The sleep slow oscillation as a traveling wave

Day-night hyperarousal in tinnitus patients

Tinnitus, which affects 12-30 % of the population, is associated with sleep disturbances and daytime dysfunction, yet the neural mechanisms that link wake-up states remain unclear. This study investigated electroencephalographic (EEG) characteristics of 51 tinnitus patients and 51 controls across wakefulness (eyes-open, eyes-closed, mental arithmetic) and sleep stages (N1, N2, N3, REM) to clarify day-night pathological mechanisms. The key findings showed persistent hyperarousal in tinnitus: wakefulness revealed enhanced gamma power (30-45 Hz) in eyes-closed and task states, while sleep demonstrated elevated gamma/beta power across all stages accompanied by reduced delta/theta power in deep sleep (N2/N3).). An analysis of sleep structure indicates impaired stability in maintaining the N2 stage among tinnitus patients, corroborating a reduction in N3 duration and an increased proportion of the N2 stage. From the wake states to the sleep stages, group × state interactions for the delta/theta power suggest an impaired state regulation capacity in tinnitus patients. Correlation clustering further revealed aberrant integration of wake-related gamma/beta activity into non-rapid eye movement sleep, indicating neuroplastic overgeneralization of wake hyperarousal into sleep. These results extend the so-called loss-of-inhibition theory to sleep, proposing that deficient low-frequency oscillations fail to suppress hyperarousal, impairing sleep-dependent neuroplasticity, and perpetuating daytime symptoms. Furthermore, this study establishes sleep as a critical therapeutic target to interrupt the 24-h dysfunctional cycle of tinnitus.

Daytime naps consolidate Cantonese tone learning through promoting cross-talker perception: The role of prior knowledge

This study investigates whether daytime naps facilitate perceptual learning of Cantonese tones and how prior knowledge mediates the consolidation effect. Ninety Mandarin native speakers were pseudo-randomly assigned to either a nap group, who napped for 1.5 h with brain activities recorded, or the non-nap group, who rested for 1.5 h. They were trained with Cantonese contour-level tonal contrasts and level-level tonal contrasts, followed by a tone identification task (trained talker) before the nap manipulation, and were re-tested (trained and novel talkers) after the nap. The results showed that naps facilitated Cantonese tone learning, with the nap group outperforming the non-nap group in the cross-talker perception. The cross-talker perception effect was specific to contour-level tonal contrasts (consistent with prior knowledge) and was associated with increased sleep spindles and slow oscillations. The findings suggest that prior knowledge plays an important role in prioritizing contour-level tonal contrasts for memory consolidation.

Temporal progression of sleep electroencephalography features in isolated rapid eye movement sleep behaviour disorder

Previous studies indicated that patients with isolated rapid eye movement (REM) sleep behaviour disorder (iRBD) exhibit alterations in spectral electroencephalographic (EEG), spindle, and slow-wave features. As it is currently unknown how these EEG features evolve over time, this study aimed to evaluate their temporal progression in patients with iRBD in comparison to controls. We included 23 patients with iRBD and 23 controls. Two polysomnographies (baseline and follow-up) were recorded with a mean (standard deviation) interval of 4.0 (2.5) years and were automatically analysed for sleep stages, spectral bandpower, spindles, and slow waves. We used linear models to evaluate differences at each time point, and linear mixed-effects models to analyse differences in temporal progression between the groups. At baseline, patients with iRBD presented EEG slowing both in REM (expressed as significantly reduced α-bandpower and increased δ-bandpower in frontal channels) and in non-REM (NREM) sleep (significantly increased slow-to-fast ratio in central channels). These differences vanished at follow-up. In both REM and NREM sleep, γ-bandpower was increased at follow-up in patients with iRBD, resulting in significantly different temporal progression between groups (in occipital channels during REM sleep and frontal channels during NREM sleep). Relative power of sleep spindles was significantly higher at baseline in patients with iRBD in frontal channels, but we observed a significant reduction over time in central channels. Finally, slow waves were significantly shorter in patients with iRBD at both time-points. Our results underscore the need of considering longitudinal data when analysing sleep EEG features in patients with iRBD. The observed temporal changes as markers of progression of neurodegeneration require further investigations.

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.

Efficacy and mechanisms of repeated closed-loop auditory exposure during slow-wave sleep for internet gaming disorder

Internet Gaming Disorder (IGD) is marked by impaired psychological and social functioning but remains without effective treatments. Cue exposure therapy (CET) is typically administered during wakefulness to help extinguish addictive memories. However, recent studies suggest that sleep may be an optimal state for memory modulation. This study aimed to assess the efficacy of repeated closed-loop exposure to game sounds during UP-state of slow-wave sleep (SWS) on IGD. 84 participants meeting DSM-5 criteria for IGD were randomly assigned to sleep intervention/control groups (SIG/SCG) or awake intervention/control groups (AIG/ACG) with two consecutive days of intervention. During SWS of two intervention nights, around 300 sounds were exposed at slow-wave UP-state. While the awake groups received similar auditory cue exposure during the awake state for two consecutive days. Cravings, playtime, and P300 amplitude in the cue reactivity task were recorded at baseline, post-intervention, and follow-up intervals (1, 2, 3, weeks, and 1 month). Results showed that the SIG significantly reduced cravings (p < 0.001), and playtime (p = 0.009) at post-intervention and follow-up, whereas awake CET showed no effect. The SIG exhibited higher low-frequency and early spindle power, along with lower late spindle power after sound exposure. Notably, the linear increase in sound-elicited late spindle power across the 20 intervention blocks over two experiment nights was positively correlated with reduced cravings post-intervention (r = 0.54, p = 0.015), especially among participants achieving a craving reduction greater than 30% after one month. Our findings suggest that closed-loop auditory exposure during SWS presents a promising, non-invasive intervention strategy for treating IGD, potentially exerting its effects by modulating late spindle power.

