Hippocampal sleep spindles preceding neocortical sleep onset in humans

Spatiotemporal EEG dynamics of the sleep onset process in preadolescence

BACKGROUND

During preadolescence the sleep electroencephalography undergoes massive qualitative and quantitative modifications. Despite these relevant age-related peculiarities, the specific EEG pattern of the wake-sleep transition in preadolescence has not been exhaustively described.

METHODS

The aim of the present study is to characterize regional and temporal electrophysiological features of the sleep onset (SO) process in a group of 23 preadolescents (9-14 years) and to compare the topographical pattern of slow wave activity and delta/beta ratio of preadolescents with the EEG pattern of young adults.

RESULTS

Results showed in preadolescence the same dynamics known for adults, but with peculiarities in the delta and beta activity, likely associated with developmental cerebral modifications: the delta power showed a widespread increase during the SO with central maxima, and the lower bins of the beta activity showed a power increase after SO. Compared to adults, preadolescents during the SO exhibited higher delta power only in the slowest bins of the band: before SO slow delta activity was higher in prefrontal, frontal and occipital areas in preadolescents, and, after SO the younger group had higher slow delta activity in occipital areas. In preadolescents delta/beta ratio was higher in more posterior areas both before and after the wake-sleep transition and, after SO, preadolescents showed also a lower delta/beta ratio in frontal areas, compared to adults.

CONCLUSION

Results point to a general higher homeostatic drive for the developing areas, consistently with plastic-related maturational modifications, that physiologically occur during preadolescence.

Considerations towards a neurobiologically-informed EEG measurement of sleepiness

Sleep is a daily experience across humans and other species, yet our understanding of how and why we sleep is presently incomplete. This is particularly prevalent in research examining the neurophysiological measurement of sleepiness in humans, where several electroencephalogram (EEG) phenomena have been linked with prolonged wakefulness. This leaves researchers without a solid basis for the measurement of homeostatic sleep need and complicates our understanding of the nature of sleep. Recent theoretical and technical advances may allow for a greater understanding of the neurobiological basis of homeostatic sleep need: this may result from increases in neuronal excitability and shifts in excitation/inhibition balance in neuronal circuits and can potentially be directly measured via the aperiodic component of the EEG. Here, we review the literature on EEG-derived markers of sleepiness in humans and argue that changes in these electrophysiological markers may actually result from neuronal activity represented by changes in aperiodic markers. We argue for the use of aperiodic markers derived from the EEG in predicting sleepiness and suggest areas for future research based on these.

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.

Coupled sleep rhythms for memory consolidation

How do passing moments turn into lasting memories? Sheltered from external tasks and distractions, sleep constitutes an optimal state for the brain to reprocess and consolidate previous experiences. Recent work suggests that consolidation is governed by the intricate interaction of slow oscillations (SOs), spindles, and ripples - electrophysiological sleep rhythms that orchestrate neuronal processing and communication within and across memory circuits. This review describes how sequential SO-spindle-ripple coupling provides a temporally and spatially fine-tuned mechanism to selectively strengthen target memories across hippocampal and cortical networks. Coupled sleep rhythms might be harnessed not only to enhance overnight memory retention, but also to combat memory decline associated with healthy ageing and neurodegenerative diseases.

Embracing sleep-onset complexity

Sleep is crucial for many vital functions and has been extensively studied. By contrast, the sleep-onset period (SOP), often portrayed as a mere prelude to sleep, has been largely overlooked and remains poorly characterized. Recent findings, however, have reignited interest in this transitional period and have shed light on its neural mechanisms, cognitive dynamics, and clinical implications. This review synthesizes the existing knowledge about the SOP in humans. We first examine the current definition of the SOP and its limits, and consider the dynamic and complex electrophysiological changes that accompany the descent to sleep. We then describe the interplay between internal and external processing during the wake-to-sleep transition. Finally, we discuss the putative cognitive benefits of the SOP and identify novel directions to better diagnose sleep-onset disorders.

Sleepiness and the transition from wakefulness to sleep

The transition from wakefulness to sleep is a progressive process that is reflected in the gradual loss of responsiveness, an alteration of cognitive functions, and a drastic shift in brain dynamics. These changes do not occur all at once. The sleep onset period (SOP) refers here to this period of transition between wakefulness and sleep. For example, although transitions of brain activity at sleep onset can occur within seconds in a given brain region, these changes occur at different time points across the brain, resulting in a SOP that can last several minutes. Likewise, the transition to sleep impacts cognitive and behavioral levels in a graded and staged fashion. It is often accompanied and preceded by a sensation of drowsiness and the subjective feeling of a need for sleep, also associated with specific physiological and behavioral signatures. To better characterize fluctuations in vigilance and the SOP, a multidimensional approach is thus warranted. Such a multidimensional approach could mitigate important limitations in the current classification of sleep, leading ultimately to better diagnoses and treatments of individuals with sleep and/or vigilance disorders. These insights could also be translated in real-life settings to either facilitate sleep onset in individuals with sleep difficulties or, on the contrary, prevent or control inappropriate sleep onsets.

From physiological awakening to pathological sleep inertia: Neurophysiological and behavioural characteristics of the sleep-to-wake transition

Sleep inertia refers to the transient physiological state of hypoarousal upon awakening, associated with various degrees of impaired neurobehavioral performance, confusion, a desire to return to sleep and often a negative emotional state. Scalp and intracranial electro-encephalography as well as functional imaging studies have provided evidence that the sleep inertia phenomenon is underpinned by an heterogenous cerebral state mixing local sleep and local wake patterns of activity, at the neuronal and network levels. Sleep inertia is modulated by homeostasis and circadian processes, sleep stage upon awakening, and individual factors; this translates into a huge variability in its intensity even under physiological conditions. In sleep disorders, especially in hypersomnolence disorders such as idiopathic hypersomnia, sleep inertia may be a daily, serious and long-lasting symptom leading to severe impairment. To date, few tools have been developed to assess sleep inertia in clinical practice. They include mainly questionnaires and behavioral tests such as the psychomotor vigilance task. Only one neurophysiological protocol has been evaluated in hypersomnia, the forced awakening test which is based on an event-related potentials paradigm upon awakening. This contrasts with the major functional consequences of sleep inertia and its potentially dangerous consequences in subjects required to perform safety-critical tasks soon after awakening. There is a great need to identify reproducible biomarkers correlated with sleep inertia-associated cognitive and behavioral impairment. These biomarkers will aim at better understanding and measuring sleep inertia in physiological and pathological conditions, as well as objectively evaluating wake-promoting treatments or non-pharmacological countermeasures to reduce this phenomenon.

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.

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.

