Sleep spindles in humans: insights from intracranial EEG and unit recordings

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 epilepsy: A clinical and pathophysiological overview

The interaction between sleep and epilepsy is complex. A better understanding of the mechanisms linking sleep and epilepsy appears increasingly important as it may improve diagnosis and therapeutic strategies in patients with epilepsy. In this narrative review, we aim to (i) provide an overview of the physiological and pathophysiological processes linking sleep and epilepsy; (ii) present common sleep disorders in patients with epilepsy; (iii) discuss how sleep and sleep disorders should be considered in new therapeutic approaches to epilepsy such as neurostimulation; and (iv) present the overall nocturnal manifestations and differential diagnosis between epileptic seizures and parasomnia.

Fast ripples reflect increased excitability that primes epileptiform spikes

The neuronal circuit disturbances that drive inter-ictal and ictal epileptiform discharges remain elusive. Using a combination of extra-operative macro-electrode and micro-electrode inter-ictal recordings in six pre-surgical patients during non-rapid eye movement sleep, we found that, exclusively in the seizure onset zone, fast ripples (200-600 Hz), but not ripples (80-200 Hz), frequently occur <300 ms before an inter-ictal intra-cranial EEG spike with a probability exceeding chance (bootstrapping, P < 1e-5). Such fast ripple events are associated with higher spectral power (P < 1e-10) and correlated with more vigorous neuronal firing than solitary fast ripple (generalized linear mixed-effects model, P < 1e-9). During the intra-cranial EEG spike that follows a fast ripple, action potential firing is lower than during an intra-cranial EEG spike alone (generalized linear mixed-effects model, P < 0.05), reflecting an inhibitory restraint of intra-cranial EEG spike initiation. In contrast, ripples do not appear to prime epileptiform spikes. We next investigated the clinical significance of pre-spike fast ripple in a separate cohort of 23 patients implanted with stereo EEG electrodes, who underwent resections. In non-rapid eye movement sleep recordings, sites containing a high proportion of fast ripple preceding intra-cranial EEG spikes correlate with brain areas where seizures begin more than solitary fast ripple (P < 1e-5). Despite this correlation, removal of these sites does not guarantee seizure freedom. These results are consistent with the hypothesis that fast ripple preceding EEG spikes reflect an increase in local excitability that primes EEG spike discharges preferentially in the seizure onset zone and that epileptogenic brain regions are necessary, but not sufficient, for initiating inter-ictal epileptiform discharges.

Sleep Spindles Favor Emotion Regulation Over Memory Consolidation of Stressors in Posttraumatic Stress Disorder

BACKGROUND

Posttraumatic stress disorder (PTSD) is a trauma-induced condition, characterized by intrusive memories and trauma-associated anxiety. Non-rapid eye movement (NREM) sleep spindles might play a crucial role in learning and consolidating declarative stressor information. However, sleep and possibly sleep spindles are also known to regulate anxiety, suggestive of a dual role for sleep spindles in the processing of stressors. Specifically, in individuals with high PTSD symptom burden, spindles might fail to regulate anxiety levels after exposure and instead might maladaptively consolidate stressor information.

METHODS

To disentangle the role of spindles in declarative memory versus anxiety regulation after stressor exposure and to examine the role of PTSD in these processes, we measured nap sleep after a cohort of 45 trauma-exposed participants were exposed to laboratory stress. Participants (high vs. low PTSD symptoms) completed 2 visits: a stress visit involving exposure to negatively valent images before nap and a control visit. In both visits, sleep was monitored via electroencephalography. A stressor recall session occurred after the nap in the stress visit.

RESULTS

Stage 2 NREM (NREM2) spindle rates were higher in stress versus control sleep, indicative of stress-induced changes in spindles. In participants with high PTSD symptoms, NREM2 spindle rates in stress sleep predicted poorer recall accuracy of stressor images relative to participants with low PTSD symptoms, while correlating with greater reduction in stressor-induced anxiety levels after sleep.

CONCLUSIONS

Contrary to our expectations, although spindles are known to play a role in declarative memory processes, our findings highlight an important role for spindles in sleep-dependent anxiety regulation in PTSD.

Pathological neurons generate ripples at the UP-DOWN transition disrupting information transfer

Objective

To confirm and investigate why pathological HFOs (pHFOs), including Ripples [80-200 Hz] and fast ripples [200-600 Hz], are generated during the UP-DOWN transition of the slow wave and if pHFOs interfere with information transmission.

Methods

We isolated 217 total units from 175.95 iEEG contact-hours of synchronized macro- and microelectrode recordings from 6 patients. Sleep slow oscillation (0.1-2 Hz) epochs were identified in the iEEG recording. iEEG HFOs that occurred superimposed on the slow wave were transformed to phasors and adjusted by the phase of maximum firing in nearby units (i.e., maximum UP). We tested whether, in the seizure onset zone (SOZ), HFOs and associated action potentials (AP) occur more often at the UP-DOWN transition. We also examined ripple temporal correlations using cross correlograms.

Results

At the group level in the SOZ, HFO and HFO-associated AP probability was highest during the UP-DOWN transition of slow wave excitability (p<<0.001). In the non-SOZ, HFO and HFO-associated AP was highest during the DOWN-UP transition (p<<0.001). At the unit level in the SOZ, 15.6% and 20% of units exhibited more robust firing during ripples (Cohen's d=0.11-0.83) and fast ripples (d=0.36-0.90) at the UP-DOWN transition (p<0.05 f.d.r corrected), respectively. By comparison, also in the SOZ, 6.6% (d=0.14-0.30) and 8.5% (d=0.33-0.41) of units had significantly less firing during ripples and fast ripples at the UP-DOWN transition, respectively. Additional data shows ripple temporal correlations, involving global slow waves, between the hippocampus, entorhinal cortex, and parahippocampal gyrus were reduced by ~50-80% in the SOZ compared to the non-SOZ (N=3).

Significance

The UP-DOWN transition of slow wave excitability facilitates the activation of pathological neurons to generate pHFOs. The pathological neurons and pHFOs disrupt ripple temporal correlations across brain regions that transfer information and may be important in memory consolidation.

How coupled slow oscillations, spindles and ripples coordinate neuronal processing and communication during human sleep

Learning and plasticity rely on fine-tuned regulation of neuronal circuits during offline periods. An unresolved puzzle is how the sleeping brain, in the absence of external stimulation or conscious effort, coordinates neuronal firing rates (FRs) and communication within and across circuits to support synaptic and systems consolidation. Using intracranial electroencephalography combined with multiunit activity recordings from the human hippocampus and surrounding medial temporal lobe (MTL) areas, we show that, governed by slow oscillation (SO) up-states, sleep spindles set a timeframe for ripples to occur. This sequential coupling leads to a stepwise increase in (1) neuronal FRs, (2) short-latency cross-correlations among local neuronal assemblies and (3) cross-regional MTL interactions. Triggered by SOs and spindles, ripples thus establish optimal conditions for spike-timing-dependent plasticity and systems consolidation. These results unveil how the sequential coupling of specific sleep rhythms orchestrates neuronal processing and communication during human sleep.