Similarities and differences between natural sleep and urethane anesthesia

Slow oscillations dominate the EEG or local field potential (LFP) of mammals during specific periods within natural sleep and anesthesia. Such similarities have led to the use of anesthesia as a model to study sleep and state-dependent changes of consciousness. Previous research has documented the similarities between the activated state of urethane anesthesia and natural REM sleep, particularly with respect to network oscillations in the theta (θ) frequency domain. Likewise, the deactivated states, characterized by large amplitude slow waves in both urethane anesthesia and non-REM sleep, have generally been regarded as similar. Here, we report striking differences between slow oscillations in the mouse parietal cortex during the deactivated state of urethane anesthesia and natural non-REM sleep. These differences are notable in the LFP, the underlying current sources, and in the modulation of unit activity. Our data show that slow network oscillations in natural sleep and anesthesia are generated by different mechanisms, despite phenomenological similarities.

Slow wave synchrony during NREM sleep tracks cognitive impairment in prodromal Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) disrupts human sleep architecture more severely than normal aging. However, it remains unclear how AD changes oscillatory neural activity during sleep, and whether such changes foreshadow cognitive decline in AD.

METHODS

We used high-density electroencephalography sleep recordings in 55 participants: (1) 21 healthy older adults, (2) 28 patients with amnestic mild cognitive impairment (aMCI)-a prodromal AD stage, and (3) 6 AD patients.

RESULTS

Cognitive performance robustly decreases with the slow wave (SW) trough amplitude and its synchronization across broad frontocentral cortical areas. Thus, across the AD spectrum, slow wave synchrony declines with cognition, as in normal aging, but at an accelerated pace. Moreover, delayed rapid eye movement (REM) sleep onset in aMCI and AD patients was associated with deficient SW activity, suggesting insufficiently restorative non-REM sleep.

DISCUSSION

These findings suggest that impaired slow waves are closely linked to cognitive impairment and mark disrupted neural activity in AD progression.

HIGHLIGHTS

Detailed analysis of high-density sleep electroencephalography was performed in amnestic mild cognitive impairment and Alzheimer's disease (AD) patients. Cognitive status robustly correlates with slow wave trough and its cortical spread. Delayed rapid eye movement sleep onset associated with AD correlates with diminished slow wave troughs. Impaired slow waves mark progressively disrupted neural activity in prodromal AD.

Machine learning classifier solving the problem of sleep stage imbalance between overnight sleep

Feature extraction follows the American Academy of Sleep Medicine (AASM) sleep score manually and applies it to machine learning with a focus on the generalization of sleep data to enable data-centric artificial intelligence. In real-world clinical testing, the manual scoring of sleep stages is time-consuming and requires significant expertise. Additionally, it is subject to interobserver subjective bias. Machine-learning techniques offer a way to overcome these limitations through automation. However, machine learning for sleep phase prediction can perform poorly for small classes. If the distribution of the training data was unbalanced, the model was trained with a bias toward the majority class. To address this, we experimented with loss function adjustment and resampling methods that assign more weight to the prediction errors of minority classes in sleep scoring to determine how to overcome the data imbalance problem. Machine learning can also be used to compare the accuracy of each channel in identifying electrodes, which should be monitored more closely in real-world clinical testing. Owing to the small amount of data available for machine learning in this study, we used various machine learning classifiers by increasing or decreasing the dataset using sampling techniques and weighting different classes of sleep stages. In our experiments, the best-performing model for classifying sleep stages had an accuracy of 91.9%, kappa of 0.899, and F1-score of 86.9%.

Deficient synaptic neurotransmission results in a persistent sleep-like cortical activity across vigilance states in mice

Growing evidence suggests that brain activity during sleep, as well as sleep regulation, are tightly linked with synaptic function and network excitability at the local and global levels. We previously reported that a mutation in synaptobrevin 2 (Vamp2) in restless (rlss) mice results in a marked increase of wakefulness and suppression of sleep, in particular REM sleep (REMS), as well as increased consolidation of sleep and wakefulness. In this study, using finer-scale in vivo electrophysiology recordings, we report that spontaneous cortical activity in rlss mice during NREM sleep (NREMS) is characterized by an occurrence of abnormally prolonged periods of complete neuronal silence (OFF-periods), often lasting several seconds, similar to the burst suppression pattern typically seen under deep anesthesia. Increased incidence of prolonged network OFF-periods was not specific to NREMS but also present in REMS and wake in rlss mice. Slow-wave activity (SWA) was generally increased in rlss mice relative to controls, while higher frequencies, including theta-frequency activity, were decreased, further resulting in diminished differences between vigilance states. The relative increase in SWA after sleep deprivation was attenuated in rlss mice, suggesting either that rlss mice experience persistently elevated sleep pressure or, alternatively, that the intrusion of sleep-like patterns of activity into the wake state attenuates the accumulation of sleep drive. We propose that a deficit in global synaptic neurotransmitter release leads to "state inertia," reflected in an abnormal propensity of brain networks to enter and remain in a persistent "default state" resembling coma or deep anesthesia.

Electroencephalography Alpha Traveling Waves as Early Predictors of Treatment Response in Major Depressive Episodes: Insights from Intermittent Photic Stimulation

Background: Early evaluation of treatment efficacy in adolescents and young adults with major depressive episodes (MDEs) remains a clinical challenge, often delaying timely therapeutic adjustments. Electroencephalography (EEG) alpha traveling waves, particularly those elicited by intermittent photic stimulation (IPS), may serve as biomarkers reflecting neural dynamics. This study aimed to investigate whether IPS-induced alpha traveling waves could predict early treatment outcomes in transitional-aged youth with MDEs. Methods: We recorded EEG signals from 119 patients aged 16-24 years at admission, prior to a standardized two-week treatment regimen. IPS was applied using multiple stimulus frequencies, and alpha traveling waves were analyzed in terms of directionality (forward vs. backward) and hemispheric lateralization. Results: Alpha traveling wave amplitudes varied across individuals, depending on stimulus frequency and hemisphere. Notably, a higher amplitude of backward alpha traveling waves at 10 Hz IPS in the left hemisphere significantly predicted positive early treatment response. In contrast, forward waves and right hemisphere responses did not show predictive value. Conclusions: IPS-induced backward alpha traveling waves in the left hemisphere may represent promising EEG biomarkers for early prediction of treatment efficacy in youth with MDEs. These findings offer a potential neurophysiological tool to support personalized treatment strategies and inform future clinical applications in adolescent and young adult depression.

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.

