Oscillating circuitries in the sleeping brain

Microglial TNFα controls daily changes in synaptic GABAARs and sleep slow waves

Microglia sense the changes in their environment. How microglia actively translate these changes into suitable cues to adapt brain physiology is unknown. We reveal an activity-dependent regulation of cortical inhibitory synapses by microglia, driven by purinergic signaling acting on P2RX7 and mediated by microglia-derived TNFα. We demonstrate that sleep induces microglia-dependent synaptic enrichment of GABAARs in a manner dependent on microglial TNFα and P2RX7. We further show that microglia-specific depletion of TNFα alters slow waves during NREM sleep and blunt memory consolidation in sleep-dependent learning tasks. Together, our results reveal that microglia orchestrate sleep-intrinsic plasticity of synaptic GABAARs, sculpt sleep slow waves, and support memory consolidation.

Coincident development and synchronization of sleep-dependent delta in the cortex and medulla

In early development, active sleep is the predominant sleep state before it is supplanted by quiet sleep. In rats, the developmental increase in quiet sleep is accompanied by the sudden emergence of the cortical delta rhythm (0.5-4 Hz) around postnatal day 12 (P12). We sought to explain the emergence of the cortical delta by assessing developmental changes in the activity of the parafacial zone (PZ), a medullary structure thought to regulate quiet sleep in adults. We recorded from the PZ in P10 and P12 rats and predicted an age-related increase in neural activity during increasing periods of delta-rich cortical activity. Instead, during quiet sleep, we discovered sleep-dependent rhythmic spiking activity-with intervening periods of total silence-phase locked to a local delta rhythm. Moreover, PZ and cortical delta were coherent at P12 but not at P10. PZ delta was also phase locked to respiration, suggesting sleep-dependent modulation of PZ activity by respiratory pacemakers in the ventral medulla. Disconnecting the main olfactory bulbs from the cortex did not diminish cortical delta, indicating that the influence of respiration on delta at this age is not mediated indirectly through nasal breathing. Finally, we observed an increase in parvalbumin-expressing terminals in the PZ across these ages, supporting a role for local GABAergic inhibition in the PZ's rhythmicity. The unexpected discovery of delta-rhythmic neural activity in the medulla-when cortical delta is also emerging-provides a new perspective on the brainstem's role in regulating sleep and promoting long-range functional connectivity in early development.

Changes in electroencephalographic microstates between evening and morning are associated with overnight sleep slow waves in healthy individuals

STUDY OBJECTIVES

Microstates are semi-stable voltage topographies that account for most of electroencephalogram (EEG) variance. However, the impact of time of the day and sleep on microstates has not been examined. To address this gap, we assessed whether microstates differed between the evening and morning and whether sleep slow waves correlated with microstate changes in healthy participants.

METHODS

Forty-five healthy participants were recruited. Each participant underwent 6 minutes of resting state EEG recordings in the evening and morning, interleaved by sleep EEGs. Evening-to-morning changes in microstate duration, coverage, and occurrence were assessed. Furthermore, correlation between microstate changes and sleep slow-wave activity (SWA) and slow-wave density (SWD) were performed.

RESULTS

Two-way ANOVAs with microstate class (A, B, C, and D) and time (evening and morning) revealed significant microstate class × time interaction for duration (F(44) = 5.571, p = 0.002), coverage (F(44) = 6.833, p = 0.001), and occurrence (F(44) = 5.715, p = 0.002). Post hoc comparisons showed significant effects for microstate C duration (padj = 0.048, Cohen's d = -0.389), coverage (padj = 0.002, Cohen's d = -0.580), and occurrence (padj = 0.002, Cohen's d = -0.606). Topographic analyses revealed inverse correlations between SWD, but not SWA, and evening-to-morning changes in microstate C duration (r = -0.51, padj = 0.002), coverage (r = -0.45, padj = 0.006), and occurrence (r = -0.38, padj = 0.033).

CONCLUSIONS

Microstate characteristics showed significant evening-to-morning changes associated with, and possibly regulated by, sleep slow waves. These findings suggest that future microstate studies should control for time of day and sleep effects.

Neuroscience in pictures: 2. Sleep, wakefulness, and mental state biology

Sleep is a vital restorative process that has occupied our curiosity for millennia. Despite our longstanding research efforts, the biology of sleep and its connection to mental states remains enigmatic. Unsurprisingly, sleep and wakefulness, the fundamental processes between which our mental states oscillate, are inseparable from our physical and mental health. Thus, clinical consideration of sleep impairments warrants a transdiagnostic approach whilst appropriately acknowledging that certain individual disorders (e.g. depression, schizophrenia) may have somewhat distinct sleep disturbances. Moreover, our knowledge of the anatomy and physiology of sleep regulation-albeit limited-forms the foundation for current treatments for sleep difficulties. This pictorial article overviews the core concepts and future of sleep neuroscience and mental state biology for trainees and practitioners in psychiatry and related professions.

Neuropsychopharmacological Induction of (Lucid) Dreams: A Narrative Review

Lucid dreaming (LD) is a physiological state of consciousness that occurs when dreamers become aware that they are dreaming, and may also control the oneiric content. In the general population, LD is spontaneously rare; thus, there is great interest in its induction. Here, we aim to review the literature on neuropsychopharmacological induction of LD. First, we describe the circadian and homeostatic processes of sleep regulation and the mechanisms that control REM sleep with a focus on neurotransmission systems. We then discuss the neurophysiology and phenomenology of LD to understand the main cortical oscillations and brain areas involved in the emergence of lucidity during REM sleep. Finally, we review possible exogenous substances-including natural plants and artificial drugs-that increase metacognition, REM sleep, and/or dream recall, thus with the potential to induce LD. We found that the main candidates are substances that increase cholinergic and/or dopaminergic transmission, such as galantamine. However, the main limitation of this technique is the complexity of these neurotransmitter systems, which challenges interpreting results in a simple way. We conclude that, despite these promising substances, more research is necessary to find a reliable way to pharmacologically induce LD.

Sleep spindles as neurophysiological biomarkers of schizophrenia

Schizophrenia (SCZ) is a complex psychiatric disorder characterized by a wide range of clinical symptoms, including disrupted sleep. In recent years, there has been growing interest in assessing alterations in sleep parameters in patients with SCZ. Sleep spindles are brief (0.5-2 s) bursts of 12- to 16-Hz rhythmic electroencephalogram (EEG) oscillatory activity occurring during non-rapid eye movement (NREM) sleep. Spindles have been implicated in several critical brain functions, including learning, memory and plasticity, and are thought to reflect the integrity of underlying thalamocortical circuits. This review aims to provide an overview of the current research investigating sleep spindles in SCZ. After briefly describing the neurophysiological features of sleep spindles, I will discuss alterations in spindle characteristics observed in SCZ, their associations with the clinical symptomatology of these patients and their putative underlying neuronal and molecular mechanisms. I will then discuss the utility of sleep spindle measures as predictors of treatment response and disease progression. Finally, I will highlight future directions for research in this emerging field, including the prospect of utilizing sleep spindles as neurophysiological biomarkers of SCZ.

