciousness and understand why it vanishes. We also learn that the ongoing state of the substrate of consciousness determines the way each experience feels regardless of how it is triggered-endogenously or exogenously. Dreaming consciousness is also a window on sleep and its functions. Dreams tell us that the sleeping brain is remarkably lively, recombining intrinsic activation patterns from a vast repertoire, freed from the requirements of ongoing behavior and cognitive control.

Which structure generates paradoxical (REM) sleep: The brainstem, the hypothalamus, the amygdala or the cortex?

Paradoxical or Rapid eye movement (REM) sleep (PS) is a state characterized by REMs, EEG activation and muscle atonia. In this review, we discuss the contribution of brainstem, hypothalamic, amygdalar and cortical structures in PS genesis. We propose that muscle atonia during PS is due to activation of glutamatergic neurons localized in the pontine sublaterodorsal tegmental nucleus (SLD) projecting to glycinergic/GABAergic pre-motoneurons localized in the ventro-medial medulla (vmM). The SLD PS-on neurons are inactivated during wakefulness and slow-wave sleep by PS-off GABAergic neurons localized in the ventrolateral periaqueductal gray (vPAG) and the adjacent deep mesencephalic reticular nucleus. Melanin concentrating hormone (MCH) and GABAergic PS-on neurons localized in the posterior hypothalamus would inhibit these PS-off neurons to initiate the state. Finally, the activation of a few limbic cortical structures during PS by the claustrum and the supramammillary nucleus as well as that of the basolateral amygdala would also contribute to PS expression. Accumulating evidence indicates that the activation of these limbic structures plays a role in memory consolidation and would communicate to the PS-generating structures the need for PS to process memory. In summary, PS generation is controlled by structures distributed from the cortex to the medullary level of the brain.

Study protocol: Cerebral characterization of sensory gating in disconnected dreaming states during propofol anesthesia using fMRI

Background

Disconnected consciousness describes a state in which subjective experience (i.e., consciousness) becomes isolated from the external world. It appears frequently during sleep or sedation, when subjective experiences remain vivid but are unaffected by external stimuli. Traditional methods of differentiating connected and disconnected consciousness, such as relying on behavioral responsiveness or on post-anesthesia reports, have demonstrated limited accuracy: unresponsiveness has been shown to not necessarily equate to unconsciousness and amnesic effects of anesthesia and sleep can impair explicit recollection of events occurred during sleep/sedation. Due to these methodological challenges, our understanding of the neural mechanisms underlying sensory disconnection remains limited.

Methods

To overcome these methodological challenges, we employ a distinctive strategy by combining a serial awakening paradigm with auditory stimulation during mild propofol sedation. While under sedation, participants are systematically exposed to auditory stimuli and questioned about their subjective experience (to assess consciousness) and their awareness of the sounds (to evaluate connectedness/disconnectedness from the environment). The data collected through interviews are used to categorize participants into connected and disconnected consciousness states. This method circumvents the requirement for responsiveness in assessing consciousness and mitigates amnesic effects of anesthesia as participants are questioned while still under sedation. Functional MRI data are concurrently collected to investigate cerebral activity patterns during connected and disconnected states, to elucidate sensory disconnection neural gating mechanisms. We examine whether this gating mechanism resides at the thalamic level or results from disruptions in information propagation to higher cortices. Furthermore, we explore the potential role of slow-wave activity (SWA) in inducing disconnected consciousness by quantifying high-frequency BOLD oscillations, a known correlate of slow-wave activity.

Discussion

This study represents a notable advancement in the investigation of sensory disconnection. The serial awakening paradigm effectively mitigates amnesic effects by collecting reports immediately after regaining responsiveness, while still under sedation. Ultimately, this research holds the potential to understand how sensory gating is achieved at the neural level. These biomarkers might be relevant for the development of sensitive anesthesia monitoring to avoid intraoperative connected consciousness and for the assessment of patients suffering from pathologically reduced consciousness.

Clinical trial registration

European Union Drug Regulating Authorities Clinical Trials Database (EudraCT), identifier 2020-003524-17.

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"-a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics.

What is sleep exactly? Global and local modulations of sleep oscillations all around the clock

Wakefulness, non-rapid eye-movement (NREM) and rapid eye-movement (REM) sleep differ from each other along three dimensions: behavioral, phenomenological, physiological. Although these dimensions often fluctuate in step, they can also dissociate. The current paradigm that views sleep as made of global NREM and REM states fail to account for these dissociations. This conundrum can be dissolved by stressing the existence and significance of the local regulation of sleep. We will review the evidence in animals and humans, healthy and pathological brains, showing different forms of local sleep and the consequences on behavior, cognition, and subjective experience. Altogether, we argue that the notion of local sleep provides a unified account for a host of phenomena: dreaming in REM and NREM sleep, NREM and REM parasomnias, intrasleep responsiveness, inattention and mind wandering in wakefulness. Yet, the physiological origins of local sleep or its putative functions remain unclear. Exploring further local sleep could provide a unique and novel perspective on how and why we sleep.

