The sleep slow oscillation as a traveling wave

Brain and brain-heart Granger causality during wakefulness and sleep

In this exploratory study we apply Granger Causality (GC) to investigate the brain-brain and brain-heart interactions during wakefulness and sleep. Our analysis includes electroencephalogram (EEG) and electrocardiogram (ECG) data during all-night polysomnographic recordings from volunteers with apnea, available from the Massachusetts General Hospital's Computational Clinical Neurophysiology Laboratory and the Clinical Data Animation Laboratory. The data is manually annotated by clinical staff at the MGH in 30 second contiguous intervals (wakefulness and sleep stages 1, 2, 3, and rapid eye movement (REM). We applied GC to 4-s non-overlapping segments of available EEG and ECG across all-night recordings of 50 randomly chosen patients. To identify differences in GC between the different sleep stages, the GC for each sleep stage was subtracted from the GC during wakefulness. Positive (negative) differences indicated that GC was greater (lower) during wakefulness compared to the specific sleep stage. The application of GC to study brain-brain and brain-heart bidirectional connections during wakefulness and sleep confirmed the importance of fronto-posterior connectivity during these two states, but has also revealed differences in ipsilateral and contralateral mechanisms of these connections. It has also confirmed the existence of bidirectional brain-heart connections that are more prominent in the direction from brain to heart. Our exploratory study has shown that GC can be successfully applied to sleep data analysis and captures the varying physiological mechanisms that are related to wakefulness and different sleep stages.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

SIGNIFICANCE

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

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

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

The human thalamus orchestrates neocortical oscillations during NREM sleep

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

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

Brain Stimulation for Improving Sleep and Memory

Over the past few decades, the importance of sleep has become increasingly recognized for many physiologic functions, including cognition. Many studies have reported the deleterious effect of sleep loss or sleep disruption on cognitive performance. Beyond ensuring adequate sleep quality and duration, discovering methods to enhance sleep to augment its restorative effects is important to improve learning in many populations, such as the military, students, age-related cognitive decline, and cognitive disorders.

Individual differences in slow wave sleep architecture relate to variation in white matter microstructure across adulthood

Sleep plays a key role in supporting brain function and resilience to brain decline. It is well known that sleep changes substantially with aging and that aging is associated with deterioration of brain structure. In this study, we sought to characterize the relationship between slow wave slope (SWslope)—a key marker of sleep architecture and an indirect proxy of sleep quality—and microstructure of white matter pathways in healthy adults with no sleep complaints. Participants were 12 young (24–27 years) and 12 older (50–79 years) adults. Sleep was assessed with nocturnal electroencephalography (EEG) and the Pittsburgh Sleep Quality Index (PSQI). White matter integrity was assessed using tract-based spatial statistics (TBSS) on tensor-based metrics such as Fractional Anisotropy (FA) and Mean Diffusivity (MD). Global PSQI score did not differ between younger (n = 11) and older (n = 11) adults (U = 50, p = 0.505), but EEG revealed that younger adults had a steeper SWslope at both frontal electrode sites (F3: U = 2, p < 0.001, F4: U = 4, p < 0.001, n = 12 younger, 10 older). There were widespread correlations between various diffusion tensor-based metrics of white matter integrity and sleep SWslope, over and above effects of age (n = 11 younger, 9 older). This was particularly evident for the corpus callosum, corona radiata, superior longitudinal fasciculus, internal and external capsule. This indicates that reduced sleep slow waves may be associated with widespread white matter deterioration. Future studies should investigate whether interventions targeted at improving sleep architecture also impact on decline in white matter microstructure in older adults.

Functional States Shape the Spatiotemporal Representation of Local and Cortex-wide Neural Activity in Mouse Sensory Cortex

The spatiotemporal representation of neural activity during rest and upon sensory stimulation in cortical areas is highly dynamic, and may be predominantly governed by cortical state. On the mesoscale level, intrinsic neuronal activity ranges from a persistent state, generally associated with a sustained depolarization of neurons, to a bimodal, slow-wave like state with bursts of neuronal activation, alternating with silent periods. These different activity states are prevalent under certain types of sedatives, or are associated with specific behavioral or vigilance conditions. Neurophysiological experiments assessing circuit activity, usually assume a constant underlying state, yet reports of variability of neuronal responses under seemingly constant conditions are common in the field. Even when a certain type of neural activity or cortical state can stably be maintained over time, the associated response properties are highly relevant for explaining experimental outcomes. Here we describe the spatiotemporal characteristics of ongoing activity and sensory evoked responses under two predominant functional states in the sensory cortices of mice: persistent activity (PA) and slow wave activity (SWA). Using electrophysiological recordings, and local and wide-field calcium recordings, we examine whether spontaneous and sensory evoked neuronal activity propagate throughout the cortex in a state dependent manner. We find that PA and SWA differ in their spatiotemporal characteristics which determine the cortical network's response to a sensory stimulus. During PA state, sensory stimulation elicits gamma-based short-latency responses which precisely follow each stimulation pulse and are prone to adaptation upon higher stimulation frequencies. Sensory responses during SWA are more variable, dependent on refractory periods following spontaneous slow waves. While spontaneous slow waves propagated in anterior-posterior direction in a majority of observations, the direction of propagation of stimulus-elicited wave depends on the sensory modality. These findings suggest that cortical state explains variance and should be considered when investigating multi-scale correlates of functional neurocircuit activity.

Mutual interaction between visual homeostatic plasticity and sleep in adult humans

Sleep and plasticity are highly interrelated, as sleep slow oscillations and sleep spindles are associated with consolidation of Hebbian-based processes. However, in adult humans, visual cortical plasticity is mainly sustained by homeostatic mechanisms, for which the role of sleep is still largely unknown. Here, we demonstrate that non-REM sleep stabilizes homeostatic plasticity of ocular dominance induced in adult humans by short-term monocular deprivation: the counterintuitive and otherwise transient boost of the deprived eye was preserved at the morning awakening (>6 hr after deprivation). Subjects exhibiting a stronger boost of the deprived eye after sleep had increased sleep spindle density in frontopolar electrodes, suggesting the involvement of distributed processes. Crucially, the individual susceptibility to visual homeostatic plasticity soon after deprivation correlated with the changes in sleep slow oscillations and spindle power in occipital sites, consistent with a modulation in early occipital visual cortex.

