Regional slow waves and spindles in human sleep

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

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

Characterising the power spectrum dynamics of the non-REM to REM sleep transition

The transition from non-rapid eye movement (NREM) to rapid eye movement (REM) sleep is considered a transitional or intermediate stage (IS), characterised by high amplitude spindles in the frontal cortex and theta activity in the occipital cortex. Early reports in rats showed an IS lasting from 1 to 5 s, but recent studies suggested a longer duration of this stage of up to 20 s. To further characterise the IS, we analysed its spectral characteristics on electrocorticogram (ECoG) recordings of the olfactory bulb (OB), primary motor (M1), primary somatosensory (S1), and secondary visual cortex (V2) in 12 Wistar male adult rats. By comparing the IS with consolidated NREM/REM epochs, our results reveal that the IS has specific power spectral patterns that fall out of the NREM and REM sleep state power distribution. Specifically, the main findings were that sigma (11-16 Hz) power in OB, M1, S1, and V2 increased during the IS compared with NREM and REM sleep, which started first in the frontal part of the brain (OB -54 s, M1 -53 s) prior to the last spindle occurrence. The beta band (17-30 Hz) power showed a similar pattern to that of the sigma band, starting -54 s before the last spindle occurrence in the M1 cortex. Notably, sigma infraslow coupling (~0.02 Hz) increased during the IS but occurred at a slower frequency (~0.01 Hz) compared with NREM sleep. Thus, we argue that the NREM to REM transition contains its own local spectral profile, in accordance with previous reports, and is more extended than described previously.

The Awakening Brain is Characterized by a Widespread and Spatiotemporally Heterogeneous Increase in High Frequencies

Morning awakening is part of everyday life. Surprisingly, information remains scarce on its underlying neurophysiological correlates. Here simultaneous polysomnography and stereo-electroencephalography recordings from 18 patients are used to assess the spectral and connectivity content of the process of awakening at a local level 15 min before and after the awakening. Awakenings from non-rapid eye movement sleep are accompanied by a widespread increase in ripple (>80 Hz) power in the fronto-temporal and parieto-insular regions, with connectivity showing an almost exclusive increase in the ripple band in the somatomotor, default, dorsal attention, and frontoparietal networks. Awakenings from rapid eye movement sleep are characterized by a widespread and almost exclusive increase in the ripple band in all available brain lobes, and connectivity increases mainly in the low ripple band in the limbic system as well as the default, dorsal attention, somatomotor, and frontoparietal networks.

Interictal network dysfunction and cognitive impairment in epilepsy

Epilepsy is diagnosed when neural networks become capable of generating excessive or hypersynchronous activity patterns that result in observable seizures. In many cases, epilepsy is associated with cognitive comorbidities that persist between seizures and negatively impact quality of life. Dysregulation of the coordinated physiological network interactions that are required for cognitive function has been implicated in mediating these enduring symptoms, but the causal mechanisms are often elusive. Here, we provide an overview of neural network abnormalities with the potential to contribute to cognitive dysfunction in epilepsy. We examine these pathological interactions across spatial and temporal scales, additionally highlighting the dynamics that arise in response to the brain's intrinsic capacity for plasticity. Understanding these processes will facilitate development of network-level interventions to address cognitive comorbidities that remain undertreated by currently available epilepsy therapeutics.

SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data

Sleep research uses electroencephalography (EEG) to infer brain activity in health and disease. Beyond standard sleep scoring, there is growing interest in advanced EEG analysis that requires extensive preprocessing to improve the signal-to-noise ratio and specialized analysis algorithms. While many EEG software packages exist, sleep research has unique needs (e.g., specific artifacts, event detection). Currently, sleep investigators use different libraries for specific tasks in a 'fragmented' configuration that is inefficient, prone to errors, and requires the learning of multiple software environments. This complexity creates a barrier for beginners. Here, we present SleepEEGpy, an open-source Python package that simplifies sleep EEG preprocessing and analysis. SleepEEGpy builds on MNE-Python, PyPREP, YASA, and SpecParam to offer an all-in-one, beginner-friendly package for comprehensive sleep EEG research, including (i) cleaning, (ii) independent component analysis, (iii) sleep event detection, (iv) spectral feature analysis, and visualization tools. A dedicated dashboard provides an overview to evaluate data and preprocessing, serving as an initial step prior to detailed analysis. We demonstrate SleepEEGpy's functionalities using overnight high-density EEG data from healthy participants, revealing characteristic activity signatures typical of each vigilance state: alpha oscillations in wakefulness, spindles and slow waves in NREM sleep, and theta activity in REM sleep. We hope that this software will be adopted and further developed by the sleep research community, and constitute a useful entry point tool for beginners in sleep EEG research.

Threshold Values of Sleep Spindles Features in Healthy Adults Using Scalp-EEG and Associations With Sleep Parameters

OBJECTIVE

Sleep spindles are an electrophysiological fingerprint of the sleeping human brain. They can be described in terms of duration, frequency, amplitude, and density, and vary widely according to age and sex. Spindles play a role in sleep and wake functions and are altered in several neurological and psychiatric disorders. This study established the first threshold values for sleep spindles in healthy adults using scalp-EEG and explored their associations with other sleep parameters.

METHODS

This observational prospective study was conducted with 80 healthy participants stratified by age and sex (40.9 years, range 19-74, 50% females). All participants underwent in-laboratory polysomnography. Sleep spindles during N2 were analyzed using an automated procedure and categorized as fast (> 13 Hz) or slow (≤ 13 Hz).

RESULTS

For fast spindles, the threshold values were duration (0.80-1.11 s), frequency (13.4-14.3 Hz), amplitude (5.2-15.2 μV), and density (1.0-5.8 spindles/min). For slow spindles, the values were duration (0.79-1.17 s), frequency (12.3-12.9 Hz), amplitude (4.1-13.2 μV), and density (0.03-3.15 spindles/min). From age 40 onwards, the density, amplitude, and duration of both types of spindles decreased; the amplitudes of both types of spindles were higher in females. Higher amplitude in fast spindles was associated with increased excessive daytime sleepiness and an increased proportion of slow-wave sleep.

INTERPRETATION

This study provides the first threshold values for sleep spindle characteristics in healthy adults. The findings emphasize the importance of investigating spindles to develop innovative biomarkers for neurological and psychiatric disorders and to gain deeper insights into the functioning of the sleeping brain.