Hypnotic treatment reverses NREM sleep disruption and EEG desynchronization in a mouse model of Fragile X syndrome to rescue memory consolidation deficits

Fragile X syndrome (FXS) is a highly-prevalent genetic cause of intellectual disability, associated with disrupted cognition and sleep abnormalities. Sleep loss itself negatively impacts cognitive function, yet the contribution of sleep loss to impaired cognition in FXS is vastly understudied. One untested possibility is that disrupted cognition in FXS is exacerbated by abnormal sleep. We hypothesized that restoration of sleep-dependent mechanisms could improve functions such as memory consolidation in FXS. We examined whether administration of ML297, a hypnotic drug acting on G-protein-activated inward-rectifying potassium channels, could restore sleep phenotypes and improve disrupted memory consolidation in Fmr1 -/y mice. Using 24-h polysomnographic recordings, we found that Fmr1 -/y mice exhibit reduced non-rapid eye movement (NREM) sleep and fragmented NREM sleep architecture, alterations in NREM EEG spectral power (including reductions in sleep spindles), and reduced EEG coherence between cortical areas. These alterations were reversed in the hours following ML297 administration. Hypnotic treatment following contextual fear or spatial learning also ameliorated disrupted memory consolidation in Fmr1 -/y mice. Hippocampal activation patterns during memory recall was altered in Fmr1 -/y mice, reflecting an altered balance of activity among principal neurons vs. parvalbumin-expressing (PV+) interneurons. This phenotype was partially reversed by post-learning ML297 administration. These studies suggest that sleep disruption could have a major impact on neurophysiological and behavioral phenotypes in FXS, and that hypnotic therapy may significantly improve disrupted cognition in this disorder.

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.

Augmenting hippocampal-prefrontal neuronal synchrony during sleep enhances memory consolidation in humans

Memory consolidation during sleep is thought to depend on the coordinated interplay between cortical slow waves, thalamocortical sleep spindles and hippocampal ripples, but direct evidence is lacking. Here, we implemented real-time closed-loop deep brain stimulation in human prefrontal cortex during sleep and tested its effects on sleep electrophysiology and on overnight consolidation of declarative memory. Synchronizing the stimulation to the active phases of endogenous slow waves in the medial temporal lobe (MTL) enhanced sleep spindles, boosted locking of brain-wide neural spiking activity to MTL slow waves, and improved coupling between MTL ripples and thalamocortical oscillations. Furthermore, synchronized stimulation enhanced the accuracy of recognition memory. By contrast, identical stimulation without this precise time-locking was not associated with, and sometimes even degraded, these electrophysiological and behavioral effects. Notably, individual changes in memory accuracy were highly correlated with electrophysiological effects. Our results indicate that hippocampo-thalamocortical synchronization during sleep causally supports human memory consolidation.

Phase-Amplitude Coupling Localizes Pathologic Brain with Aid of Behavioral Staging in Sleep

Low frequency brain rhythms facilitate communication across large spatial regions in the brain and high frequency rhythms are thought to signify local processing among nearby assemblies. A heavily investigated mode by which these low frequency and high frequency phenomenon interact is phase-amplitude coupling (PAC). This phenomenon has recently shown promise as a novel electrophysiologic biomarker, in a number of neurologic diseases including human epilepsy. In 17 medically refractory epilepsy patients undergoing phase-2 monitoring for the evaluation of surgical resection and in whom temporal depth electrodes were implanted, we investigated the electrophysiologic relationships of PAC in epileptogenic (seizure onset zone or SOZ) and non-epileptogenic tissue (non-SOZ). That this biomarker can differentiate seizure onset zone from non-seizure onset zone has been established with ictal and pre-ictal data, but less so with interictal data. Here we show that this biomarker can differentiate SOZ from non-SOZ interictally and is also a function of interictal epileptiform discharges. We also show a differential level of PAC in slow-wave-sleep relative to NREM1-2 and awake states. Lastly, we show AUROC evaluation of the localization of SOZ is optimal when utilizing beta or alpha phase onto high-gamma or ripple band. The results suggest an elevated PAC may reflect an electrophysiology-based biomarker for abnormal/epileptogenic brain regions.

Fast ripples reflect increased excitability that primes epileptiform spikes

The neuronal circuit disturbances that drive interictal and ictal epileptiform discharges remains elusive. Using a combination of extraoperative macro- and micro-electrode interictal recordings in six presurgical patients during non-rapid eye movement (REM) sleep we found that, exclusively in the seizure onset zone, fast ripples (FR; 200-600Hz), but not ripples (80-200 Hz), frequently occur <300 msec before an interictal intracranial EEG (iEEG) spike with a probability exceeding chance (bootstrapping, p<1e-5). Such FR events are associated with higher spectral power (p<1e-10) and correlated with more vigorous neuronal firing than solitary FR (generalized linear mixed-effects model, GLMM, p<1e-3) irrespective of FR power. During the iEEG spike that follows a FR, action potential firing is lower than during a iEEG spike alone (GLMM, p<1e-10), reflecting an inhibitory restraint of iEEG spike initiation. In contrast, ripples do not appear to prime epileptiform spikes. We next investigated the clinical significance of pre-spike FR in a separate cohort of 23 patients implanted with stereo EEG electrodes who underwent resections. In non-REM sleep recordings, sites containing a high proportion of FR preceding iEEG spikes correlate with brain areas where seizures begin more than solitary FR (p<1e-5). Despite this correlation, removal of these sites does not guarantee seizure freedom. These results are consistent with the hypothesis that FR preceding EEG spikes reflect an increase in local excitability that primes EEG spike discharges preferentially in the seizure onset zone and that epileptogenic brain regions are necessary, but not sufficient, for initiating interictal epileptiform discharges.

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.

A questionnaire measure of adult attachment anxiety correlates with frontal hemispheric asymmetry in sleep spindle activity

Subjects with high levels of attachment anxiety and neuroticism were proposed to be characterized by higher relative right rather than left frontal activity. Since sleep spindles are argued to reflect enhanced offline neuroplasticity, higher spindle activity measured over the right frontal areas relative to the corresponding left frontal ones could index higher attachment anxiety and neuroticism. Our aim was to explore the relationship between the lateralization patterns of frontally dominant slow sleep spindles and questionnaire measures of adult attachment anxiety and neuroticism. Thirty-four healthy subjects (male = 19; Mage = 31.64; SDage = 9.5) were enrolled in our preliminary study. Second night EEG/polysomnography records and questionnaire measures of personality (Zuckerman-Kuhlman Personality Questionnaire) and adult attachment (Relationship Scales Questionnaire) were collected. Frontal slow sleep spindles were measured by the Individual Adjustment Method (IAM), whereas hemispheric asymmetry indexes of spindle occurrence rate, duration, and amplitude were derived as normalized left-right differences (electrode pairs: Fp1-Fp2, F3-F4, and F7-F8). Relative right lateralization of frontolateral and frontopolar slow sleep spindle density and mid-frontal slow spindle duration were associated with attachment anxiety, but spindle lateralization was less closely related to neuroticism. The relationships between frontal slow spindle laterality and attachment anxiety remained statistically significant even after controlling for the effect of neuroticism, whereas attachment avoidance-independence was not correlated with frontal slow spindle lateralization. Right frontal lateralization of slow sleep spindle activity might indicate attachment status in terms of the negative view of the self.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41105-022-00426-0.