Pitolisant alleviates brain network dysfunction and cognitive deficits in a mouse model of Alzheimer's disease

Histamine H3 receptor (H3R) antagonists regulate histamine release that modulates neuronal activity and cognitive function. Although H3R is elevated in Alzheimer's disease (AD) patients, whether H3R antagonists can rescue AD-associated neural impairments and cognitive deficits remains unknown. Pitolisant is a clinically approved H3R antagonist/inverse agonist that treats narcolepsy. Here, we find that pitolisant reverses AD-like pathophysiology and cognitive impairments in an AD mouse model. Behavioral assays and in vivo wide-field Ca2+ imaging revealed that recognition memory, learning flexibility, and slow-wave impairment were all improved following the 15-day pitolisant treatment. Improved recognition memory was tightly correlated with slow-wave coherence, suggesting slow waves serve as a biomarker for treatment response and for AD drug screening. Furthermore, pitolisant reduced amyloid-β deposition and dystrophic neurites surrounding plaques, and enhanced neuronal lysosomal activity, inhibiting which blocked cognitive and slow-wave restoration. Our findings identify pitolisant as a potential therapeutic agent for AD treatments.

The Space-Time Organisation of Sleep Slow Oscillations as Potential Biomarker for Hypersomnolence

Research suggests that the spatial profile of slow wave activity (SWA) could be altered in hypersomnolence. Slow oscillations (SOs; 0.5-1.5 Hz), single waveform events contributing to SWA, can be labelled as Global, Frontal, or Local depending on their presentation on the scalp. We showed that SO space-time types differentiate in their amplitudes, coordination with sleep spindles, and propagation patterns. This study applies our data-driven analysis to the nocturnal sleep of adults with and without hypersomnolence and major depressive disorder (MDD) to explore the potential relevance of SO space-time patterns as hypersomnolence signatures in the sleep EEG. We leverage an existing dataset of nocturnal polysomnography with high-density EEG in 83 adults, organised in four groups depending on the presence/absence of hypersomnolence and on the presence/absence of MDD. Group comparisons were conducted considering either two groups (hypersomnolence status) or the four groups separately. Data shows enhanced Frontal SO activity compared with Global activity in hypersomnolence, with or without MDD, and a loss of Global SO amplitude at central regions in hypersomnolence without MDD compared to controls. As Global SOs travel fronto-parietally, we interpret these results as likely driven by a loss of coordination of Global SO activity in hypersomnolence without MDD, resulting in an overabundance of Frontal SOs. This study suggests that characteristics of Frontal SO and Global SOs may have the potential to differentiate individuals with hypersomnolence without MDD, and that the space-time organisation of SOs could be a mechanistically relevant indicator of changes in sleep brain dynamics related to hypersomnolence.

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.

Phase-Amplitude Coupling of NREM Sleep Oscillations Shows Between-Night Stability and is Related to Overnight Memory Gains

There is growing evidence in humans linking the temporal coupling between spindles and slow oscillations during NREM sleep with the overnight stabilization of memories encoded from daytime experiences in humans. However, whether the type and strength of learning influence that relationship is still unknown. Here we tested whether the amount or type of verbal word-pair learning prior to sleep affects subsequent phase-amplitude coupling (PAC) between spindles and slow oscillations (SO). We measured the strength and preferred timing of such coupling in the EEG of 41 healthy human participants over a post-learning and control night to compare intra-individual changes with inter-individual differences. We leveraged learning paradigms of varying word-pair (WP) load: 40 WP learned to a minimum criterion of 60% correct (n = 11); 40 WP presented twice (n = 15); 120 WP presented twice (n = 15). There were no significant differences in the preferred phase or strength between the control and post-learning nights, in all learning conditions. We observed an overnight consolidation effect (improved performance at delayed recall) for the criterion learning condition only, and only in this condition was the overnight change in memory performance significantly positively correlated with the phase of SO-spindle coupling. These results suggest that the coupling of brain oscillations during human NREM sleep is stable traits that are not modulated by the amount of pre-sleep learning, yet are implicated in the sleep-dependent consolidation of memory-especially when overnight gains in memory are observed.

H-current modulation of cortical Up and Down states

Understanding the link between cellular processes and brain function remains a key challenge in neuroscience. One crucial aspect is the interplay between specific ion channels and network dynamics. This work reveals a role for h-current, a hyperpolarization-activated cationic current, in shaping cortical slow oscillations. Cortical slow oscillations are generated not only during slow wave sleep and deep anaesthesia, but also in association with disorders of consciousness and brain lesions. Cortical slow oscillations exhibit rhythmic periods of activity (Up states) alternating with silent periods (Down states). By progressively reducing h-current in both cortical slices and in a computational model, we observed Up states transformed into prolonged plateaus of sustained firing, while Down states were also significantly extended. This transformation led to a fivefold reduction in oscillation frequency. In a biophysical recurrent network model, we identified the cellular mechanisms underlying this transformation of network dynamics: an increased neuronal input resistance and membrane time constant, increasing neuronal responsiveness to even weak inputs. A partial block of h-current therefore resulted in a change in brain state. HCN (hyperpolarization-activated cyclic nucleotide-gated) channels, which generate h-current, are known targets for neuromodulation, suggesting potential pathways for dynamic control of brain rhythms. KEY POINTS: We investigated the role of h-current in shaping emergent cortical slow oscillation dynamics, specifically Up and Down states, in cortical slices. Blocking h-current transformed Up states into prolonged plateaus of sustained firing, lasting up to 4 s. Down states were also significantly elongated and the oscillatory frequency decreased. A biophysical model of the cortical network replicated these findings and allowed us to explore the underlying mechanisms. An increase in cellular input resistance and time constant led to a rise in network excitability, synaptic responsiveness and firing rates. Our results highlight the significant role of h-current in controlling cortical slow rhythmic patterns, making it a relevant target for neuromodulators regulating brain states.

Protocol for detecting and analyzing non-oscillatory traveling waves from high-spatiotemporal-resolution human electrophysiological recordings

Innovations in electrophysiological recordings and computational analytic techniques enable high-resolution analysis of neural traveling waves. Here, we present a protocol for the detection and analysis of traveling waves from multi-day microelectrode array human electrophysiological recordings through a multi-linear regression statistical approach using point estimator data. We describe steps for traveling wave detection, feature characterization, and propagation pattern analysis. This protocol may improve our understanding of the coordination of neurons during non-oscillatory neural dynamics. For complete details on the use and execution of this protocol, please refer to Smith et al.1.