Noninvasive Intracranial Source Signal Localization and Decoding with High Spatiotemporal Resolution

High spatiotemporal resolution of noninvasive electroencephalography (EEG) signals is an important prerequisite for fine brain-computer manipulation. However, conventional scalp EEG has a low spatial resolution due to the volume conductor effect, making it difficult to accurately identify the intent of brain-computer manipulation. In recent years, transcranial focused ultrasound modulated EEG technology has increasingly become a research hotspot, which is expected to acquire noninvasive acoustoelectric coupling signals with a high spatial and temporal resolution. In view of this, this study established a transcranial focused ultrasound numerical simulation model and experimental platform based on a real brain model and a 128-array phased array, further constructed a 3-dimensional transcranial multisource dipole localization and decoding numerical simulation model and experimental platform based on the acoustic field platform, and developed a high-precision localization and decoding algorithm. The results show that the simulation-guided phased-array acoustic field experimental platform can achieve accurate focusing in both pure water and transcranial conditions within a safe threshold, with a modulation range of 10 mm, and the focal acoustic pressure can be enhanced by more than 200% compared with that of transducer self-focusing. In terms of dipole localization decoding results, the proposed algorithm in this study has a localization signal-to-noise ratio of 24.18 dB, which is 50.59% higher than that of the traditional algorithm, and the source signal decoding accuracy is greater than 0.85. This study provides a reliable experimental basis and technical support for high-spatiotemporal-resolution noninvasive EEG signal acquisition and precise brain-computer manipulation.

Sleep Modulates Neural Timescales and Spatiotemporal Integration in the Human Cortex

Spontaneous neural dynamics manifest across multiple temporal and spatial scales, which are thought to be intrinsic to brain areas and exhibit hierarchical organization across the cortex. In wake, a hierarchy of timescales is thought to naturally emerge from microstructural properties, gene expression, and recurrent connections. A fundamental question is timescales' organization and changes in sleep, where physiological needs are different. Here, we describe two measures of neural timescales, obtained from broadband activity and gamma power, which display complementary properties. We leveraged intracranial electroencephalography in 106 human epilepsy patients (48 females) to characterize timescale changes from wake to sleep across the cortical hierarchy. We show that both broadband and gamma timescales are globally longer in sleep than in wake. While broadband timescales increase along the sensorimotor-association axis, gamma ones decrease. During sleep, slow waves can explain the increase of broadband and gamma timescales, but only broadband ones show a positive association with slow-wave density across the cortex. Finally, we characterize spatial correlations and their relationship with timescales as a proxy for spatiotemporal integration, finding high integration at long distances in wake for broadband and at short distances in sleep for gamma timescales. Our results suggest that mesoscopic neural populations possess different timescales that are shaped by anatomy and are modulated by the sleep/wake cycle.

Molecular dynamics of amyloid-β transport in Alzheimer's disease: Exploring therapeutic plasma exchange with albumin replacement - Current insights and future perspectives

INTRODUCTION

The complex process of amyloid-β (Aβ) transportation across the blood-brain and blood-cerebrospinal fluid barriers is crucial for preventing Aβ accumulation, which linked to dementia and neurodegeneration. This review explores therapeutic plasma exchange with albumin replacement in Alzheimer's disease, based on the dynamics of amyloid-β between the brain, plasma, and cerebrospinal fluid.

METHODOLOGY

A comprehensive literature review was conducted using PubMed/Medline, Cochrane Library, and open databases (bioRxiv, MedRixv, preprint.org) up to April 30, 2023. The first search utilized the following MeSH terms and keywords: 'Plasma Exchange', 'Plasmapheresis', 'Therapeutic plasma exchange', 'Apheresis', 'Aβ', 'p-tau', 'Total-tau', 'Alzheimer's disease', 'Cognitive dysfunction', 'neurodegenerative diseases', 'centrifugation', 'membranous', and 'filtration' in the Title/Abstract, yielding 146 results. A second search with the keywords: 'Albumin', 'Aβ', 'BBB', 'Alzheimer's dementia', and 'Nerve degeneration' resulted in 125 additional articles for analysis. Finally, a third search using keywords: 'Albumin structural domains', 'Albumin-Aβ interactions', 'Albumin-endothelial interactions', and 'Post-Translational Modification' produced 193 results for further review.

RESULTS/DISCUSSION

Therapeutic plasma exchange shows potential as a disease-modifying therapy for dementia, specifically for Alzheimer's disease. Additionally, the promising role of albumin supplementation in cognitive improvement has attracted attention. However, clinical evidence supporting therapeutic plasma exchange for dementia remains limited, necessitating further research and development to mitigate potential adverse effects. A deeper understanding of the molecular dynamics of Aβ transportation and the mechanisms of therapeutic plasma exchange is essential. A critical evaluation of existing evidence highlights the importance of balancing potential benefits with associated risks, which will guide the development and application of these treatments in neurodegenerative diseases.

Mechanistic insights into the interaction between epilepsy and sleep

Epidemiological evidence has demonstrated associations between sleep and epilepsy, but we lack a mechanistic understanding of these associations. If sleep affects the pathophysiology of epilepsy and the risk of seizures, as suggested by correlative evidence, then understanding these effects could provide crucial insight into the basic mechanisms that underlie the development of epilepsy and the generation of seizures. In this Review, we provide in-depth discussion of the associations between epilepsy and sleep at the cellular, network and system levels and consider the mechanistic underpinnings of these associations. We also discuss the clinical relevance of these associations, highlighting how they could contribute to improvements in the management of epilepsy. A better understanding of the mechanisms that govern the interactions between epilepsy and sleep could guide further research and the development of novel approaches to the management of epilepsy.

Chronic insomnia, REM sleep instability and emotional dysregulation: A pathway to anxiety and depression?

The world-wide prevalence of insomnia disorder reaches up to 10% of the adult population. Women are more often afflicted than men, and insomnia disorder is a risk factor for somatic and mental illness, especially depression and anxiety disorders. Persistent hyperarousals at the cognitive, emotional, cortical and/or physiological levels are central to most theories regarding the pathophysiology of insomnia. Of the defining features of insomnia disorder, the discrepancy between minor objective polysomnographic alterations of sleep continuity and substantive subjective impairment in insomnia disorder remains enigmatic. Microstructural alterations, especially in rapid eye movement sleep ("rapid eye movement sleep instability"), might explain this mismatch between subjective and objective findings. As rapid eye movement sleep represents the most highly aroused brain state during sleep, it might be particularly prone to fragmentation in individuals with persistent hyperarousal. In consequence, mentation during rapid eye movement sleep may be toned more as conscious-like wake experience, reflecting pre-sleep concerns. It is suggested that this instability of rapid eye movement sleep is involved in the mismatch between subjective and objective measures of sleep in insomnia disorder. Furthermore, as rapid eye movement sleep has been linked in previous works to emotional processing, rapid eye movement sleep instability could play a central role in the close association between insomnia and depressive and anxiety disorders.