The adolescent pattern of sleep spindle development revealed by HD-EEG

Sleep spindles are developmentally relevant cortical oscillatory patterns; however, they have mostly been studied by considering the entire spindle frequency range (11-15 Hz) without a distinction between the functionally and topographically different slow and fast spindles, using relatively few electrodes and analysing wide age-ranges. Here, we employ high-density night sleep electroencephalography in three age-groups between 12 and 20 years of age (30 females and 30 males) and analyse the adolescent developmental pattern of the four major parameters of slow and fast sleep spindles. Most of our findings corroborate those very few previous studies that also make a distinction between slow and fast spindles in their developmental analysis. We find spindle frequency increasing with age. A spindle density change is not obvious in our study. We confirm the declining tendencies for amplitude and duration, although within narrower, more specific age-windows than previously determined. Spindle frequency seems to be higher in females in the oldest age-group. Based on the pattern of our findings, we suggest that high-density electroencephalography, specifically targeting slow and fast spindle ranges and relatively narrow age-ranges would advance the understanding of both adolescent cortical maturation and development and the functional relevance of sleep spindles in general.

Sleep spindles, stress and PTSD: The state of the science and future directions

Sleep spindles are a signature feature of non-REM (NREM) sleep, with demonstrated relationships to sleep maintenance and learning and memory. Because PTSD is characterized by disturbances in sleep maintenance and in stress learning and memory, there is now a growing interest in examining the role of sleep spindles in the neurobiology of PTSD. This review provides an overview of methods for measuring and detecting sleep spindles as they pertain to human PTSD and stress research, presents a critical review of early findings examining sleep spindles in PTSD and stress neurobiology, and proposes several directions for future research. In doing so, this review underscores the extensive heterogeneity in sleep spindle measurement and detection methods, the wide range of spindle features that may be and have been examined, the many persisting unknowns about the clinical and functional relevance of those features, and the problems considering PTSD as a homogeneous group in between-group comparisons. This review also highlights the progress that has been made in this field and underscores the strong rationale for ongoing work in this area.

Amygdala and hippocampus dialogue with neocortex during human sleep and wakefulness

ABSTRACT

Previous studies have described synchronic electroencephalographic (EEG) patterns of the background activity that is characteristic of several vigilance states.

STUDY OBJECTIVES

To explore whether the background synchronous activity of the amygdala-hippocampal-neocortical circuit is modified during sleep in the delta, theta, alpha, sigma, beta, and gamma bands characteristic of each sleep state.

METHODS

By simultaneously recording intracranial and noninvasive scalp EEG (10-20 system) in epileptic patients who were candidates for neurosurgery, we explored synchronous activity among the amygdala, hippocampus, and neocortex during wakefulness (W), Non-Rapid Eye Movement (NREM), and Rapid-Eye Movement (REM) sleep.

RESULTS

Our findings reveal that hippocampal-cortical synchrony in the sleep spindle frequencies was spread across the cortex and was higher during NREM versus W and REM, whereas the amygdala showed punctual higher synchronization with the temporal lobe. Contrary to expectations, delta synchrony between the amygdala and frontal lobe and between the hippocampus and temporal lobe was higher during REM than NREM. Gamma and alpha showed higher synchrony between limbic structures and the neocortex during wakefulness versus sleep, while synchrony among deep structures showed a mixed pattern. On the one hand, amygdala-hippocampal synchrony resembled cortical activity (i.e. higher gamma and alpha synchrony in W); on the other, it showed its own pattern in slow frequency oscillations.

CONCLUSIONS

This is the first study to depict diverse patterns of synchronic interaction among the frequency bands during distinct vigilance states in a broad human brain circuit with direct anatomical and functional connections that play a crucial role in emotional processes and memory.

Role of cerebellum in sleep-dependent memory processes

The activities and role of the cerebellum in sleep have, until recently, been largely ignored by both the sleep and cerebellum fields. Human sleep studies often neglect the cerebellum because it is at a position in the skull that is inaccessible to EEG electrodes. Animal neurophysiology sleep studies have focussed mainly on the neocortex, thalamus and the hippocampus. However, recent neurophysiological studies have shown that not only does the cerebellum participate in the sleep cycle, but it may also be implicated in off-line memory consolidation. Here we review the literature on cerebellar activity during sleep and the role it plays in off-line motor learning, and introduce a hypothesis whereby the cerebellum continues to compute internal models during sleep that train the neocortex.

John EK, Potesta CV, Gallagher MJ, Macdonald RL, Zhou C. Sleep slow-wave oscillations trigger seizures in a genetic epilepsy model of Dravet syndrome

Sleep is the preferential period when epileptic spike-wave discharges appear in human epileptic patients, including genetic epileptic seizures such as Dravet syndrome with multiple mutations including SCN1A mutation and GABAA receptor γ2 subunit Gabrg2Q390X mutation in patients, which presents more severe epileptic symptoms in female patients than male patients. However, the seizure onset mechanism during sleep still remains unknown. Our previous work has shown that the sleep-like state-dependent homeostatic synaptic potentiation can trigger epileptic spike-wave discharges in one transgenic heterozygous Gabrg2+/Q390X knock-in mouse model.1 Here, using this heterozygous knock-in mouse model, we hypothesized that slow-wave oscillations themselves in vivo could trigger epileptic seizures. We found that epileptic spike-wave discharges in heterozygous Gabrg2+/Q390X knock-in mice exhibited preferential incidence during non-rapid eye movement sleep period, accompanied by motor immobility/facial myoclonus/vibrissal twitching and more frequent spike-wave discharge incidence appeared in female heterozygous knock-in mice than male heterozygous knock-in mice. Optogenetically induced slow-wave oscillations in vivo significantly increased epileptic spike-wave discharge incidence in heterozygous Gabrg2+/Q390X knock-in mice with longer duration of non-rapid eye movement sleep or quiet-wakeful states. Furthermore, suppression of slow-wave oscillation-related homeostatic synaptic potentiation by 4-(diethylamino)-benzaldehyde injection (i.p.) greatly attenuated spike-wave discharge incidence in heterozygous knock-in mice, suggesting that slow-wave oscillations in vivo did trigger seizure activity in heterozygous knock-in mice. Meanwhile, sleep spindle generation in wild-type littermates and heterozygous Gabrg2+/Q390X knock-in mice involved the slow-wave oscillation-related homeostatic synaptic potentiation that also contributed to epileptic spike-wave discharge generation in heterozygous Gabrg2+/Q390X knock-in mice. In addition, EEG spectral power of delta frequency (0.1-4 Hz) during non-rapid eye movement sleep was significantly larger in female heterozygous Gabrg2+/Q390X knock-in mice than that in male heterozygous Gabrg2+/Q390X knock-in mice, which likely contributes to the gender difference in seizure incidence during non-rapid eye movement sleep/quiet-wake states of human patients. Overall, all these results indicate that slow-wave oscillations in vivo trigger the seizure onset in heterozygous Gabrg2+/Q390X knock-in mice, preferentially during non-rapid eye movement sleep period and likely generate the sex difference in seizure incidence between male and female heterozygous Gabrg2+/Q390X knock-in mice.