DELTA-RHYTHMIC ACTIVITY IN THE MEDULLA DEVELOPS COINCIDENT WITH CORTICAL DELTA IN SLEEPING INFANT RATS

In early development, active sleep is the predominant sleep state before it is supplanted by quiet sleep. In rats, the developmental increase in quiet sleep is accompanied by the sudden emergence of the cortical delta rhythm (0.5-4 Hz) around postnatal day 12 (P12). We sought to explain the emergence of cortical delta by assessing developmental changes in the activity of the parafacial zone (PZ), a medullary structure thought to regulate quiet sleep in adults. We recorded from PZ in P10 and P12 rats and predicted an age-related increase in neural activity during increasing periods of delta-rich cortical activity. Instead, during quiet sleep we discovered sleep-dependent rhythmic spiking activity-with intervening periods of total silence-phase-locked to a local delta rhythm. Moreover, PZ and cortical delta were coherent at P12, but not at P10. PZ delta was also phase-locked to respiration, suggesting sleep-dependent modulation of PZ activity by respiratory pacemakers in the ventral medulla. Disconnecting the main olfactory bulbs from the cortex did not diminish cortical delta, indicating that the influence of respiration on delta at this age is not mediated indirectly through nasal breathing. Finally, we observed an increase in parvalbumin-expressing terminals in PZ across these ages, supporting a role for GABAergic inhibition in PZ's rhythmicity. The discovery of delta-rhythmic neural activity in the medulla-when cortical delta is also emerging-opens a new path to understanding the brainstem's role in regulating sleep and synchronizing rhythmic activity throughout the brain.

Proof-of-concept evidence for high-density EEG investigation of sleep slow wave traveling in First-Episode Psychosis

Schizophrenia is thought to reflect aberrant connectivity within cortico-cortical and reentrant thalamo-cortical loops, which physiologically integrate and coordinate the function of multiple cortical and subcortical structures. Despite extensive research, reliable biomarkers of such "dys-connectivity" remain to be identified at the onset of psychosis, and before exposure to antipsychotic drugs. Because slow waves travel across the brain during sleep, they represent an ideal paradigm to study pathological conditions affecting brain connectivity. Here, we provide proof-of-concept evidence for a novel approach to investigate slow wave traveling properties in First-Episode Psychosis (FEP) with high-density electroencephalography (EEG). Whole-night sleep recordings of 5 drug-naïve FEP and 5 age- and gender-matched healthy control subjects were obtained with a 256-channel EEG system. One patient was re-recorded after 6 months and 3 years of continuous clozapine treatment. Slow wave detection and traveling properties were obtained with an open-source toolbox. Slow wave density and slow wave traveled distance (measured as the line of longest displacement) were significantly lower in patients (p < 0.05). In the patient who was tested longitudinally during effective clozapine treatment, slow wave density normalized, while traveling distance only partially recovered. These preliminary findings suggest that slow wave traveling could be employed in larger samples to detect cortical "dys-connectivity" at psychosis onset.

BNST GABAergic neurons modulate wakefulness over sleep and anesthesia

The neural circuits underlying sleep-wakefulness and general anesthesia have not been fully investigated. The GABAergic neurons in the bed nucleus of the stria terminalis (BNST) play a critical role in stress and fear that relied on heightened arousal. Nevertheless, it remains unclear whether BNST GABAergic neurons are involved in the regulation of sleep-wakefulness and anesthesia. Here, using in vivo fiber photometry combined with electroencephalography, electromyography, and video recordings, we found that BNST GABAergic neurons exhibited arousal-state-dependent alterations, with high activities in both wakefulness and rapid-eye movement sleep, but suppressed during anesthesia. Optogenetic activation of these neurons could initiate and maintain wakefulness, and even induce arousal from anesthesia. However, chronic lesion of BNST GABAergic neurons altered spontaneous sleep-wakefulness architecture during the dark phase, but not induction and emergence from anesthesia. Furthermore, we also discovered that the BNST-ventral tegmental area pathway might participate in promoting wakefulness and reanimation from steady-state anesthesia. Collectively, our study explores new elements in neural circuit mechanisms underlying sleep-wakefulness and anesthesia, which may contribute to a more comprehensive understanding of consciousness and the development of innovative anesthetics.

A Thalamocortical Perspective on Sleep Spindle Alterations in Neurodevelopmental Disorders

Purpose of Review

Neurodevelopmental disorders are a group of conditions that affect the development and function of the nervous system, typically arising early in life. These disorders can have various genetic, environmental, and/or neural underpinnings, which can impact the thalamocortical system. Sleep spindles, brief bursts of oscillatory activity that occur during NREM sleep, provide a unique in vivo measure of the thalamocortical system. In this manuscript, we review the development of the thalamocortical system and sleep spindles in rodent models and humans. We then utilize this as a foundation to discuss alterations in sleep spindle activity in four of the most pervasive neurodevelopmental disorders-intellectual disability, attention deficit hyperactivity disorder, autism, and schizophrenia.

Recent Findings

Recent work in humans has shown alterations in sleep spindles across several neurodevelopmental disorders. Simultaneously, rodent models have elucidated the mechanisms which may underlie these deficits in spindle activity. This review merges recent findings from these two separate lines of research to draw conclusions about the pathogenesis of neurodevelopmental disorders.

Summary

We speculate that deficits in the thalamocortical system associated with neurodevelopmental disorders are exquisitely reflected in sleep spindle activity. We propose that sleep spindles may represent a promising biomarker for drug discovery, risk stratification, and treatment monitoring.

Complexity Measures for EEG Microstate Sequences: Concepts and Algorithms

EEG microstate sequence analysis quantifies properties of ongoing brain electrical activity which is known to exhibit complex dynamics across many time scales. In this report we review recent developments in quantifying microstate sequence complexity, we classify these approaches with regard to different complexity concepts, and we evaluate excess entropy as a yet unexplored quantity in microstate research. We determined the quantities entropy rate, excess entropy, Lempel-Ziv complexity (LZC), and Hurst exponents on Potts model data, a discrete statistical mechanics model with a temperature-controlled phase transition. We then applied the same techniques to EEG microstate sequences from wakefulness and non-REM sleep stages and used first-order Markov surrogate data to determine which time scales contributed to the different complexity measures. We demonstrate that entropy rate and LZC measure the Kolmogorov complexity (randomness) of microstate sequences, whereas excess entropy and Hurst exponents describe statistical complexity which attains its maximum at intermediate levels of randomness. We confirmed the equivalence of entropy rate and LZC when the LZ-76 algorithm is used, a result previously reported for neural spike train analysis (Amigó et al., Neural Comput 16:717-736, https://doi.org/10.1162/089976604322860677 , 2004). Surrogate data analyses prove that entropy-based quantities and LZC focus on short-range temporal correlations, whereas Hurst exponents include short and long time scales. Sleep data analysis reveals that deeper sleep stages are accompanied by a decrease in Kolmogorov complexity and an increase in statistical complexity. Microstate jump sequences, where duplicate states have been removed, show higher randomness, lower statistical complexity, and no long-range correlations. Regarding the practical use of these methods, we suggest that LZC can be used as an efficient entropy rate estimator that avoids the estimation of joint entropies, whereas entropy rate estimation via joint entropies has the advantage of providing excess entropy as the second parameter of the same linear fit. We conclude that metrics of statistical complexity are a useful addition to microstate analysis and address a complexity concept that is not yet covered by existing microstate algorithms while being actively explored in other areas of brain research.

Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep

Auditory processing and the complexity of neural activity can both indicate residual consciousness levels and differentiate states of arousal. However, how measures of neural signal complexity manifest in neural activity following environmental stimulation and, more generally, how the electrophysiological characteristics of auditory responses change in states of reduced consciousness remain under-explored. Here, we tested the hypothesis that measures of neural complexity and the spectral slope would discriminate stages of sleep and wakefulness not only in baseline electroencephalography (EEG) activity but also in EEG signals following auditory stimulation. High-density EEG was recorded in 21 participants to determine the spatial relationship between these measures and between EEG recorded pre- and post-auditory stimulation. Results showed that the complexity and the spectral slope in the 2-20 Hz range discriminated between sleep stages and had a high correlation in sleep. In wakefulness, complexity was strongly correlated to the 20-40 Hz spectral slope. Auditory stimulation resulted in reduced complexity in sleep compared to the pre-stimulation EEG activity and modulated the spectral slope in wakefulness. These findings confirm our hypothesis that electrophysiological markers of arousal are sensitive to sleep/wake states in EEG activity during baseline and following auditory stimulation. Our results have direct applications to studies using auditory stimulation to probe neural functions in states of reduced consciousness.

Developmental alcohol exposure is exhausting: Sleep and the enduring consequences of alcohol exposure during development

Prenatal alcohol exposure is the leading nongenetic cause of human intellectual impairment. The long-term impacts of prenatal alcohol exposure on health and well-being are diverse, including neuropathology leading to behavioral, cognitive, and emotional impairments. Additionally negative effects also occur on the physiological level, such as the endocrine, cardiovascular, and immune systems. Among these diverse impacts is sleep disruption. In this review, we describe how prenatal alcohol exposure affects sleep, and potential mechanisms of those effects. Furthermore, we outline the evidence that sleep disruption across the lifespan may be a mediator of some cognitive and behavioral impacts of developmental alcohol exposure, and thus may represent a promising target for treatment.

Slow-wave sleep and REM sleep without atonia predict motor progression in Parkinson's disease

BACKGROUND

Growing evidence supports the potential role of sleep in the motor progression of Parkinson's disease (PD). Slow-wave sleep (SWS) and rapid eye movement (REM) sleep without atonia (RWA) are important sleep parameters. The association between SWS and RWA with PD motor progression and their predictive value have not yet been elucidated.

METHODS

We retro-prospectively analyzed clinical and polysomnographic data of 136 patients with PD. The motor symptoms were assessed using Unified Parkinson's Disease Rating Scale Part III (UPDRS III) at baseline and follow-up to determine its progression. Partial correlation analysis was used to explore the cross-sectional associations between slow-wave energy (SWE), RWA and clinical symptoms. Longitudinal analyses were performed using Cox regression and linear mixed-effects models.

RESULTS

Among 136 PD participants, cross-sectional partial correlation analysis showed SWE decreased with the prolongation of the disease course (P = 0.046), RWA density was positively correlated with Hoehn & Yahr (H-Y) stage (tonic RWA, P < 0.001; phasic RWA, P = 0.002). Cox regression analysis confirmed that low SWE (HR = 1.739, 95% CI = 1.038-2.914; P = 0.036; FDR-P = 0.036) and high tonic RWA (HR = 0.575, 95% CI = 0.343-0.963; P = 0.032; FDR-P = 0.036) were predictors of motor symptom progression. Furthermore, we found that lower SWE predicted faster rate of axial motor progression (P < 0.001; FDR-P < 0.001) while higher tonic RWA density was associated with faster rate of rigidity progression (P = 0.006; FDR-P = 0.024) using linear mixed-effects models.

CONCLUSIONS

These findings suggest that SWS and RWA might represent markers of different motor subtypes progression in PD.

Reduced slow wave density is associated with worse positive symptoms in clinical high risk: An objective readout of symptom severity for early treatment interventions?

Individuals at clinical high risk for psychosis (CHR) present subsyndromal psychotic symptoms that can escalate and lead to the transition to a diagnosable psychotic disorder. Identifying biological parameters that are sensitive to these symptoms can therefore help objectively assess their severity and guide early interventions in CHR. Reduced slow wave oscillations (∼1 Hz) during non-rapid eye movement sleep were recently observed in first-episode psychosis patients and were linked to the intensity of their positive symptoms. Here, we collected overnight high-density EEG recordings from 37 CHR and 32 healthy control (HC) subjects and compared slow wave (SW) activity and other SW parameters (i.e., density and negative peak amplitude) between groups. We also assessed the relationships between clinical symptoms and SW parameters in CHR. While comparisons between HC and the entire CHR group showed no SW differences, CHR individuals with higher positive symptom severity (N = 18) demonstrated a reduction in SW density in an EEG cluster involving bilateral prefrontal, parietal, and right occipital regions compared to matched HC individuals. Furthermore, we observed a negative correlation between SW density and positive symptoms across CHR individuals, suggesting a potential target for early treatment interventions.

Distributed fMRI dynamics predict distinct EEG rhythms across sleep and wakefulness

The brain exhibits rich oscillatory dynamics that vary across tasks and states, such as the EEG oscillations that define sleep. These oscillations play critical roles in cognition and arousal, but the brainwide mechanisms underlying them are not yet described. Using simultaneous EEG and fast fMRI in subjects drifting between sleep and wakefulness, we developed a machine learning approach to investigate which brainwide fMRI dynamics predict alpha (8-12 Hz) and delta (1-4 Hz) rhythms. We predicted moment-by-moment EEG power from fMRI activity in held-out subjects, and found that information about alpha power was represented by a remarkably small set of regions, segregated in two distinct networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale across the cortex. These results identify distributed networks that predict delta and alpha rhythms, and establish a computational framework for investigating fMRI brainwide dynamics underlying EEG oscillations.