How we sleep: From brain states to processes

All our lives, we alternate between wakefulness and sleep with direct consequences on our ability to interact with our environment, the dynamics and contents of our subjective experience, and our brain activity. Consequently, sleep has been extensively characterised in terms of behavioural, phenomenological, and physiological changes, the latter constituting the gold standard of sleep research. The common view is thus that sleep represents a collection of discrete states with distinct neurophysiological signatures. However, recent findings challenge such a monolithic view of sleep. Indeed, there can be sharp discrepancies in time and space in the activity displayed by different brain regions or networks, making it difficult to assign a global vigilance state to such a mosaic of contrasted dynamics. Viewing sleep as a multidimensional continuum rather than a succession of non-overlapping and mutually exclusive states could account for these local aspects of sleep. Moving away from the focus on sleep states, sleep can also be investigated through the brain processes that are present in sleep, if not necessarily specific to sleep. This focus on processes rather than states allows to see sleep for what it does rather than what it is, avoiding some of the limitations of the state perspective and providing a powerful heuristic to understand sleep. Indeed, what is sleep if not a process itself that makes up wake up every morning with a brain cleaner, leaner and less cluttered.

New bands in the sleep stages of spider monkeys (Ateles geoffroyi): Electroencephalographic correlations and spatial distribution

The study of electroencephalographic (EEG) signals in nonhuman primates has led to important discoveries in neurophysiology and sleep behavior. Several studies have analyzed digital EEG data from primate species with prehensile tails, like the spider monkey, and principal component analysis has led to the identification of new EEG bands and their spatial distribution during sleep and wakefulness in these monkeys. However, the spatial location of the EEG correlations of these new bands during the sleep-wake cycle in the spider monkey has not yet been explored. Thus, the objective of this study was to determine the spatial distribution of EEG correlations in the new bands during wakefulness, rapid eye movement (REM) sleep, and non-REM sleep in this species. EEG signals were obtained from the scalp of six monkeys housed in experimental conditions in a laboratory setting. Regarding the 1-21 Hz band, a significant correlation between left frontal and central regions was recorded during non-REM 2 sleep. In the REM sleep, a significant correlation between these cortical areas was seen in two bands: 1-3 and 3-13 Hz. This reflects a modification of the degree of coupling between the cortical areas studied, associated with the distinct stages of sleep. The intrahemispheric EEG correlation found between left perceptual and motor regions during sleep in the spider monkey could indicate activation of a neural circuit for the processing of environmental information that plays a critical role in monitoring the danger of nocturnal predation.

Sleep and vigilance states: Embracing spatiotemporal dynamics

The classic view of sleep and vigilance states is a global stationary perspective driven by the interaction between neuromodulators and thalamocortical systems. However, recent data are challenging this view by demonstrating that vigilance states are highly dynamic and regionally complex. Spatially, sleep- and wake-like states often co-occur across distinct brain regions, as in unihemispheric sleep, local sleep in wakefulness, and during development. Temporally, dynamic switching prevails around state transitions, during extended wakefulness, and in fragmented sleep. This knowledge, together with methods monitoring brain activity across multiple regions simultaneously at millisecond resolution with cell-type specificity, is rapidly shifting how we consider vigilance states. A new perspective incorporating multiple spatial and temporal scales may have important implications for considering the governing neuromodulatory mechanisms, the functional roles of vigilance states, and their behavioral manifestations. A modular and dynamic view highlights novel avenues for finer spatiotemporal interventions to improve sleep function.

A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior

Sleep and wake are understood to be slow, long-lasting processes that span the entire brain. Brain states correlate with many neurophysiological changes, yet the most robust and reliable signature of state is enriched in rhythms between 0.1 and 20 Hz. The possibility that the fundamental unit of brain state could be a reliable structure at the scale of milliseconds and microns has not been addressed due to the physical limits associated with oscillation-based definitions. Here, by analyzing high resolution neural activity recorded in 10 anatomically and functionally diverse regions of the murine brain over 24 h, we reveal a mechanistically distinct embedding of state in the brain. Sleep and wake states can be accurately classified from on the order of 100 to 101 ms of neuronal activity sampled from 100 μm of brain tissue. In contrast to canonical rhythms, this embedding persists above 1,000 Hz. This high frequency embedding is robust to substates and rapid events such as sharp wave ripples and cortical ON/OFF states. To ascertain whether such fast and local structure is meaningful, we leveraged our observation that individual circuits intermittently switch states independently of the rest of the brain. Brief state discontinuities in subsets of circuits correspond with brief behavioral discontinuities during both sleep and wake. Our results suggest that the fundamental unit of state in the brain is consistent with the spatial and temporal scale of neuronal computation, and that this resolution can contribute to an understanding of cognition and behavior.

Extracting electromyographic signals from multi-channel LFPs using independent component analysis without direct muscular recording

Electromyography (EMG) has been commonly used for the precise identification of animal behavior. However, it is often not recorded together with in vivo electrophysiology due to the need for additional surgeries and setups and the high risk of mechanical wire disconnection. While independent component analysis (ICA) has been used to reduce noise from field potential data, there has been no attempt to proactively use the removed "noise," of which EMG signals are thought to be one of the major sources. Here, we demonstrate that EMG signals can be reconstructed without direct EMG recording using the "noise" ICA component from local field potentials. The extracted component is highly correlated with directly measured EMG, termed IC-EMG. IC-EMG is useful for measuring an animal's sleep/wake, freezing response, and non-rapid eye movement (NREM)/REM sleep states consistently with actual EMG. Our method has advantages in precise and long-term behavioral measurement in wide-ranging in vivo electrophysiology experiments.