Spotlight on Sleep Stage Classification Based on EEG

The recommendations for identifying sleep stages based on the interpretation of electrophysiological signals (electroencephalography [EEG], electro-oculography [EOG], and electromyography [EMG]), derived from the Rechtschaffen and Kales manual, were published in 2007 at the initiative of the American Academy of Sleep Medicine, and regularly updated over years. They offer an important tool to assess objective markers in different types of sleep/wake subjective complaints. With the aims and advantages of simplicity, reproducibility and standardization of practices in research and, most of all, in sleep medicine, they have overall changed little in the way they describe sleep. However, our knowledge on sleep/wake physiology and sleep disorders has evolved since then. High-density electroencephalography and intracranial electroencephalography studies have highlighted local regulation of sleep mechanisms, with spatio-temporal heterogeneity in vigilance states. Progress in the understanding of sleep disorders has allowed the identification of electrophysiological biomarkers better correlated with clinical symptoms and outcomes than standard sleep parameters. Finally, the huge development of sleep medicine, with a demand for explorations far exceeding the supply, has led to the development of alternative studies, which can be carried out at home, based on a smaller number of electrophysiological signals and on their automatic analysis. In this perspective article, we aim to examine how our description of sleep has been constructed, has evolved, and may still be reshaped in the light of advances in knowledge of sleep physiology and the development of technical recording and analysis tools. After presenting the strengths and limitations of the classification of sleep stages, we propose to challenge the "EEG-EOG-EMG" paradigm by discussing the physiological signals required for sleep stages identification, provide an overview of new tools and automatic analysis methods and propose avenues for the development of new approaches to describe and understand sleep/wake states.

Regional variability in intracerebral properties of NREM to REM sleep transitions in humans

Transitions between wake and sleep states show a progressive pattern underpinned by local sleep regulation. In contrast, little evidence is available on non-rapid eye movement (NREM) to rapid eye movement (REM) sleep boundaries, considered as mainly reflecting subcortical regulation. Using polysomnography (PSG) combined with stereoelectroencephalography (SEEG) in humans undergoing epilepsy presurgical evaluation, we explored the dynamics of NREM-to-REM transitions. PSG was used to visually score transitions and identify REM sleep features. SEEG-based local transitions were determined automatically with a machine learning algorithm using features validated for automatic intra-cranial sleep scoring (10.5281/zenodo.7410501). We analyzed 2988 channel-transitions from 29 patients. The average transition time from all intracerebral channels to the first visually marked REM sleep epoch was 8 s ± 1 min 58 s, with a great heterogeneity between brain areas. Transitions were observed first in the lateral occipital cortex, preceding scalp transition by 1 min 57 s ± 2 min 14 s (d = -0.83), and close to the first sawtooth wave marker. Regions with late transitions were the inferior frontal and orbital gyri (1 min 1 s ± 2 min 1 s, d = 0.43, and 1 min 1 s ± 2 min 5 s, d = 0.43, after scalp transition). Intracranial transitions were earlier than scalp transitions as the night advanced (last sleep cycle, d = -0.81). We show a reproducible gradual pattern of REM sleep initiation, suggesting the involvement of cortical mechanisms of regulation. This provides clues for understanding oneiric experiences occurring at the NREM/REM boundary.

Functional MRI reveals brain-wide actions of thalamically-initiated oscillatory activities on associative memory consolidation

As a key oscillatory activity in the brain, thalamic spindle activities are long believed to support memory consolidation. However, their propagation characteristics and causal actions at systems level remain unclear. Using functional MRI (fMRI) and electrophysiology recordings in male rats, we found that optogenetically-evoked somatosensory thalamic spindle-like activities targeted numerous sensorimotor (cortex, thalamus, brainstem and basal ganglia) and non-sensorimotor limbic regions (cortex, amygdala, and hippocampus) in a stimulation frequency- and length-dependent manner. Thalamic stimulation at slow spindle frequency (8 Hz) and long spindle length (3 s) evoked the most robust brain-wide cross-modal activities. Behaviorally, evoking these global cross-modal activities during memory consolidation improved visual-somatosensory associative memory performance. More importantly, parallel visual fMRI experiments uncovered response potentiation in brain-wide sensorimotor and limbic integrative regions, especially superior colliculus, periaqueductal gray, and insular, retrosplenial and frontal cortices. Our study directly reveals that thalamic spindle activities propagate in a spatiotemporally specific manner and that they consolidate associative memory by strengthening multi-target memory representation.

The effects of bifrontal anodal transcranial direct current stimulation (tDCS) on sleepiness and vigilance in partially sleep-deprived subjects: A multidimensional study

In recent years, transcranial electrical stimulation techniques have demonstrated their ability to modulate our levels of sleepiness and vigilance. However, the outcomes differ among the specific aspects considered (physiological, behavioural or subjective). This study aimed to observe the effects of bifrontal anodal transcranial direct current stimulation. Specifically, we tested the ability of this stimulation protocol to reduce sleepiness and increase vigilance in partially sleep-deprived healthy participants. Twenty-three subjects underwent a within-subject sham-controlled stimulation protocol. We compared sleepiness and vigilance levels before and after the two stimulation conditions (active versus sham) by using behavioural (reaction-time task), subjective (self-report scales) and physiological (sleep-onset latency and electroencephalogram power [n = 20] during the Maintenance of Wakefulness Test) measures. We showed the efficacy of the active stimulation in reducing physiological sleepiness and preventing vigilance drop compared with the sham stimulation. Consistently, we observed a reduction of perceived sleepiness following the active stimulation for both self-report scales. However, the stimulation effect on subjective measures was not statistically significant probably due to the underpowered sample size for these measures, and to the possible influence of motivational and environmental factors. Our findings confirm the ability of this technique to influence vigilance and sleepiness, pointing out the potential for new treatment developments based on transcranial electrical stimulation.

[The phenomenon of awakening from sleep and underlying neurophysiological and autonomic mechanisms]

Current research has shown that sleep is not a global process evenly covering the entire brain. The heterogeneity of wakefulness levels in different parts of the brain and differences in their activation thresholds are especially pronounced during the transitions between sleep and wakefulness. During awakening, subcortical brain structures activate first, followed by sensory and motor cortical regions, whereas the associative cortex wakes up much later. Awakening, unlike falling asleep, is not a smooth process. It begins with a short-term sharp activation of the autonomic nervous system and some wake-promoting brain regions. The amplitude of this activity burst is out of proportion to obvious physiological needs and exceeds that observed in later periods of quiet wakefulness. The review discusses the similarities and differences between awakening from sleep and hibernation in hibernating rodents. Data on some clinical consequences of impaired awakening mechanisms are also provided.

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.