The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold

Sleep spindles are important for sleep quality and cognitive functions, with their coordination with slow oscillations (SOs) potentially organizing cross-region reactivation of memory traces. Here, we describe the organization of spindles on the electrode manifold and their relation to SOs. We analyzed the sleep night EEG of 34 subjects and detected spindles and SOs separately at each electrode. We compared spindle properties (frequency, duration, and amplitude) in slow wave sleep (SWS) and Stage 2 sleep (S2); and in spindles that coordinate with SOs or are uncoupled. We identified different topographical spindle types using clustering analysis that grouped together spindles co-detected across electrodes within a short delay (±300 ms). We then analyzed the properties of spindles of each type, and coordination to SOs. We found that SWS spindles are shorter than S2 spindles, and spindles at frontal electrodes have higher frequencies in S2 compared to SWS. Furthermore, S2 spindles closely following an SO (about 10% of all spindles) show faster frequency, shorter duration, and larger amplitude than uncoupled ones. Clustering identified Global, Local, Posterior, Frontal-Right and Left spindle types. At centro-parietal locations, Posterior spindles show faster frequencies compared to other types. Furthermore, the infrequent SO-spindle complexes are preferentially recruiting Global SO waves coupled with fast Posterior spindles. Our results suggest a non-uniform participation of spindles to complexes, especially evident in S2. This suggests the possibility that different mechanisms could initiate an SO-spindle complex compared to SOs and spindles separately. This has implications for understanding the role of SOs-spindle complexes in memory reactivation.

Selective blockade of rat brain T-type calcium channels provides insights on neurophysiological basis of arousal dependent resting state functional magnetic resonance imaging signals

A number of studies point to slow (0.1–2 Hz) brain rhythms as the basis for the resting-state functional magnetic resonance imaging (rsfMRI) signal. Slow waves exist in the absence of stimulation, propagate across the cortex, and are strongly modulated by vigilance similar to large portions of the rsfMRI signal. However, it is not clear if slow rhythms serve as the basis of all neural activity reflected in rsfMRI signals, or just the vigilance-dependent components. The rsfMRI data exhibit quasi-periodic patterns (QPPs) that appear to increase in strength with decreasing vigilance and propagate across the brain similar to slow rhythms. These QPPs can complicate the estimation of functional connectivity (FC) via rsfMRI, either by existing as unmodeled signal or by inducing additional wide-spread correlation between voxel-time courses of functionally connected brain regions. In this study, we examined the relationship between cortical slow rhythms and the rsfMRI signal, using a well-established pharmacological model of slow wave suppression. Suppression of cortical slow rhythms led to significant reduction in the amplitude of QPPs but increased rsfMRI measures of intrinsic FC in rats. The results suggest that cortical slow rhythms serve as the basis of only the vigilance-dependent components (e.g., QPPs) of rsfMRI signals. Further attenuation of these non-specific signals enhances delineation of brain functional networks.

fMRI spectral signatures of sleep

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

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

Transient brain-wide coactivations and structured transitions revealed in hemodynamic imaging data

Brain-wide patterns in resting human brains, as either structured functional connectivity (FC) or recurring brain states, have been widely studied in the neuroimaging literature. In particular, resting-state FCs estimated over windowed timeframe neuroimaging data from sub-minutes to minutes using correlation or blind source separation techniques have reported many brain-wide patterns of significant behavioral and disease correlates. The present pilot study utilized a novel whole-head cap-based high-density diffuse optical tomography (DOT) technology, together with data-driven analysis methods, to investigate recurring transient brain-wide patterns in spontaneous fluctuations of hemodynamic signals at the resolution of single timeframes from thirteen healthy adults in resting conditions. Our results report that a small number, i.e., six, of brain-wide coactivation patterns (CAPs) describe major spatiotemporal dynamics of spontaneous hemodynamic signals recorded by DOT. These CAPs represent recurring brain states, showing spatial topographies of hemispheric symmetry, and exhibit highly anticorrelated pairs. Moreover, a structured transition pattern among the six brain states is identified, where two CAPs with anterior-posterior spatial patterns are significantly involved in transitions among all brain states. Our results further elucidate two brain states of global positive and negative patterns, indicating transient neuronal coactivations and co-deactivations, respectively, over the entire cortex. We demonstrate that these two brain states are responsible for the generation of a subset of peaks and troughs in global signals (GS), supporting the recent reports on neuronal relevance of hemodynamic GS. Collectively, our results suggest that transient neuronal events (i.e., CAPs), global brain activity, and brain-wide structured transitions co-exist in humans and these phenomena are closely related, which extend the observations of similar neuronal events recently reported in animal hemodynamic data. Future studies on the quantitative relationship among these transient events and their relationships to windowed FCs along with larger sample size are needed to understand their changes with behaviors and diseased conditions.

Alterations of sleep oscillations in Alzheimer's disease: A potential role for GABAergic neurons in the cortex, hippocampus, and thalamus

Sleep abnormalities are widely reported in patients with Alzheimer's disease (AD) and are linked to cognitive impairments. Sleep abnormalities could be potential biomarkers to detect AD since they are often observed at the preclinical stage. Moreover, sleep could be a target for early intervention to prevent or slow AD progression. Thus, here we review changes in brain oscillations observed during sleep, their connection to AD pathophysiology and the role of specific brain circuits. Slow oscillations (0.1-1 Hz), sleep spindles (8-15 Hz) and their coupling during non-REM sleep are consistently reduced in studies of patients and in AD mouse models although the timing and magnitude of these alterations depends on the pathophysiological changes and the animal model studied. Changes in delta (1-4 Hz) activity are more variable. Animal studies suggest that hippocampal sharp-wave ripples (100-250 Hz) are also affected. Reductions in REM sleep amount and slower oscillations during REM are seen in patients but less consistently in animal models. Thus, changes in a variety of sleep oscillations could impact sleep-dependent memory consolidation or restorative functions of sleep. Recent mechanistic studies suggest that alterations in the activity of GABAergic neurons in the cortex, hippocampus and thalamic reticular nucleus mediate sleep oscillatory changes in AD and represent a potential target for intervention. Longitudinal studies of the timing of AD-related sleep abnormalities with respect to pathology and dysfunction of specific neural networks are needed to identify translationally relevant biomarkers and guide early intervention strategies to prevent or delay AD progression.