Dreaming conundrum

Dreaming, a common yet mysterious cognitive phenomenon, is an involuntary process experienced by individuals during sleep. Although the fascination with dreams dates back to ancient times and gained therapeutic significance through psychoanalysis in the early twentieth century, its scientific investigation only gained momentum with the discovery of rapid eye movement (REM) sleep in the 1950s. This review synthesises current research on the neurobiological and psychological aspects of dreaming, including factors influencing dream recall and content, neurophysiological correlates, and experimental models, and discusses the implications for clinical practice.

The yin and yang of sleep-wake regulation: gender gap in need for sleep persists across the human lifespan

PURPOSE

Experiments on sleep regulation under the condition of self-selected light-dark and wake-sleep cycle revealed a larger sleep fraction in women than men. In questionnaire studies, women usually report a greater need for sleep than men. They also sleep more than men in overall and at most life course stages. However, much of the gap was explained by work/family responsibilities and gendered time tradeoffs. We tested whether females from childhood to late adulthood spend longer time in bed than males on weekends, and whether on weekdays, their time in bed fails to become longer due to the socially rather than biologically governed earlier weekday wake ups.

METHODS

A set of 340 paired subsamples of male and female sleep times was analyzed. The averaged sleep times were calculated and simulated.

RESULTS

Female-male difference in weekend time in bed attained the value of 0.26 h (the 0.16-h earlier bedtime was combined with the 0.10-h later risetime). The gender gap in weekday risetime was non-significant and the direction of gender difference in weekday time in bed was inconsistent across the lifespan. Model-based simulations provided support for the prediction that the male-female gap in sleep desire can reflect a higher level of sleep slow-wave activity (SWA) in females found in the vast majority of published studies.

CONCLUSION

Since SWA is strongly indicative of age and age-associated change in neuronal networks, the persistency of female's greater desire for sleep can be explained by the underlying sex difference in age-associated structural changes in these networks.

A continuous approach to explain insomnia and subjective-objective sleep discrepancy

Understanding insomnia is crucial for improving its diagnosis and treatment. However, many subjective complaints about insomnia do not align with objective measures of sleep quality, as is the case in subjective-objective sleep discrepancy (SOSD). We address this discrepancy by measuring sleep intrusions and instability in polysomnographic recordings from a large clinical database. Using machine learning, we develop personalized models to infer hypnodensities-a continuous and probabilistic measure of sleep dynamics-, and analyze them via information theory to measure intrusions and instability in a principled way. We find that insomnia with SOSD involves sleep intrusions during intra-sleep wakefulness, while insomnia without SOSD shows wake intrusions during sleep, indicating distinct etiologies. By mapping these metrics to standard sleep features, we provide a continuous and interpretable framework for measuring sleep quality. This approach integrates and values subjective insomnia complaints with physiological data for a more accurate view of sleep quality and its disorders.

Monitoring Sleep Quality Through Low α-Band Activity in the Prefrontal Cortex Using a Portable Electroencephalogram Device: Longitudinal Study

BACKGROUND

The pursuit of sleep quality has become an important aspect of people's global quest for overall health. However, the objective neurobiological features corresponding to subjective perceptions of sleep quality remain poorly understood. Although previous studies have investigated the relationship between electroencephalogram (EEG) and sleep, the lack of longitudinal follow-up studies raises doubts about the reproducibility of their findings.

OBJECTIVE

Currently, there is a gap in research regarding the stable associations between EEG data and sleep quality assessed through multiple data collection sessions, which could help identify potential neurobiological targets related to sleep quality.

METHODS

In this study, we used a portable EEG device to collect resting-state prefrontal cortex EEG data over a 3-month follow-up period from 42 participants (27 in the first month, 25 in the second month, and 40 in the third month). Each month, participants' sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) to estimate their recent sleep quality.

RESULTS

We found that there is a significant and consistent positive correlation between low α band activity in the prefrontal cortex and PSQI scores (r=0.45, P<.001). More importantly, this correlation remained consistent across all 3-month follow-up recordings (P<.05), regardless of whether we considered the same cohort or expanded the sample size. Furthermore, we discovered that the periodic component of the low α band primarily contributed to this significant association with PSQI.

CONCLUSIONS

These findings represent the first identification of a stable and reliable neurobiological target related to sleep quality through multiple follow-up sessions. Our results provide a solid foundation for future applications of portable EEG devices in monitoring sleep quality and screening for sleep disorders in a broad population.

Clinical and intracranial electrophysiological signatures of post-operative and post-ictal delirium

OBJECTIVES

(1) Gain insight into the mechanisms of postoperative delirium (POD). (2) Determine mechanistic overlap with post-ictal delirium (PID). Epilepsy patients undergoing intracranial electrophysiological monitoring can experience both POD and PID, and thus are suitable subjects for these investigations.

METHODS

POD was assessed daily after surgery. PID was assessed following seizures. Resting state data were collected following delirium assessments, during a control period, and during sleep. Slow-wave activity (SWA: 1-4 Hz) and resting state functional connectivity were compared between different time points and according to delirium status.

RESULTS

POD was present in 6 of 20 participants. Post-operatively, SWA was globally elevated in all participants but highest in POD+ participants. POD+ participants exhibited altered functional connectivity compared to POD-. These differences persisted even after resolution of delirium. PID was present in 7 of 15 participants and was predicted by seizures involving prefrontal cortex. PID+ participants exhibited higher post-ictal SWA versus PID-; no differences in functional connectivity were observed. Post-operative and post-ictal SWA was comparable to sleep in some participants.

CONCLUSIONS

Elevated SWA may predispose patients to both post-operative and post-ictal delirium and may indicate overlapping mechanisms.

SIGNIFICANCE

Delirium treatments focused on SWA may be most effective for ameliorating cognitive symptoms.

Simultaneous activation of different subtypes of dopamine receptors may lead to activation of homeostatic sleep regulatory mechanisms

Dopaminergic system gains importance in homeostatic sleep regulation, but the role of different dopamine receptors is not well-defined. 72 h rat electrocorticogram and sleep recordings were made after single application of dopaminergic drugs in clinical use or at least underwent clinical trials. The non-selective agonist apomorphine evoked short pharmacological sleep deprivation with intense wakefulness followed by pronounced sleep rebound. D2 agonist bromocriptine induced moderate and extended increase in wakefulness without a homeostatic sleep replacement but downregulated slow wave sleep need for 72 h. Selective D1 agonist SKF-38393 failed to induce enhanced waking sufficient for sleep replacement. High-dose D2 antagonism by sulpiride temporarily enhanced wakefulness. All drugs evoked extended (72 h) sleep changes after single application. Opposite sleep changes could be seen after the application of different doses in case of both bromocriptine and sulpiride. Theta, beta and gamma power reflected intensity differences in drug-induced wakefulness stages. Apomorphine- and high sulpiride dose-induced waking showed elevated power in all three frequency bands. Bromocriptine-induced wakefulness dominated by beta activity. Enhancement of more, than one type of electrocorticogram activities during wakefulness was a prerequisite for the activation of sleep homeostasis. According to present data, D1- or D2-like receptor agonism are not separately involved in the homeostatic regulation of slow wave sleep. Simultaneous and non-selective agonism on DA receptors is the most effective way to elicit intense W, which is followed by slow wave sleep rebound. REM sleep rebound could be evoked by D2 agonism. Rebound indicates the activation of homeostatic sleep regulation, but with unknown exact mechanisms.