Conserved Population Dynamics in the Cerebro-Cerebellar System between Waking and Sleep

Despite the importance of the cerebellum for motor learning, and the recognized role of sleep in motor memory consolidation, surprisingly little is known about neural activity in the sleeping cerebro-cerebellar system. Here, we used wireless recording from primary motor cortex (M1) and the cerebellum in three female monkeys to examine the relationship between patterns of single-unit spiking activity observed during waking behavior and in natural sleep. Across the population of recorded units, we observed similarities in the timing of firing relative to local field potential features associated with both movements during waking and up state during sleep. We also observed a consistent pattern of asymmetry in pairwise cross-correlograms, indicative of preserved sequential firing in both wake and sleep at low frequencies. Despite the overall similarity in population dynamics between wake and sleep, there was a global change in the timing of cerebellar activity relative to motor cortex, from contemporaneous in the awake state to motor cortex preceding the cerebellum in sleep. We speculate that similar population dynamics in waking and sleep may imply that cerebellar internal models are activated in both states, despite the absence of movement when asleep. Moreover, spindle frequency coherence between the cerebellum and motor cortex may provide a mechanism for cerebellar computations to influence sleep-dependent learning processes in the motor cortex.SIGNIFICANCE STATEMENT It is well known that sleep can lead to improved motor performance. One possibility is that off-line learning results from neural activity during sleep in brain areas responsible for the control of movement. In this study we show for the first time that neuronal patterns in the cerebro-cerebellar system are conserved during both movements and sleep up-states, albeit with a shift in the relative timing between areas. Additionally, we show the presence of simultaneous M1-cerebellar spike coherence at spindle frequencies associated with up-state replay and postulate that this is a mechanism whereby a cerebellar internal model can shape plasticity in neocortical circuits during sleep.

Differential thalamocortical interactions in slow and fast spindle generation: A computational model

Cortical slow oscillations (SOs) and thalamocortical sleep spindles are two prominent EEG rhythms of slow wave sleep. These EEG rhythms play an essential role in memory consolidation. In humans, sleep spindles are categorized into slow spindles (8-12 Hz) and fast spindles (12-16 Hz), with different properties. Slow spindles that couple with the up-to-down phase of the SO require more experimental and computational investigation to disclose their origin, functional relevance and most importantly their relation with SOs regarding memory consolidation. To examine slow spindles, we propose a biophysical thalamocortical model with two independent thalamic networks (one for slow and the other for fast spindles). Our modeling results show that fast spindles lead to faster cortical cell firing, and subsequently increase the amplitude of the cortical local field potential (LFP) during the SO down-to-up phase. Slow spindles also facilitate cortical cell firing, but the response is slower, thereby increasing the cortical LFP amplitude later, at the SO up-to-down phase of the SO cycle. Neither the SO rhythm nor the duration of the SO down state is affected by slow spindle activity. Furthermore, at a more hyperpolarized membrane potential level of fast thalamic subnetwork cells, the activity of fast spindles decreases, while the slow spindles activity increases. Together, our model results suggest that slow spindles may facilitate the initiation of the following SO cycle, without however affecting expression of the SO Up and Down states.

Dexmedetomidine-mediated sleep phase modulation ameliorates motor and cognitive performance in a chronic blast-injured mouse model

Multiple studies have shown that blast injury is followed by sleep disruption linked to functional sequelae. It is well established that improving sleep ameliorates such functional deficits. However, little is known about longitudinal brain activity changes after blast injury. In addition, the effects of directly modulating the sleep/wake cycle on learning task performance after blast injury remain unclear. We hypothesized that modulation of the sleep phase cycle in our injured mice would improve post-injury task performance. Here, we have demonstrated that excessive sleep electroencephalographic (EEG) patterns are accompanied by prominent motor and cognitive impairment during acute stage after secondary blast injury (SBI) in a mouse model. Over time we observed a transition to more moderate and prolonged sleep/wake cycle disturbances, including changes in theta and alpha power. However, persistent disruptions of the non-rapid eye movement (NREM) spindle amplitude and intra-spindle frequency were associated with lasting motor and cognitive deficits. We, therefore, modulated the sleep phase of injured mice using subcutaneous (SC) dexmedetomidine (Dex), a common, clinically used sedative. Dex acutely improved intra-spindle frequency, theta and alpha power, and motor task execution in chronically injured mice. Moreover, dexmedetomidine ameliorated cognitive deficits a week after injection. Our results suggest that SC Dex might potentially improve impaired motor and cognitive behavior during daily tasks in patients that are chronically impaired by blast-induced injuries.

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.

Alterations in TRN-anterodorsal thalamocortical circuits affect sleep architecture and homeostatic processes in oxidative stress vulnerable Gclm-/- mice

Schizophrenia is associated with alterations of sensory integration, cognitive processing and both sleep architecture and sleep oscillations in mouse models and human subjects, possibly through changes in thalamocortical dynamics. Oxidative stress (OxS) damage, including inflammation and the impairment of fast-spiking gamma-aminobutyric acid neurons have been hypothesized as a potential mechanism responsible for the onset and development of schizophrenia. Yet, the link between OxS and perturbation of thalamocortical dynamics and sleep remains unclear. Here, we sought to investigate the effects of OxS on sleep regulation by characterizing the dynamics of thalamocortical networks across sleep-wake states in a mouse model with a genetic deletion of the modifier subunit of glutamate-cysteine ligase (Gclm knockout, KO) using high-density electrophysiology in freely-moving mice. We found that Gcml KO mice exhibited a fragmented sleep architecture and impaired sleep homeostasis responses as revealed by the increased NREM sleep latencies, decreased slow-wave activities and spindle rate after sleep deprivation. These changes were associated with altered bursting activity and firing dynamics of neurons from the thalamic reticularis nucleus, anterior cingulate and anterodorsal thalamus. Administration of N-acetylcysteine (NAC), a clinically relevant antioxidant, rescued the sleep fragmentation and spindle rate through a renormalization of local neuronal dynamics in Gclm KO mice. Collectively, these findings provide novel evidence for a link between OxS and the deficits of frontal TC network dynamics as a possible mechanism underlying sleep abnormalities and impaired homeostatic responses observed in schizophrenia.

Deep-spindle: An automated sleep spindle detection system for analysis of infant sleep spindles

BACKGROUND

Sleep spindles are an indicator of the development and integrity of the central nervous system in infants. Identifying sleep spindles manually in EEG is time-consuming and typically requires experienced experts. Automated detection of sleep spindles would greatly facilitate this analysis. Deep learning methods have been widely used recently in EEG analysis.