Paradoxical pharmacological dissociations result from drugs that enhance delta oscillations but preserve consciousness

Low-frequency (<4 Hz) neural activity, particularly in the delta band, is generally indicative of loss of consciousness and cortical down states, particularly when it is diffuse and high amplitude. Remarkably, however, drug challenge studies of several diverse classes of pharmacological agents-including drugs which treat epilepsy, activate GABA_B receptors, block acetylcholine receptors, or produce psychedelic effects-demonstrate neural activity resembling cortical down states even as the participants remain conscious. Of those substances that are safe to use in healthy volunteers, some may be highly valuable research tools for investigating which neural activity patterns are sufficient for consciousness or its absence.

Regional variation in cholinergic terminal activity determines the non-uniform occurrence of cortical slow waves during REM sleep in mice

Sleep consists of two basic stages: non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. NREM sleep is characterized by slow high-amplitude cortical electroencephalogram (EEG) signals, while REM sleep is characterized by desynchronized cortical rhythms. Despite this, recent electrophysiological studies have suggested the presence of slow waves (SWs) in local cortical areas during REM sleep. Electrophysiological techniques, however, have been unable to resolve the regional structure of these activities because of relatively sparse sampling. Here, we map functional gradients in cortical activity during REM sleep using mesoscale imaging in mice and show local SW patterns occurring mainly in somatomotor and auditory cortical regions with minimum presence within the default mode network. The role of the cholinergic system in local desynchronization during REM sleep is also explored by calcium imaging of cholinergic activity within the cortex and analyzing structural data. We demonstrate weaker cholinergic projections and terminal activity in regions exhibiting frequent SWs during REM sleep.

Detection of neuronal OFF periods as low amplitude neural activity segments

BACKGROUND

During non-rapid eye movement sleep (NREM), alternating periods of synchronised high (ON period) and low (OFF period) neuronal activity are associated with high amplitude delta band (0.5-4 Hz) oscillations in neocortical electrophysiological signals termed slow waves. As this oscillation is dependent crucially on hyperpolarisation of cortical cells, there is an interest in understanding how neuronal silencing during OFF periods leads to the generation of slow waves and whether this relationship changes between cortical layers. A formal, widely adopted definition of OFF periods is absent, complicating their detection. Here, we grouped segments of high frequency neural activity containing spikes, recorded as multiunit activity from the neocortex of freely behaving mice, on the basis of amplitude and asked whether the population of low amplitude (LA) segments displayed the expected characteristics of OFF periods.

RESULTS

Average LA segment length was comparable to previous reports for OFF periods but varied considerably, from as short as 8 ms to > 1 s. LA segments were longer and occurred more frequently in NREM but shorter LA segments also occurred in half of rapid eye movement sleep (REM) epochs and occasionally during wakefulness. LA segments in all states were associated with a local field potential (LFP) slow wave that increased in amplitude with LA segment duration. We found that LA segments > 50 ms displayed a homeostatic rebound in incidence following sleep deprivation whereas short LA segments (< 50 ms) did not. The temporal organisation of LA segments was more coherent between channels located at a similar cortical depth.

CONCLUSION

We corroborate previous studies showing neural activity signals contain uniquely identifiable periods of low amplitude with distinct characteristics from the surrounding signal known as OFF periods and attribute the new characteristics of vigilance-state-dependent duration and duration-dependent homeostatic response to this phenomenon. This suggests that ON/OFF periods are currently underdefined and that their appearance is less binary than previously considered, instead representing a continuum.

Neural circuit plasticity for complex non-declarative sensorimotor memory consolidation during sleep

Evidence is accumulating that the brain actively consolidates long-term memory during sleep. Motor skill memory is a form of non-declarative procedural memory and can be coordinated with multi-sensory processing such as visual, tactile, and, auditory. Conversely, perception is affected by body movement signal from motor brain regions. Although both cortical and subcortical brain regions are involved in memory consolidation, cerebral cortex activity can be recorded and manipulated noninvasively or minimally invasively in humans and animals. NREM sleep, which is important for non-declarative memory consolidation, is characterized by slow and spindle waves representing thalamo-cortical population activity. In animals, electrophysiological recording, optical imaging, and manipulation approaches have revealed multi-scale cortical dynamics across learning and sleep. In the sleeping cortex, neural activity is affected by prior learning and neural circuits are continually reorganized. Here I outline how sensorimotor coordination is formed through awake learning and subsequent sleep.

The role of calcium and CaMKII in sleep

Sleep is an evolutionarily conserved phenotype shared by most of the animals on the planet. Prolonged wakefulness will result in increased sleep need or sleep pressure. However, its mechanisms remain elusive. Recent findings indicate that Ca²⁺ signaling, known to control diverse physiological functions, also regulates sleep. This review intends to summarize research advances in Ca²⁺ and Ca²⁺/calmodulin-dependent protein kinase II (CaMKII) in sleep regulation. Significant changes in sleep phenotype have been observed through calcium-related channels, receptors, and pumps. Mathematical modeling for neuronal firing patterns during NREM sleep suggests that these molecules compose a Ca²⁺-dependent hyperpolarization mechanism. The intracellular Ca²⁺ may then trigger sleep induction and maintenance through the activation of CaMKII, one of the sleep-promoting kinases. CaMKII and its multisite phosphorylation status may provide a link between transient calcium dynamics typically observed in neurons and sleep-wake dynamics observed on the long-time scale.