Brain-wide neural co-activations in resting human

Spontaneous neural activity in human as assessed with resting-state functional magnetic resonance imaging (fMRI) exhibits brain-wide coordinated patterns in the frequency of < 0.1 Hz. However, understanding of fast brain-wide networks at the timescales of neuronal events (milliseconds to sub-seconds) and their spatial, spectral, and transitional characteristics remain limited due to the temporal constraints of hemodynamic signals. With milli-second resolution and whole-head coverage, scalp-based electroencephalography (EEG) provides a unique window into brain-wide networks with neuronal-timescale dynamics, shedding light on the organizing principles of brain functions. Using the state-of-the-art signal processing techniques, we reconstructed cortical neural tomography from resting-state EEG and extracted component-based co-activation patterns (cCAPs). These cCAPs revealed brain-wide intrinsic networks and their dynamics, indicating the configuration/reconfiguration of resting human brains into recurring and transitional functional states, which are featured with the prominent spatial phenomena of global patterns and anti-state pairs of co-(de)activations. Rich oscillational structures across a wide frequency band (i.e., 0.6 Hz, 5 Hz, and 10 Hz) were embedded in the nonstationary dynamics of these functional states. We further identified a superstructure that regulated between-state immediate and long-range transitions involving the entire set of identified cCAPs and governed a significant aspect of brain-wide network dynamics. These findings demonstrated how resting-state EEG data can be functionally decomposed using cCAPs to reveal rich dynamic structures of brain-wide human neural activations.

Spatiotemporal patterns of spontaneous brain activity: a mini-review

The brain exists in a state of constant activity in the absence of any external sensory input. The spatiotemporal patterns of this spontaneous brain activity have been studied using various recording and imaging techniques. This has enabled considerable progress to be made in elucidating the cellular and network mechanisms that are involved in the observed spatiotemporal dynamics. This mini-review outlines different spatiotemporal dynamic patterns that have been identified in four commonly used modalities: electrophysiological recordings, optical imaging, functional magnetic resonance imaging, and electroencephalography. Signal sources for each modality, possible sources of the observed dynamics, and future directions are also discussed.

Slow oscillations promote long-range effective communication: The key for memory consolidation in a broken-down network

A prominent and robust finding in cognitive neuroscience is the strengthening of memories during nonrapid eye movement (NREM) sleep, with slow oscillations (SOs;<1Hz) playing a critical role in systems-level consolidation. However, NREM generally shows a breakdown in connectivity and reduction of synaptic plasticity with increasing depth: a brain state seemingly unfavorable to memory consolidation. Here, we present an approach to address this apparent paradox that leverages an event-related causality measure to estimate directional information flow during NREM in epochs with and without SOs. Our results confirm that NREM is generally a state of dampened neural communication but reveals that SOs provide two windows of enhanced large-scale communication before and after the SO trough. These peaks in communication are significantly higher when SOs are coupled with sleep spindles compared with uncoupled SOs. To probe the functional relevance of these SO-selective peaks of information flow, we tested the temporal and topographic conditions that predict overnight episodic memory improvement. Our results show that global, long-range communication during SOs promotes sleep-dependent systems consolidation of episodic memories. A significant correlation between peaks of information flow and memory improvement lends predictive validity to our measurements of effective connectivity. In other words, we were able to predict memory improvement based on independent electrophysiological observations during sleep. This work introduces a noninvasive approach to understanding information processing during sleep and provides a mechanism for how systems-level brain communication can occur during an otherwise low connectivity sleep state. In short, SOs are a gating mechanism for large-scale neural communication, a necessary substrate for systems consolidation and long-term memory formation.

Adenosine-A2A Receptor Signaling Plays a Crucial Role in Sudden Unexpected Death in Epilepsy

Adenosinergic activities are suggested to participate in SUDEP pathophysiology; this study aimed to evaluate the adenosine hypothesis of SUDEP and specifically the role of adenosine A_2A receptor (A_2AR) in the development of a SUDEP mouse model with relevant clinical features. Using a combined paradigm of intrahippocampal and intraperitoneal administration of kainic acid (KA), we developed a boosted-KA model of SUDEP in genetically modified adenosine kinase (ADK) knockdown (Adk^+/-) mice, which has reduced ADK in the brain. Seizure activity was monitored using video-EEG methods, and in vivo recording of local field potential (LFP) was used to evaluate neuronal activity within the nucleus tractus solitarius (NTS). Our boosted-KA model of SUDEP was characterized by a delayed, postictal sudden death in epileptic mice. We demonstrated a higher incidence of SUDEP in Adk^+/- mice (34.8%) vs. WTs (8.0%), and the ADK inhibitor, 5-Iodotubercidin, further increased SUDEP in Adk^+/- mice (46.7%). We revealed that the NTS level of ADK was significantly increased in epileptic WTs, but not in epileptic Adk^+/- mutants, while the A_2AR level in NTS was increased in epileptic (WT and Adk^+/-) mice vs. non-epileptic controls. The A_2AR antagonist, SCH58261, significantly reduced SUDEP events in Adk^+/- mice. LFP data showed that SCH58261 partially restored KA injection-induced suppression of gamma oscillation in the NTS of epileptic WT mice, whereas SCH58261 increased theta and beta oscillations in Adk^+/- mutants after KA injection, albeit with no change in gamma oscillations. These LFP findings suggest that SCH58261 and KA induced changes in local neuronal activities in the NTS of epileptic mice. We revealed a crucial role for NTS A_2AR in SUDEP pathophysiology suggesting A_2AR as a potential therapeutic target for SUDEP risk prevention.

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

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

Sigma oscillations protect or reinstate motor memory depending on their temporal coordination with slow waves

Targeted memory reactivation (TMR) during post-learning sleep is known to enhance motor memory consolidation but the underlying neurophysiological processes remain unclear. Here, we confirm the beneficial effect of auditory TMR on motor performance. At the neural level, TMR enhanced slow wave (SW) characteristics. Additionally, greater TMR-related phase-amplitude coupling between slow (0.5–2 Hz) and sigma (12–16 Hz) oscillations after the SW peak was related to higher TMR effect on performance. Importantly, sounds that were not associated to learning strengthened SW-sigma coupling at the SW trough. Moreover, the increase in sigma power nested in the trough of the potential evoked by the unassociated sounds was related to the TMR benefit. Altogether, our data suggest that, depending on their precise temporal coordination during post learning sleep, slow and sigma oscillations play a crucial role in either memory reinstatement or protection against irrelevant information; two processes that critically contribute to motor memory consolidation.