Unraveling the neurophysiological correlates of phase-specific enhancement of motor memory consolidation via slow-wave closed-loop targeted memory reactivation

Memory consolidation can be enhanced during sleep using targeted memory reactivation (TMR) and closed-loop (CL) acoustic stimulation on the up-phase of slow oscillations (SOs). Here, we test whether applying TMR at specific phases of the SOs (up vs. down vs. no reactivation) can influence the behavioral and neural correlates of motor memory consolidation in healthy young adults. Results show that up- (as compared to down-) state cueing results in greater performance improvement. Sleep electrophysiological data indicate that up- (as compared to down-) stimulated SOs exhibits higher amplitude and greater peak-nested sigma power. Task-related functional magnetic resonance images reveal that up-state cueing strengthens activity in - and segregation of - striato-motor and hippocampal networks; and that these modulations are related to the beneficial effect of TMR on sleep features and performance. Overall, these findings highlight the potential of CL-TMR to induce phase-specific modulations of motor performance, sleep oscillations and brain responses during motor memory consolidation.

Topography of putative bi-directional interaction between hippocampal sharp-wave ripples and neocortical slow oscillations

Systems consolidation relies on coordination between hippocampal sharp-wave ripples (SWRs) and neocortical UP/DOWN states during sleep. However, whether this coupling exists across the neocortex and the mechanisms enabling it remains unknown. By combining electrophysiology in mouse hippocampus (HPC) and retrosplenial cortex (RSC) with wide-field imaging of the dorsal neocortex, we found spatially and temporally precise bi-directional hippocampo-neocortical interaction. HPC multi-unit activity and SWR probability were correlated with UP/DOWN states in the default mode network (DMN), with the highest modulation by the RSC in deep sleep. Further, some SWRs were preceded by the high rebound excitation accompanying DMN DOWN → UP transitions, whereas large-amplitude SWRs were often followed by DOWN states originating in the RSC. We explain these electrophysiological results with a model in which the HPC and RSC are weakly coupled excitable systems capable of bi-directional perturbation and suggest that the RSC may act as a gateway through which SWRs can perturb downstream cortical regions via cortico-cortical propagation of DOWN states.

Pulsed inhibition of corticospinal excitability by the thalamocortical sleep spindle

Thalamocortical sleep spindles, i.e., oscillatory bursts at ∼12-15 Hz of waxing and waning amplitude, are a hallmark feature of non-rapid eye movement (NREM) sleep and believed to play a key role in memory reactivation and consolidation. Generated in the thalamus and projecting to neocortex and hippocampus, they are phasically modulated by neocortical slow oscillations (<1 Hz) and in turn phasically modulate hippocampal sharp-wave ripples (>80 Hz). This hierarchical cross-frequency nesting, where slower oscillations group faster ones into certain excitability phases, may enable phase-dependent plasticity in the neocortex, and spindles have thus been considered windows of plasticity in the sleeping brain. However, the assumed phasic excitability modulation had not yet been demonstrated for spindles. Utilizing a recently developed real-time spindle detection algorithm, we applied spindle phase-triggered transcranial magnetic stimulation (TMS) to the primary motor cortex (M1) hand area to characterize the corticospinal excitability profile of spindles via motor evoked potentials (MEP). MEPs showed net suppression during spindles, driven by a "pulse of inhibition" during its falling flank with no inhibition or facilitation during its peak, rising flank, or trough. This unidirectional ("asymmetric") modulation occurred on top of the general sleep-related inhibition during spindle-free NREM sleep and did not extend into the refractory post-spindle periods. We conclude that spindles exert "asymmetric pulsed inhibition" on corticospinal excitability. These findings and the developed real-time spindle targeting methods enable future studies to investigate the causal role of spindles in phase-dependent synaptic plasticity and systems memory consolidation during sleep by repetitively targeting relevant spindle phases.

Spatiotemporal network dynamics and structural correlates in the human cerebral cortex in vitro

Elucidating human cerebral cortex function is essential for understanding the physiological basis of both healthy and pathological brain states. We obtained extracellular local field potential recordings from slices of neocortical tissue from refractory epilepsy patients. Multi-electrode recordings were combined with histological information, providing a two-dimensional spatiotemporal characterization of human cortical dynamics in control conditions and following modulation of the excitation/inhibition balance. Slices expressed spontaneous rhythmic activity consistent with slow wave activity, comprising alternating active (Up) and silent (Down) states (Up-duration: 0.08 ± 0.03 s, Down-duration: 2.62 ± 2.12 s, frequency: 0.75 ± 0.39 Hz). Up states propagated from deep to superficial layers, with faster propagation speeds than in other species (vertical: 64.6 mm/s; horizontal: 65.9 mm/s). GABAA blockade progressively transformed the emergent activity into epileptiform discharges, marked by higher firing rates, faster network recruitment and propagation, and infraslow rhythmicity (0.01 Hz). This dynamical characterization broadens our understanding of the mechanistic organization of the human cortical network at the micro- and mesoscale.

Brain under surgical anesthesia: focus on nociception and attention network

Surgery endangers the integrity of the body through a continuous stream of noxious stimuli. General anesthesia helps patients cope with the surgery situation. In the first part of our literature review, we present our new knowledge about nociception as described by Sherrington. Anesthesiology researchers have discovered the common mechanism of action of various anesthetics for loss of consciousness (LOC). We review the neural correlates of anesthesia. Maintaining the unconscious state created by anesthetics during surgery is only possible by continuously counteracting nociception. Finally, we present the role of the opioid receptor system in antinociception. Understanding all these processes can help expand our knowledge about nociception, pain and formation of consciousness.

Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep

STUDY OBJECTIVES

To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality.

METHODS

Power spectra from PSGs of 8716 participants, including from the MrOS Sleep Study and the Sleep Heart Health Study, were analyzed in deep learning-based survival models. The best-performing model was further examined using SHapley Additive Explanation (SHAP) for data-driven sleep-stage specific definitions of power bands, which were evaluated in predicting mortality using Cox Proportional Hazards models.