Neuronal traveling waves form preferred pathways using synaptic plasticity

Traveling waves of neuronal spiking activity are commonly observed across the brain, but their intrinsic function is still a matter of investigation. Experiments suggest that they may be valuable in the consolidation of memory or learning, indicating that consideration of traveling waves in the presence of plasticity might be important. A possible outcome of this consideration is that the synaptic pathways, necessary for the propagation of these waves, will be modified by the waves themselves. This will create a feedback loop where both the traveling waves and the strengths of the available synaptic pathways will change. To computationally investigate this, we model a sheet of cortical tissue by considering a quasi two-dimensional network of model neurons locally connected with plastic synaptic weights using Spike-Timing Dependent Plasticity (STDP). By using different stimulation conditions (central, stochastic, and alternating stimulation), we demonstrate that starting from a random network, traveling waves with STDP will form and strengthen propagation pathways. With progressive formation of traveling waves, we observe increases in synaptic weight along the direction of wave propagation, increases in propagation speed when pathways are strengthened over time, and an increase in the local order of synaptic weights. We also present evidence that the interaction between traveling waves and plasticity can serve as a mechanism of network-wide competition between available pathways. With an improved understanding of the interactions between traveling waves and synaptic plasticity, we can approach a fuller understanding of mechanisms of learning, computation, and processing within the brain.

Infant sleep EEG features at 4 months as biomarkers of neurodevelopment at 18 months

BACKGROUND

Sleep parameters evolve in parallel with neurodevelopment. Sleep participates in synaptic homeostasis and memory consolidation and infant sleep parameters correlate with later aspects of early childhood cognition.

METHODS

Typically developing, term-born infants had a diurnal sleep-EEG at 4 months and Griffiths III developmental assessment at 18 months. EEG analysis included sleep macrostructure (i.e. durations of total sleep and sleep stages, and latencies to sleep and REM), sleep spindle features, and quantitative EEG features (qEEG): interhemispheric connectivity and spectral power. We assessed the correlations between these EEG features and Griffiths III quotients.

RESULTS

Sleep recordings from 92 infants were analyzed. Sleep latency was positively associated with the Griffiths III Foundations of Learning subscale and N3 sleep duration was positively correlated with the Personal-Social-Emotional subscale. Sleep spindle synchrony was negatively associated with Eye and Hand Coordination, Personal-Social-Emotional, Gross Motor, and General Development quotients. Sleep spindle duration was negatively associated with the Personal-Social-Emotional and Gross Motor subscales. In some sleep states, delta 1 and 2 EEG spectral power and interhemispheric coherence measures were correlated with subscale quotients.

CONCLUSION

Certain sleep features in the EEG of 4-month-old infants are associated with neurodevelopment at 18 months and may be useful early biomarkers of neurodevelopment.

IMPACT

This study shows that the EEG during infant sleep may provide insights into later neurodevelopmental outcomes. We have examined novel EEG sleep spindle features and shown that spindle duration and synchrony may help predict neurodevelopmental outcomes. Sleep macrostructure elements such as latency to sleep, N3 duration, and qEEG features such as interhemispheric coherence and spectral power measures at 4 months may be useful for the assessment of future neurodevelopmental outcomes. Due to exceptional neuroplasticity in infancy, EEG biomarkers of neurodevelopment may support early and targeted intervention to optimize outcomes.

CircRNA Itm2b induces oxidative stress via the interaction with Sirt1-Nox4 to aggravate sleep disturbances after traumatic brain injury

Sleep disorders (SD) are common sequelae following traumatic brain injury (TBI) and may be linked to mitochondrial oxidative stress dysregulation after TBI. Increasing evidence showed that circRNAs play crucial roles in nervous system diseases. However, the involvement of circRNAs in sleep disturbances after TBI is not characterized. In this study, differentially expressed circRNAs were identified by RNA sequencing. Sleep quality in TBI patients was assessed through sleep scales and electroencephalograms. Further experiments were conducted to investigate the role of circItm2b. We found that circItm2b was elevated and involved sleep disorder in TBI patients. Over-expression of circItm2b might aggravate sleep disturbances in mice after TBI. Mechanically, circItm2b regulates Nox4 expression through binding Sirt1, which influences mitochondrial oxidative stress-caused circadian protein losses. Moreover, the knockdown of circItm2b attenuated mitochondrial oxidative stress-induced circadian proteins losses via circItm2b/Sirt1/Nox4 axis after TBI, which might suggest that circItm2b may serve as a prognostic marker for improving sleep disorders and represent a promising therapeutic target for TBI-related sleep disturbances.

Neural Oscillations in the Somatosensory and Motor Cortex Distinguish Dexmedetomidine-Induced Anesthesia and Sleep in Rats

BACKGROUND

Anesthesia is featured by behavioral and physiological characteristics such as decreased sensory and motor function, loss of consciousness, etc. Some anesthetics such as dexmedetomidine (DEX), induce electroencephalogram signatures close to non-rapid eye movement sleep. Studies have shown that sleep is primarily driven by the activation of subcortical sleep-promoting neural pathways.

AIMS

However, the neuronal level electrophysiology features of anesthesia and how they differ from sleep is still not fully understood.

MATERIALS AND METHODS

In the present study, we recorded neuronal activity simultaneously from somatosensory cortex (S1) and motor cortex (M1) during awake, sleep, and DEX-induced anesthesia in rats.

RESULTS

The results show that DEX increased local field potential (LFP) power across a relatively wide band (1-25 Hz) in both S1 and M1. The coherence between S1 LFP and M1 LFP increased significantly in the delta and alpha bands. Power spectrum analysis during DEX-induced anesthesia revealed relatively high power in the delta and alpha bands, but low power in the theta and beta bands. Overall, the firing rate of individual neurons decreased after DEX. Correlation analysis of firing rate and LFP power indicate that more neurons were correlated, either positively or negatively, with LFPs during DEX-induced anesthesia compared to sleep.

DISCUSSION

Although these results showed enhancement of cortical LFP power in both DEX-induced anesthesia and sleep, different patterns of spike-field correlation suggest that the two states may be regulated by different cortical mechanisms.

CONCLUSION

Distinguishing anesthesia from sleep with neural oscillations could lead to more personalized, safer, and more effective approaches to managing consciousness in medical settings, with the potential for broad applications in neuroscience and clinical practice.