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

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

Circuits and components of delta wave regulation

Sleep is vital and the deepest stages of sleep occur within Non-rapid-eye-movement sleep (NREM), defined by high electroencephalographic power in the delta (~0.5-4Hz) wave frequency range. Delta waves are thought to facilitate a myriad of physical and mental health functions. This review aims to comprehensively cover the historical and recent advances in the understanding of the mechanisms orchestrating NREM delta waves. We discuss a complete neurocircuit - focusing on one leg of the circuit at a time - and delve deeply into the molecular mechanistic components that contribute to NREM delta wave regulation. We also discuss the relatively localized nature in which these mechanisms have been defined, and how likely they might generalize across distinct sensory and higher order modalities in the brain.

The natural frequencies of the resting human brain: An MEG-based atlas

Brain oscillations are considered to play a pivotal role in neural communication. However, detailed information regarding the typical oscillatory patterns of individual brain regions is surprisingly scarce. In this study we applied a multivariate data-driven approach to create an atlas of the natural frequencies of the resting human brain on a voxel-by-voxel basis. We analysed resting-state magnetoencephalography (MEG) data from 128 healthy adult volunteers obtained from the Open MEG Archive (OMEGA). Spectral power was computed in source space in 500 ms steps for 82 frequency bins logarithmically spaced from 1.7 to 99.5 Hz. We then applied k-means clustering to detect characteristic spectral profiles and to eventually identify the natural frequency of each voxel. Our results provided empirical confirmation of the canonical frequency bands and revealed a region-specific organisation of intrinsic oscillatory activity, following both a medial-to-lateral and a posterior-to-anterior gradient of increasing frequency. In particular, medial fronto-temporal regions were characterised by slow rhythms (delta/theta). Posterior regions presented natural frequencies in the alpha band, although with differentiated generators in the precuneus and in sensory-specific cortices (i.e., visual and auditory). Somatomotor regions were distinguished by the mu rhythm, while the lateral prefrontal cortex was characterised by oscillations in the high beta range (>20 Hz). Importantly, the brain map of natural frequencies was highly replicable in two independent subsamples of individuals. To the best of our knowledge, this is the most comprehensive atlas of ongoing oscillatory activity performed to date. Critically, the identification of natural frequencies is a fundamental step towards a better understanding of the functional architecture of the human brain.

Human lesions and animal studies link the claustrum to perception, salience, sleep and pain

The claustrum is the most densely interconnected region in the human brain. Despite the accumulating data from clinical and experimental studies, the functional role of the claustrum remains unknown. Here, we systematically review claustrum lesion studies and discuss their functional implications. Claustral lesions are associated with an array of signs and symptoms, including changes in cognitive, perceptual and motor abilities; electrical activity; mental state; and sleep. The wide range of symptoms observed following claustral lesions do not provide compelling evidence to support prominent current theories of claustrum function such as multisensory integration or salience computation. Conversely, the lesions studies support the hypothesis that the claustrum regulates cortical excitability. We argue that the claustrum is connected to, or part of, multiple brain networks that perform both fundamental and higher cognitive functions. As a multifunctional node in numerous networks, this may explain the manifold effects of claustrum damage on brain and behaviour.

Zolpidem Maintains Memories for Negative Emotions Across a Night of Sleep

Zolpidem, a common medication for sleep complaints, also shows secondary, unexpected memory benefits. We previously found that zolpidem prior to a nap enhanced negative, highly arousing picture memory. As zolpidem is typically administered at night, how it affects overnight emotional memory processing is relevant. We used a double-blind, placebo-controlled, within-subject, cross-over design to investigate if zolpidem boosted negative compared to neutral picture memory. Subjects learned both pictures sets in the morning. That evening, subjects were administered zolpidem or placebo and slept in the lab. Recognition was tested that evening and the following morning. We found that zolpidem maintained negative picture memory compared to forgetting in the placebo condition. Furthermore, zolpidem increased slow-wave sleep time, decreased rapid eye movement sleep time, and increased the fast spindle range in NREM. Our results suggest that zolpidem may enhance negative memory longevity and salience. These findings raise concerns for zolpidem administration to certain clinical populations.

Associations of Reduced Sympathetic Neural Activity and Elevated Baroreflex Sensitivity With Non-Rapid Eye Movement Sleep: Evidence From Electroencephalogram- and Electrocardiogram-Based Sleep Staging

OBJECTIVE

Autonomic neural controls in sleep regulation have been previously demonstrated; however, whether these alternations can be observed by different sleep staging approaches remains unclear. Two established methods for sleep staging-the standardized visual scoring and the cardiopulmonary coupling (CPC) analysis based on electrocardiogram-were used to explore the cardiovascular profiles of sleep.

METHODS

Overnight polysomnography was recorded together with continuous beat-to-beat blood pressure. Cortical activity, heart rate variability, blood pressure variability, and baroreflex sensitivity during sleep stages from 24 nights of sleep were obtained from 15 normotensive participants and analyzed.

RESULTS

Non-rapid eye movement sleep (NREM) from visual scoring and restful sleep (RS) of CPC both showed the highest delta power of electroencephalogram (EEG) and lowest beta activity of EEG in comparison with other sleep stages (p < .001); likewise, the lowest total power of heart rate variability and suppressed vascular-sympathetic activity, reflected by low-frequency power of blood pressure variability, as well as a trend in elevated baroreflex sensitivity, were observed in the NREM or RS. This suppressed vascular-sympathetic activity during stable sleep further exhibited a significant correlation with increased slow-wave activity (NREM: r = -0.292 ± 0.34, p = .002; RS: r = -0.209 ± 0.30, p = .010).

CONCLUSIONS

Autonomic nervous system is evidently associated with stable sleep, as indicated by the similar findings obtained from sleep stages categorized by standardized visual scoring or CPC analysis. Such association between cardiovascular neural activity and sleep EEGs can be observed regardless of the sleep staging approach followed.