RESULTS

Survival analyses, adjusted for known covariates, identified multiple EEG frequency bands across all sleep stages predicting all-cause mortality. For EEG, we found an all-cause mortality hazard ratio (HR) of 0.90 (CI: 95% 0.85 to 0.96) for 12-15 Hz in N2, 0.86 (CI: 95% 0.82 to 0.91) for 0.75-1.5 Hz in N3, and 0.87 (CI: 95% 0.83 to 0.92) for 14.75-33.5 Hz in rapid-eye-movement sleep. For EOG, we found several low-frequency effects including an all-cause mortality HR of 1.19 (CI: 95% 1.11 to 1.28) for 0.25 Hz in N3, 1.11 (CI: 95% 1.03 to 1.21) for 0.75 Hz in N1, and 1.11 (CI: 95% 1.03 to 1.20) for 1.25-1.75 Hz in wake. The gain in the concordance index (C-index) for all-cause mortality is minimal, with only a 0.24% increase: The best single mortality predictor was EEG N3 (0-0.5 Hz) with a C-index of 77.78% compared to 77.54% for confounders alone.

CONCLUSIONS

Spectral power features, possibly reflecting abnormal sleep microstructure, are associated with mortality risk. These findings add to a growing literature suggesting that sleep contains incipient predictors of health and mortality.

Mediating role of obstructive sleep apnea in altering slow-wave activity and elevating Alzheimer's disease risk: Pilot study from a northern Taiwan cohort

OBJECTIVES

Obstructive sleep apnea is associated with alterations in slow-wave activity during sleep, potentially increasing the risk of Alzheimer's disease. This study investigated the associations between obstructive sleep apnea manifestations such as respiratory events, hypoxia, arousal, slow-wave patterns, and neurochemical biomarker levels.

METHODS

Individuals with suspected obstructive sleep apnea underwent polysomnography. Sleep disorder indices, oxygen metrics, and slow-wave activity data were obtained from the polysomnography, and blood samples were taken the following morning to determine the plasma levels of total tau (T-Tau) and amyloid beta-peptide 42 (Aβ42) by using an ultrasensitive immunomagnetic reduction assay. Subsequently, the participants were categorized into groups with low and high Alzheimer's disease risk on the basis of their computed product Aβ42 × T-Tau. Intergroup differences and the associations and mediation effects between sleep-related parameters and neurochemical biomarkers were analyzed.

RESULTS

Forty-two participants were enrolled, with 21 assigned to each of the low- and high-risk groups. High-risk individuals had a higher apnea-hypopnea index, oxygen desaturation index (≥3%, ODI-3%), fraction of total sleep time with oxygen desaturation (SpO2-90% TST), and arousal index and greater peak-to-peak amplitude and slope in slow-wave activity, with a correspondingly shorter duration, than did low-risk individuals. Furthermore, indices such as the apnea-hypopnea index, ODI-3% and SpO2-90% TST were found to indirectly affect slow-wave activity, thereby raising the Aβ42 × T-Tau level.

CONCLUSIONS

Obstructive sleep apnea manifestations, such as respiratory events and hypoxia, may influence slow-wave sleep activity (functioning as intermediaries) and may be linked to elevated neurochemical biomarker levels. However, a longitudinal study is necessary to determine causal relationships among these factors.

STATEMENT OF SIGNIFICANCE

This research aims to bridge gaps in understanding how obstructive sleep apnea is associated with an elevated risk of Alzheimer's disease, providing valuable knowledge for sleep and cognitive health.

Slow oscillation-sleep spindle coupling is associated with fear extinction retention in trauma-exposed individuals

Posttraumatic stress disorder (PTSD) can be characterized as a disorder of fear learning and memory, in which there is a failure to retain memory for the extinction of conditioned fear. Sleep has been implicated in successful extinction retention. The coupling of sleep spindles to slow oscillations (SOs) during non-rapid eye movement sleep has been shown to broadly underpin sleep's beneficial effect on memory consolidation. However, the role of this oscillatory coupling in the retention of extinction memories is unknown. In a large sample of 124 trauma-exposed individuals, we investigated SO-spindle coupling in relation to fear extinction memory. We found that participants with a PTSD diagnosis, relative to trauma-exposed controls, showed significantly altered SO-spindle timing, such that PTSD participants exhibited spindle coupling further away from the peak of the SO. Across participants, the amount of coupling significantly predicted extinction retention, with coupled spindles uniquely predicting successful extinction retention compared to uncoupled spindles. These results suggest that SO-spindle coupling is critical for successful retention of extinguished fear, and that SO-spindle coupling dynamics are altered in PTSD. These alterations in the mechanics of sleep may have substantial clinical implications, meriting further investigation.

Multi-region processing during sleep for memory and cognition

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

Acceptance and commitment therapy for nurses' sleep, rumination, psychological flexibility, and it's neural mechanism: A randomized controlled fNIRS study

BACKGROUND

Given nurses often face sleep problems, this study compares two internet-delivered Acceptance and Commitment Therapy (ACT) interventions to improve sleep quality (SQ), psychological flexibility (PF), reduce rumination, and explore neural mechanisms. Methods: 477 nurses were randomly assigned to ACT linear psychotherapy model (LINEAR), ACT loop psychotherapy model (LOOP) and wait-list group. SQ, rumination, and PF were assessed with questionnaires. Prefrontal cortical activation changes were measured using functional near-infrared spectroscopy. Results: The linear mixed-effects model demonstrated significant improvements in SQ, PF, and reduced rumination compared to pre-intervention for both models through enhanced psychological flexibility. LOOP showed a significantly superior effect compared to LINEAR. DLPFC activation increased following both interventions, with LOOP additionally stimulating the frontopolar area. Changes in the DLPFC mediated the relationship between intervention and outcome improvements. Frontopolar changes mediated SQ improvements but not rumination or PF. No significant changes in functional connectivity were observed during the verbal fluency task. Conclusions: Both interventions improved outcome variables, with LOOP being notably more effective, offering a novel approach. Mediation analyses highlight the role of DLPFC activation in understanding ACT's mechanisms and targeting insomnia treatment, while the mechanisms of LOOP's superior effect warrant further research. Trial Registration: Chinese Clinical Trial Registry (ChiCTR2200063533). https://www.chictr.org.cn/.