Frequency-dependent connectivity in large-scale resting-state brain networks during sleep

Functional connectivity (FC) during sleep has been shown to break down as non-rapid eye movement (NREM) sleep deepens before returning to a state closer to wakefulness during rapid eye movement (REM) sleep. However, the specific spatial and temporal signatures of these fluctuations in connectivity patterns remain poorly understood. This study aimed to investigate how frequency-dependent network-level FC fluctuates during nocturnal sleep in healthy young adults using high-density electroencephalography (hdEEG). Specifically, we examined source-localized FC in resting-state networks during NREM2, NREM3 and REM sleep (sleep stages scored using a semi-automatic procedure) in the first three sleep cycles of 29 participants. Our results showed that FC within and between all resting-state networks decreased from NREM2 to NREM3 sleep in multiple frequency bands and all sleep cycles. The data also highlighted a complex modulation of connectivity patterns during the transition to REM sleep whereby delta and sigma bands hosted a persistence of the connectivity breakdown in all networks. In contrast, a reconnection occurred in the default mode and the attentional networks in frequency bands characterizing their organization during wake (i.e., alpha and beta bands, respectively). Finally, all network pairs (except the visual network) showed higher gamma-band FC during REM sleep in cycle three compared to earlier sleep cycles. Altogether, our results unravel the spatial and temporal characteristics of the well-known breakdown in connectivity observed as NREM sleep deepens. They also illustrate a complex pattern of connectivity during REM sleep that is consistent with network- and frequency-specific breakdown and reconnection processes.

Single closed-loop acoustic stimulation targeting memory consolidation suppressed hippocampal ripple and thalamo-cortical spindle activity in mice

Brain rhythms of sleep reflect neuronal activity underlying sleep-associated memory consolidation. The modulation of brain rhythms, such as the sleep slow oscillation (SO), is used both to investigate neurophysiological mechanisms as well as to measure the impact of sleep on presumed functional correlates. Previously, closed-loop acoustic stimulation in humans targeted to the SO Up-state successfully enhanced the slow oscillation rhythm and phase-dependent spindle activity, although effects on memory retention have varied. Here, we aim to disclose relations between stimulation-induced hippocampo-thalamo-cortical activity and retention performance on a hippocampus-dependent object-place recognition task in mice by applying acoustic stimulation at four estimated SO phases compared to sham condition. Across the 3-h retention interval at the beginning of the light phase closed-loop stimulation failed to improve retention significantly over sham. However, retention during SO Up-state stimulation was significantly higher than for another SO phase. At all SO phases, acoustic stimulation was accompanied by a sharp increase in ripple activity followed by about a second-long suppression of hippocampal sharp wave ripple and longer maintained suppression of thalamo-cortical spindle activity. Importantly, dynamics of SO-coupled hippocampal ripple activity distinguished SOUp-state stimulation. Non-rapid eye movement (NREM) sleep was not impacted by stimulation, yet preREM sleep duration was effected. Results reveal the complex effect of stimulation on the brain dynamics and support the use of closed-loop acoustic stimulation in mice to investigate the inter-regional mechanisms underlying memory consolidation.

Mapping sleep's oscillatory events as a biomarker of Alzheimer's disease

INTRODUCTION

Memory-associated neural circuits produce oscillatory events including theta bursts (TBs), sleep spindles (SPs), and slow waves (SWs) in sleep electroencephalography (EEG). Changes in the "coupling" of these events may indicate early Alzheimer's disease (AD) pathogenesis.

METHODS

We analyzed 205 aging adults using single-channel sleep EEG, cerebrospinal fluid (CSF) AD biomarkers, and Clinical Dementia Rating® (CDR®) scale. We mapped SW-TB and SW-SP neural circuit coupling precision to amyloid positivity, cognitive impairment, and CSF AD biomarkers.

RESULTS

Cognitive impairment correlated with lower TB spectral power in SW-TB coupling. Cognitively unimpaired, amyloid positive individuals demonstrated lower precision in SW-TB and SW-SP coupling compared to amyloid negative individuals. Significant biomarker correlations were found in oscillatory event coupling with CSF Aβ42 /Aβ40 , phosphorylated- tau181 , and total-tau.

DISCUSSION

Sleep-dependent memory processing integrity in neural circuits can be measured for both SW-TB and SW-SP coupling. This breakdown associates with amyloid positivity, increased AD pathology, and cognitive impairment.

HIGHLIGHTS

At-home sleep EEG is a potential biomarker of neural circuits linked to memory. Circuit precision is associated with amyloid positivity in asymptomatic aging adults. Levels of CSF amyloid and tau also correlate with circuit precision in sleep EEG. Theta burst EEG power is decreased in very early mild cognitive impairment. This technique may enable inexpensive wearable EEGs for monitoring brain health.

Abnormal patterns of sleep and waking behaviors are accompanied by neocortical oscillation disturbances in an Ank3 mouse model of epilepsy-bipolar disorder comorbidity

ANK3 is a leading bipolar disorder (BD) candidate gene in humans and provides a unique opportunity for studying epilepsy-BD comorbidity. Previous studies showed that deletion of Ank3-1b, a BD-associated variant of Ank3 in mice leads to increased firing threshold and diminished action potential dynamic range of parvalbumin (PV) interneurons and absence epilepsy, thus providing a biological mechanism linking epilepsy and BD. To explore the behavioral overlap of these disorders, we characterized behavioral patterns of Ank3-1b KO mice during overnight home-cage activity and examined network activity during these behaviors using paired video and EEG recordings. Since PV interneurons contribute to the generation of high-frequency gamma oscillations, we anticipated changes in the power of neocortical EEG signals in the gamma frequency range (> 25 Hz) during behavioral states related to human BD symptoms, including abnormal sleep, hyperactivity, and repetitive behaviors. Ank3-1b KO mice exhibited an overall increase in slow gamma (~25-45 Hz) power compared to controls, and slow gamma power correlated with seizure phenotype severity across behaviors. During sleep, increased slow gamma power correlated with decreased time spent in the rapid eye movement (REM) stage of sleep. Seizures were more common during REM sleep compared to non-REM (NREM) sleep. We also found that Ank3-1b KO mice were hyperactive and exhibited a repetitive behavior phenotype that co-occurred with increased slow gamma power. Our results identify a novel EEG biomarker associating Ank3 genetic variation with BD and epilepsy and suggest modulation of gamma oscillations as a potential therapeutic target.

Efficient, continual, and generalized learning in the brain - neural mechanism of Mental Schema 2.0

There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose "Mental Schema 2.0," a new computational property underlying the brain's unique learning ability that can be implemented in ANNs.

Cortical Synchrony and Information Flow during Transition from Wakefulness to Light Non-Rapid Eye Movement Sleep

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.

Serum C-reactive protein level and sleep characteristics in obstructive sleep apnea syndrome comorbid with panic disorder: a preliminary study

OBJECTIVE

Investigate the sleep characteristics of patients with obstructive sleep apnea syndrome (OSAS) comorbidity with panic disorder (PD), exploring its potential association with serum C-reactive protein (CRP) levels.