Spatiotemporal patterns of sleep spindle activity in human anterior thalamus and cortex

Sleep spindles (8 - 16 Hz) are transient electrophysiological events during non-rapid eye movement sleep. While sleep spindles are routinely observed in the cortex using scalp electroencephalography (EEG), recordings of their thalamic counterparts have not been widely studied in humans. Based on a few existing studies, it has been hypothesized that spindles occur as largely local phenomena. We investigated intra-thalamic and thalamocortical spindle co-occurrence, which may underlie thalamocortical communication. We obtained scalp EEG and thalamic recordings from 7 patients that received bilateral deep brain stimulation (DBS) electrodes to the anterior thalamus for the treatment of drug resistant focal epilepsy. Spindles were categorized into subtypes based on their main frequency (i.e., slow (10±2 Hz) or fast (14±2 Hz)) and their level of thalamic involvement (spanning one channel, or spreading uni- or bilaterally within the thalamus). For the first time, we contrasted observed spindle patterns with permuted data to estimate random spindle co-occurrence. We found that multichannel spindle patterns were systematically coordinated at the thalamic and thalamocortical level. Importantly, distinct topographical patterns of thalamocortical spindle overlap were associated with slow and fast subtypes of spindles. These observations provide further evidence for coordinated spindle activity in thalamocortical networks.

Asymmetry in sleep spindles and motor outcome in infants with unilateral brain injury

AIM

To determine whether interhemispheric difference in sleep spindles in infants with perinatal unilateral brain injury could link to a pathological network reorganization that underpins the development of unilateral cerebral palsy (CP).

METHOD

This was a multicentre retrospective study of 40 infants (19 females, 21 males) with unilateral brain injury. Sleep spindles were detected and quantified with an automated algorithm from electroencephalograph records performed at 2 months to 5 months of age. The clinical outcomes after 18 months were compared to spindle power asymmetry (SPA) between hemispheres in different brain regions.

RESULTS

We found a significantly increased SPA in infants who later developed unilateral CP (n=13, with the most robust interhemispheric difference seen in the central spindles. The best individual-level prediction of unilateral CP was seen in the centro-occipital spindles with an overall accuracy of 93%. An empiric cut-off level for SPA at 0.65 gave a positive predictive value of 100% and a negative predictive value of 93% for later development of unilateral CP.

INTERPRETATION

Our data suggest that automated analysis of interhemispheric SPA provides a potential biomarker of unilateral CP at a very early age. This holds promise for guiding the early diagnostic process in infants with a perinatally identified brain injury.

WHAT THIS PAPER ADDS

Unilateral perinatal brain injury may affect the development of electroencephalogram (EEG) sleep spindles. Interhemispheric asymmetry in sleep spindles can be quantified with automated EEG analysis. Spindle power asymmetry can be a potential biomarker of unilateral cerebral palsy.

Cortico-cortical and thalamo-cortical connectivity during non-REM and REM sleep: Insights from intracranial recordings in humans

OBJECTIVE

To study changes of thalamo-cortical and cortico-cortical connectivity during wakefulness, non-Rapid Eye Movement (non-REM) sleep, including N2 and N3 stages, and REM sleep, using stereoelectroencephalography (SEEG) recording in humans.

METHODS

We studied SEEG recordings of ten patients during wakefulness, non-REM sleep and REM sleep, in seven brain regions of interest including the thalamus. We calculated directed and undirected functional connectivity using a measure of non-linear correlation coefficient h².

RESULTS

The thalamus was more connected to other brain regions during N2 stage and REM sleep than during N3 stage during which cortex was more connected than the thalamus. We found two significant directed links: the first from the prefrontal region to the lateral parietal region in the delta band during N3 sleep and the second from the thalamus to the insula during REM sleep.

CONCLUSIONS

These results showed that cortico-cortical connectivity is more prominent in N3 stage than in N2 and REM sleep. During REM sleep we found significant thalamo-insular connectivity, with a driving role of the thalamus.

SIGNIFICANCE

We found a pattern of cortical connectivity during N3 sleep concordant with antero-posterior traveling slow waves. The thalamus seemed particularly involved as a hub of connectivity during REM sleep.

The human thalamus orchestrates neocortical oscillations during NREM sleep

A hallmark of non-rapid eye movement sleep is the coordinated interplay of slow oscillations (SOs) and sleep spindles. Traditionally, a cortico-thalamo-cortical loop is suggested to coordinate these rhythms: neocortically-generated SOs trigger spindles in the thalamus that are projected back to neocortex. Here, we used intrathalamic recordings from human epilepsy patients to test this canonical interplay. We show that SOs in the anterior thalamus precede neocortical SOs (peak −50 ms), whereas concurrently-recorded SOs in the mediodorsal thalamus are led by neocortical SOs (peak +50 ms). Sleep spindles, detected in both thalamic nuclei, preceded their neocortical counterparts (peak −100 ms) and were initiated during early phases of thalamic SOs. Our findings indicate an active role of the anterior thalamus in organizing sleep rhythms in the neocortex and highlight the functional diversity of thalamic nuclei in humans. The thalamic coordination of sleep oscillations could have broad implications for the mechanisms underlying memory consolidation.

Slow oscillations, which are instrumental to memory consolidation, have been assumed to be solely generated in neocortex. Here, the authors show that the anterior thalamus might play a fundamental role in organizing slow oscillations in human sleep.

Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load

Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow oscillations and spindles. What is the spatial scale of sleep rhythms? To answer this question, we adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves in high-noise settings for analysis of neural recordings in sleep. We then studied sleep spindles in non-human primate electrocorticography (ECoG), human electroencephalogram (EEG), and clinical intracranial electroencephalogram (iEEG) recordings in the human. Within each recording type, we find widespread spindles occur much more frequently than previously reported. We then analyzed the spatiotemporal patterns of these large-scale, multi-area spindles and, in the EEG recordings, how spindle patterns change following a visual memory task. Our results reveal a potential role for widespread, multi-area spindles in consolidation of memories in networks widely distributed across primate cortex.

The brain processes memories as we sleep, generating rhythms of electrical activity called ‘sleep spindles’. Sleep spindles were long thought to be a state where the entire brain was fully synchronized by this rhythm. This was based on EEG recordings, short for electroencephalogram, a technique that uses electrodes on the scalp to measure electrical activity in the outermost layer of the brain, the cortex. But more recent intracranial recordings of people undergoing brain surgery have challenged this idea and suggested that sleep spindles may not be a state of global brain synchronization, but rather localised to specific areas.

Mofrad et al. sought to clarify the extent to which spindles co-occur at multiple sites in the brain, which could shed light on how networks of neurons coordinate memory storage during sleep. To analyse highly variable brain wave recordings, Mofrad et al. adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves. The resulting algorithm, designed to more sensitively detect spindles amongst other brain activity, was then applied to a range of sleep recordings from humans and macaque monkeys.