Spontaneous neuronal oscillations in the human insula are hierarchically organized traveling waves

The insula plays a fundamental role in a wide range of adaptive human behaviors, but its electrophysiological dynamics are poorly understood. Here, we used human intracranial electroencephalographic recordings to investigate the electrophysiological properties and hierarchical organization of spontaneous neuronal oscillations within the insula. We analyzed the neuronal oscillations of the insula directly and found that rhythms in the theta and beta frequency oscillations are widespread and spontaneously present. These oscillations are largely organized along the anterior–posterior (AP) axis of the insula. Both the left and right insula showed anterior-­to-posterior decreasing gradients for the power of oscillations in the beta frequency band. The left insula also showed a posterior-to-anterior decreasing frequency gradient and an anterior-to-posterior decreasing power gradient in the theta frequency band. In addition to measuring the power of these oscillations, we also examined the phase of these signals across simultaneous recording channels and found that the insula oscillations in the theta and beta bands are traveling waves. The strength of the traveling waves in each frequency was positively correlated with the amplitude of each oscillation. However, the theta and beta traveling waves were uncoupled to each other in terms of phase and amplitude, which suggested that insular traveling waves in the theta and beta bands operate independently. Our findings provide new insights into the spatiotemporal dynamics and hierarchical organization of neuronal oscillations within the insula, which, given its rich connectivity with widespread cortical regions, indicates that oscillations and traveling waves have an important role in intrainsular and interinsular communications.

Nine-day continuous recording of EEG and 2-hour of high-density EEG under chronic sleep restriction in mice

This work provides an EEG dataset collected from nine mice during the sleep deprivation (SD) paradigm for the sleep science community. It includes 9-day of continuous recording of the frontal and parietal EEG, accelerometer, and 2-hour of high-density EEG (HD-EEG) under SD and SD-free conditions. Eighteen hours of SD were conducted on 5 consecutive days. The HD-EEG data were saved in the EEGLAB format and stored as the brain imaging data structure (BIDS). These datasets can be used to (i) compare mouse HD-EEG to human HD-EEG, (ii) track oscillatory activities of the sleep EEG (e.g., slow waves, spindles) across the cortical regions under different conditions of sleep pressure, and (iii) investigate the cortical traveling waves in the mouse brain. We also provided Python code for basic analyses of this dataset, including the detection of slow waves and sleep spindles. We hope that our dataset will reveal hidden activities during sleep and lead to a better understanding of the functions and mechanisms of sleep.

Thalamic activity during scalp slow waves in humans

Slow waves are major pacemakers of NREM sleep oscillations. While slow waves themselves are mainly generated by cortical neurons, it is not clear what role thalamic activity plays in the generation of some oscillations grouped by slow waves, and to what extent thalamic activity during slow waves is itself driven by corticothalamic inputs. To address this question, we simultaneously recorded both scalp EEG and local field potentials from six thalamic nuclei (bilateral anterior, mediodorsal and ventral anterior) in fifteen epileptic patients (age-range: 17-64 years, 7 females) undergoing Deep Brain Stimulation Protocol and assessed the temporal evolution of thalamic activity relative to scalp slow waves using time-frequency analysis. We found that thalamic activity in all six nuclei during scalp slow waves is highly similar to what is observed on the scalp itself. Slow wave downstates are characterized by delta, theta and alpha activity and followed by beta, high sigma and low sigma activity during subsequent upstates. Gamma activity in the thalamus is not significantly grouped by slow waves. Theta and alpha activity appeared first on the scalp, but sigma activity appeared first in the thalamus. These effects were largely independent from the scalp region in which SWs were detected and the precise identity of thalamic nuclei. Our results suggest that while small thalamocortical neuron assemblies may initiate cortical oscillations, especially in the sleep spindle range, the large-scale neuronal activity in the thalamus which is detected by field potentials is principally driven by global cortical activity, and thus it is highly similar to what is observed on the scalp.

Brain-wide interactions during hippocampal sharp wave ripples

During periods of disengagement from the environment, transient population bursts, known as sharp wave ripples (SPW-Rs), occur sporadically. While numerous experiments have characterized the bidirectional relationship between SPW-Rs and activity in chosen brain areas, the topographic relationship between different segments of the hippocampus and brain-wide target areas has not been studied at high temporal and spatial resolution. Yet, such knowledge is necessary to infer the direction of communication. We analyzed two publicly available datasets with simultaneous high-density silicon probe recordings from across the mouse forebrain. We found that SPW-Rs coincide with a transient brain-wide increase in functional connectivity. In addition, we show that the diversity in SPW-R features, such as their incidence, magnitude, and intrahippocampal topography in the septotemporal axis, are correlated with slower excitability fluctuations in cortical and subcortical areas. Further, variations in SPW-R features correlated with the timing, sign, and magnitude of downstream responses with large-amplitude SPW-Rs followed by transient silence in extrahippocampal structures. Our findings expand on previous results and demonstrate that the activity patterns in extrahippocampal structures depend both on the intrahippocampal topographic origin and magnitude of hippocampal SPW-Rs.

Translational Approaches to Influence Sleep and Arousal

Sleep disorders are widespread in society and are prevalent in military personnel and in Veterans. Disturbances of sleep and arousal mechanisms are common in neuropsychiatric disorders such as schizophrenia, post-traumatic stress disorder, anxiety and affective disorders, traumatic brain injury, dementia, and substance use disorders. Sleep disturbances exacerbate suicidal ideation, a major concern for Veterans and in the general population. These disturbances impair quality of life, affect interpersonal relationships, reduce work productivity, exacerbate clinical features of other disorders, and impair recovery. Thus, approaches to improve sleep and modulate arousal are needed. Basic science research on the brain circuitry controlling sleep and arousal led to the recent approval of new drugs targeting the orexin/hypocretin and histamine systems, complementing existing drugs which affect GABA_A receptors and monoaminergic systems. Non-invasive brain stimulation techniques to modulate sleep and arousal are safe and show potential but require further development to be widely applicable. Invasive viral vector and deep brain stimulation approaches are also in their infancy but may be used to modulate sleep and arousal in severe neurological and psychiatric conditions. Behavioral, pharmacological, non-invasive brain stimulation and cell-specific invasive approaches covered here suggest the potential to selectively influence arousal, sleep initiation, sleep maintenance or sleep-stage specific phenomena such as sleep spindles or slow wave activity. These manipulations can positively impact the treatment of a wide range of neurological and psychiatric disorders by promoting the restorative effects of sleep on memory consolidation, clearance of toxic metabolites, metabolism, and immune function and by decreasing hyperarousal.