EEG brain networks modulation during sleep onset: the effects of aging

The aim of the present study is to investigate differences in brain networks modulation during the pre- and post-sleep onset period, both within and between two groups of young and older individuals. Thirty-six healthy elderly and 40 young subjects participated. EEG signals were recorded during pre- and post-sleep onset periods and functional connectivity analysis, specifically focusing on the small world (SW) index, applied to EEG data (i.e., frequency bands) was examined. Significant differences in SW values were found between the pre-sleep and post-sleep onset phases in both young and older groups, with a reduction in the SW index in the theta band common to both groups. Additionally, an increase in the SW index in the beta band was exclusive to the elderly group during the post-sleep onset period, while an increase in the sigma band was exclusive to the young group. Furthermore, differences between the young and elderly groups were found during both phases, including a decrease in the SW index within the delta band, an increment in the sigma and beta bands in the elderly compared to the young group during the pre-sleep onset period, and a notable absence of sigma band modulation in the elderly group during the post-sleep onset condition. These findings provide insights into age-related changes in sleep-related brain network dynamics and their potential impact on sleep quality and cognitive functions, prompting interventions aimed at supporting healthy aging and addressing age-related cognitive decline.

Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme

Interictal epileptiform discharges (IEDs) such as spikes and sharp waves represent pathological electrophysiological activities occurring in epilepsy patients between seizures. IEDs occur preferentially during non-rapid eye movement (NREM) sleep and are associated with impaired memory and cognition. Despite growing interest, most studies involving IED detections rely on visual annotations or employ simple amplitude threshold approaches. Alternatively, advanced computerized detection methods are not standardized or publicly available. To address this gap, we introduce a novel dataset comprising multichannel intracranial electroencephalography (iEEG) data recorded at two medical centers during overnight sleep with IED annotations performed by expert neurologists. Utilizing these annotations to train machine learning models via a gradient-boosting algorithm, we demonstrate automated IED detection with high precision (94.4%) and sensitivity (94.3%) that can generalize across individuals and surpass performance of a leading commercial software. The dataset featuring multi-channel annotations with sub-second resolution including hippocampus and medial temporal lobe (MTL) regions is made publicly available, together with the detection algorithm, to advance research on detection methodology, epilepsy, sleep, and cognition.

Membrane potential states gate synaptic consolidation in human neocortical tissue

Synaptic mechanisms that contribute to human memory consolidation remain largely unexplored. Consolidation critically relies on sleep. During slow wave sleep, neurons exhibit characteristic membrane potential oscillations known as UP and DOWN states. Coupling of memory reactivation to these slow oscillations promotes consolidation, though the underlying mechanisms remain elusive. Here, we performed axonal and multineuron patch-clamp recordings in acute human brain slices, obtained from neurosurgeries, to show that sleep-like UP and DOWN states modulate axonal action potentials and temporarily enhance synaptic transmission between neocortical pyramidal neurons. Synaptic enhancement by UP and DOWN state sequences facilitates recruitment of postsynaptic action potentials, which in turn results in long-term stabilization of synaptic strength. In contrast, synapses undergo lasting depression if presynaptic neurons fail to recruit postsynaptic action potentials. Our study offers a mechanistic explanation for how coupling of neural activity to slow waves can cause synaptic consolidation, with potential implications for brain stimulation strategies targeting memory performance.

Cortical evoked activity is modulated by the sleep state in a ferret model of tinnitus. A cross-case study

Subjective tinnitus is a phantom auditory perception in the absence of an actual acoustic stimulus that affects 15% of the global population. In humans, tinnitus is often associated with disturbed sleep and, interestingly, there is an overlap between the brain areas involved in tinnitus and regulation of NREM sleep. We used eight adult ferrets exposed to mild noise trauma as an animal model of tinnitus. We assessed the phantom percept using two operant paradigms sensitive to tinnitus, silent gap detection and silence detection, before and, in a subset of animals, up to six months after the mild acoustic trauma. The integrity of the auditory brainstem was assessed over the same period using auditory brainstem response recordings. Following noise overexposure, ferrets developed lasting, frequency-specific impairments in operant behaviour and evoked brainstem activity. To explore the interaction between sleep and tinnitus, in addition to tracking the behavioural markers of noise-induced tinnitus and hearing impairment after noise overexposure, we evaluated sleep-wake architecture and spontaneous and auditory-evoked EEG activity across vigilance states. Behavioural performance and auditory-evoked activity measurements after noise overexposure suggested distinct degrees of tinnitus and hearing impairment between individuals. Animals that developed signs of tinnitus consistently developed sleep impairments, suggesting a link between the emergence of noise-induced hearing loss and/or tinnitus and sleep disruption. However, neural markers of tinnitus were reduced during sleep, suggesting that sleep may transiently mitigate tinnitus. These results reveal the importance of sleep-wake states in tinnitus and suggest that understanding the neurophysiological link between sleep and tinnitus may provide a new angle for research into the causes of phantom percepts and inform future treatments.

Development of slow oscillation-spindle coupling from infancy to toddlerhood

Sleep has been demonstrated to support memory formation from early life on. The precise temporal coupling of slow oscillations (SOs) with spindles has been suggested as a mechanism facilitating this consolidation process in thalamocortical networks. Here, we investigated the development of sleep spindles and SOs and their coordinate interplay by comparing frontal, central, and parietal electroencephalogram recordings during a nap between infants aged 2-3 months (n = 31) and toddlers aged 14-17 months (n = 49). Spindles and SOs showed quite different maturational patterns between age groups, as to topography, amplitude, and density. Notably, spindle-SO co-occurrence in the infants did not exceed chance levels and was increased to significant levels only in the toddlers. In the infants, the slow SO upstate over frontocortical regions was even associated with a significant decrease in spindles, contrasting with the adult-like increase in spindles seen in toddlers. These results point to an immature processing in thalamocortical networks during sleep in early infancy, possibly diminishing the efficacy of sleep-dependent memory formation at this age.

Interhemispheric asynchrony of NREM EEG at the beginning and end of sleep describes evening vigilance performance in patients undergoing diagnostic polysomnography

Objective.Obstructive sleep apnea (OSA) is associated with deficits in vigilance. This work explored the temporal patterns of OSA-related events during sleep and vigilance levels measured by the psychomotor vigilance test (PVT) in patients undergoing polysomnography (PSG) for suspected OSA.Approach.The PVT was conducted prior to in-laboratory PSG for 80 patients suspected of having OSA. Three groups were formed based on PVT-RT-outcomes and participants were randomly allocated into Training (n= 55) and Test (n= 25) samples. Sleep epochs of non-rapid-eye movement (NREM) electroencephalographic (EEG) asynchrony data, and REM and NREM data for respiratory, arousal, limb movement and desaturation events were analysed. The data were segmented by sleep stage, by sleep blocks (SB) of stable Stage N2, Stage N3, mixed-stage NREM sleep (NXL), and, by Time of Night (TN) across sleep. Models associating this data with PVT groups were developed and tested.Main Results.Amodel using NREM EEG asynchrony data segmented by SB and TN achieved 81.9% accuracy in the Test Cohort. Models based on interhemispheric asynchrony SB data and OSA data segmented by TN achieved 80.6% and 79.5% respectively.Significance.Novel data segmentation methods via blocks of NXL and TN have improved our understanding of the relationship between sleep, OSA and vigilance.