PATIENTS AND METHODS

Fifty-four patients (25 OSAS patients with PD and 29 without PD) and 25 healthy controls (HCs) were included. The Self-rating anxiety scale (SAS), self-rating depression scale (SDS), and Pittsburgh sleep quality index (PSQI) were used to assess the mood and sleep quality of the subjects. All patients had circulating CRP levels and polysomnography was performed.

RESULTS

OSAS with PD had higher SAS, SDS, PSQI than the OSAS without PD. Compared to OSAS without PD, OSAS with PD had higher percentage of non- rapid eye movement sleep 1 and 2 (N1 and N2%), sleep latency, and a lower percentage of rapid eye movement sleep (REM%). Respiratory-related microarousal index, AHI, and time below 90% oxygen saturation (T90) were low, and the lowest oxygen saturation (LO2) was high. Serum CRP levels in OSAS patients with PD were lower than that in OSAS patients without PD, but higher than that in HCs. In OSAS patients with PD, serum CRP levels were negatively correlated with wake time after sleep onset and SAS scores but positively correlated with sleep efficiency and N2%. Serum CRP levels were positively correlated with T90 and negatively correlated with LO2.

CONCLUSION

OSAS patients with PD had worse sleep quality, less severe OSAS, and low serum CRP levels. Serum CRP levels in OSAS patients with PD were associated with poorer sleep quality and duration of hypoxia rather than AHI.

Memory ability and retention performance relate differentially to sleep depth and spindle type

Temporal interactions between non-rapid eye movement (NREM) sleep rhythms especially the coupling between cortical slow oscillations (SO, ∼1 Hz) and thalamic spindles (∼12 Hz) have been proposed to contribute to multi-regional interactions crucial for memory processing and cognitive ability. We investigated relationships between NREM sleep depth, sleep spindles and SO-spindle coupling regarding memory ability and memory consolidation in healthy humans. Findings underscore the functional relevance of spindle dynamics (slow versus fast), SO-phase, and most importantly NREM sleep depth for cognitive processing. Cross-frequency coupling analyses demonstrated stronger precise temporal coordination of slow spindles to SO down-state in N2 for subjects with higher general memory ability. A GLM model underscored this relationship, and furthermore that fast spindle properties were predictive of overnight memory consolidation. Our results suggest cognitive fingerprints dependent on conjoint fine-tuned SO-spindle temporal coupling, spindle properties, and brain sleep state.

Sleep and the hypothalamus

Neural substrates of wakefulness, rapid eye movement sleep (REMS), and non-REMS (NREMS) in the mammalian hypothalamus overlap both anatomically and functionally with cellular networks that support physiological and behavioral homeostasis. Here, we review the roles of sleep neurons of the hypothalamus in the homeostatic control of thermoregulation or goal-oriented behaviors during wakefulness. We address how hypothalamic circuits involved in opposing behaviors such as core body temperature and sleep compute conflicting information and provide a coherent vigilance state. Finally, we highlight some of the key unresolved questions and challenges, and the promise of a more granular view of the cellular and molecular diversity underlying the integrative role of the hypothalamus in physiological and behavioral homeostasis.

Hippocampal injections of soluble amyloid-beta oligomers alter electroencephalographic activity during wake and slow-wave sleep in rats

BACKGROUND

Soluble amyloid-beta oligomers (Aβo) begin to accumulate in the human brain one to two decades before a clinical diagnosis of Alzheimer's disease (AD). The literature supports that soluble Aβo are implicated in synapse and neuronal losses in the brain regions such as the hippocampus. This region importantly contributes to explicit memory, the first type of memory affected in AD. During AD preclinical and prodromal stages, people are also experiencing wake/sleep alterations such as insomnia (e.g., difficulty initiating sleep, decreased sleep duration), excessive daytime sleepiness, and sleep schedule modifications. In addition, changes in electroencephalographic (EEG) activity during wake and sleep have been reported in AD patients and animal models. However, the specific contribution of Aβo to wake/sleep alterations is poorly understood and was investigated in the present study.

METHODS

Chronic hippocampal injections of soluble Aβo were conducted in male rats and combined with EEG recording to determine the progressive impact of Aβ pathology specifically on wake/sleep architecture and EEG activity. Bilateral injections were conducted for 6 consecutive days, and EEG acquisition was done before, during, and after Aβo injections. Immunohistochemistry was used to assess neuron numbers in the hippocampal dentate gyrus (DG).

RESULTS

Aβo injections did not affect the time spent in wakefulness, slow wave sleep (SWS), and paradoxical sleep but altered EEG activity during wake and SWS. More precisely, Aβo increased slow-wave activity (SWA; 0.5-5 Hz) and low-beta activity (16-20 Hz) during wake and decreased theta (5-9 Hz) and alpha (9-12 Hz) activities during SWS. Moreover, the theta activity/SWA ratio during wake and SWS was decreased by Aβo. These effects were significant only after 6 days of Aβo injections and were found with alterations in neuron counts in the DG.

CONCLUSIONS

We found multiple modifications of the wake and SWS EEG following Aβo delivery to the hippocampus. These findings expose a specific EEG signature of Aβ pathology and can serve the development of non-invasive and cost-effective markers for the early diagnosis of AD or other amyloid-related diseases.

Somatostatin neurons in prefrontal cortex initiate sleep-preparatory behavior and sleep via the preoptic and lateral hypothalamus

The prefrontal cortex (PFC) enables mammals to respond to situations, including internal states, with appropriate actions. One such internal state could be 'tiredness'. Here, using activity tagging in the mouse PFC, we identified particularly excitable, fast-spiking, somatostatin-expressing, γ-aminobutyric acid (GABA) (PFCSst-GABA) cells that responded to sleep deprivation. These cells projected to the lateral preoptic (LPO) hypothalamus and the lateral hypothalamus (LH). Stimulating PFCSst-GABA terminals in the LPO hypothalamus caused sleep-preparatory behavior (nesting, elevated theta power and elevated temperature), and stimulating PFCSst-GABA terminals in the LH mimicked recovery sleep (non-rapid eye-movement sleep with higher delta power and lower body temperature). PFCSst-GABA terminals had enhanced activity during nesting and sleep, inducing inhibitory postsynaptic currents on diverse cells in the LPO hypothalamus and the LH. The PFC also might feature in deciding sleep location in the absence of excessive fatigue. These findings suggest that the PFC instructs the hypothalamus to ensure that optimal sleep takes place in a suitable place.