Tonic excitation of nucleus reuniens decreases prefrontal-hippocampal coordination during slow-wave states

The nucleus reuniens of the thalamus (RE) is an important node between the medial prefrontal cortex (mPFC) and the hippocampus (HPC). Previously, we have shown that its mode of activity and its influence in mPFC-HPC communication is dependent upon brain state. During slow-wave states, RE units are closely and rhythmically coupled to the ongoing mPFC-slow oscillation (SO), while during activated (theta) states, RE neurons fire in an arrhythmic and tonically active manner. Inactivating the RE selectively impoverishes coordination of the SO between mPFC and HPC and interestingly, both mPFC and RE stimulation during the SO cause larger responses in the HPC than during theta. It is unclear if the activity patterns within the RE across states may play a role in both phenomena. Here, we optogenetically excited RE neurons in a tonic fashion to assess the impact on mPFC-HPC coupling. This stimulation decreased the influence of mPFC stimulation in the HPC during SO states, in a manner similar to what is observed across state changes into theta. Importantly, this type of stimulation had no effect on evoked responses during theta. Perhaps more interestingly, tonic optogenetic excitation of the RE also decreased mPFC-HPC SO coherence. Thus, it may not be the integrity of the RE per se that is responsible for efficient communication between mPFC and HPC, but rather the particular state in which RE neurons find themselves. Our results have direct implications for how distant brain regions can communicate most effectively, an issue that is ultimately important for activity-dependent processes occurring during slow-wave sleep-dependent memory consolidation.

Propofol Anesthesia Alters Cortical Traveling Waves

Oscillatory dynamics in cortex seem to organize into traveling waves that serve a variety of functions. Recent studies show that propofol, a widely used anesthetic, dramatically alters cortical oscillations by increasing slow-delta oscillatory power and coherence. It is not known how this affects traveling waves. We compared traveling waves across the cortex of non-human primates before, during, and after propofol-induced loss of consciousness (LOC). After LOC, traveling waves in the slow-delta (∼1 Hz) range increased, grew more organized, and traveled in different directions relative to the awake state. Higher frequency (8-30 Hz) traveling waves, by contrast, decreased, lost structure, and switched to directions where the slow-delta waves were less frequent. The results suggest that LOC may be due, in part, to increases in the strength and direction of slow-delta traveling waves that, in turn, alter and disrupt traveling waves in the higher frequencies associated with cognition.

Dysfunctional Overnight Memory Consolidation in Patients with Schizophrenia in Comparison to Healthy Controls: Disturbed Slow-Wave Sleep as Contributing Factor?

INTRODUCTION

Memory deficiency has been shown in schizophrenia patients, but results on the role of sleep parameters in overnight consolidation of associative verbal memory are still missing. Therefore, the aim of our study was to elucidate underlying processes of impaired sleep-related consolidation of associative word pairs in schizophrenia including standard sleep parameters as well as sleep spindle counts and spectral analysis.

METHODS

Eighteen stably medicated schizophrenia patients and 24 healthy age-matched controls performed an associative declarative memory task before and after polysomnographic recordings. Part of the participants expected verbal associative memory testing in the morning, while the others did not. Furthermore, participants filled in self-rating questionnaires of schizophrenia-typical experiences (Eppendorf Schizophrenia Inventory [ESI] and Psychotic Symptom Rating Scale).

RESULTS

Schizophrenia patients performed worse in verbal declarative memory in the evening as well as in overnight consolidation (morning compared to evening performance). While duration of slow-wave sleep was nearly comparable between groups, schizophrenia patients showed lower sleep spindle count, reduced delta power during slow-wave sleep, and reduced spindle power during the slow oscillation (SO) up-state. In healthy but not in schizophrenia patients, a linear relationship between overnight memory consolidation and slow-wave sleep duration as well as delta power was evident. No significant effect with respect to the expectation of memory retrieval was evident in our data. Additionally, we observed a negative linear relationship between total number of sleep spindles and ESI score in healthy participants.

DISCUSSION/CONCLUSION

As expected, schizophrenia patients showed deficient overnight verbal declarative memory consolidation as compared to healthy controls. Reduced sleep spindles, delta power, and spindle power during the SO up-state may link sleep and memory deficiency in schizophrenia. Additionally, the absence of a linear relationship between sleep-related memory consolidation and slow-wave sleep as well as delta power suggests further functional impairments in schizophrenia. Note that this conclusion is based on observational data. Future studies should investigate if stimulation of delta waves during sleep could improve memory performance and thereby quality of life in schizophrenia.

A Multivariate Functional Connectivity Approach to Mapping Brain Networks and Imputing Neural Activity in Mice

Temporal correlation analysis of spontaneous brain activity (e.g., Pearson "functional connectivity," FC) has provided insights into the functional organization of the human brain. However, bivariate analysis techniques such as this are often susceptible to confounding physiological processes (e.g., sleep, Mayer-waves, breathing, motion), which makes it difficult to accurately map connectivity in health and disease as these physiological processes affect FC. In contrast, a multivariate approach to imputing individual neural networks from spontaneous neuroimaging data could be influential to our conceptual understanding of FC and provide performance advantages. Therefore, we analyzed neural calcium imaging data from Thy1-GCaMP6f mice while either awake, asleep, anesthetized, during low and high bouts of motion, or before and after photothrombotic stroke. A linear support vector regression approach was used to determine the optimal weights for integrating the signals from the remaining pixels to accurately predict neural activity in a region of interest (ROI). The resultant weight maps for each ROI were interpreted as multivariate functional connectivity (MFC), resembled anatomical connectivity, and demonstrated a sparser set of strong focused positive connections than traditional FC. While global variations in data have large effects on standard correlation FC analysis, the MFC mapping methods were mostly impervious. Lastly, MFC analysis provided a more powerful connectivity deficit detection following stroke compared to traditional FC.

Learning New Vocabulary Implicitly During Sleep Transfers With Cross-Modal Generalization Into Wakefulness

New information can be learned during sleep but the extent to which we can access this knowledge after awakening is far less understood. Using a novel Associative Transfer Learning paradigm, we show that, after hearing unknown Japanese words with sounds referring to their meaning during sleep, participants could identify the images depicting the meaning of newly acquired Japanese words after awakening (N = 22). Moreover, we demonstrate that this cross-modal generalization is implicit, meaning that participants remain unaware of this knowledge. Using electroencephalography, we further show that frontal slow-wave responses to auditory stimuli during sleep predicted memory performance after awakening. This neural signature of memory formation gradually emerged over the course of the sleep phase, highlighting the dynamics of associative learning during sleep. This study provides novel evidence that the formation of new associative memories can be traced back to the dynamics of slow-wave responses to stimuli during sleep and that their implicit transfer into wakefulness can be generalized across sensory modalities.