METHOD

We have developed a deep learning-based automated sleep spindle detection system, Deep-spindle, which employs a convolutional neural network (CNN) combined with a bidirectional Long Short-Term Memory (LSTM) network, which could assist in the analysis of infant sleep spindles. Deep-spindle was trained on the EEGs of ex-term infants to estimate the number and duration of sleep spindles. The ex-term EEG on channel F4-C4 was split into training (N=81) and validation (N=30) sets. An additional 30 ex-term EEG and 54 ex-preterm infant EEGs (channel F4-C4 and F3-C3) were used as an independent test set.

RESULT

Deep-spindle detected the number of sleep spindles with 91.9% to 96.5% sensitivity and 95.3% to 96.7% specificity, and estimated sleep spindle duration with a percent error of 13.1% to 19.1% in the independent test set. For each detected spindle event, the user is presented with amplitude, power spectral density and the spectrogram of the corresponding spindle EEG, and the probability of the event being a sleep spindle event, providing the user with insight into why the event is predicted as a sleep spindle to provide confidence in the predictions.

CONCLUSION

The Deep-spindle system can reduce physicians' workload, demonstrating the potential to assist physicians in the automated analysis of sleep spindles in infants.

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.

Local networks from different parts of the human cerebral cortex generate and share the same population dynamic

A major goal of neuroscience is to reveal mechanisms supporting collaborative actions of neurons in local and larger-scale networks. However, no clear overall principle of operation has emerged despite decades-long experimental efforts. Here, we used an unbiased method to extract and identify the dynamics of local postsynaptic network states contained in the cortical field potential. Field potentials were recorded by depth electrodes targeting a wide selection of cortical regions during spontaneous activities, and sensory, motor, and cognitive experimental tasks. Despite different architectures and different activities, all local cortical networks generated the same type of dynamic confined to one region only of state space. Surprisingly, within this region, state trajectories expanded and contracted continuously during all brain activities and generated a single expansion followed by a contraction in a single trial. This behavior deviates from known attractors and attractor networks. The state-space contractions of particular subsets of brain regions cross-correlated during perceptive, motor, and cognitive tasks. Our results imply that the cortex does not need to change its dynamic to shift between different activities, making task-switching inherent in the dynamic of collective cortical operations. Our results provide a mathematically described general explanation of local and larger scale cortical dynamic.

John EK, Potesta CV, Gallagher MJ, Zhou C. Artificial sleep-like up/down-states induce synaptic plasticity in cortical neurons from mouse brain slices

During non-rapid eye movement (NREM) sleep, cortical neuron activity alternates between a depolarized (firing, up-state) and a hyperpolarized state (down-state) coinciding with delta electroencephalogram (EEG) slow-wave oscillation (SWO, 0. 5-4 Hz) in vivo. Recently, we have found that artificial sleep-like up/down-states can potentiate synaptic strength in layer V cortical neurons ex vivo. Using mouse coronal brain slices, whole cell voltage-clamp recordings were made from layer V cortical pyramidal neurons to record spontaneous excitatory synaptic currents (sEPSCs) and inhibitory synaptic currents (sIPSCs). Artificial sleep-like up/down-states (as SWOs, 0.5 Hz, 10 min, current clamp mode) were induced by injecting sinusoidal currents into layer V cortical neurons. Baseline pre-SWO recordings were recorded for 5 min and post-SWO recordings for at least 25-30 min. Compared to pre-SWO sEPSCs or sIPSCs, post-SWO sEPSCs or sIPSCs in layer V cortical neurons exhibited significantly larger amplitudes and a higher frequency for 30 min. This finding suggests that both sEPSCs and sIPSCs could be potentiated in layer V cortical neurons by the low-level activity of SWOs, and sEPSCs and sIPSCs maintained a balance in layer V cortical neurons during pre- and post-SWO periods. Overall, this study presents an ex vivo method to show SWO's ability to induce synaptic plasticity in layer V cortical neurons, which may underlie sleep-related synaptic potentiation for sleep-related memory consolidation in vivo.

Enhancement of Hippocampal-Thalamocortical Temporal Coordination during Slow-Frequency Long-Duration Anterior Thalamic Spindles

Temporal nesting of cortical slow oscillations, thalamic spindles, and hippocampal ripples indicates multiregional neuronal interactions required for memory consolidation. However, how the thalamic activity during spindles organizes hippocampal dynamics remains largely undetermined. We analyzed simultaneous recordings of anterodorsal thalamus and CA1 in male mice to determine the contribution of thalamic spindles in cross-regional synchronization. Our results indicated that temporal hippocampo-thalamocortical coupling was more enhanced during slower and longer thalamic spindles. Additionally, spindles occurring closer to slow oscillation trough were more strongly coupled to ripples. We found that the temporal association between CA1 spiking/ripples and thalamic spindles was stronger following spatial exploration compared with baseline sleep. We further developed a hippocampal-thalamocortical model to explain the mechanism underlying the duration and frequency-dependent coupling of thalamic spindles to hippocampal activity. Our findings shed light on our understanding of the functional role of thalamic activity during spindles on multiregional information transfer.SIGNIFICANCE STATEMENT The contribution of thalamic spindles with differential properties to cross-regional synchronization and information transfer still remains poorly understood. Using simultaneous anterodorsal thalamic and hippocampal recordings from naturally sleeping mice before and after exploration, we found strong coupling of CA1 units to anterodorsal thalamic spindles and increase of this coupling following spatial experience. We further showed that the temporal coupling of CA1 units and hippocampal ripples with thalamic spindles and the spindle-associated modulation of CA1 units with ripples were stronger for spindles with slower frequency of oscillations. Our experimental as well as computational findings using a hippocampal-thalamocortical model provide the first demonstration that spindle frequency and duration can provide valuable information about the underlying multiregional interactions essential for memory consolidation computations.

Entrainment to sleep spindles reflects dissociable patterns of connectivity between cortex and basal ganglia

Sleep spindles are crucial for learning in the cortex and basal ganglia (BG) because they facilitate the reactivation of previously active neuronal ensembles. Studying field potentials (FPs) and spiking in the cortex and BG during sleep in non-human primates following pre-sleep learning, we show that FP sleep spindles are widespread in the BG and are similar to cortical spindles in morphology, spectral content, and response to the pre-sleep task. Further, BG spindles are concordant with electroencephalogram (EEG) spindles and associated with increased cortico-BG correlation. However, spindles across the BG differ markedly in their entrainment of local spiking. The spiking activity of striatal projection neurons exhibits consistent phase locking to striatal and EEG spindles, producing phase windows of peaked cross-region spindling. In contrast, firing in other BG nuclei is not entrained to either local or EEG sleep spindles. These results suggest corticostriatal synapses as the main hub for offline cortico-BG communication.

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.