Pontine Waves Accompanied by Short Hippocampal Sharp Wave-Ripples During Non-rapid Eye Movement Sleep

Ponto-geniculo-occipital or pontine (P) waves have long been recognized as an electrophysiological signature of rapid eye movement (REM) sleep. However, P-waves can be observed not just during REM sleep, but also during non-REM (NREM) sleep. Recent studies have uncovered that P-waves are functionally coupled with hippocampal sharp wave ripples (SWRs) during NREM sleep. However, it remains unclear to what extent P-waves during NREM sleep share their characteristics with P-waves during REM sleep and how the functional coupling to P-waves modulates SWRs. Here, we address these issues by performing multiple types of electrophysiological recordings and fiber photometry in both sexes of mice. P-waves during NREM sleep share their waveform shapes and local neural ensemble dynamics at a short (~100 milliseconds) timescale with their REM sleep counterparts. However, the dynamics of mesopontine cholinergic neurons are distinct at a longer (~10 seconds) timescale: although P-waves are accompanied by cholinergic transients, the cholinergic tone gradually reduces before P-wave genesis during NREM sleep. While P-waves are coupled to hippocampal theta rhythms during REM sleep, P-waves during NREM sleep are accompanied by a rapid reduction in hippocampal ripple power. SWRs coupled with P-waves are short-lived and hippocampal neural firing is also reduced after P-waves. These results demonstrate that P-waves are part of coordinated sleep-related activity by functionally coupling with hippocampal ensembles in a state-dependent manner.

A Spatial-Temporal Transformer Architecture Using Multi-Channel Signals for Sleep Stage Classification

Sleep stage classification is a fundamental task in diagnosing and monitoring sleep diseases. There are 2 challenges that remain open: (1) Since most methods only rely on input from a single channel, the spatial-temporal relationship of sleep signals has not been fully explored. (2) Lack of sleep data makes models hard to train from scratch. Here, we propose a vision Transformer-based architecture to process multi-channel polysomnogram signals. The method is an end-to-end framework that consists of a spatial encoder, a temporal encoder, and an MLP head classifier. The spatial encoder using a pre-trained Vision Transformer captures spatial information from multiple PSG channels. The temporal encoder utilizing the self-attention mechanism understands transitions between nearby epochs. In addition, we introduce a tailored image generation method to extract features within multi-channel and reshape them for transfer learning. We validate our method on 3 datasets and outperform the state-of-the-art algorithms. Our method fully explores the spatial-temporal relationship among different brain regions and addresses the problem of data insufficiency in clinical environments. Benefiting from reformulating the problem as image classification, the method could be applied to other 1D-signal problems in the future.

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.

Hyperpolarization-Activated Cation Channels Shape the Spiking Frequency Preference of Human Cortical Layer 5 Pyramidal Neurons

Discerning the contribution of specific ionic currents to complex neuronal dynamics is a difficult, but important, task. This challenge is exacerbated in the human setting, although the widely characterized uniqueness of the human brain compared with preclinical models necessitates the direct study of human neurons. Neuronal spiking frequency preference is of particular interest given its role in rhythm generation and signal transmission in cortical circuits. Here, we combine the frequency-dependent gain (FDG), a measure of spiking frequency preference, and novel in silico analyses to dissect the contributions of individual ionic currents to the suprathreshold features of human layer 5 (L5) neurons captured by the FDG. We confirm that a contemporary model of such a neuron, primarily constrained to capture subthreshold activity driven by the hyperpolarization-activated cyclic nucleotide gated (h-) current, replicates key features of the in vitro FDG both with and without h-current activity. With the model confirmed as a viable approximation of the biophysical features of interest, we applied new analysis techniques to quantify the activity of each modeled ionic current in the moments before spiking, revealing unique dynamics of the h-current. These findings motivated patch-clamp recordings in analogous rodent neurons to characterize their FDG, which confirmed that a biophysically detailed model of these neurons captures key interspecies differences in the FDG. These differences are correlated with distinct contributions of the h-current to neuronal activity. Together, this interdisciplinary and multispecies study provides new insights directly relating the dynamics of the h-current to suprathreshold spiking frequency preference in human L5 neurons.

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.

The Metabotropic Glutamate 5 Receptor in Sleep and Wakefulness: Focus on the Cortico-Thalamo-Cortical Oscillations

Sleep is an essential innate but complex behaviour which is ubiquitous in the animal kingdom. Our knowledge of the distinct neural circuit mechanisms that regulate sleep and wake states in the brain are, however, still limited. It is therefore important to understand how these circuits operate during health and disease. This review will highlight the function of mGlu5 receptors within the thalamocortical circuitry in physiological and pathological sleep states. We will also evaluate the potential of targeting mGlu5 receptors as a therapeutic strategy for sleep disorders that often co-occur with epileptic seizures.

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

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

Astrocytic modulation of neuronal signalling

Neuronal signalling is a key element in neuronal communication and is essential for the proper functioning of the CNS. Astrocytes, the most prominent glia in the brain play a key role in modulating neuronal signalling at the molecular, synaptic, cellular, and network levels. Over the past few decades, our knowledge about astrocytes and their functioning has evolved from considering them as merely a brain glue that provides structural support to neurons, to key communication elements. Astrocytes can regulate the activity of neurons by controlling the concentrations of ions and neurotransmitters in the extracellular milieu, as well as releasing chemicals and gliotransmitters that modulate neuronal activity. The aim of this review is to summarise the main processes through which astrocytes are modulating brain function. We will systematically distinguish between direct and indirect pathways in which astrocytes affect neuronal signalling at all levels. Lastly, we will summarize pathological conditions that arise once these signalling pathways are impaired focusing on neurodegeneration.

Silencing KCC2 in mouse dorsal hippocampus compromises spatial and contextual memory

Delayed upregulation of the neuronal chloride extruder KCC2 underlies the progressive shift in GABA signaling polarity during development. Conversely, KCC2 downregulation is observed in a variety of neurological and psychiatric disorders often associated with cognitive impairment. Reduced KCC2 expression and function in mature networks may disrupt GABA signaling and promote anomalous network activities underlying these disorders. However, the causal link between KCC2 downregulation, altered brain rhythmogenesis, and cognitive function remains elusive. Here, by combining behavioral exploration with in vivo electrophysiology we assessed the impact of chronic KCC2 downregulation in mouse dorsal hippocampus and showed it compromises both spatial and contextual memory. This was associated with altered hippocampal rhythmogenesis and neuronal hyperexcitability, with increased burst firing in CA1 neurons during non-REM sleep. Reducing neuronal excitability with terbinafine, a specific Task-3 leak potassium channel opener, occluded the impairment of contextual memory upon KCC2 knockdown. Our results establish a causal relationship between KCC2 expression and cognitive performance and suggest that non-epileptiform rhythmopathies and neuronal hyperexcitability are central to the deficits caused by KCC2 downregulation in the adult mouse brain.

Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation

Drowsy driving is a common, but underestimated phenomenon in terms of associated risks as it often results in crashes causing fatalities and serious injuries. It is a challenging task to alert or reduce the driver's drowsy state using non-invasive techniques. In this study, a drowsiness reduction strategy has been developed and analyzed using exposure to different light colors and recording the corresponding electrical and biological brain activities. 31 subjects were examined by dividing them into 2 classes, a control group, and a healthy group. Fourteen EEG and 42 fNIRS channels were used to gather neurological data from two brain regions (prefrontal and visual cortices). Experiments shining 3 different colored lights have been carried out on them at certain times when there is a high probability to get drowsy. The results of this study show that there is a significant increase in HbO of a sleep-deprived participant when he is exposed to blue light. Similarly, the beta band of EEG also showed an increased response. However, the study found that there is no considerable increase in HbO and beta band power in the case of red and green light exposures. In addition to that, values of other physiological signals acquired such as heart rate, eye blinking, and self-reported Karolinska Sleepiness Scale scores validated the findings predicted by the electrical and biological signals. The statistical significance of the signals achieved has been tested using repeated measures ANOVA and t-tests. Correlation scores were also calculated to find the association between the changes in the data signals with the corresponding changes in the alertness level.

Sleep-A brain-state serving systems memory consolidation

Although long-term memory consolidation is supported by sleep, it is unclear how it differs from that during wakefulness. Our review, focusing on recent advances in the field, identifies the repeated replay of neuronal firing patterns as a basic mechanism triggering consolidation during sleep and wakefulness. During sleep, memory replay occurs during slow-wave sleep (SWS) in hippocampal assemblies together with ripples, thalamic spindles, neocortical slow oscillations, and noradrenergic activity. Here, hippocampal replay likely favors the transformation of hippocampus-dependent episodic memory into schema-like neocortical memory. REM sleep following SWS might balance local synaptic rescaling accompanying memory transformation with a sleep-dependent homeostatic process of global synaptic renormalization. Sleep-dependent memory transformation is intensified during early development despite the immaturity of the hippocampus. Overall, beyond its greater efficacy, sleep consolidation differs from wake consolidation mainly in that it is supported, rather than impaired, by spontaneous hippocampal replay activity possibly gating memory formation in neocortex.

Sleep fMRI with simultaneous electrophysiology at 9.4 T in male mice

Sleep is ubiquitous and essential, but its mechanisms remain unclear. Studies in animals and humans have provided insights of sleep at vastly different spatiotemporal scales. However, challenges remain to integrate local and global information of sleep. Therefore, we developed sleep fMRI based on simultaneous electrophysiology at 9.4 T in male mice. Optimized un-anesthetized mouse fMRI setup allowed manifestation of NREM and REM sleep, and a large sleep fMRI dataset was collected and openly accessible. State dependent global patterns were revealed, and state transitions were found to be global, asymmetrical and sequential, which can be predicted up to 17.8 s using LSTM models. Importantly, sleep fMRI with hippocampal recording revealed potentiated sharp-wave ripple triggered global patterns during NREM than awake state, potentially attributable to co-occurrence of spindle events. To conclude, we established mouse sleep fMRI with simultaneous electrophysiology, and demonstrated its capability by revealing global dynamics of state transitions and neural events.

P2X7 receptor-activated microglia in cortex is critical for sleep disorder under neuropathic pain

Neuropathic pain (NP) is associated with sleep disturbances, which may substantially influence the quality of life. Clinical and animal studies demonstrated that neurotransmitter is one of the main contributors to cause sleep disturbances induced by NP. Recently, it was reported that P2X7 receptors (P2X7R) are widely expressed in microglia, which serves crucial role in neuronal activity in the pain and sleep-awake cycle. In this study, we adopted the chronic constriction injury (CCI) model to establish the progress of chronic pain and investigated whether P2X7R of microglia in cortex played a critical role in sleep disturbance induced by NP. At electroencephalogram (EEG) level, sleep disturbance was observed in mice treated with CCI as they exhibited mechanical and thermal hypersensitivity, and inhibition of P2X7R ameliorated these changes. We showed a dramatic high level of P2X7R and Iba-1 co-expression in the cortical region, and the inhibition of P2X7R also adversely affected it. Furthermore, the power of LFPs in ventral posterior nucleus (VP) and primary somatosensory cortex (S1) which changed in the CCI group was adverse after the inhibition of P2X7R. Furthermore, inhibition of P2X7R also decreased the VP-S1 coherence which increased in CCI group. Nuclear magnetic resonance demonstrated inhibition of P2X7R decreased glutamate (Glu) levels in thalamic and cortical regions which were significantly increased in the CCI mice. Our findings provide evidence that NP has a critical effect on neuronal activity linked to sleep and may built up a new target for the development of sleep disturbances under chronic pain conditions.

State-dependent and region-specific alterations of cerebellar connectivity across stable human wakefulness and NREM sleep states

Sleep regulation and functioning may rely on systematic coordination throughout the whole brain, including the cerebellum. However, whether and how interactions between the cerebellum and other brain regions vary across sleep stages remain poorly understood. Here, using simultaneous EEG-fMRI recordings captured from 73 participants during wakefulness and non-rapid eye movement (NREM) sleep, we constructed cerebellar connectivity among intrinsic functional networks with intra-cerebellar, neocortical and subcortical regions. We uncovered that cerebellar connectivity exhibited sleep-dependent alterations: slight differences between wakefulness and N1/N2 sleep and greater changes in N3 sleep than other states. Region-specific cerebellar connectivity changes between N2 sleep and N3 sleep were also revealed: general breakdown of intra-cerebellar connectivity, enhancement of limbic-cerebellar connectivity and alterations of cerebellar connectivity with spatially specific neocortices. Further correlation analysis showed that functional connectivity between the cerebellar Control II network and regions (including the insula, hippocampus, and amygdala) correlated with delta power during N3 and beta power during N2 sleep. These findings systematically reveal altered cerebellar connectivity among intrinsic networks from wakefulness to deep sleep and highlight the potential role of the cerebellum in sleep regulation and functioning.

Alterations in microglial morphology concentrate in the habitual sleeping period of the mouse

In nocturnal animals, waking appears during the dark period while maximal non-rapid-eye-movement sleep (NREMS) with electroencephalographic slow-wave-activity (SWA) takes place at the beginning of the light period. Vigilance states associate with variable levels of neuronal activity: waking with high-frequency activity patterns while during NREMS, SWA influences neuronal activity in many brain areas. On a glial level, sleep deprivation modifies microglial morphology, but only few studies have investigated microglia through the physiological sleep-wake cycle. To quantify microglial morphology (territory, volume, ramification) throughout the 24 h light-dark cycle, we collected brain samples from inbred C57BL male mice (n = 51) every 3 h and applied a 3D-reconstruction method for microglial cells on the acquired confocal microscopy images. As microglia express regional heterogeneity and are influenced by local neuronal activity, we chose to investigate three interconnected and functionally well-characterized brain areas: the somatosensory cortex (SC), the dorsal hippocampus (HC), and the basal forebrain (BF). To temporally associate microglial morphology with vigilance stages, we performed a 24 h polysomnography in a separate group of animals (n = 6). In line with previous findings, microglia displayed de-ramification in the 12 h light- and hyper-ramification in the 12 h dark period. Notably, we found that the decrease in microglial features was most prominent within the early hours of the light period, co-occurring with maximal sleep SWA. By the end of the light period, all features reached maximum levels and remained steadily elevated throughout the dark period with minor regional differences. We propose that vigilance-stage specific neuronal activity, and SWA, could modify microglial morphology.