Capacity for consciousness under ketamine anaesthesia is selectively associated with activity in posteromedial cortex in rats

It remains unclear how specific cortical regions contribute to the brain's overall capacity for consciousness. Clarifying this could help distinguish between theories of consciousness. Here, we investigate the association between markers of regionally specific (de)activation and the brain's overall capacity for consciousness. We recorded electroencephalographic responses to cortical electrical stimulation in six rats and computed Perturbational Complexity Index state-transition (PCIST), which has been extensively validated as an index of the capacity for consciousness in humans. We also estimated the balance between activation and inhibition of specific cortical areas with the ratio between high and low frequency power from spontaneous electroencephalographic activity at each electrode. We repeated these measurements during wakefulness, and during two levels of ketamine anaesthesia: with the minimal dose needed to induce behavioural unresponsiveness and twice this dose. We found that PCIST was only slightly reduced from wakefulness to light ketamine anaesthesia, but dropped significantly with deeper anaesthesia. The high-dose effect was selectively associated with reduced high frequency/low frequency ratio in the posteromedial cortex, which strongly correlated with PCIST. Conversely, behavioural unresponsiveness induced by light ketamine anaesthesia was associated with similar spectral changes in frontal, but not posterior cortical regions. Thus, activity in the posteromedial cortex correlates with the capacity for consciousness, as assessed by PCIST, during different depths of ketamine anaesthesia, in rats, independently of behaviour. These results are discussed in relation to different theories of consciousness.

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

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

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Complexity of isoflurane-induced slow waves reliably determines anesthesia level

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

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

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Local excitability underpins the cortical reorganization in fading anesthesia

Sleep Power Topography in Children with Attention Deficit Hyperactivity Disorder (ADHD)

OBJECTIVE

Recent years saw an increasing interest towards sleep microstructure abnormalities in attention-deficit/hyperactivity disorder (ADHD). However, the existing literature on sleep electroencephalographic (EEG) power in ADHD is still controversial, often based on single electrode recordings, and mainly focused on slow wave activity (SWA) during NREM sleep. This study aimed to systematically investigate sleep power topography in all traditional frequency bands, in all sleep stages and across sleep cycles using high-density EEG (HD-EEG).

METHOD

Thirty drug-naïve children with ADHD (10.5 ± 2.1 years, 21 male) and 23 typically developing (TD) control participants (mean age: 10.2 ± 1.6 years, 13 male) were included in the current analysis. Signal power topography was computed in classical frequency bands during sleep, contrasted between groups and sleep cycles, and correlated with measures of ADHD severity, cognitive functioning and estimated total sleep time.

RESULTS

Compared to TD subjects, patients with ADHD consistently displayed a widespread increase in low-frequency activity (between 3 and 10 Hz) during NREM sleep, but not during REM sleep and wake before sleep onset. Such a difference involved a wide centro-posterior cluster of channels in the upper SWA range, in Theta, and low-Alpha. Between-group difference was maximal in sleep stage N3 in the first sleep cycle, and positively correlated with average total sleep time.

CONCLUSIONS

These results support the concept that children with ADHD, compared to TD peers, have a higher sleep pressure and altered sleep homeostasis, which possibly interfere with (and delay) cortical maturation.

Boosting Recovery During Sleep by Means of Auditory Stimulation

Sufficient recovery during sleep is the basis of physical and psychological well-being. Understanding the physiological mechanisms underlying this restorative function is essential for developing novel approaches to promote recovery during sleep. Phase-targeted auditory stimulation (PTAS) is an increasingly popular technique for boosting the key electrophysiological marker of recovery during sleep, slow-wave activity (SWA, 1-4 Hz EEG power). However, it is unknown whether PTAS induces physiological sleep. In this study, we demonstrate that, when applied during deep sleep, PTAS accelerates SWA decline across the night which is associated with an overnight improvement in attentional performance. Thus, we provide evidence that PTAS enhances physiological sleep and demonstrate under which conditions this occurs most efficiently. These findings will be important for future translation into clinical populations suffering from insufficient recovery during sleep.

Prefrontal-Hippocampal Pathways Through the Nucleus Reuniens Are Functionally Biased by Brain State

Circuit-level communication between disparate brain regions is fundamental for the complexities of the central nervous system operation. Co-ordinated bouts of rhythmic activity between the prefrontal cortex (PFC) and hippocampus (HPC), in particular, are important for mnemonic processes. This is true during awake behavior, as well as during offline states like sleep. We have recently shown that the anatomically interposed thalamic nucleus reuniens (RE) has a role in coordinating slow-wave activity between the PFC and HPC. Here, we took advantage of spontaneous brain state changes occurring during urethane anesthesia in order to assess if PFC-HPC communication was modified during activated (theta) vs. deactivated (slow oscillation: SO) states. These forebrain states are highly similar to those expressed during rapid eye movement (REM) and non-REM stages of natural sleep, respectively. Evoked potentials and excitatory current sinks in the HPC were consistently larger during SO states, regardless of whether PFC or RE afferents were stimulated. Interestingly, PFC stimulation during theta appeared to preferentially use a cortico-cortical pathway, presumably involving the entorhinal cortex as opposed to the more direct RE to HPC conduit. Optogenetic and chemogenetic manipulations of the RE suggested that this state-dependent biasing was mediated by responding in the RE itself. Finally, the phase of both ongoing rhythms also appeared to be an important factor in modulating HPC responses, with maximal field excitatory postsynaptic potentials (EPSPs) occurring during the negative-going phase of both rhythms. Thus, forebrain state plays an important role in how communication takes place across the PFC and HPC, with the RE as a determining factor in how this is shaped. Furthermore, ongoing sleep-like rhythms influence the coordination and perhaps potentiate excitatory processing in this extended episodic memory circuit. Our results have direct implications for activity-dependent processes relevant to sleep-dependent memory consolidation.