The analyses revealed that widespread and complex patterns of spindle rhythms, spanning multiple areas in the cortex of the brain, actually appear much more frequently than previously thought. This finding was consistent across all the recordings analysed, even recordings under the skull, which provide the clearest window into brain circuits. Further analyses found that these multi-area spindles occurred more often in sleep after people had completed tasks that required holding many visual scenes in memory, as opposed to control conditions with fewer visual scenes.

In summary, Mofrad et al. show that neuroscientists had previously not appreciated the complex and dynamic patterns in this sleep rhythm. These patterns in sleep spindles may be able to adapt based on the demands needed for memory storage, and this will be the subject of future work. Moreover, the findings support the idea that sleep spindles help coordinate the consolidation of memories in brain circuits that stretch across the cortex. Understanding this mechanism may provide insights into how memory falters in aging and sleep-related diseases, such as Alzheimer’s disease. Lastly, the algorithm developed by Mofrad et al. stands to be a useful tool for analysing other rhythmic waveforms in noisy recordings.

SleepSEEG: Automatic sleep scoring using intracranial EEG recordings only

OBJECTIVE

To perform automatic sleep scoring based only on intracranial EEG, without the need for scalp electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), in order to study sleep, epilepsy, and their interaction.

APPROACH

Data from 33 adult patients was used for development and training of the automatic scoring algorithm using both oscillatory and non-oscillatory spectral features. The first step consisted in unsupervised clustering of channels based on feature variability. For each cluster the classification was done in two steps, a multiclass tree followed by binary classification trees to distinguish the more challenging stage N1. The test data consisted in 11 patients, in whom the classification was done independently for each channel and then combined to get a single stage per epoch.

MAIN RESULTS

An overall agreement of 78% was observed in the test set between the sleep scoring of the algorithm and two human experts scoring based on scalp EEG, EOG and EMG. Balanced sensitivity and specificity were obtained for the different sleep stages. The performance was excellent for stages W, N2, and N3, and good for stage R, but with high variability across patients. The performance for the challenging stage N1 was poor, but at a similar level as for published algorithms based on scalp EEG. High confidence epochs in different stages (other than N1) can be identified with median per patient specificity >80%.

SIGNIFICANCE

The automatic algorithm can perform sleep scoring of long term recordings of patients with intracranial electrodes undergoing presurgical evaluation in the absence of scalp EEG, EOG and EMG, which are normally required to define sleep stages but are difficult to use in the context of intracerebral studies. It also constitutes a valuable tool to generate hypotheses regarding local aspects of sleep, and will be significant for sleep evaluation in clinical epileptology and neuroscience research.

Predictors of hyperkinetic seizures

OBJECTIVE

To identify predisposing factors for hyperkinetic seizure occurrence in a representative cohort of surgically treated patients with drug-resistant focal epilepsy.

METHODS

We retrospectively recruited all seizure-free patients after epilepsy surgery with a postoperative follow-up ≥12 months. Patients were classified as presenting with hyperkinetic seizures if at least 2 episodes occurred during their disease history, based on clear-cut anamnestic description and/or video-EEG/stereo-EEG recordings. We performed univariable and multivariable logistic regression models to study the association between the occurrence of hyperkinetic seizures and some predictors.

RESULTS

From a pool of 1758 consecutive patients who underwent surgery from 1996 to 2017, we identified 974 seizure-free cases. Considering at least 1-year follow-up, 937 cases were included (511 males, 91 patients with hyperkinetic seizures). Variables significantly associated with an increased risk of hyperkinetic seizure occurrence were (1) presence of epilepsy with sleep-related seizures (SRE) (P < 0.001); (2) histological diagnosis of type II focal cortical dysplasia (FCD) (P < 0.001); (3) resection including the frontal lobe (P = 0.002) (4) duration of epilepsy at surgery (P < 0.001) and (5) high seizure frequency at surgery (weekly: P = 0.02 - daily: P = 0.05). A resection including the occipital lobe reduced the risk of hyperkinetic seizures (P = 0.05). About 63% of patients had hyperkinetic seizure onset before 12 years and it was rarely reported before 5 years of age.

SIGNIFICANCE

Our findings underlie the role of SRE, type II FCD and frontal epileptogenic zone as predictors of hyperkinetic seizure occurrence and highlight an age-dependent effect in favoring hyperkinetic manifestations.

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.

Hippocampal spindles and barques are normal intracranial electroencephalographic entities

OBJECTIVE

To assess whether hippocampal spindles and barques are markers of epileptogenicity.

METHODS

Focal epilepsy patients that underwent stereo-electroencephalography implantation with at least one electrode in their hippocampus were selected (n = 75). The occurrence of spindles and barques in the hippocampus was evaluated in each patient. We created pairs of pathologic and pathology-free groups according to two sets of criteria: 1. Non-invasive diagnostic criteria (patients grouped according to focal epilepsy classification). 2. Intracranial neurophysiological criteria (patient's hippocampi grouped according to their seizure onset involvement).

RESULTS

Hippocampal spindles and barques appear equally often in both pathologic and pathology-free groups, both for non-invasive (P_spindles = 0.73; P_barques = 0.46) and intracranial criteria (P_spindles = 0.08; P_barques = 0.26). In Engel Class I patients, spindles occurred with similar incidence both within the non-invasive (P = 0.67) and the intracranial criteria group (P = 0.20). Barques were significantly more frequent in extra-temporal lobe epilepsy defined by either non-invasive (P = 0.01) or intracranial (P = 0.01) criteria.

CONCLUSIONS

Both spindles and barques are normal entities of the hippocampal intracranial electroencephalogram. The presence of barques may also signify lack of epileptogenic properties in the hippocampus.

SIGNIFICANCE

Understanding that hippocampal spindles and barques do not reflect epileptogenicity is critical for correct interpretation of epilepsy surgery evaluations and appropriate surgical treatment selection.

The Regional EEG Pattern of the Sleep Onset Process in Older Adults

Healthy aging is characterized by macrostructural sleep changes and alterations of regional electroencephalographic (EEG) sleep features. However, the spatiotemporal EEG pattern of the wake-sleep transition has never been described in the elderly. The present study aimed to assess the topographical and temporal features of the EEG during the sleep onset (SO) in a group of 36 older participants (59-81 years). The topography of the 1 Hz bins' EEG power and the time course of the EEG frequency bands were assessed. Moreover, we compared the delta activity and delta/beta ratio between the older participants and a group of young adults. The results point to several peculiarities in the elderly: (a) the generalized post-SO power increase in the slowest frequencies did not include the 7 Hz bin; (b) the alpha power revealed a frequency-specific pattern of post-SO modifications; (c) the sigma activity exhibited only a slight post-SO increase, and its highest bins showed a frontotemporal power decrease. Older adults showed a generalized reduction of delta power and delta/beta ratio in both pre- and post-SO intervals compared to young adults. From a clinical standpoint, the regional EEG activity may represent a target for brain stimulation techniques to reduce SO latency and sleep fragmentation.