Defining slow wave sleep without slow waves

Recent research by Parks, Schneider, and colleagues demonstrates that brain states during rodent sleep can be predicted from neural activity on millisecond and micrometer scales. These findings contradict the traditional view that defines sleep by brain-wide oscillations. Instead, this work posits that nonoscillatory activity governs different brain states.

The human claustrum tracks slow waves during sleep

Slow waves are a distinguishing feature of non-rapid-eye-movement (NREM) sleep, an evolutionarily conserved process critical for brain function. Non-human studies suggest that the claustrum, a small subcortical nucleus, coordinates slow waves. We show that, in contrast to neurons from other brain regions, claustrum neurons in the human brain increase their spiking activity and track slow waves during NREM sleep, suggesting that the claustrum plays a role in coordinating human sleep architecture.

Louis EK, McCarter SJ, Bukartyk J, Przybelski SA, Kamykowski MG, Spychalla AJ, Machulda MM, Boeve BF, Petersen RC, Jack CR, Lowe VJ, Graff-Radford J, Worrell GA, Somers VK, Varga AW, Vemuri P. Non-rapid eye movement sleep slow-wave activity features are associated with amyloid accumulation in older adults with obstructive sleep apnoea

Obstructive sleep apnoea (OSA) is associated with an increased risk for cognitive impairment and dementia, which likely involves Alzheimer's disease pathology. Non-rapid eye movement slow-wave activity (SWA) has been implicated in amyloid clearance, but it has not been studied in the context of longitudinal amyloid accumulation in OSA. This longitudinal retrospective study aims to investigate the relationship between polysomnographic and electrophysiological SWA features and amyloid accumulation. From the Mayo Clinic Study of Aging cohort, we identified 71 participants ≥60 years old with OSA (mean baseline age = 72.9 ± 7.5 years, 60.6% male, 93% cognitively unimpaired) who had at least 2 consecutive Amyloid Pittsburgh Compound B (PiB)-PET scans and a polysomnographic study within 5 years of the baseline scan and before the second scan. Annualized PiB-PET accumulation [global ΔPiB(log)/year] was estimated by the difference between the second and first log-transformed global PiB-PET uptake estimations divided by the interval between scans (years). Sixty-four participants were included in SWA analysis. SWA was characterized by the mean relative spectral power density (%) in slow oscillation (SO: 0.5-0.9 Hz) and delta (1-3.9 Hz) frequency bands and by their downslopes (SO-slope and delta-slope, respectively) during the diagnostic portion of polysomnography. We fit linear regression models to test for associations among global ΔPiB(log)/year, SWA features (mean SO% and delta% or mean SO-slope and delta-slope), and OSA severity markers, after adjusting for age at baseline PiB-PET, APOE ɛ4 and baseline amyloid positivity. For 1 SD increase in SO% and SO-slope, global ΔPiB(log)/year increased by 0.0033 (95% CI: 0.0001; 0.0064, P = 0.042) and 0.0069 (95% CI: 0.0009; 0.0129, P = 0.026), which were comparable to 32% and 59% of the effect size associated with baseline amyloid positivity, respectively. Delta-slope was associated with a reduction in global ΔPiB(log)/year by -0.0082 (95% CI: -0.0143; -0.0021, P = 0.009). Sleep apnoea severity was not associated with amyloid accumulation. Regional associations were stronger in the pre-frontal region. Both slow-wave slopes had more significant and widespread regional associations. Annualized PiB-PET accumulation was positively associated with SO and SO-slope, which may reflect altered sleep homeostasis due to increased homeostatic pressure in the setting of unmet sleep needs, increased synaptic strength, and/or hyper-excitability in OSA. Delta-slope was inversely associated with PiB-PET accumulation, suggesting it may represent residual physiological activity. Further investigation of SWA dynamics in the presence of sleep disorders before and after treatment is necessary for understanding the relationship between amyloid accumulation and SWA physiology.

Multi-scale spiking network model of human cerebral cortex

Although the structure of cortical networks provides the necessary substrate for their neuronal activity, the structure alone does not suffice to understand the activity. Leveraging the increasing availability of human data, we developed a multi-scale, spiking network model of human cortex to investigate the relationship between structure and dynamics. In this model, each area in one hemisphere of the Desikan-Killiany parcellation is represented by a $1\,\mathrm{mm^{2}}$ column with a layered structure. The model aggregates data across multiple modalities, including electron microscopy, electrophysiology, morphological reconstructions, and diffusion tensor imaging, into a coherent framework. It predicts activity on all scales from the single-neuron spiking activity to the area-level functional connectivity. We compared the model activity with human electrophysiological data and human resting-state functional magnetic resonance imaging (fMRI) data. This comparison reveals that the model can reproduce aspects of both spiking statistics and fMRI correlations if the inter-areal connections are sufficiently strong. Furthermore, we study the propagation of a single-spike perturbation and macroscopic fluctuations through the network. The open-source model serves as an integrative platform for further refinements and future in silico studies of human cortical structure, dynamics, and function.

Weekday and weekend sleep times across the human lifespan: a model-based simulation

PURPOSE

The shifts in the opposite directions, toward later and earlier sleep timing, occur during the transition through adolescence and adulthood, respectively. Such a n-shape of age-associated change in sleep timing does not resemble the inverse relationship of sleep duration with ages. Age-associated variation in the parameters of the mechanisms of circadian and homeostatic regulation of sleep would underlie these different shapes of relationship of sleep times with ages. Here, we searched for a parsimonious explanation of these different shapes by simulating sleep times on weekdays and weekends with one of the variants of the two-process model of sleep regulation.

METHODS

Using mean age of a sample with reported sleep times on weekdays and weekends, the whole set of 1404 such samples was subdivided into 15 age subsets. Simulations of sleep times in these subsets were performed with and without the suggestion of age-associated variation in the circadian phase.

RESULTS

Simulations showed that the age-associated decay of slow-wave activity can parsimoniously explain not only the parallel decreases in weekend sleep duration and rate of the buildup of sleep pressure during the wake phase of the sleep-wake cycle, but also both the delay and advance of sleep timing during the transition through adolescence and adulthood, respectively.