Effects of auditory sleep modulation approaches on brain oscillatory and cardiovascular dynamics

Slow waves, the hallmark feature of deep nonrapid eye movement sleep, do potentially drive restorative effects of sleep on brain and body functions. Sleep modulation techniques to elucidate the functional role of slow waves thus have gained large interest. Auditory slow wave stimulation is a promising tool; however, directly comparing auditory stimulation approaches within a night and analyzing induced dynamic brain and cardiovascular effects are yet missing. Here, we tested various auditory stimulation approaches in a windowed, 10 s ON (stimulations) followed by 10 s OFF (no stimulations), within-night stimulation design and compared them to a SHAM control condition. We report the results of three studies and a total of 51 included nights and found a large and global increase in slow-wave activity (SWA) in the stimulation window compared to SHAM. Furthermore, slow-wave dynamics were most pronouncedly increased at the start of the stimulation and declined across the stimulation window. Beyond the changes in brain oscillations, we observed, for some conditions, a significant increase in the mean interval between two heartbeats within a stimulation window, indicating a slowing of the heart rate, and increased heart rate variability derived parasympathetic activity. Those cardiovascular changes were positively correlated with the change in SWA, and thus, our findings provide insight into the potential of auditory slow wave enhancement to modulate cardiovascular restorative conditions during sleep. However, future studies need to investigate whether the potentially increased restorative capacity through slow-wave enhancements translates into a more rested cardiovascular system on a subsequent day.

Prolongation of cortical sleep spindles during hippocampal interictal epileptiform discharges in epilepsy patients

OBJECTIVE

Memory deficits are frequent among patients with epilepsies affecting the temporal lobe. Hippocampal interictal epileptic discharges (hIEDs), the presumed epileptic exaggeration of sharp wave-ripples (SWRs), are known to contribute to memory dysfunction, but the potential underlying mechanism is unknown. The precise temporal coordination between hippocampal SWRs and corticothalamic spindles during sleep is critical for memory consolidation. Moreover, previous investigation indicated that hIEDs induce neocortical spindlelike oscillation. In the present study, we aimed to assess the influence of hIEDs on neocortical spindles.

METHODS

We analyzed the spindle characteristics (duration, amplitude, frequency) of 21 epilepsy patients implanted with foramen ovale (FO) electrodes during a whole night sleep. Scalp sleep spindles were categorized based on their temporal relationship to hIEDs detected on the FO electrodes. Three groups were created: (1) spindles coinciding with hIEDs, (2) spindles "induced" by hIEDs, and (3) spindles without hIED co-occurrence.

RESULTS

We found that spindles co-occurring with hIEDs had altered characteristics in all measured properties, lasted longer by 126 ± 48 ms (mean ± SD), and had higher amplitude by 3.4 ± 3.2 μV, and their frequency range shifted toward the higher frequencies within the 13-15-Hz range. Also, hIED-induced spindles had identical oscillatory properties to spindles without any temporal relationships with hIEDs. In more than half of our subjects, clear temporal coherence was revealed between hIEDs and spindles, but the direction of the coupling was patient-specific.

SIGNIFICANCE

We investigated the effect of hippocampal IEDs on neocortical spindle activity and found spindle alterations in cases of spindle-hIED co-occurrence, but not in cases of hIED-initiated spindles. We propose that this is a marker of a pathologic process, where IEDs may have direct effect on spindle generation. It could mark a potential mechanism whereby IEDs disrupt memory processes, and also provide a potential therapeutic target to treat memory disturbances in epilepsy.

Memory-enhancing properties of sleep depend on the oscillatory amplitude of norepinephrine

Sleep has a complex micro-architecture, encompassing micro-arousals, sleep spindles and transitions between sleep stages. Fragmented sleep impairs memory consolidation, whereas spindle-rich and delta-rich non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep promote it. However, the relationship between micro-arousals and memory-promoting aspects of sleep remains unclear. In this study, we used fiber photometry in mice to examine how release of the arousal mediator norepinephrine (NE) shapes sleep micro-architecture. Here we show that micro-arousals are generated in a periodic pattern during NREM sleep, riding on the peak of locus-coeruleus-generated infraslow oscillations of extracellular NE, whereas descending phases of NE oscillations drive spindles. The amplitude of NE oscillations is crucial for shaping sleep micro-architecture related to memory performance: prolonged descent of NE promotes spindle-enriched intermediate state and REM sleep but also associates with awakenings, whereas shorter NE descents uphold NREM sleep and micro-arousals. Thus, the NE oscillatory amplitude may be a target for improving sleep in sleep disorders.

fMRI spectral signatures of sleep

The conventional wisdom that sleep is a global state, affecting the whole brain uniformly and simultaneously, was overturned by the discovery of local sleep, where individual neuronal populations were found to be asleep and the rest of the brain awake. However, due to the difficulty of monitoring local neuronal states in humans, our understanding of local sleep remains limited. Using simultaneous functional MRI (fMRI) and electroencephalography, we find that the oscillations of brain hemodynamic activity provide signatures of sleep at a local neuronal population level. We show that the fMRI signatures of sleep can be employed to monitor local neuronal states and investigate which brain regions are the first to fall asleep or wake up at wake–sleep transitions.

Sleep can be distinguished from wake by changes in brain electrical activity, typically assessed using electroencephalography (EEG). The hallmark of nonrapid-eye-movement (NREM) sleep is the shift from high-frequency, low-amplitude wake EEG to low-frequency, high-amplitude sleep EEG dominated by spindles and slow waves. Here we identified signatures of sleep in brain hemodynamic activity, using simultaneous functional MRI (fMRI) and EEG. We found that, at the transition from wake to sleep, fMRI blood oxygen level–dependent (BOLD) activity evolved from a mixed-frequency pattern to one dominated by two distinct oscillations: a low-frequency (<0.1 Hz) oscillation prominent in light sleep and correlated with the occurrence of spindles, and a high-frequency oscillation (>0.1 Hz) prominent in deep sleep and correlated with the occurrence of slow waves. The two oscillations were both detectable across the brain but exhibited distinct spatiotemporal patterns. During the falling-asleep process, the low-frequency oscillation first appeared in the thalamus, then the posterior cortex, and lastly the frontal cortex, while the high-frequency oscillation first appeared in the midbrain, then the frontal cortex, and lastly the posterior cortex. During the waking-up process, both oscillations disappeared first from the thalamus, then the frontal cortex, and lastly the posterior cortex. The BOLD oscillations provide local signatures of spindle and slow wave activity. They may be employed to monitor the regional occurrence of sleep or wakefulness, track which regions are the first to fall asleep or wake up at the wake–sleep transitions, and investigate local homeostatic sleep processes.

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.