Dynamics of hippocampus and orbitofrontal cortex activity during arousing reactions from sleep: An intracranial electroencephalographic study

Sleep is punctuated by transient elevations of vigilance level called arousals or awakenings depending on their durations. Understanding the dynamics of brain activity modifications during these transitional phases could help to better understand the changes in cognitive functions according to vigilance states. In this study, we investigated the activity of memory‐related areas (hippocampus and orbitofrontal cortex) during short (3 s to 2 min) arousing reactions detected from thalamic activity, using intracranial recordings in four drug‐resistant epilepsy patients. The average power of the signal between 0.5 and 128 Hz was compared across four time windows: 10 s of preceding sleep, the first part and the end of the arousal/awakening, and 10 s of wakefulness. We observed that (a) in most frequency bands, the spectral power during hippocampal arousal/awakenings is intermediate between wakefulness and sleep whereas frontal cortex shows an early increase in low and fast activities during non‐rapid‐eye‐movement (NREM) sleep arousals/awakenings; (b) this pattern depends on the preceding sleep stage with fewer modifications for REM than for non‐REM sleep arousal/awakenings, potentially reflecting the EEG similarities between REM sleep and wakefulness; (c) a greater activation at the arousing reaction onset in the prefrontal cortex predicts longer arousals/awakenings. Our findings suggest that hippocampus and prefrontal arousals/awakenings are progressive phenomena modulated by sleep stage, and, in the neocortex, by the intensity of the early activation. This pattern of activity could underlie the link between sleep stage, arousal/awakening duration and restoration of memory abilities including dream recall.

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.

Microsleep versus Sleep Onset Latency during Maintenance Wakefulness Tests: Which One Is the Best Marker of Sleepiness?

The interpretation of the Maintenance Wakefulness Test (MWT) relies on sleep onset detection. However, microsleeps (MSs), i.e., brief periods of sleep intrusion during wakefulness, may occur before sleep onset. We assessed the prevalence of MSs during the MWT and their contribution to the diagnosis of residual sleepiness in patients treated for obstructive sleep apnea (OSA) or hypersomnia. The MWT of 98 patients (89 OSA, 82.6% male) were analyzed for MS scoring. Polysomnography parameters and clinical data were collected. The diagnostic value for detecting sleepiness (Epworth Sleepiness Scale > 10) of sleep onset latency (SOL) and of the first MS latency (MSL) was assessed by the area under the receiver operating characteristic (ROC) curve (AUC, 95% CI). At least one MS was observed in 62.2% of patients. MSL was positively correlated with SOL (r = 0.72, p < 0.0001) but not with subjective scales, clinical variables, or polysomnography parameters. The use of SOL or MSL did not influence the diagnostic performance of the MWT for subjective sleepiness assessment (AUC = 0.66 95% CI (0.56, 0.77) versus 0.63 95% CI (0.51, 0.74)). MSs are frequent during MWTs performed in patients treated for sleep disorders, even in the absence of subjective sleepiness, and may represent physiological markers of the wake-to-sleep transition.

Changes in EEG permutation entropy in the evening and in the transition from wake to sleep

Quantifying the complexity of the EEG signal during prolonged wakefulness and during sleep is gaining interest as an additional mean to characterize the mechanisms associated with sleep and wakefulness regulation. Here, we characterized how EEG complexity, as indexed by Multiscale Permutation Entropy (MSPE), changed progressively in the evening prior to light off and during the transition from wakefulness to sleep. We further explored whether MSPE was able to discriminate between wakefulness and sleep around sleep onset and whether MSPE changes were correlated with spectral measures of the EEG related to sleep need during concomitant wakefulness (theta power-Ptheta: 4-8 Hz). To address these questions, we took advantage of large datasets of several hundred of ambulatory EEG recordings of individual of both sexes aged 25-101 years. Results show that MSPE significantly decreases before light off (i.e. before sleep time) and in the transition from wakefulness to sleep onset. Furthermore, MSPE allows for an excellent discrimination between pre-sleep wakefulness and early sleep. Finally, we show that MSPE is correlated with concomitant Ptheta. Yet, the direction of the latter correlation changed from before light-off to the transition to sleep. Given the association between EEG complexity and consciousness, MSPE may track efficiently putative changes in consciousness preceding sleep onset. An MSPE stands as a comprehensive measure that is not limited to a given frequency band and reflects a progressive change brain state associated with sleep and wakefulness regulation. It may be an effective mean to detect when the brain is in a state close to sleep onset.

Sex differences within sleep in gonadally intact rats

Sleep impacts diverse physiological and neural processes and is itself affected by the menstrual cycle; however, few studies have examined the effects of the estrous cycle on sleep in rodents. Studies of disease mechanisms in females therefore lack critical information regarding estrous cycle influences on relevant sleep characteristics. We recorded electroencephalographic (EEG) activity from multiple brain regions to assess sleep states as well as sleep traits such as spectral power and interregional spectral coherence in freely cycling females across the estrous cycle and compared with males. Our findings show that the high hormone phase of proestrus decreases the amount of nonrapid eye movement (NREM) sleep and rapid eye movement (REM) sleep and increases the amount of time spent awake compared with other estrous phases and to males. This spontaneous sleep deprivation of proestrus was followed by a sleep rebound in estrus which increased NREM and REM sleep. In proestrus, spectral power increased in the delta (0.5-4 Hz) and the gamma (30-60 Hz) ranges during NREM sleep, and increased in the theta range (5-9 Hz) during REM sleep during both proestrus and estrus. Slow-wave activity (SWA) and cortical sleep spindle density also increased in NREM sleep during proestrus. Finally, interregional NREM and REM spectral coherence increased during proestrus. This work demonstrates that the estrous cycle affects more facets of sleep than previously thought and reveals both sex differences in features of the sleep-wake cycle related to estrous phase that likely impact the myriad physiological processes influenced by sleep.