Neural complexity is a common denominator of human consciousness across diverse regimes of cortical dynamics

What is the common denominator of consciousness across divergent regimes of cortical dynamics? Does consciousness show itself in decibels or in bits? To address these questions, we introduce a testbed for evaluating electroencephalogram (EEG) biomarkers of consciousness using dissociations between neural oscillations and consciousness caused by rare genetic disorders. Children with Angelman syndrome (AS) exhibit sleep-like neural dynamics during wakefulness. Conversely, children with duplication 15q11.2-13.1 syndrome (Dup15q) exhibit wake-like neural dynamics during non-rapid eye movement (NREM) sleep. To identify highly generalizable biomarkers of consciousness, we trained regularized logistic regression classifiers on EEG data from wakefulness and NREM sleep in children with AS using both entropy measures of neural complexity and spectral (i.e., neural oscillatory) EEG features. For each set of features, we then validated these classifiers using EEG from neurotypical (NT) children and abnormal EEGs from children with Dup15q. Our results show that the classification performance of entropy-based EEG biomarkers of conscious state is not upper-bounded by that of spectral EEG features, which are outperformed by entropy features. Entropy-based biomarkers of consciousness may thus be highly adaptable and should be investigated further in situations where spectral EEG features have shown limited success, such as detecting covert consciousness or anesthesia awareness.

Neuronal-spiking-based closed-loop stimulation during cortical ON- and OFF-states in freely moving mice

The slow oscillation is a central neuronal dynamic during sleep, and is generated by alternating periods of high and low neuronal activity (ON- and OFF-states). Mounting evidence causally links the slow oscillation to sleep's functions, and it has recently become possible to manipulate the slow oscillation non-invasively and phase-specifically. These developments represent promising clinical avenues, but they also highlight the importance of improving our understanding of how ON/OFF-states affect incoming stimuli and what role they play in neuronal plasticity. Most studies using closed-loop stimulation rely on the electroencephalogram and local field potential signals, which reflect neuronal ON- and OFF-states only indirectly. Here we develop an online detection algorithm based on spiking activity recorded from laminar arrays in mouse motor cortex. We find that online detection of ON- and OFF-states reflects specific phases of spontaneous local field potential slow oscillation. Our neuronal-spiking-based closed-loop procedure offers a novel opportunity for testing the functional role of slow oscillation in sleep-related restorative processes and neural plasticity.

Cortical regulation of two-stage rapid eye movement sleep

Rapid eye movement (REM) sleep is a sleep state characterized by skeletal muscle paralysis and cerebral cortical activation. Yet, global cortical dynamics and their role in regulating REM sleep remain unclear. Here we show that in mice, REM sleep is accompanied by highly patterned cortical activity waves, with the retrosplenial cortex (RSC) as a major initiation site. Two-photon imaging of layer 2/3 pyramidal neurons of the RSC revealed two distinct patterns of population activities during REM sleep. These activities encoded two sequential REM sleep substages, characterized by contrasting facial movement and autonomic activity and by distinguishable electroencephalogram theta oscillations. Closed-loop optogenetic inactivation of RSC during REM sleep altered cortical activity dynamics and shortened REM sleep duration via inhibition of the REM substage transition. These results highlight an important role for the RSC in dictating cortical dynamics and regulating REM sleep progression.

Recurrent Hippocampo-neocortical sleep-state divergence in humans

Sleep is assumed to be a unitary, global state in humans and most other animals that is coordinated by executive centers in the brain stem, hypothalamus, and basal forebrain. However, the common observation of unihemispheric sleep in birds and marine mammals, as well as the recently discovered nonpathological regional sleep in rodents, calls into question whether the whole human brain might also typically exhibit different states between brain areas at the same time. We analyzed sleep states independently from simultaneously recorded hippocampal depth electrodes and cortical scalp electrodes in eight human subjects who were implanted with depth electrodes for pharmacologically intractable epilepsy evaluation. We found that the neocortex and hippocampus could be in nonsimultaneous states, on average, one-third of the night and that the hippocampus often led in asynchronous state transitions. Nonsimultaneous bout lengths varied from 30 s to over 30 min. These results call into question the conclusions of studies, across phylogeny, that measure only surface cortical state but seek to assess the functions and drivers of sleep states throughout the brain.

Brain states in freely behaving marmosets

Study Objectives

We evaluated common marmosets as a perspective animal model to study human sleep and wake states.

Methods

Using wireless neurologger recordings, we performed longitudinal multichannel local field potential (LFP) cortical, hippocampal, neck muscle, and video recordings in three freely behaving marmosets. The brain states were formally identified using self-organizing maps.

Results

Marmosets were generally awake during the day with occasional 1–2 naps, and they slept during the night. Major electrographic patterns fall in five clearly distinguished categories: wakefulness, drowsiness, light and deep NREM sleep, and REM. Marmosets typically had 14–16 sleep cycles per night, with either gradually increasing or relatively low, but stable delta power within the cycle. Overall, the delta power decreased throughout the night sleep. Marmosets demonstrated prominent high amplitude somatosensory mu-rhythm (10–15 Hz), accompanied with neocortical ripples, and alternated with occipital alpha rhythm (10–15 Hz). NREM sleep was characterized with the presence of high amplitude slow waves, sleep spindles and ripples in neocortex, and sharp-wave-ripple complexes in CA1. Light and deep stages differed in levels of delta and sigma power and muscle tone. REM sleep was defined with low muscle tone and activated LFP with predominant beta-activity and rare spindle-like or mu-like events.