Cortico-Hippocampal Oscillations Are Associated With the Developmental Onset of Hippocampal-Dependent Memory

Hippocampal-dependent memories emerge late during postnatal development, aligning with hippocampal maturation. During sleep, the two-stage memory formation model states that through hippocampal-neocortical interactions, cortical slow-oscillations (SO), thalamocortical Spindles, and hippocampal sharp-wave ripples (SWR) are synchronized, allowing for the consolidation of hippocampal-dependent memories. However, evidence supporting this hypothesis during development is still lacking. Therefore, we performed successive object-in-place tests during a window of memory emergence and recorded in vivo the occurrence of SO, Spindles, and SWR during sleep, immediately after the memory encoding stage of the task. We found that hippocampal-dependent memory emerges at the end of the 4th postnatal week independently of task overtraining. Furthermore, we observed that those animals with better performance in the memory task had increased Spindle density and duration and lower density of SWR. Moreover, we observed changes in the SO-Spindle and Spindle-SWR temporal-coupling during this developmental period. Our results provide new evidence for the onset of hippocampal-dependent memory and its relationship to the oscillatory phenomenon occurring during sleep that helps us understand how memory consolidation models fit into the early stages of postnatal development.

Temporal relations between peripheral and central arousals in good and poor sleepers

Good sleepers and patients with insomnia symptoms (poor sleepers) were tracked with two measures of arousal; conventional polysomnography (PSG) for electroencephalogram (EEG) assessed cortical arousals, and a peripheral arterial tonometry device was used for the detection of peripheral nervous system (PNS) arousals associated with vasoconstrictions. The relationship between central (cortical) and peripheral (autonomic) arousals was examined by evaluating their close temporal dynamics. Cortical arousals almost invariably were preceded and followed by peripheral activations, while large peripheral autonomic arousals were followed by cortical arousals only half of the time. The temporal contiguity of these two types of arousals was altered in poor sleepers, and poor sleepers displayed a higher number of cortical and peripheral arousals compared with good sleepers. Given the difference in the number of peripheral autonomic arousals between good and poor sleepers, an evaluation of such arousals could become a means of physiologically distinguishing poor sleepers.

Beyond traditional sleep scoring: Massive feature extraction and data-driven clustering of sleep time series

The widely used guidelines for sleep staging were developed for the visual inspection of electrophysiological recordings by the human eye. As such, these rules reflect a limited range of features in these data and are therefore restricted in accurately capturing the physiological changes associated with sleep. Here we present a novel analysis framework that extensively characterizes sleep dynamics using over 7700 time-series features from the hctsa software. We used clustering to categorize sleep epochs based on the similarity of their time-series features, without relying on established scoring conventions. The resulting sleep structure overlapped substantially with that defined by visual scoring. However, we also observed discrepancies between our approach and traditional scoring. This divergence principally stemmed from the extensive characterization by hctsa features, which captured distinctive time-series properties within the traditionally defined sleep stages that are overlooked with visual scoring. Lastly, we report time-series features that are highly discriminative of stages. Our framework lays the groundwork for a data-driven exploration of sleep sub-stages and has significant potential to identify new signatures of sleep disorders and conscious sleep states.

Human Spindle Variability

In humans, sleep spindles are 10- to 16-Hz oscillations lasting approximately 0.5-2 s. Spindles, along with cortical slow oscillations, may facilitate memory consolidation by enabling synaptic plasticity. Early recordings of spindles at the scalp found anterior channels had overall slower frequency than central-posterior channels. This robust, topographical finding led to dichotomizing spindles as "slow" versus "fast," modeled as two distinct spindle generators in frontal versus posterior cortex. Using a large dataset of intracranial stereoelectroencephalographic (sEEG) recordings from 20 patients (13 female, 7 male) and 365 bipolar recordings, we show that the difference in spindle frequency between frontal and parietal channels is comparable to the variability in spindle frequency within the course of individual spindles, across different spindles recorded by a given site, and across sites within a given region. Thus, fast and slow spindles only capture average differences that obscure a much larger underlying overlap in frequency. Furthermore, differences in mean frequency are only one of several ways that spindles differ. For example, compared with parietal, frontal spindles are smaller, tend to occur after parietal when both are engaged, and show a larger decrease in frequency within-spindles. However, frontal and parietal spindles are similar in being longer, less variable, and more widespread than occipital, temporal, and Rolandic spindles. These characteristics are accentuated in spindles which are highly phase-locked to posterior hippocampal spindles. We propose that rather than a strict parietal-fast/frontal-slow dichotomy, spindles differ continuously and quasi-independently in multiple dimensions, with variability due about equally to within-spindle, within-region, and between-region factors.SIGNIFICANCE STATEMENT Sleep spindles are 10- to 16-Hz neural oscillations generated by cortico-thalamic circuits that promote memory consolidation. Spindles are often dichotomized into slow-anterior and fast-posterior categories for cognitive and clinical studies. Here, we show that the anterior-posterior difference in spindle frequency is comparable to that observed between different cycles of individual spindles, between spindles from a given site, or from different sites within a region. Further, we show that spindles vary on other dimensions such as duration, amplitude, spread, primacy and consistency, and that these multiple dimensions vary continuously and largely independently across cortical regions. These findings suggest that multiple continuous variables rather than a strict frequency dichotomy may be more useful biomarkers for memory consolidation or psychiatric disorders.

Non-rapid eye movement sleep and wake neurophysiology in schizophrenia

Motivated by the potential of objective neurophysiological markers to index thalamocortical function in patients with severe psychiatric illnesses, we comprehensively characterized key non-rapid eye movement (NREM) sleep parameters across multiple domains, their interdependencies, and their relationship to waking event-related potentials and symptom severity. In 72 schizophrenia (SCZ) patients and 58 controls, we confirmed a marked reduction in sleep spindle density in SCZ and extended these findings to show that fast and slow spindle properties were largely uncorrelated. We also describe a novel measure of slow oscillation and spindle interaction that was attenuated in SCZ. The main sleep findings were replicated in a demographically distinct sample, and a joint model, based on multiple NREM components, statistically predicted disease status in the replication cohort. Although also altered in patients, auditory event-related potentials elicited during wake were unrelated to NREM metrics. Consistent with a growing literature implicating thalamocortical dysfunction in SCZ, our characterization identifies independent NREM and wake EEG biomarkers that may index distinct aspects of SCZ pathophysiology and point to multiple neural mechanisms underlying disease heterogeneity. This study lays the groundwork for evaluating these neurophysiological markers, individually or in combination, to guide efforts at treatment and prevention as well as identifying individuals most likely to benefit from specific interventions.

Probing the Neural Circuitry Targets of Neurotoxicants In Vivo Through High Density Silicon Probe Brain Implants

Adverse effects of drugs on the human nervous system are rarely possible to anticipate based on preclinical neurotoxicity data, thus propagating the centuries long single most important obstacle to drug discovery and development for disorders of the nervous system. An emerging body of evidence indicates that in vivo electrophysiology using chronically implanted high-density electrodes (ciHDE) in freely moving animals is a rigorous method with enhanced potential for use in translational research. In particular, the structure and function of the hippocampal trisynaptic circuit (HTC) is conserved from rodents to primates, including Homo sapiens, suggesting that the effects of therapeutic agents and other potential neurologically active agents, whether beneficial or adverse, are likely to translate across species when interrogated using a conserved neural circuitry platform. This review explores science advances in the rapidly moving field of in vivo ciHDE in animal models of learning and memory. For this reason we focus on the HTC, where substantial research has investigated neural circuitry level responses and specific behaviors that reflect memory permitting a test of the ground truth validity of the findings. Examples of changes in neural network activity induced by endogenous neurotoxicants associated with neurodegenerative diseases, as well as exogenous therapeutics, drugs, and neurotoxicants are presented. Several illustrative examples of relevant findings that involve longer range neural circuitry outside of the HTC are discussed. Lastly, the limitations of in vivo ciHDE as applied to preclinical neurotoxicology are discussed with a view toward leveraging circuitry level actions to enhance our ability to project the specificity of in vitro target engagement with the desired psychopharmacological or neurological outcome. At the same time, the goal of reducing or eliminating significant neurotoxic adverse events in human is the desired endpoint. We believe that this approach will lead to enhanced discovery of high value neuroactive therapeutics that target neural circuitry domains as their primary mechanism of action, thus enhancing their ultimate contribution toward discovery of precision therapeutics.