Sleep-Related Declarative Memory Consolidation in Children and Adolescents with Developmental Dyslexia

Sleep has a crucial role in memory processes, and maturational changes in sleep electrophysiology are involved in cognitive development. Albeit both sleep and memory alterations have been observed in Developmental Dyslexia (DD), their relation in this population has been scarcely investigated, particularly concerning topographical aspects. The study aimed to compare sleep topography and associated sleep-related declarative memory consolidation in participants with DD and normal readers (NR). Eleven participants with DD and 18 NR (9-14 years old) underwent a whole-night polysomnography. They were administered a word pair task before and after sleep to assess for declarative memory consolidation. Memory performance and sleep features (macro and microstructural) were compared between the groups, and the intercorrelations between consolidation rate and sleep measures were assessed. DD showed a deeper worsening in memory after sleep compared to NR and reduced slow spindles in occipito-parietal and left fronto-central areas. Our results suggest specific alterations in local sleep EEG (i.e., sleep spindles) and in sleep-dependent memory consolidation processes in DD. We highlight the importance of a topographical approach, which might shed light on potential alteration in regional cortical oscillation dynamics in DD. The latter might represent a target for therapeutic interventions aimed at enhancing cognitive functioning in DD.

The brain network organization during sleep onset after deprivation

OBJECTIVE

Aim of the present study is to investigate the alterations of brain networks derived from EEG analysis in pre- and post-sleep onset conditions after 40 h of sleep deprivation (SD) compared to sleep onset after normal sleep in 39 healthy subjects.

METHODS

Functional connectivity analysis was made on electroencelographic (EEG) cortical sources of current density and small world (SW) index was evaluated in the EEG frequency bands (delta, theta, alpha, sigma and beta).

RESULTS

Comparing pre- vs. post-sleep onset conditions after a night of SD a significant decrease of SW in delta and theta bands in post-sleep onset condition was found together with an increase of SW in sigma band. Comparing pre-sleep onset after sleep SD versus pre-sleep onset after a night of normal sleep a decreased of SW index in beta band in pre-sleep onset in SD compared to pre-sleep onset in normal sleep was evidenced.

CONCLUSIONS

Brain functional network architecture is influenced by the SD in different ways. Brain networks topology during wake resting state needs to be further explored to reveal SD-related changes in order to prevent possible negative effects of SD on behaviour and brain function during wakefulness.

SIGNIFICANCE

The SW modulations as revealed by the current study could be used as an index of an altered balance between brain integration and segregation processes after SD.

Thalamic and neocortical differences in the relationship between the time course of delta and sigma power during NREM sleep in humans

Sleep spindles and slow waves are the hallmarks of non-rapid eye movement (NREM) sleep and are produced by the dynamic interplay between thalamic and cortical regions. Several studies in both human and animal models have focused their attention on the relationship between electroencephalographic (EEG) spindles and slow waves during NREM, using the power in the sigma and delta bands as a surrogate for the production of spindles and slow waves. A typical report is an overall inverse relationship between the time course of sigma and delta power as measured by a single correlation coefficient both within and across NREM episodes. Here we analysed stereotactically implanted intracerebral electrode (Stereo-EEG [SEEG]) recordings during NREM simultaneously acquired from thalamic and from several neocortical sites in six neurosurgical patients. We investigated the relationship between the time course of delta and sigma power and found that, although at the cortical level it shows the expected inverse relationship, these two frequency bands follow a parallel time course at the thalamic level. Both these observations were consistent across patients and across different cortical as well as thalamic regions. These different temporal dynamics at the neocortical and thalamic level are discussed, considering classical as well as more recent interpretations of the neurophysiological determinants of sleep spindles and slow waves. These findings may also help understanding the regulatory mechanisms of these fundamental sleep EEG graphoelements across different brain compartments.

Diversity of simultaneous sleep in the motor cortex and hippocampus in rats

We investigated the homogeneity/heterogeneity of spontaneous sleep, simultaneously recorded in the motor cortex and the hippocampus of control rats, and particularly analysed simultaneous and non-simultaneous motor cortical and hippocampal non-rapid eye movement (NREM)/rapid eye movement (REM) sleep. We demonstrate that the sleep architectures of the motor cortex and hippocampus are different in control rats. There was an increase of NREM duration and a decrease of REM duration in the hippocampus versus the motor cortex. In terms of duration, NREM state is the most heterogeneous in the hippocampus, whereas the REM state is the most heterogeneous in the motor cortex. Whereas the hippocampal NREM duration was increased due to the prolongation of NREM episodes, the hippocampal REM duration decreased due to the decreased number of REM episodes. The heterogeneity of sleep in the motor cortex and hippocampus in control rats was particularly expressed through the inverse alteration of sigma amplitude during NREM sleep and beta/gamma amplitudes during REM sleep in the hippocampus, along with the delta, sigma, beta and gamma amplitudes only during non-simultaneous NREM/REM sleep in the hippocampus. We demonstrated the brain structure-related and NREM/REM state-related heterogeneity of the motor cortical and hippocampal local sleep in control rats. The distinctly altered local NREM/REM states, alongside their episode dynamics and electroencephalographic (EEG) microstructures, suggest the importance of both the local neuronal network substrate and the NREM/REM neurochemical substrate in the control mechanisms of sleep.

Hippocampal Interictal Spikes during Sleep Impact Long-Term Memory Consolidation

OBJECTIVE

Non-rapid eye movement (NREM) sleep is supposed to play a key role in long-term memory consolidation transferring information from hippocampus to neocortex. However, sleep also activates epileptic activities in medial temporal regions. This study investigated whether interictal hippocampal spikes during sleep would impair long-term memory consolidation.

METHOD

We prospectively measured visual and verbal memory performance in 20 patients with epilepsy investigated with stereoelectroencephalography (SEEG) at immediate, 30-minute, and 1-week delays, and studied the correlations between interictal hippocampal spike frequency during waking and the first cycle of NREM sleep and memory performance, taking into account the number of seizures occurring during the consolidation period and other possible confounding factors, such as age and epilepsy duration.

RESULTS

Retention of verbal memory over 1 week was negatively correlated with hippocampal spike frequency during sleep, whereas no significant correlation was found with hippocampal interictal spikes during waking. No significant result was found for visual memory. Regression tree analysis showed that the number of seizures was the first factor that impaired the verbal memory retention between 30 minutes and 1 week. When the number of seizures was below 5, spike frequency during sleep higher than 13 minutes was associated with impaired memory retention over 1 week.

INTERPRETATION

Our results show that activation of interictal spikes in the hippocampus during sleep and seizures specifically impair long-term memory consolidation. We hypothesize that hippocampal interictal spikes during sleep interrupt hippocampal-neocortical transfer of information. ANN NEUROL 2020;87:976-987.