Conclusions

Multiple features of sleep–wake state distribution and electrographic patterns associated with behavioral states in marmosets closely match human states, although marmoset have shorter sleep cycles. This demonstrates that marmosets represent an excellent model to study origin of human electrographical rhythms and brain states.

A Two-Stream Graph Convolutional Network Based on Brain Connectivity for Anesthetized States Analysis

Investigating neural mechanisms of anesthesia process and developing efficient anesthetized state detection methods are especially on high demand for clinical consciousness monitoring. Traditional anesthesia monitoring methods are not involved with the topological changes between electrodes covering the prefrontal-parietal cortices, by investigating electrocorticography (ECoG). To fill this gap, a framework based on the two-stream graph convolutional network (GCN) was proposed, i.e., one stream for extracting topological structure features, and the other one for extracting node features. The two-stream graph convolutional network includes GCN Model 1 and GCN Model 2. For GCN Model 1, brain connectivity networks were constructed by using phase lag index (PLI), representing different structure features. A common adjacency matrix was founded through the dual-graph method, the structure features were expressed on nodes. Therefore, the traditional spectral graph convolutional network can be directly applied on the graphs with changing topological structures. On the other hand, the average of the absolute signal amplitudes was calculated as node features, then a fully connected matrix was constructed as the adjacency matrix of these node features, as the input of GCN Model 2. This method learns features of both topological structure and nodes of the graph, and uses a dual-graph approach to enhance the focus on topological structure features. Based on the ECoG signals of monkeys, results show that this method which can distinguish awake state, moderate sedation and deep sedation achieved an accuracy of 92.75% in group-level experiments and mean accuracy of 93.50% in subject-level experiments. Our work verifies the excellence of the graph convolutional network in anesthesia monitoring, the high recognition accuracy also shows that the brain network may carry neurological markers associated with anesthesia.

Vigilance and Behavioral State-Dependent Modulation of Cortical Neuronal Activity throughout the Sleep/Wake Cycle

GABAergic inhibitory neurons, through their molecular, anatomic, and physiological diversity, provide a substrate for the modulation of ongoing cortical circuit activity throughout the sleep/wake cycle. Here, we investigated neuronal activity dynamics of parvalbumin (PV), vasoactive intestinal polypeptide (VIP), and somatostatin (SST) neurons in naturally sleeping head-restrained mice at the level of layer 2/3 of the primary somatosensory barrel cortex of mice. Through calcium imaging and targeted single-unit loose-patch or whole-cell recordings, we found that PV action potential firing activity was largest during both rapid eye movement (REM) and nonrapid eye movement (NREM) sleep stages, that VIP neurons were most active during REM sleep, and that the overall activity of SST neurons remained stable throughout the sleep/wake cycle. Analysis of neuronal activity dynamics uncovered rapid decreases in PV cell firing at wake onset followed by a progressive recovery during wake. Simultaneous local field potential (LFP) recordings further revealed that except for SST neurons, a large proportion of neurons were modulated by ongoing delta and theta oscillations. During NREM sleep spindles, PV and SST activity increased and decreased, respectively. Finally, we uncovered the presence of whisking behavior in mice during REM sleep and show that the activity of VIP and SST is differentially modulated during awake and sleeping whisking bouts, which may provide a neuronal substrate for internal brain representations occurring during sleep.SIGNIFICANCE STATEMENT In the sensory cortex, the balance between excitation and inhibition is believed to be highly dynamic throughout the sleep/wake cycle, shaping the response of cortical circuits to external stimuli while allowing the formation of newly encoded memory. Using in vivo two-photon calcium imaging or targeted single-unit recordings combined with LFP recordings, we describe the vigilance state and whisking-behavior-dependent activity of excitatory pyramidal and inhibitory GABAergic neurons in the supragranular layers of mouse somatosensory cortex. Interneuronal activity was found to be differentially modulated by ongoing delta and theta waves, sleep spindles, and a novel type of whisking observed during REM sleep, potentially providing a neuronal substrate for internal brain representations occurring during sleep.

Can Daytime Transcranial Direct Current Stimulation Treatment Change the Sleep Electroencephalogram Complexity of REM Sleep in Depressed Patients? A Double-Blinded, Randomized, Placebo-Controlled Trial

STUDY OBJECTIVES

The purpose of this study was to determine the effects of daytime transcranial direct current stimulation (tDCS) on sleep electroencephalogram (EEG) in patients with depression.

METHODS

The study was a double-blinded, randomized, controlled clinical trial. A total of 37 patients diagnosed with a major depression were recruited; 19 patients (13 females and 6 males mean age 44.79 ± 15.25 years) received tDCS active stimulation and 18 patients (9 females and 9 males; mean age 43.61 ± 11.89 years) received sham stimulation. Ten sessions of daytime tDCS were administered with the anode over F3 and the cathode over F4. Each session delivered a 2 mA current for 30 min per 10 working days. Hamilton-24 and Montgomery scales were used to assess the severity of depression, and polysomnography (PSG) was used to assess sleep structure and EEG complexity. Eight intrinsic mode functions (IMFs) were computed from each EEG signal in a channel. The sample entropy of the cumulative sum of the IMFs were computed to acquire high-dimensional multi-scale complexity information of EEG signals.