Barbeau E, Pariente J, Dahan L, Valton L. Sleep: The Tip of the Iceberg in the Bidirectional Link Between Alzheimer's Disease and Epilepsy

The observation that a pathophysiological link might exist between Alzheimer's disease (AD) and epilepsy dates back to the identification of the first cases of the pathology itself and is now strongly supported by an ever-increasing mountain of literature. An overwhelming majority of data suggests not only a higher prevalence of epilepsy in Alzheimer's disease compared to healthy aging, but also that AD patients with a comorbid epileptic syndrome, even subclinical, have a steeper cognitive decline. Moreover, clinical and preclinical investigations have revealed a marked sleep-related increase in the frequency of epileptic activities. This characteristic might provide clues to the pathophysiological pathways underlying this comorbidity. Furthermore, the preferential sleep-related occurrence of epileptic events opens up the possibility that they might hasten cognitive decline by interfering with the delicately orchestrated synchrony of oscillatory activities implicated in sleep-related memory consolidation. Therefore, we scrutinized the literature for mechanisms that might promote sleep-related epileptic activity in AD and, possibly dementia onset in epilepsy, and we also aimed to determine to what degree and through which processes such events might alter the progression of AD. Finally, we discuss the implications for patient care and try to identify a common basis for methodological considerations for future research and clinical practice.

A systematic scoping review of the effects of central nervous system active drugs on sleep spindles and sleep-dependent memory consolidation

Sleep spindles are key electroencephalogram (EEG) oscillatory events that occur during non-rapid eye movement (NREM) sleep. Deficits in sleep spindles are present in populations with sleep and neurological disorders, and in severe mental illness. Pharmacological manipulation of these waveforms is of growing interest with therapeutic potential in targeting spindle deficits relating to memory impairment. This review integrates studies that provide insight into the feasibility of manipulating sleep spindles by using psychoactive drug classes, with consequent effects on sleep-dependent memory. Most studies showed that benzodiazepines and Z-drugs consistently enhanced sleep spindle activity unlike other psychoactive drug classes reviewed. However, how these spindle enhancements translate into improved sleep-dependent memory remains to be fully elucidated. From the few studies that examined both spindles and memory, preliminary evidence suggests that zolpidem may have some therapeutic potential to enhance declarative memory through boosting sleep spindle activity. There is a greater need to standardise methodological approaches for identifying and quantifying spindle activity as well as more exploratory studies to elucidate the role of spindle enhancement for other types of memory.

Evaluation of electroencephalography biomarkers for Angelman syndrome during overnight sleep

Angelman syndrome (AS) is a neurodevelopmental disorder caused by loss‐of‐function mutations in the maternal copy of the UBE3A gene. AS is characterized by intellectual disability, impaired speech and motor skills, epilepsy, and sleep disruptions. Multiple treatment strategies to re‐express functional neuronal UBE3A from the dormant paternal allele were successful in rodent models of AS and have now moved to early phase clinical trials in children. Developing reliable and objective AS biomarkers is essential to guide the design and execution of current and future clinical trials. Our prior work quantified short daytime electroencephalograms (EEGs) to define promising biomarkers for AS. Here, we asked whether overnight sleep is better suited to detect AS EEG biomarkers. We retrospectively analyzed EEGs from 12 overnight sleep studies from individuals with AS with age and sex‐matched Down syndrome and neurotypical controls, focusing on low frequency (2–4 Hz) delta rhythms and sleep spindles. Delta EEG rhythms were increased in individuals with AS during all stages of overnight sleep, but overnight sleep did not provide additional benefit over wake in the ability to detect increased delta. Abnormal sleep spindles were not reliably detected in EEGs from individuals with AS during overnight sleep, suggesting that delta rhythms represent a more reliable biomarker. Overall, we conclude that periods of wakefulness are sufficient, and perhaps ideal, to quantify delta EEG rhythms for use as AS biomarkers.

Slow waves form expanding, memory-rich mesostates steered by local excitability in fading anesthesia

In the arousal process, the brain restores its integrative activity from the synchronized state of slow wave activity (SWA). The mechanisms underpinning this state transition remain, however, to be elucidated. Here we simultaneously probed neuronal assemblies throughout the whole cortex with micro-electrocorticographic recordings in mice. We investigated the progressive shaping of propagating SWA at different levels of isoflurane. We found a form of memory of the wavefront shapes at deep anesthesia, tightly alternating posterior-anterior-posterior patterns. At low isoflurane, metastable patterns propagated in more directions, reflecting an increased complexity. The wandering across these mesostates progressively increased its randomness, as predicted by simulations of a network of spiking neurons, and confirmed in our experimental data. The complexity increase is explained by the elevated excitability of local assemblies with no modifications of the network connectivity. These results shed new light on the functional reorganization of the cortical network as anesthesia fades out.

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Complexity of isoflurane-induced slow waves reliably determines anesthesia level

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In deep anesthesia, the propagation strictly alternates between front-back-front patterns

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In light anesthesia, there is a continuum of directions and faster propagation

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Local excitability underpins the cortical reorganization in fading anesthesia

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.

Nonrapid eye movement sleep characteristics and relations with motor, memory, and cognitive ability from infancy to preadolescence

Sleep plays a critical role in neural neurodevelopment. Hallmarks of sleep reflected in the electroencephalogram during nonrapid eye movement (NREM) sleep are associated with learning processes, cognitive ability, memory, and motor functioning. Research in adults is well-established; however, the role of NREM sleep in childhood is less clear. Growing evidence suggests the importance of two NREM sleep features: slow-wave activity and sleep spindles. These features may be critical for understanding maturational change and the functional role of sleep during development. Here, we review the literature on NREM sleep from infancy to preadolescence to provide insight into the network dynamics of the developing brain. The reviewed findings show distinct relations between topographical and maturational aspects of slow waves and sleep spindles; however, the direction and consistency of these relationships vary, and associations with cognitive ability remain unclear. Future research investigating the role of NREM sleep and development would benefit from longitudinal approaches, increased control for circadian and homeostatic influences, and in early childhood, studies recording daytime naps and overnight sleep to yield increased precision for detecting age-related change. Such evidence could help explicate the role of NREM sleep and provide putative physiological markers of neurodevelopment.