CONCLUSION

The almost functional relationships were revealed between the age-related changes in sleep duration, rate of the buildup of sleep pressure, and slow-wave activity that is a good electrophysiological marker of cortical metabolic rate and synaptic density, strength and efficacy.

A visual paired associate learning (vPAL) paradigm to study memory consolidation during sleep

Sleep improves the consolidation and long-term stability of newly formed memories and associations. Most research on human declarative memory and its consolidation during sleep uses word-pair associations requiring exhaustive learning. In the present study, we present the visual paired association learning (vPAL) paradigm, in which participants learn new associations between images of celebrities and animals. The vPAL is based on a one-shot exposure that resembles learning in natural conditions. We tested if vPAL can reveal a role for sleep in memory consolidation by assessing the specificity of memory recognition, and the cued recall performance, before and after sleep. We found that a daytime nap improved the stability of recognition memory and discrimination abilities compared to identical intervals of wakefulness. By contrast, cued recall of associations did not exhibit significant sleep-dependent effects. High-density electroencephalography during naps further revealed an association between sleep spindle density and stability of recognition memory. Thus, the vPAL paradigm opens new avenues for future research on sleep and memory consolidation across ages and heterogeneous populations in health and disease.

Coordinated NREM sleep oscillations among hippocampal subfields modulate synaptic plasticity in humans

The integration of hippocampal oscillations during non-rapid eye movement (NREM) sleep is crucial for memory consolidation. However, how cardinal sleep oscillations bind across various subfields of the human hippocampus to promote information transfer and synaptic plasticity remains unclear. Using human intracranial recordings from 25 epilepsy patients, we find that hippocampal subfields, including DG/CA3, CA1, and SUB, all exhibit significant delta and spindle power during NREM sleep. The DG/CA3 displays strong coupling between delta and ripple oscillations with all the other hippocampal subfields. In contrast, the regions of CA1 and SUB exhibit more precise coordination, characterized by event-level triple coupling between delta, spindle, and ripple oscillations. Furthermore, we demonstrate that the synaptic plasticity within the hippocampal circuit, as indexed by delta-wave slope, is linearly modulated by spindle power. In contrast, ripples act as a binary switch that triggers a sudden increase in delta-wave slope. Overall, these results suggest that different subfields of the hippocampus regulate one another through diverse layers of sleep oscillation synchronization, collectively facilitating information processing and synaptic plasticity during NREM sleep.

Oscillatory and nonoscillatory sleep electroencephalographic biomarkers of the epileptic network

OBJECTIVE

In addition to the oscillatory brain activity, the nonoscillatory (scale-free) components of the background electroencephalogram (EEG) may provide further information about the complexity of the underlying neuronal network. As epilepsy is considered a network disease, such scale-free metrics might help to delineate the epileptic network. Here, we performed an analysis of the sleep oscillatory (spindle, slow wave, and rhythmic spectral power) and nonoscillatory (H exponent) intracranial EEG using multiple interictal features to estimate whether and how they deviate from normalcy in 38 adults with drug-resistant epilepsy.

METHODS

To quantify intracranial EEG abnormalities within and outside the seizure onset areas, patients' values were adjusted based on normative maps derived from the open-access Montreal Neurological Institute open iEEG Atlas. In a subset of 29 patients who underwent resective surgery, we estimated the predictive value of these features to identify the epileptogenic zone in those with a good postsurgical outcome.

RESULTS

We found that distinct sleep oscillatory and nonoscillatory metrics behave differently across the epileptic network, with the strongest differences observed for (1) a reduction in spindle activity (spindle rates and rhythmic sigma power in the 10-16 Hz band), (2) a higher rhythmic gamma power (30-80 Hz), and (3) a higher H exponent (steeper 1/f slope). As expected, epileptic spikes were also highest in the seizure onset areas. Furthermore, in surgical patients, the H exponent achieved the highest performance (balanced accuracy of .76) for classifying resected versus nonresected channels in good outcome patients.

SIGNIFICANCE

This work suggests that nonoscillatory components of the intracranial EEG signal could serve as promising interictal sleep candidates of epileptogenicity in patients with drug-resistant epilepsy. Our findings further advance the understanding of epilepsy as a disease, whereby absence or loss of sleep physiology may provide information complementary to pathological epileptic processes.

Prefrontal synaptic regulation of homeostatic sleep pressure revealed through synaptic chemogenetics

Sleep is regulated by homeostatic processes, yet the biological basis of sleep pressure that accumulates during wakefulness, triggers sleep, and dissipates during sleep remains elusive. We explored a causal relationship between cellular synaptic strength and electroencephalography delta power indicating macro-level sleep pressure by developing a theoretical framework and a molecular tool to manipulate synaptic strength. The mathematical model predicted that increased synaptic strength promotes the neuronal "down state" and raises the delta power. Our molecular tool (synapse-targeted chemically induced translocation of Kalirin-7, SYNCit-K), which induces dendritic spine enlargement and synaptic potentiation through chemically induced translocation of protein Kalirin-7, demonstrated that synaptic potentiation of excitatory neurons in the prefrontal cortex (PFC) increases nonrapid eye movement sleep amounts and delta power. Thus, synaptic strength of PFC excitatory neurons dictates sleep pressure in mammals.

Oscillatory-Quality of sleep spindles links brain state with sleep regulation and function

Here, we characterized the dynamics of sleep spindles, focusing on their damping, which we estimated using a metric called oscillatory-Quality (o-Quality), derived by fitting an autoregressive model to electrophysiological signals, recorded from the cortex in mice. The o-Quality of sleep spindles correlates weakly with their amplitude, shows marked laminar differences and regional topography across cortical regions, reflects the level of synchrony within and between cortical networks, is strongly modulated by sleep-wake history, reflects the degree of sensory disconnection, and correlates with the strength of coupling between spindles and slow waves. As most spindle events are highly localized and not detectable with conventional low-density recording approaches, o-Quality thus emerges as a valuable metric that allows us to infer the spread and dynamics of spindle activity across the brain and directly links their spatiotemporal dynamics with local and global regulation of brain states, sleep regulation, and function.

E-Suture: Mixed-Conducting Suture for Medical Devices

Modern implantable bioelectronics demand soft, biocompatible components that make robust, low-impedance connections with the body and circuit elements. Concurrently, such technologies must demonstrate high efficiency, with the ability to interface between the body's ionic and external electronic charge carriers. Here, a mixed-conducting suture, the e-suture, is presented. Composed of silk, the conducting polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), and insulating jacketing polymers,the resulting e-suture has mixed-conducting properties at the interface with biological tissue as well as effective insulation along its length. The e-suture can be mechanically integrated into electronics, enabling the acquisition of biopotentials such as electrocardiograms, electromyograms, and local field potentials (LFP). Chronic, in vivo acquisition of LFP with e-sutures remains stable for months with robust brain activity patterns. Furthermore, e-sutures can establish electrophoretic-based local drug delivery, potentially offering enhanced anatomical targeting and decreased side effects associated with systemic administration, while maintaining an electrically conducting interface for biopotential monitoring. E-sutures expand on the conventional role of sutures and wires by providing a soft, biocompatible, and mechanically sound structure that additionally has multifunctional capacity for sensing, stimulation, and drug delivery.