RESULTS

The complexity of Rapid Eye Movement (REM) EEG signals significantly decreased intrinsic multi-scale entropy (iMSE) (1.732 ± 0.057 vs. 1.605 ± 0.046, P = 0.0004 in the case of the C4 channel, IMF 1:4 and scale 7) after tDCS active stimulation. The complexity of the REM EEG signals significantly increased iMSE (1.464 ± 0.101 vs. 1.611 ± 0.085, P = 0.001 for C4 channel, IMF 1:4 and scale 7) after tDCS sham stimulation. There was no significant difference in the Hamilton-24 (P = 0.988), Montgomery scale score (P = 0.726), and sleep structure (N1% P = 0.383; N2% P = 0.716; N3% P = 0.772) between the two groups after treatment.

CONCLUSION

Daytime tDCS changed the complexity of sleep in the REM stage, and presented as decreased intrinsic multi-scale entropy, while no changes in sleep structure occurred. This finding indicated that daytime tDCS may be an effective method to improve sleep quality in depressed patients. Trial registration This trial has been registered at the ClinicalTrials.gov (protocol ID: TCHIRB-10409114, in progress).

Sound disrupts sleep-associated brain oscillations in rodents in a meaning-dependent manner

Sleep is essential but places animals at risk. Filtering acoustic information according to its relevance, a process generally known as sensory gating, is crucial during sleep to ensure a balance between rest and danger detection. The mechanisms of this sensory gating and its specificity are not understood. Here, we tested the effect that sounds of different meaning have on sleep-associated ongoing oscillations. We recorded EEG and EMG from mice during REM and NREM sleep while presenting sounds with or without behavioural relevance. We found that sound presentation per se, in the form of a neutral sound, elicited a weak or no change in the power of sleep-state-dependent EEG during REM and NREM sleep. In contrast, the presentation of a sound previously conditioned in an aversive task, elicited a clear and fast decrease in the EEG power during both sleep phases, suggesting a transition to lighter sleep without awakening. The observed changes generally weakened over training days and were not present in animals that failed to learn. Interestingly, the effect could be generalized to unfamiliar neutral sounds if presented following conditioned training, an effect that depended on sleep phase and sound type. The data demonstrate that sounds are differentially gated during sleep depending on their meaning and that this process is reflected in disruption of sleep-associated brain oscillations without behavioural arousal.

Tinnitus: at a crossroad between phantom perception and sleep

Sensory disconnection from the environment is a hallmark of sleep and is crucial for sleep maintenance. It remains unclear, however, whether internally generated percepts—phantom percepts—may overcome such disconnection and, in turn, how sleep and its effect on sensory processing and brain plasticity may affect the function of the specific neural networks underlying such phenomena. A major hurdle in addressing this relationship is the methodological difficulty to study sensory phantoms, due to their subjective nature and lack of control over the parameters or neural activity underlying that percept. Here, we explore the most prevalent phantom percept, subjective tinnitus—or tinnitus for short—as a model to investigate this. Tinnitus is the permanent perception of a sound with no identifiable corresponding acoustic source. This review offers a novel perspective on the functional interaction between brain activity across the sleep–wake cycle and tinnitus. We discuss characteristic features of brain activity during tinnitus in the awake and the sleeping brain and explore its effect on sleep functions and homeostasis. We ask whether local changes in cortical activity in tinnitus may overcome sensory disconnection and prevent the occurrence of global restorative sleep and, in turn, how accumulating sleep pressure may temporarily alleviate the persistence of a phantom sound. Beyond an acute interaction between sleep and neural activity, we discuss how the effects of sleep on brain plasticity may contribute to aberrant neural circuit activity and promote tinnitus consolidation. Tinnitus represents a unique window into understanding the role of sleep in sensory processing. Clarification of the underlying relationship may offer novel insights into therapeutic interventions in tinnitus management.

Restricted truncal sagittal movements of rapid eye movement behaviour disorder

Unlike sleep-walkers, patients with rapid-eye-movement-behaviour disorder (RBD) rarely leave the bed during the re-enactment of their dreams. RBD movements may be independent of spatial co-ordinates of the ‘outside-world’, and instead rely on (allocentric) brain-generated virtual space-maps, as evident by patients’ limited truncal/axial movements. To confirm this, a semiology analysis of video-polysomnography records of 38 RBD patients was undertaken and paradoxically restricted truncal/thoraco-lumbar movements during complex dream re-enactments demonstrated.

Local sleep: A new concept in brain plasticity

Traditionally, sleep and wakefulness have been considered as two global, mutually exclusive states. However, this view has been challenged by the discovery that sleep and wakefulness are actually locally regulated and that islands of these two states may often coexist in the same individual. Importantly, such a local regulation seems to be the key for many essential functions of sleep, including the maintenance of cognitive efficiency and the consolidation of new skills and memories. Indeed, local changes in sleep-related oscillations occur in brain areas that are used and involved in learning during wakefulness. In turn, these changes directly modulate experience-dependent brain adaptations and the consolidation of newly acquired memories. In line with these observations, alterations in the regional balance between wake- and sleep-like activity have been shown to accompany many pathologic conditions, including psychiatric and neurologic disorders. In the last decade, experimental research has started to shed light on the mechanisms involved in the local regulation of sleep and wakefulness. The results of this research have opened new avenues of investigation regarding the function of sleep and have revealed novel potential targets for the treatment of several pathologic conditions.

Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning

BACKGROUND

Wide-field calcium imaging (WFCI) allows for monitoring of cortex-wide neural dynamics in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming and often suffers from low inter- and intra-rater reliability and invasiveness. Therefore, an automated sleep state classification method that operates on WFCI data alone is needed.

NEW METHOD

A hybrid, two-step method is proposed. In the first step, spatial-temporal WFCI data is mapped to multiplex visibility graphs (MVGs). Subsequently, a two-dimensional convolutional neural network (2D CNN) is employed on the MVGs to be classified as wakefulness, NREM and REM.

RESULTS

Sleep states were classified with an accuracy of 84% and Cohen's κ of 0.67. The method was also effectively applied on a binary classification of wakefulness/sleep (accuracy=0.82, κ = 0.62) and a four-class wakefulness/sleep/anesthesia/movement classification (accuracy=0.74, κ = 0.66). Gradient-weighted class activation maps revealed that the CNN focused on short- and long-term temporal connections of MVGs in a sleep state-specific manner. Sleep state classification performance when using individual brain regions was highest for the posterior area of the cortex and when cortex-wide activity was considered.

COMPARISON WITH EXISTING METHOD

On a 3-hour WFCI recording, the MVG-CNN achieved a κ of 0.65, comparable to a κ of 0.60 corresponding to the human EEG/EMG-based scoring.

CONCLUSIONS

The hybrid MVG-CNN method accurately classifies sleep states from WFCI data and will enable future sleep-focused studies with WFCI.

fMRI Evidence for Default Mode Network Deactivation Associated with Rapid Eye Movements in Sleep

System-specific brain responses—time-locked to rapid eye movements (REMs) in sleep—are characteristically widespread, with robust and clear activation in the primary visual cortex and other structures involved in multisensory integration. This pattern suggests that REMs underwrite hierarchical processing of visual information in a time-locked manner, where REMs index the generation and scanning of virtual-world models, through multisensory integration in dreaming—as in awake states. Default mode network (DMN) activity increases during rest and reduces during various tasks including visual perception. The implicit anticorrelation between the DMN and task-positive network (TPN)—that persists in REM sleep—prompted us to focus on DMN responses to temporally-precise REM events. We timed REMs during sleep from the video recordings and quantified the neural correlates of REMs—using functional MRI (fMRI)—in 24 independent studies of 11 healthy participants. A reanalysis of these data revealed that the cortical areas exempt from widespread REM-locked brain activation were restricted to the DMN. Furthermore, our analysis revealed a modest temporally-precise REM-locked decrease—phasic deactivation—in key DMN nodes, in a subset of independent studies. These results are consistent with hierarchical predictive coding; namely, permissive deactivation of DMN at the top of the hierarchy (leading to the widespread cortical activation at lower levels; especially the primary visual cortex). Additional findings indicate REM-locked cerebral vasodilation and suggest putative mechanisms for dream forgetting.

Brain Connectivity Analysis in Anesthetized and Awake States: an ECoG Study in Monkeys

Increasingly, studies have shown that changes in brain network topology accompany loss of consciousness such that the functional connectivity of the prefrontal-parietal network differs significantly in anesthetized and awake states. In this work, anesthetized and awake segments of electrocorticography were selected from two monkeys. Using phase lag index, functional connectivity matrices were built in multiple frequency bands. Quantifying topological changes in brain network through graph-theoretic properties revealed significant differences between the awake and anesthetized states. Compared to the awake state, there were distinct increases in overall and Delta prefrontal-frontal connectivity, and decreases in Alpha, Beta1 and Beta2 prefrontal-frontal connectivity during the anesthetized state, which indicate a change in the topology of the small-world network. Using functional connectivity features we achieved a satisfactory classification accuracy (93.68%). Our study demonstrates that functional connectivity features are of sufficient power to distinguish awake versus anesthetized state.Clinical Relevance- This explores the brain network topology in awake and anesthetized states, and provides new ideas for clinical depth of anesthesia monitoring.

The interconnected causes and consequences of sleep in the brain

Sleep is essential for brain function in a surprisingly diverse set of ways. In the short term, lack of sleep leads to impaired memory and attention; in the longer term, it produces neurological dysfunction or even death. I discuss recent advances in understanding how sleep maintains the physiological health of the brain through interconnected systems of neuronal activity and fluid flow. The neural dynamics that appear during sleep are intrinsically coupled to its consequences for blood flow, cerebrospinal fluid dynamics, and waste clearance. Recognizing these linked causes and consequences of sleep has shed new light on why sleep is important for such disparate aspects of brain function.

Sleep Instability Correlates with Attentional Impairment in Boys with Attention Deficit Hyperactivity Disorder

Theoretical models of sleep and attention deficit hyperactivity disorder (ADHD) suggest that symptoms of ADHD are associated with daytime sleepiness, but it has received little support. The present study aimed at testing an alternative model involving the association of attentional instability with sleep instability, i.e., sleep stage transitions and arousals. Twelve ADHD and 15 healthy control (HC) boys aged between 8 and 12 years old underwent polysomnography recording and attentional testing. The microarousal index, the number of awakenings, and the number of stage shifts between stages 1, 2, 3, 4 and REM sleep throughout the night were computed as sleep stability parameters. Attentional functioning was assessed using the Continuous Performance Test-II. We found significantly higher sleep instability in ADHD compared to HC. Sleep arousals and stage transitions (micro arousal index, stage 4/3 and 2/4 transitions) in ADHD significantly correlated with lower attentional scores. No association whatsoever was found between sleep instability and attentional functioning in HC. The results show that sleep instability is associated with lower attentional performance in boys with ADHD, but not in HC. This could be compatible with a model according to which attention and sleep stability share a common neural substrate in ADHD.