The human K-complex: Insights from combined scalp-intracranial EEG recordings

Sleep spindles and K-complexes (KCs) are a hallmark of N2 sleep. While the functional significance of spindles is comparatively well investigated, there is still ongoing debate about the role of the KC: it is unclear whether it is a cortical response to an arousing stimulus (either external or internal) or whether it has sleep-promoting properties. Invasive intracranial EEG recordings from individuals with drug-resistant epilepsy offer a unique opportunity to study in-situ human brain physiology. To better understand the function of the KC, we aimed to (i) investigate the intracranial correlates of spontaneous scalp KCs, and (ii) compare the intracranial activity of scalp KCs associated or not with arousals. Whole-night recordings from adults with drug-resistant focal epilepsy who underwent combined intracranial-scalp EEG for pre-surgical evaluation at the Montreal Neurological Institute between 2010 and 2018 were selected. KCs were visually marked in the scalp and categorized according to the presence of microarousals: (i) Pre-microarousal KCs; (ii) KCs during an ongoing microarousal; and (iii) KCs without microarousal. Power in different spectral bands was computed to compare physiological intracranial EEG activity at the time of scalp KCs relative to the background, as well as to compare microarousal subcategories. A total of 1198 scalp KCs selected from 40 subjects were analyzed, resulting in 32,504 intracranial KC segments across 992 channels. Forty-seven percent of KCs were without microarousal, 30% were pre-microarousal, and 23% occurred during microarousals. All scalp KCs were accompanied by widespread cortical increases in delta band power (0.3-4 ​Hz) relative to the background: the highest percentages were observed in the parietal (60-65%) and frontal cortices (52-58%). Compared to KCs without microarousal, pre-microarousal KCs were accompanied by increases (66%) in beta band power (16-30 ​Hz) in the motor cortex, which was present before the peak of the KC. In addition, spatial distribution of spectral power changes following each KC without microarousal revealed that certain brain regions were associated with increases in delta power (25-62%) or decreases in alpha/beta power (11-24%), suggesting a sleep-promoting pattern, whereas others were accompanied by increases of higher frequencies (12-27%), suggesting an arousal-related pattern. This study shows that KCs can be generated across widespread cortical areas. Interestingly, the motor cortex shows awake-like EEG activity before the onset of KCs followed by microarousals. Our findings also highlight region-specific sleep- or arousal-promoting responses following KCs, suggesting a dual role for the human KC.

Sleep Spindles: Mechanisms and Functions

Sleep spindles are burstlike signals in the electroencephalogram (EEG) of the sleeping mammalian brain and electrical surface correlates of neuronal oscillations in thalamus. As one of the most inheritable sleep EEG signatures, sleep spindles probably reflect the strength and malleability of thalamocortical circuits that underlie individual cognitive profiles. We review the characteristics, organization, regulation, and origins of sleep spindles and their implication in non-rapid-eye-movement sleep (NREMS) and its functions, focusing on human and rodent. Spatially, sleep spindle-related neuronal activity appears on scales ranging from small thalamic circuits to functional cortical areas, and generates a cortical state favoring intracortical plasticity while limiting cortical output. Temporally, sleep spindles are discrete events, part of a continuous power band, and elements grouped on an infraslow time scale over which NREMS alternates between continuity and fragility. We synthesize diverse and seemingly unlinked functions of sleep spindles for sleep architecture, sensory processing, synaptic plasticity, memory formation, and cognitive abilities into a unifying sleep spindle concept, according to which sleep spindles 1) generate neural conditions of large-scale functional connectivity and plasticity that outlast their appearance as discrete EEG events, 2) appear preferentially in thalamic circuits engaged in learning and attention-based experience during wakefulness, and 3) enable a selective reactivation and routing of wake-instated neuronal traces between brain areas such as hippocampus and cortex. Their fine spatiotemporal organization reflects NREMS as a physiological state coordinated over brain and body and may indicate, if not anticipate and ultimately differentiate, pathologies in sleep and neurodevelopmental, -degenerative, and -psychiatric conditions.

Sleepiness as a Local Phenomenon

Sleep occupies a third of our life and is a primary need for all animal species studied so far. Nonetheless, chronic sleep restriction is a growing source of morbidity and mortality in both developed and developing countries. Sleep loss is associated with the subjective feeling of sleepiness and with decreased performance, as well as with detrimental effects on general health, cognition, and emotions. The ideas that small brain areas can be asleep while the rest of the brain is awake and that local sleep may account for at least some of the cognitive and behavioral manifestations of sleepiness are making their way into the scientific community. We herein clarify the different ways sleep can intrude into wakefulness, summarize recent scientific advances in the field, and offer some hypotheses that help framing sleepiness as a local phenomenon.

Oscillatory patterns in the electroencephalogram at sleep onset

Falling asleep is a gradually unfolding process. We investigated the role of various oscillatory activities including sleep spindles and alpha and delta oscillations at sleep onset (SO) by automatically detecting oscillatory events. We used two datasets of healthy young males, eight with four baseline recordings, and eight with a baseline and recovery sleep after 40 h of sustained wakefulness. We analyzed the 2-min interval before SO (stage 2) and the five consecutive 2-min intervals after SO. The incidence of delta/theta events reached its maximum in the first 2-min episode after SO, while the frequency of them was continuously decreasing from stage 1 onwards, continuing over SO and further into deeper sleep. Interestingly, this decrease of the frequencies of the oscillations were not affected by increased sleep pressure, in contrast to the incidence which increased. We observed an increasing number of alpha events after SO, predominantly frontally, with their prevalence varying strongly across individuals. Sleep spindles started to occur after SO, with first an increasing then a decreasing incidence and a continuous decrease in their frequency. Again, the frequency of the spindles was not altered after sleep deprivation. Oscillatory events revealed derivation dependent aspects. However, these regional aspects were not specific of the process of SO but rather reflect a general sleep related phenomenon. No individual traits of SO features (incidence and frequency of oscillations) and their dynamics were observed. Delta/theta events are important features for the analysis of SO in addition to slow waves.

Spatiotemporal Dynamics of Sleep Spindle Sources Across NREM Sleep Cycles

The existence of two different types of sleep spindles (slow and fast) is well-established, according to their topographical distribution at scalp- and cortical-level. Our aim was to provide a systematic investigation focused on the temporal evolution of sleep spindle sources during non-rapid eye movement (NREM) sleep. Spindle activity was recorded and automatically detected in 20 healthy subjects. Low resolution brain electromagnetic tomography (LORETA) was applied for the EEG source localization. Aiming to evaluate the time course of the detected slow and fast spindle sources, we considered the first four NREM sleep cycles and divided each cycle into five intervals of equal duration. We confirmed the preferential localization in the frontal (Brodmann area 10) and parietal (Brodmann area 7) cortical regions, respectively for slow (11.0-12.5) and fast (13.0-14.5) spindles. Across subsequent NREM sleep episodes, the maximal source activation remained systematically located in Brodmann area 10 and Brodmann area 7, showing the topographical stability of the detected generators. However, a different time course was observed as a function of the type of spindles: a linear decrease across subsequent cycles was found for slow spindle but not for fast spindle source. The intra-cycle variations followed a "U" shaped curve for both spindle source, with a trough around third and fourth interval (middle part) and the highest values at the beginning and the end of the considered temporal window. We confirmed the involvement of the frontal and parietal brain regions in spindle generation, showing for the first time their changes within and between consecutive NREM sleep episodes. Our results point to a correspondence between the scalp-recorded electrical activity and the underlying source topography, supporting the notion that spindles are not uniform phenomena: complex region- and time-specific patterns are involved in their generation and manifestation.