Sleep and circadian rhythms modeling: From hypothalamic regulatory networks to cortical dynamics and behavior

Sleep and circadian rhythms are regulated by dynamic physiologic processes that operate across multiple spatial and temporal scales. These include, but are not limited to, genetic oscillators, clearance of waste products from the brain, dynamic interplay among brain regions, and propagation of local dynamics across the cortex. The combination of these processes, modulated by environmental cues, such as light-dark cycles and work schedules, represents a complex multiscale system that regulates sleep-wake cycles and brain dynamics. Physiology-based mathematical models have successfully explained the mechanisms underpinning dynamics at specific scales and are a useful tool to investigate interactions across multiple scales. They can help answer questions such as how do electroencephalographic (EEG) features relate to subthalamic neuron activity? Or how are local cortical dynamics regulated by the homeostatic and circadian mechanisms? In this chapter, we review two types of models that are well-positioned to consider such interactions. Part I of the chapter focuses on the subthalamic sleep regulatory networks and a model of arousal dynamics capable of predicting sleep, circadian rhythms, and cognitive outputs. Part II presents a model of corticothalamic circuits, capable of predicting spatial and temporal EEG features. We then discuss existing approaches and unsolved challenges in developing unified multiscale models.

Influence of sleep on seizures and interictal epileptiform discharges in epilepsy

Sleep significantly influences seizure occurrence and interictal epileptiform discharges (IEDs) in patients with epilepsy. Sleep-related epilepsy, where seizures occur exclusively or predominantly during sleep, has been observed in various epilepsy syndromes. Understanding the influence of sleep on seizures and IEDs is crucial in the diagnosis, classification, and management of epilepsy. Although there is a bidirectional relationship between sleep and epilepsy, this review focuses on the influence of sleep on seizures and IEDs in epilepsy. Seizures are more common during non-rapid eye movement (NREM) sleep, particularly during stage N2, and are suppressed during rapid eye movement (REM) sleep. Sleep also activates IEDs, increasing the diagnostic yield of EEG recordings. The rate of IEDs increases during NREM sleep, reaches its maximum during stage N3, and decreases during REM sleep. Sleep affects the electrical field of IEDs, with an increase of spiking fields during NREM sleep and a decrease during REM sleep. In the localization of epileptogenic foci, REM sleep is less sensitive but more specific than NREM sleep. Thalamocortical EEG synchronization during NREM sleep and desynchronization during REM sleep underlie their opposing effects on seizures and IEDs. Accumulating evidence has suggested an antiseizure effect of orexinergic antagonism in animal studies. Interventions that promote REM sleep, including orexinergic antagonists, should be studied in the future as novel treatment strategies for epilepsy.

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.

Hemodynamic cortical ripples through cyclicity analysis

A fine-grained understanding of dynamics in cortical networks is crucial to unpacking brain function. Resting-state functional magnetic resonance imaging (fMRI) gives rise to time series recordings of the activity of different brain regions, which are aperiodic and lack a base frequency. Cyclicity analysis, a novel technique robust under time reparametrizations, is effective in recovering the temporal ordering of such time series, collectively considered components of a multidimensional trajectory. Here, we extend this analytical method for characterizing the dynamic interaction between distant brain regions and apply it to the data from the Human Connectome Project. Our analysis detected cortical traveling waves of activity propagating along a spatial axis, resembling cortical hierarchical organization with consistent lead-lag relationships between specific brain regions in resting-state scans. In fMRI scans involving tasks, we observed short bursts of task-modulated strong temporal ordering that dominate overall lead-lag relationships between pairs of regions in the brain that align temporally with stimuli from the tasks. Our results suggest a possible role played by waves of excitation sweeping through brain regions that underlie emergent cognitive functions.

Brainwide hemodynamics predict neural rhythms across sleep and wakefulness in humans

The brain exhibits rich oscillatory dynamics that play critical roles in vigilance and cognition, such as the neural rhythms that define sleep. These rhythms continuously fluctuate, signaling major changes in vigilance, but the brainwide dynamics underlying these oscillations are unknown. Using simultaneous EEG and fast fMRI in humans drifting between sleep and wakefulness, we developed a machine learning approach to investigate which brainwide fMRI networks predict alpha (8-12 Hz) and delta (1-4 Hz) fluctuations. We predicted moment-to-moment EEG power variations from fMRI activity in held-out subjects, and found that information about alpha rhythms was highly separable in two networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale across the cortex. These results identify the large-scale network patterns that underlie alpha and delta rhythms, and establish a novel framework for investigating multimodal, brainwide dynamics.

Can Neuromodulation Improve Sleep and Psychiatric Symptoms?

PURPOSE OF REVIEW

In this review, we evaluate recent studies that employ neuromodulation, in the form of non-invasive brain stimulation, to improve sleep in both healthy participants, and patients with psychiatric disorders. We review studies using transcranial electrical stimulation, transcranial magnetic stimulation, and closed-loop auditory stimulation, and consider both subjective and objective measures of sleep improvement.

RECENT FINDINGS

Neuromodulation can alter neuronal activity underlying sleep. However, few studies utilizing neuromodulation report improvements in objective measures of sleep. Enhancements in subjective measures of sleep quality are replicable, however, many studies conducted in this field suffer from methodological limitations, and the placebo effect is robust. Currently, evidence that neuromodulation can effectively enhance sleep is lacking. For the field to advance, methodological issues must be resolved, and the full range of objective measures of sleep architecture, alongside subjective measures of sleep quality, must be reported. Additionally, validation of effective modulation of neuronal activity should be done with neuroimaging.

A Whole-Brain Model of the Aging Brain During Slow Wave Sleep

Age-related brain changes affect sleep and are reflected in properties of sleep slow-waves, however, the precise mechanisms behind these changes are still not completely understood. Here, we adapt a previously established whole-brain model relating structural connectivity changes to resting state dynamics, and extend it to a slow-wave sleep brain state. In particular, starting from a representative connectome at the beginning of the aging trajectory, we have gradually reduced the inter-hemispheric connections, and simulated sleep-like slow-wave activity. We show that the main empirically observed trends, namely a decrease in duration and increase in variability of the slow waves are captured by the model. Furthermore, comparing the simulated EEG activity to the source signals, we suggest that the empirically observed decrease in amplitude of the slow waves is caused by the decrease in synchrony between brain regions.