Conscious experiences and high-density EEG patterns predicting subjective sleep depth

What accounts for feeling deeply asleep? Standard sleep recordings only incompletely reflect subjective aspects of sleep and some individuals with so-called sleep misperception frequently feel awake although sleep recordings indicate clear-cut sleep. To identify the determinants of sleep perception, we performed 787 awakenings in 20 good sleepers and 10 individuals with sleep misperception and interviewed them about their subjective sleep depth while they underwent high-density EEG sleep recordings. Surprisingly, in good sleepers, sleep was subjectively lightest in the first 2 h of non-rapid eye movement (NREM) sleep, generally considered the deepest sleep, and deepest in rapid eye movement (REM) sleep. Compared to good sleepers, sleep misperceptors felt more frequently awake during sleep and reported lighter REM sleep. At the EEG level, spatially widespread high-frequency power was inversely related to subjective sleep depth in NREM sleep in both groups and in REM sleep in misperceptors. Subjective sleep depth positively correlated with dream-like qualities of reports of mental activity. These findings challenge the widely held notion that slow wave sleep best accounts for feeling deeply asleep. Instead, they indicate that subjective sleep depth is inversely related to a neurophysiological process that predominates in early NREM sleep, becomes quiescent in REM sleep, and is reflected in high-frequency EEG activity. In sleep misperceptors, this process is more frequently active, more spatially widespread, and abnormally persists into REM sleep. These findings help identify the neuromodulatory systems involved in subjective sleep depth and are relevant for studies aiming to improve subjective sleep quality.

A role for the cortex in sleep-wake regulation

Cortical and subcortical circuitry are thought to play distinct roles in the generation of sleep oscillations and global state control, respectively. Here we silenced a subset of neocortical layer 5 pyramidal and archicortical dentate gyrus granule cells in male mice by ablating SNAP25. This markedly increased wakefulness and reduced rebound of electroencephalographic slow-wave activity after sleep deprivation, suggesting a role for the cortex in both vigilance state control and sleep homeostasis.

The distinctive sleep pattern of the human calcarine cortex: a stereo-electroencephalographic study

STUDY OBJECTIVES

The aim of this study was to describe the spontaneous electroencephalographic (EEG) features of sleep in the human calcarine cortex, comparing them with the well-established pattern of the parietal cortex.

METHODS

We analyzed presurgical intracerebral EEG activity in calcarine and parietal cortices during non-rapid eye movement (NREM) and rapid eye movement (REM) sleep in seven patients with drug-resistant focal epilepsy. The time course of the EEG spectral power and NREM vs REM differences was assessed. Sleep spindles were automatically detected. To assess homeostatic dynamics, we considered the first vs second half of the night ratio in the delta frequency range (0.5-4 Hz) and the rise rate of delta activity during the first sleep cycle.

RESULTS

While the parietal area showed the classically described NREM and REM sleep hallmarks, the calcarine cortex exhibited a distinctive pattern characterized by: (1) the absence of sleep spindles; (2) a large similarity between EEG power spectra of NREM and REM; and (3) reduced signs of homeostatic dynamics, with a decreased delta ratio between the first and the second half of the night, a reduced rise rate of delta activity during the first NREM sleep cycle, and lack of correlation between these measures.

CONCLUSIONS

Besides describing for the first time the peculiar sleep EEG pattern in the human calcarine cortex, our findings provide evidence that different cortical areas may exhibit specific sleep EEG pattern, supporting the view of sleep as a local process and promoting the idea that the functional role of sleep EEG features should be considered at a regional level.

Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics

Functional connectivity (FC) metrics describe brain inter-regional interactions and may complement information provided by common power-based analyses. Here, we investigated whether the FC-metrics weighted Phase Lag Index (wPLI) and weighted Symbolic Mutual Information (wSMI) may unveil functional differences across four stages of vigilance-wakefulness (W), NREM-N2, NREM-N3, and REM sleep-with respect to each other and to power-based features. Moreover, we explored their possible contribution in identifying differences between stages characterized by distinct levels of consciousness (REM+W vs. N2+N3) or sensory disconnection (REM vs. W). Overnight sleep and resting-state wakefulness recordings from 24 healthy participants (27 ± 6 years, 13F) were analyzed to extract power and FC-based features in six classical frequency bands. Cross-validated linear discriminant analyses (LDA) were applied to investigate the ability of extracted features to discriminate (1) the four vigilance stages, (2) W+REM vs. N2+N3, and (3) W vs. REM. For the four-way vigilance stages classification, combining features based on power and both connectivity metrics significantly increased accuracy relative to considering only power, wPLI, or wSMI features. Delta-power and connectivity (0.5-4 Hz) represented the most relevant features for all the tested classifications, in line with a possible involvement of slow waves in consciousness and sensory disconnection. Sigma-FC, but not sigma-power (12-16 Hz), was found to strongly contribute to the differentiation between states characterized by higher (W+REM) and lower (N2+N3) probabilities of conscious experiences. Finally, alpha-FC resulted as the most relevant FC-feature for distinguishing among wakefulness and REM sleep and may thus reflect the level of disconnection from the external environment.