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

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

Does slow oscillation-spindle coupling contribute to sleep-dependent memory consolidation? A Bayesian meta-analysis

The active system consolidation theory suggests that information transfer between the hippocampus and cortex during sleep underlies memory consolidation. Neural oscillations during sleep, including the temporal coupling between slow oscillations (SO) and sleep spindles (SP), may play a mechanistic role in memory consolidation. However, differences in analytical approaches and the presence of physiological and behavioral moderators have led to inconsistent conclusions. This meta-analysis, comprising 23 studies and 297 effect sizes, focused on four standard phase-amplitude coupling measures including coupling phase, strength, percentage, and SP amplitude, and their relationship with memory retention. We developed a standardized approach to incorporate non-normal circular-linear correlations. We found strong evidence supporting that precise and strong SO-fast SP coupling in the frontal lobe predicts memory consolidation. The strength of this association is mediated by memory type, aging, and dynamic spatio-temporal features, including SP frequency and cortical topography. In conclusion, SO-SP coupling should be considered as a general physiological mechanism for memory consolidation.

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

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

Using spectral continuity to extract breathing rate from heart rate and its applications in sleep physiology

Introduction: ECG Derived Respiration (EDR) are a set of methods used for extracting the breathing rate from the Electrocardiogram (ECG). Recent studies revealed a tight connection between breathing rate and more specifically the breathing patterns during sleep and several related pathologies. Yet, while breathing rate and more specifically the breathing pattern is recognised as a vital sign it is less employed than Electroencephalography (EEG) and heart rate in sleep and polysomnography studies. Methods: This study utilised open-access data from the ISRUC sleep database to test a novel spectral-based EDR technique (scEDR). In contrast to previous approaches, the novel method emphasizes spectral continuity and not only the power of the different spectral peaks. scEDR is then compared against a more widely used spectral EDR method that selects the frequency with the highest power as the respiratory frequency (Max Power EDR). Results: scEDR yielded improved performance against the more widely used Max Power EDR in terms of accuracy across all sleep stages and the whole sleep. This study further explores the breathing rate across sleep stages, providing evidence in support of a putative sleep stage "REM0" which was previously proposed based on analysis of the Heart Rate Variability (HRV) but not yet widely discussed. Most importantly, this study observes that the frequency distribution of the heart rate during REM0 is closer to REM than other NREM periods even though most of REM0 was previously classified as NREM sleep by sleep experts following either the original or revised sleep staging criteria. Discussion: Based on the results of the analysis, this study proposes scEDR as a potential low-cost and non-invasive method for extracting the breathing rate using the heart rate during sleep with further studies required to validate its accuracy in awake subjects. In this study, the autonomic balance across different sleep stages, including REM0, was examined using HRV as a metric. The results suggest that sympathetic activity decreases as sleep progresses to NREM3 until it reaches a level similar to the awake state in REM through a transition from REM0.

Thalamic epileptic spikes disrupt sleep spindles in patients with epileptic encephalopathy

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Epileptic encephalopathies share the trait of spike-wave activation during non-REM sleep (EE-SWAS), a sleep stage dominated by sleep spindles, which are brain oscillations known to coordinate offline memory consolidation. Epileptic activity has been proposed to hijack the circuits driving these thalamocortical oscillations, thereby contributing to cognitive impairment. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we provide evidence for epileptic spike interference of thalamic sleep spindle production in patients with EE-SWAS. First, we show that epileptic spikes and sleep spindles are both predicted by slow oscillations during stage two sleep (N2), but at different phases of the slow oscillation. Next, we demonstrate that sleep-activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate increases after a cortical spike, P ≈ 0). We then show that epileptic spikes in the thalamus increase the thalamic spindle refractory period (P ≈ 0). Finally, we show that in three patients with EE-SWAS, there is a downregulation of sleep spindles for 30 s after each thalamic spike (P < 0.01). These direct human thalamocortical observations support a proposed mechanism for epileptiform activity to impact cognitive function, wherein epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy with spike and wave activation during sleep.

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.

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

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

Exploring the local field potential signal from the subthalamic nucleus for phase-targeted auditory stimulation in Parkinson's disease

BACKGROUND

Enhancing slow waves, the electrophysiological (EEG) manifestation of non-rapid eye movement (NREM) sleep, could potentially benefit patients with Parkinson's disease (PD) by improving sleep quality and slowing disease progression. Phase-targeted auditory stimulation (PTAS) is an approach to enhance slow waves, which are detected in real-time in the surface EEG signal.

OBJECTIVE

We aimed to test whether the local-field potential of the subthalamic nucleus (STN-LFP) can be used to detect frontal slow waves and assess the electrophysiological changes related to PTAS.

METHODS

We recruited patients diagnosed with PD and undergoing Percept™ PC neurostimulator (Medtronic) implantation for deep brain stimulation of STN (STN-DBS) in a two-step surgery. Patients underwent three full-night recordings, including one between-surgeries recording and two during rehabilitation, one with DBS+ (on) and one with DBS- (off). Surface EEG and STN-LFP signals from Percept PC were recorded simultaneously, and PTAS was applied during sleep in all three recording sessions.

RESULTS

Our results show that during NREM sleep, slow waves of the cortex and STN are time-locked. PTAS application resulted in power and coherence changes, which can be detected in STN-LFP.

CONCLUSION

Our findings suggest the feasibility of implementing PTAS using solely STN-LFP signal for slow wave detection, thus without a need for an external EEG device alongside the implanted neurostimulator. Moreover, we propose options for more efficient STN-LFP signal preprocessing, including different referencing and filtering to enhance the reliability of cortical slow wave detection in STN-LFP recordings.

Contributions of Basal Ganglia Circuits to Perception, Attention, and Consciousness

Research into ascending sensory pathways and cortical networks has generated detailed models of perception. These same cortical regions are strongly connected to subcortical structures, such as the basal ganglia (BG), which have been conceptualized as playing key roles in reinforcement learning and action selection. However, because the BG amasses experiential evidence from higher and lower levels of cortical hierarchies, as well as higher-order thalamus, it is well positioned to dynamically influence perception. Here, we review anatomical, functional, and clinical evidence to demonstrate how the BG can influence perceptual processing and conscious states. This depends on the integrative relationship between cortex, BG, and thalamus, which allows contributions to sensory gating, predictive processing, selective attention, and representation of the temporal structure of events.

Spatiotemporal EEG dynamics of the sleep onset process in preadolescence

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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