Binaural beats at 0.25 Hz shorten the latency to slow-wave sleep during daytime naps

Binaural beats can entrain neural oscillations and modulate behavioral states. However, the effect of binaural beats, particularly those with slow frequencies (< 1 Hz), on sleep remains poorly understood. We hypothesized that 0.25-Hz beats can entrain neural oscillations and enhance slow-wave sleep by shortening its latency or increasing its duration. To investigate this, we included 12 healthy participants (six women; mean age, 25.4 ± 2.6 years) who underwent four 90-min afternoon nap sessions, comprising a sham condition (without acoustic stimulation) and three binaural-beat conditions (0, 0.25, or 1 Hz) with a 250-Hz carrier tone. The acoustic stimuli, delivered through earphones, were sustained throughout the 90-min nap period. Both N2- and N3- latencies were shorter in the 0.25-Hz binaural beats condition than in the sham condition. We observed no significant results regarding neural entrainment at slow frequencies, such as 0.25 and 1 Hz, and the modulation of sleep oscillations, including delta and sigma activity, by binaural beats. In conclusion, this study demonstrated the potential of binaural beats at slow frequencies, specifically 0.25 Hz, for inducing slow-wave sleep in generally healthy populations.

Topography of putative bidirectional interaction between hippocampal sharp wave ripples and neocortical slow oscillations

Systems consolidation relies on coordination between hippocampal sharp-wave ripples (SWRs) and neocortical UP/DOWN states during sleep. However, whether this coupling exists across neocortex and the mechanisms enabling it remain unknown. By combining electrophysiology in mouse hippocampus (HPC) and retrosplenial cortex (RSC) with widefield imaging of dorsal neocortex, we found spatially and temporally precise bidirectional hippocampo-neocortical interaction. HPC multi-unit activity and SWR probability was correlated with UP/DOWN states in mouse default mode network, with highest modulation by RSC in deep sleep. Further, some SWRs were preceded by the high rebound excitation accompanying DMN DOWN→UP transitions, while large-amplitude SWRs were often followed by DOWN states originating in RSC. We explain these electrophysiological results with a model in which HPC and RSC are weakly coupled excitable systems capable of bi-directional perturbation and suggest RSC may act as a gateway through which SWRs can perturb downstream cortical regions via cortico-cortical propagation of DOWN states.

Coupling of Slow Oscillations in the Prefrontal and Motor Cortex Predicts Onset of Spindle Trains and Persistent Memory Reactivations

Sleep is known to drive the consolidation of motor memories. During nonrapid eye movement (NREM) sleep, the close temporal proximity between slow oscillations (SOs) and spindles ("nesting" of SO-spindles) is known to be essential for consolidation, likely because it is closely associated with the reactivation of awake task activity. Interestingly, recent work has found that spindles can occur in temporal clusters or "trains." However, it remains unclear how spindle trains are related to the nesting phenomenon. Here, we hypothesized that spindle trains are more likely when SOs co-occur in the prefrontal and motor cortex. We conducted simultaneous neural recordings in the medial prefrontal cortex (mPFC) and primary motor cortex (M1) of male rats training on the reach-to-grasp motor task. We found that intracortically recorded M1 spindles are organized into distinct temporal clusters. Notably, the occurrence of temporally precise SOs between mPFC and M1 was a strong predictor of spindle trains. Moreover, reactivation of awake task patterns is much more persistent during spindle trains in comparison with that during isolated spindles. Together, our work suggests that the precise coupling of SOs across mPFC and M1 may be a potential driver of spindle trains and persistent reactivation of motor memory during NREM sleep.

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.

Transformers and cortical waves: encoders for pulling in context across time

The capabilities of transformer networks such as ChatGPT and other large language models (LLMs) have captured the world's attention. The crucial computational mechanism underlying their performance relies on transforming a complete input sequence - for example, all the words in a sentence - into a long 'encoding vector' that allows transformers to learn long-range temporal dependencies in naturalistic sequences. Specifically, 'self-attention' applied to this encoding vector enhances temporal context in transformers by computing associations between pairs of words in the input sequence. We suggest that waves of neural activity traveling across single cortical areas, or multiple regions on the whole-brain scale, could implement a similar encoding principle. By encapsulating recent input history into a single spatial pattern at each moment in time, cortical waves may enable a temporal context to be extracted from sequences of sensory inputs, the same computational principle as that used in transformers.

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.

Artificial neural network for evaluating sleep spindles and slow waves after transcranial magnetic stimulation in a child with autism

Sleep spindles (SS) and slow waves (SW) serve as indicators of the integrity of thalamocortical connections, which are often compromised in individuals with autism spectrum disorder (ASD). Transcranial magnetic stimulation (TMS) can modulate brain activity associated with ASD. This study evaluated the effects of TMS on SS and SW in an 11-year-old male with ASD who received 17 sessions of TMS on the dorsolateral prefrontal cortex. Both SS and SW were detected before and after TMS and were analyzed using self-organizing maps (SOM). Using the SOM, a subset of SS and SW was identified that exhibited structural changes after TMS.

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.

Whole-Brain Electrophysiology and Calcium Imaging in Drosophila during Sleep and Wake

Sleep is likely a whole-brain phenomenon, with most of the brain probably benefiting from this state of decreased arousal. Recent advances in our understanding of some potential sleep functions, such as metabolite clearance and synaptic homeostasis, make it evident why the whole brain is likely impacted by sleep: All neurons have synapses, and all neurons produce waste metabolites. Sleep experiments in the fly Drosophila melanogaster suggest that diverse sleep functions appear to be conserved across all animals. Studies of brain activity during sleep in humans typically involve multidimensional data sets, such as those acquired by electroencephalograms (EEGs) or functional magnetic resonance imaging (fMRI), and these whole-brain read-outs often reveal important qualities of different sleep stages, such as changes in frequency dynamics or connectivity. Recently, various techniques have been developed that allow for the recording of neural activity simultaneously across multiple regions of the fly brain. These whole-brain-recording approaches will be important for better understanding sleep physiology and function, as they provide a more comprehensive view of neural dynamics during sleep and wake in a relevant model system. Here, we present a brief summary of some of the findings derived from sleep activity recording studies in sleeping Drosophila flies and discuss the value of electrophysiological versus calcium imaging techniques. Although these involve very different preparations, they both highlight the value of multidimensional data for studying sleep in this model system, like the use of both EEG and fMRI in humans.