Brain dynamics during the sleep onset transition: An EEG source localization study

EEG source localization is an essential tool to reveal the cortical sources underlying brain oscillatory activity. We applied LORETA, a technique of EEG source localization, to identify the principal brain areas involved in the process of falling asleep (sleep onset, SO). We localized the contributing brain areas of activity in the classical frequency bands and tracked their temporal evolution (in 2-min intervals from 2 min prior to SO up to 10 min after SO) during a baseline night and subsequent recovery sleep after total sleep deprivation of 40 h.

Delta activity (0.5–5 Hz) gradually increased both in baseline and recovery sleep, starting in frontal areas and finally involving the entire cortex. This increase was steeper in the recovery condition. The evolution of sigma activity (12–16 Hz) resembled an inverted U-shape in both conditions and the activity was most salient in the parietal cortex. In recovery, sigma activity reached its maximum faster than in baseline, but attained lower levels. Theta activity (5–8 Hz) increased with time in large parts of the occipital lobe (baseline and recovery) and in recovery involved additionally frontal areas. Changes in alpha activity (8–12 Hz) at sleep onset involved large areas of the cortex, whereas activity in the beta range (16–24 Hz) was restricted to small cortical areas. The dynamics in recovery could be considered as a “fast-forward version” of the one in baseline.

Our results confirm that the process of falling asleep is neither spatially nor temporally a uniform process and that different brain areas might be falling asleep at a different speed potentially reflecting use dependent aspects of sleep regulation.

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LORETA is a valuable tool to reveal cortical sources of brain activity at sleep onset.

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Spectral bands had location dependent dynamics; brain areas fell asleep asynchronously

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BA 11 was the most relevant brain region associated with delta activity.

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Spindle dynamics resembled an inverted U-shape.

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During recovery from sleep deprivation capacity for spindle generation was reduced.

The Spatiotemporal Pattern of the Human Electroencephalogram at Sleep Onset After a Period of Prolonged Wakefulness

During the sleep onset (SO) process, the human electroencephalogram (EEG) is characterized by an orchestrated pattern of spatiotemporal changes. Sleep deprivation (SD) strongly affects both wake and sleep EEG, but a description of the topographical EEG power spectra and oscillatory activity during the wake-sleep transition after a period of prolonged wakefulness is still missing. The increased homeostatic sleep pressure should induce an earlier onset of sleep-related EEG oscillations. The aim of the present study was to assess the spatiotemporal EEG pattern at SO following SD. A dataset of a previous study was analyzed. We assessed the spatiotemporal EEG changes (19 cortical derivations) during the SO (5 min before vs. 5 min after the first epoch of Stage 2) of a recovery night after 40 h of SD in 39 healthy subjects, analyzing the EEG power spectra (fast Fourier transform) and the oscillatory activity [better oscillation (BOSC) detection method]. The spatiotemporal pattern of the EEG power spectra mostly confirmed the changes previously observed during the wake-sleep transition at baseline. The comparison between baseline and recovery showed a wide increase of the post- vs. pre-SO ratio during the recovery night in the frequency bins ≤10 Hz. We found a predominant alpha oscillatory rhythm in the pre-SO period, while after SO the theta oscillatory activity was prevalent. The oscillatory peaks showed a generalized increase in all frequency bands from delta to sigma with different predominance, while beta activity increased only in the fronto-central midline derivations. Overall, the analysis of the EEG power replicated the topographical pattern observed during a baseline night of sleep but with a stronger intensity of the SO-induced changes in the frequencies ≤10 Hz, and the detection of the rhythmic activity showed the rise of several oscillations at SO after SD that was not observed during the wake-sleep transition at baseline (e.g., alpha and frontal theta in correspondence of their frequency peaks). Beyond confirming the local nature of the EEG pattern at SO, our results show that SD has an impact on the spatiotemporal modulation of cortical activity during the falling-asleep process, inducing the earlier emergence of sleep-related EEG oscillations.

Alterations of Sleep and Sleep Oscillations in the Hemiparkinsonian Rat

Our previous studies in the rat model of Parkinson’s disease (PD) cholinopathy demonstrated the sleep-related alterations in electroencephalographic (EEG) oscillations at the cortical and hippocampal levels, cortical drives, and sleep spindles (SSs) as the earliest functional biomarkers preceding hypokinesia. Our aim in this study was to follow the impact of a unilateral substantia nigra pars compacta (SNpc) lesion in rat on the cortical and hippocampal sleep architectures and their EEG microstructures, as well as the cortico-hippocampal synchronizations of EEG oscillations, and the SS and high voltage sleep spindle (HVS) dynamics during NREM and REM sleep. We performed unilateral SNpc lesions using two different concentrations/volumes of 6-hydroxydopamine (6-OHDA; 12 μg/1 μl or 12 μg/2 μl). Whereas the unilateral dopaminergic neuronal loss >50% throughout the overall SNpc rostro-caudal dimension prolonged the Wake state, with no change in the NREM or REM duration, there was a long-lasting theta amplitude augmentation across all sleep states in the motor cortex (MCx), but also in the CA1 hippocampus (Hipp) during both Wake and REM sleep. We demonstrate that SS are the hallmarks of NREM sleep, but that they also occur during REM sleep in the MCx and Hipp of the control rats. Whereas SS are always longer in REM vs. NREM sleep in both structures, they are consistently slower in the Hipp. The dopaminergic neuronal loss increased the density of SS in both structures and shortened them in the MCx during NREM sleep, without changing the intrinsic frequency. Conversely, HVS are the hallmarks of REM sleep in the control rats, slower in the Hipp vs. MCx, and the dopaminergic neuronal loss increased their density in the MCx, but shortened them more consistently in the Hipp during REM sleep. In addition, there was an altered synchronization of the EEG oscillations between the MCx and Hipp in different sleep states, particularly the theta and sigma coherences during REM sleep. We provide novel evidence for the importance of the SNpc dopaminergic innervation in sleep regulation, theta rhythm generation, and SS/HVS dynamics control. We suggest the importance of the underlying REM sleep regulatory substrate to HVS generation and duration and to the cortico-hippocampal synchronizations of EEG oscillations in hemiparkinsonian rats.