Breathing coordinates cortico-hippocampal dynamics in mice during offline states

Network dynamics have been proposed as a mechanistic substrate for the information transfer across cortical and hippocampal circuits. However, little is known about the mechanisms that synchronize and coordinate these processes across widespread brain regions during offline states. Here we address the hypothesis that breathing acts as an oscillatory pacemaker, persistently coupling distributed brain circuit dynamics. Using large-scale recordings from a number of cortical and subcortical brain regions in behaving mice, we uncover the presence of an intracerebral respiratory corollary discharge, that modulates neural activity across these circuits. During offline states, the respiratory modulation underlies the coupling of hippocampal sharp-wave ripples and cortical DOWN/UP state transitions, which mediates systems memory consolidation. These results highlight breathing, a perennial brain rhythm, as an oscillatory scaffold for the functional coordination of the limbic circuit that supports the segregation and integration of information flow across neuronal networks during offline states.

Local sleep: A new concept in brain plasticity

Traditionally, sleep and wakefulness have been considered as two global, mutually exclusive states. However, this view has been challenged by the discovery that sleep and wakefulness are actually locally regulated and that islands of these two states may often coexist in the same individual. Importantly, such a local regulation seems to be the key for many essential functions of sleep, including the maintenance of cognitive efficiency and the consolidation of new skills and memories. Indeed, local changes in sleep-related oscillations occur in brain areas that are used and involved in learning during wakefulness. In turn, these changes directly modulate experience-dependent brain adaptations and the consolidation of newly acquired memories. In line with these observations, alterations in the regional balance between wake- and sleep-like activity have been shown to accompany many pathologic conditions, including psychiatric and neurologic disorders. In the last decade, experimental research has started to shed light on the mechanisms involved in the local regulation of sleep and wakefulness. The results of this research have opened new avenues of investigation regarding the function of sleep and have revealed novel potential targets for the treatment of several pathologic conditions.

EEG as a marker of brain plasticity in clinical applications

Neural networks are dynamic, and the brain has the capacity to reorganize itself. This capacity is named neuroplasticity and is fundamental for many processes ranging from learning and adaptation to new environments to the response to brain injuries. Measures of brain plasticity involve several techniques, including neuroimaging and neurophysiology. Electroencephalography, often used together with other techniques, is a common tool for prognostic and diagnostic purposes, and cortical reorganization is reflected by EEG measurements. Changes of power bands in different cortical areas occur with fatigue and in response to training stimuli leading to learning processes. Sleep has a fundamental role in brain plasticity, restoring EEG bands alterations and promoting consolidation of learning. Exercise and physical inactivity have been extensively studied as both strongly impact brain plasticity. Indeed, EEG studies showed the importance of the physical activity to promote learning and the effects of inactivity or microgravity on cortical reorganization to cope with absent or altered sensorimotor stimuli. Finally, this chapter will describe some of the EEG changes as markers of neural plasticity in neurologic conditions, focusing on cerebrovascular and neurodegenerative diseases. In conclusion, neuroplasticity is the fundamental mechanism necessary to ensure adaptation to new stimuli and situations, as part of the dynamicity of life.

Associations between cortical activation and network interaction during sleep

Cortical activation and network interaction, two characterizations of the cortical states, are separately studied in most previous studies. To further clarify the underlying mechanism, the association between these two indicators during sleep was investigated in this study. Twenty healthy individuals were enrolled and all of them underwent overnight polysomnography (PSG) recording. The relative spectral powers and the phase transfer entropy (PTE) of various frequency components were extracted from 6 electroencephalographic (EEG) channels, to assess the cortical activation and network interaction, respectively. Pearson correlation coefficient was employed to estimate their associations. The results suggested that there was a negative correlation between spectral power and phase transfer entropy in δ and α frequency bands during sleep. As the sleep deepened, an increased negative correlation in the δ frequency band was noted, but the negative correlation became less extreme in the α frequency band. The extremum of the correlation coefficient was noted in δ of N3, and α of Wake. Overall, this study provides a connection between these two cortical activity assessments, especially reveals the variable characteristics of different frequency components, which is conducive to better understand sleep state.

Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep

During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approach, good fits to human resting-state fMRI data and sleep EEG data are found at values of the adaptation strength close to a bifurcation where the model produces a balance between local and global SOs with realistic spatiotemporal statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the undirected brain graph, traveling from anterior to posterior regions. These traveling waves are caused by heterogeneities in the brain network in which the connection strengths between brain areas determine which areas transition to a down-state first, and thus initiate traveling waves across the cortex. Our results demonstrate the utility of whole-brain models for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome.

Cortical traveling waves reflect state-dependent hierarchical sequencing of local regions in the human connectome network

Recent human studies using electrocorticography have demonstrated that alpha and theta band oscillations form traveling waves on the cortical surface. According to neural synchronization theories, the cortical traveling waves may group local cortical regions and sequence them by phase synchronization; however these contributions have not yet been assessed. This study aimed to evaluate the functional contributions of traveling waves using connectome-based network modeling. In the simulation, we observed stable traveling waves on the entire cortical surface wherein the topographical pattern of these phases was substantially correlated with the empirically obtained resting-state networks, and local radial waves also appeared within the size of the empirical networks (< 50 mm). Importantly, individual regions in the entire network were instantaneously sequenced by their internal frequencies, and regions with higher intrinsic frequency were seen in the earlier phases of the traveling waves. Based on the communication-through-coherence theory, this phase configuration produced a hierarchical organization of each region by unidirectional communication between the arbitrarily paired regions. In conclusion, cortical traveling waves reflect the intrinsic frequency-dependent hierarchical sequencing of local regions, global traveling waves sequence the set of large-scale cortical networks, and local traveling waves sequence local regions within individual cortical networks.

Brain Data in Pediatric Disorders of Consciousness: Special Considerations

SUMMARY

The diagnosis and management of disorders of consciousness in children continue to present a clinical, research, and ethical challenge. Though the practice guidelines for diagnosis and management of disorders of consciousness in adults are supported by decades of empirical and pragmatic evidence, similar guidelines for infants and children are lacking. The maturing conscious experience and the limited behavioral repertoire to report consciousness in this age group restrict extrapolation from the adult literature. Equally challenging is the process of heightened structural and functional neuroplasticity in the developing brain, which adds a layer of complexity to the investigation of the neural correlates of consciousness in infants and children. This review discusses the clinical assessment of pediatric disorders of consciousness and delineates the diagnostic and prognostic utility of neurophysiological and neuroimaging correlates of consciousness. The potential relevance of these correlates for the developing brain based on existing theoretical models of consciousness in adults is outlined.