Sleep oscillations related to memory consolidation during aromatases inhibitors for breast cancer

Aromatase inhibitors (AIs) are associated with sleep difficulties in breast cancer (BC) patients. Sleep is known to favor memory consolidation through the occurrence of specific oscillations, i.e., slow waves (SW) and sleep spindles, allowing a dialogue between prefrontal cortex and the hippocampus. Interestingly, neuroimaging studies in BC patients have consistently shown structural and functional modifications in these two brain regions. With the aim to evaluate sleep oscillations related to memory consolidation during AIs, we collected polysomnography data in BC patients treated (AI+, n = 17) or not (AI-, n = 17) with AIs compared to healthy controls (HC, n = 21). None of the patients had received chemotherapy and radiotherapy was finished since at least 6 months, that limit the confounding effects of other treatments than AIs. Fast and slow spindles were detected during sleep stage 2 at centro-parietal and frontal electrodes respectively. SW were detected at frontal electrodes during stage 3. Here, we show lower frontal SW densities in AI + patients compared to HC. These results concord with previous reports about frontal cortical alterations in cancer following AIs administration. Moreover, AI + patients tended to have lower spindle density at C4 electrode. Regression analyses showed that, in both patient groups, spindle density at C4 electrode explained a large variance of memory performances. Slow spindle characteristics did not differ between groups and sleep oscillations characteristics of AI- patients did not differ significantly from those of both AI + patients and HC. Overall, our results add to the compelling evidence of the systemic effects of AIs previously reported in animals, with deleterious effects on cortical activity during sleep and associated memory consolidation in the current study. There is thus a need to further investigate sleep modifications during AIs administration. Longitudinal studies are needed to confirm these findings and investigation in other cancers on this topic should be conducted.

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.

Targeted memory reactivation to augment treatment in post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) is a psychiatric disorder with traumatic memories at its core. Post-treatment sleep may offer a unique time window to increase therapeutic efficacy through consolidation of therapeutically modified traumatic memories. Targeted memory reactivation (TMR) enhances memory consolidation by presenting reminder cues (e.g., sounds associated with a memory) during sleep. Here, we applied TMR in PTSD patients to strengthen therapeutic memories during sleep after one treatment session with eye movement desensitization and reprocessing (EMDR). PTSD patients received either slow oscillation (SO) phase-targeted TMR, using modeling-based closed-loop neurostimulation (M-CLNS) with EMDR clicks as a reactivation cue (n = 17), or sham stimulation (n = 16). Effects of TMR on sleep were assessed through high-density polysomnography. Effects on treatment outcome were assessed through subjective, autonomic, and fMRI responses to script-driven imagery (SDI) of the targeted traumatic memory and overall PTSD symptom level. Compared to sham stimulation, TMR led to stimulus-locked increases in SO and spindle dynamics, which correlated positively with PTSD symptom reduction in the TMR group. Given the role of SOs and spindles in memory consolidation, these findings suggest that TMR may have strengthened the consolidation of the EMDR-treatment memory. Clinically, TMR vs. sham stimulation resulted in a larger reduction of avoidance level during SDI. TMR did not disturb sleep or trigger nightmares. Together, these data provide first proof of principle that TMR may be a safe and viable future treatment augmentation strategy for PTSD. The required follow-up studies may implement multi-night TMR or TMR during REM sleep to further establish the clinical effect of TMR for traumatic memories.

Cerebral Gray Matter May Not Explain Sleep Slow-Wave Characteristics after Severe Brain Injury

Sleep slow waves are the hallmark of deeper non-rapid eye movement sleep. It is generally assumed that gray matter properties predict slow-wave density, morphology, and spectral power in healthy adults. Here, we tested the association between gray matter volume (GMV) and slow-wave characteristics in 27 patients with moderate-to-severe traumatic brain injury (TBI, 32.0 ± 12.2 years old, eight women) and compared that with 32 healthy controls (29.2 ± 11.5 years old, nine women). Participants underwent overnight polysomnography and cerebral MRI with a 3 Tesla scanner. A whole-brain voxel-wise analysis was performed to compare GMV between groups. Slow-wave density, morphology, and spectral power (0.4-6 Hz) were computed, and GMV was extracted from the thalamus, cingulate, insula, precuneus, and orbitofrontal cortex to test the relationship between slow waves and gray matter in regions implicated in the generation and/or propagation of slow waves. Compared with controls, TBI patients had significantly lower frontal and temporal GMV and exhibited a subtle decrease in slow-wave frequency. Moreover, higher GMV in the orbitofrontal cortex, insula, cingulate cortex, and precuneus was associated with higher slow-wave frequency and slope, but only in healthy controls. Higher orbitofrontal GMV was also associated with higher slow-wave density in healthy participants. While we observed the expected associations between GMV and slow-wave characteristics in healthy controls, no such associations were observed in the TBI group despite lower GMV. This finding challenges the presumed role of GMV in slow-wave generation and morphology.

BDNF-TrkB signaling orchestrates the buildup process of local sleep

Sleep debt accumulates during wakefulness, leading to increased slow wave activity (SWA) during sleep, an encephalographic marker for sleep need. The use-dependent demands of prior wakefulness increase sleep SWA locally. However, the circuitry and molecular identity of this "local sleep" remain unclear. Using pharmacology and optogenetic perturbations together with transcriptomics, we find that cortical brain-derived neurotrophic factor (BDNF) regulates SWA via the activation of tyrosine kinase B (TrkB) receptor and cAMP-response element-binding protein (CREB). We map BDNF/TrkB-induced sleep SWA to layer 5 (L5) pyramidal neurons of the cortex, independent of neuronal firing per se. Using mathematical modeling, we here propose a model of how BDNF's effects on synaptic strength can increase SWA in ways not achieved through increased firing alone. Proteomic analysis further reveals that TrkB activation enriches ubiquitin and proteasome subunits. Together, our study reveals that local SWA control is mediated by BDNF-TrkB-CREB signaling in L5 excitatory cortical neurons.