SSAVE: A tool for analysis and visualization of sleep periods using electroencephalography data

Human sleep architecture is structured with repeated episodes of rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep. An overnight sleep study facilitates identification of macro and micro changes in the pattern and duration of sleep stages associated with sleep disorders and other aspects of human mental and physical health. Overnight sleep studies record, in addition to electroencephalography (EEG) and other electro-physiological signals, a sequence of sleep-stage annotations. SSAVE, introduced here, is open-source software that takes sleep-stage annotations and EEG signals as input, identifies and characterizes periods of NREM and REM sleep, and produces a hypnogram and its time-matched EEG spectrogram. SSAVE fills an important gap for the rapidly growing field of sleep medicine by providing an easy-to-use tool for sleep-period identification and visualization. SSAVE can be used as a Python package, a desktop standalone tool or through a web portal. All versions of the SSAVE tool can be found on: https://manticore.niehs.nih.gov/ssave.

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.

Sleep slow waves' negative-to-positive-phase transition: a marker of cognitive and apneic status in aging

The sleep slow-wave (SW) transition between negative and positive phases is thought to mirror synaptic strength and likely depends on brain health. This transition shows significant age-related changes but has not been investigated in pathological aging. The present study aimed at comparing the transition speed and other characteristics of SW between older adults with amnestic mild cognitive impairment (aMCI) and cognitively normal (CN) controls with and without obstructive sleep apnea (OSA). We also examined the association of SW characteristics with the longitudinal changes of episodic memory and executive functions and the degree of subjective cognitive complaints. aMCI (no/mild OSA = 17; OSA = 15) and CN (no/mild OSA = 20; OSA = 17) participants underwent a night of polysomnography and a neuropsychological evaluation at baseline and 18 months later. Participants with aMCI had a significantly slower SW negative-to-positive-phase transition speed and a higher proportion of SW that are "slow-switchers" than CN participants. These SW measures in the frontal region were significantly correlated with memory decline and cognitive complaints in aMCI and cognitive improvements in CN participants. The transition speed of the SW that are "fast-switchers" was significantly slower in OSA compared to no or mild obstructive sleep apnea participants. The SW transition-related metrics showed opposite correlations with the longitudinal episodic memory changes depending on the participants' cognitive status. These relationships were particularly strong in participants with aMCI. As the changes of the SW transition-related metrics in pathological aging might reflect synaptic alterations, future studies should investigate whether these new metrics covary with biomarker levels of synaptic integrity in this population.

Microglial TNFα orchestrates protein phosphorylation in the cortex during the sleep period and controls homeostatic sleep

Sleep intensity is adjusted by the length of previous awake time, and under tight homeostatic control by protein phosphorylation. Here, we establish microglia as a new cellular component of the sleep homeostasis circuit. Using quantitative phosphoproteomics of the mouse frontal cortex, we demonstrate that microglia-specific deletion of TNFα perturbs thousands of phosphorylation sites during the sleep period. Substrates of microglial TNFα comprise sleep-related kinases such as MAPKs and MARKs, and numerous synaptic proteins, including a subset whose phosphorylation status encodes sleep need and determines sleep duration. As a result, microglial TNFα loss attenuates the build-up of sleep need, as measured by electroencephalogram slow-wave activity and prevents immediate compensation for loss of sleep. Our data suggest that microglia control sleep homeostasis by releasing TNFα which acts on neuronal circuitry through dynamic control of phosphorylation.

Coupling between the prelimbic cortex, nucleus reuniens, and hippocampus during NREM sleep remains stable under cognitive and homeostatic demands

The interplay between the medial prefrontal cortex and hippocampus during non-rapid eye movement (NREM) sleep contributes to the consolidation of contextual memories. To assess the role of the thalamic nucleus reuniens (Nre) in this interaction, we investigated the coupling of neuro-oscillatory activities among prelimbic cortex, Nre, and hippocampus across sleep states and their role in the consolidation of contextual memories using multi-site electrophysiological recordings and optogenetic manipulations. We showed that ripples are time-locked to the Up state of cortical slow waves, the transition from UP to DOWN state in thalamic slow waves, the troughs of cortical spindles, and the peaks of thalamic spindles during spontaneous sleep, rebound sleep and sleep following a fear conditioning task. In addition, spiking activity in Nre increased before hippocampal ripples, and the phase-locking of hippocampal ripples and thalamic spindles during NREM sleep was stronger after acquisition of a fear memory. We showed that optogenetic inhibition of Nre neurons reduced phase-locking of ripples to cortical slow waves in the ventral hippocampus whilst their activation altered the preferred phase of ripples to slow waves in ventral and dorsal hippocampi. However, none of these optogenetic manipulations of Nre during sleep after acquisition of fear conditioning did alter sleep-dependent memory consolidation. Collectively, these results showed that Nre is central in modulating hippocampus and cortical rhythms during NREM sleep.

Sleep Staging Framework with Physiologically Harmonized Sub-Networks

Sleep screening is an important tool for both healthcare and neuroscientific research. Automatic sleep scoring is an alternative to the time-consuming gold-standard manual scoring procedure. Recently there have seen promising results on automatic stage scoring by extracting spatio-temporal features via deep neural networks from electroencephalogram (EEG). However, such methods fail to consistently yield good performance due to a missing piece in data representation: the medical criterion of the sleep scoring task on top of EEG features. We argue that capturing stage-specific features that satisfy the criterion of sleep medicine is non-trivial for automatic sleep scoring. This paper considers two criteria: Transient stage marker and Overall profile of EEG features, then we propose a physiologically meaningful framework for sleep stage scoring via mixed deep neural networks. The framework consists of two sub-networks: feature extraction networks, constructed in consideration of the physiological characteristics of sleep, and an attention-based scoring decision network. Moreover, we quantize the framework for potential use under an IoT setting. For proof-of-concept, the performance of the proposed framework is demonstrated by introducing multiple sleep datasets with the largest comprising 42,560 h recorded from 5,793 subjects. From the experiment results, the proposed method achieves a competitive stage scoring performance, especially for Wake, N2, and N3, with higher F1 scores of 0.92, 0.86, and 0.88, respectively. Moreover, the feasibility analysis of framework quantization provides a potential for future implementation in the edge computing field and clinical settings.