Spontaneous neural activity during human slow wave sleep

Sleep disturbances are associated with cortical and subcortical atrophy in alcohol use disorder

Sleep disturbances are prominent in patients with alcohol use disorder (AUD) and predict relapse. So far, the mechanisms underlying sleep disruptions in AUD are poorly understood. Because sleep-related regions vastly overlap with regions, where patients with AUD showed pronounced grey matter (GM) reduction; we hypothesized that GM structure could contribute to sleep disturbances associated with chronic alcohol use. We combined sleep EEG recording and high-resolution structural brain imaging to examine the GM-sleep associations in 36 AUD vs. 26 healthy controls (HC). The patterns of GM-sleep associations differed for N3 vs. REM sleep and for AUD vs. HC. For cortical thickness (CT), CT-sleep associations were significant in AUD but not in HC and were lateralized such that lower CT in right hemisphere was associated with shorter N3, whereas in left hemisphere was associated with shorter REM sleep. For the GM density (GMD), we observed a more extensive positive GMD-N3 association in AUD (right orbitofrontal cortex, cerebellum, dorsal cingulate and occipital cortex) than in HC (right orbitofrontal cortex), and the GMD-REM association was positive in AUD (midline, motor and paralimbic regions) whereas negative in HC (the left supramarginal gyrus). GM structure mediated the effect of chronic alcohol use on the duration of N3 and the age by alcohol effect on REM sleep. Our findings provide evidence that sleep disturbances in AUD were associated with GM reductions. Targeting sleep-related regions might improve sleep in AUD and enhance sleep-induced benefits in cognition and emotional regulation for recovery.

Trail Making Test Performance Using a Touch-Sensitive Tablet: Behavioral Kinematics and Electroencephalography

The Trail Making Test (TMT) is widely used to probe brain function and is performed with pen and paper, involving Parts A (linking numbers) and B (alternating between linking numbers and letters). The relationship between TMT performance and the underlying brain activity remains to be characterized in detail. Accordingly, sixteen healthy young adults performed the TMT using a touch-sensitive tablet to capture enhanced performance metrics, such as the speed of linking movements, during simultaneous electroencephalography (EEG). Linking and non-linking periods were derived as estimates of the time spent executing and preparing movements, respectively. The seconds per link (SPL) was also used to quantify TMT performance. A strong effect of TMT Part A and B was observed on the SPL value as expected (Part B showing increased SPL value); whereas the EEG results indicated robust effects of linking and non-linking periods in multiple frequency bands, and effects consistent with the underlying cognitive demands of the test.

Autonomic dysregulation and sleep homeostasis in insomnia

STUDY OBJECTIVES

Insomnia is common in older adults, and is associated with poor health, including cognitive impairment and cardio-metabolic disease. Although the mechanisms linking insomnia with these comorbidities remain unclear, age-related changes in sleep and autonomic nervous system (ANS) regulation might represent a shared mechanistic pathway. In this study, we assessed the relationship between ANS activity with indices of objective and subjective sleep quality in older adults with insomnia.

METHODS

Forty-three adults with chronic insomnia and 16 age-matched healthy sleeper controls were studied. Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), objective sleep quality by electroencephalogram spectral components derived from polysomnography, and ANS activity by measuring 24-h plasma cortisol and norepinephrine (NE).

RESULTS

Sleep cycle analysis displayed lower slow oscillatory (SO: 0.5-1.25 Hz) activity in the first cycle in insomnia compared to controls. In insomnia, 24-h cortisol levels were higher and 24-h NE levels were lower than controls. In controls, but not in insomnia, there was a significant interaction between NE level during wake and SO activity levels across the sleep cycles, such that in controls but not in insomnia, NE level during wake was positively associated with the amount of SO activity in the first cycle. In insomnia, lower 24-h NE level and SO activity in the first sleep cycle were associated with poorer subjective sleep quality.

CONCLUSION

Dysregulation of autonomic activity may be an underlying mechanism that links objective and subjective measures of sleep quality in older adults with insomnia, and potentially contribute to adverse health outcomes.

Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a very promising non‐invasive neuroimaging technique. However, EEG data obtained from the simultaneous EEG–fMRI are strongly influenced by MRI‐related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. When compared to the GA correction, the BCG correction is more challenging to remove due to its inherent variabilities and dynamic changes over time. The standard BCG correction (i.e., average artefact subtraction [AAS]), require detecting cardiac pulses from simultaneous electrocardiography (ECG) recording. However, ECG signals are also distorted and will become problematic for detecting reliable cardiac peaks. In this study, we focused on a beamforming spatial filtering technique to attenuate all unwanted source activities outside of the brain. Specifically, we applied the beamforming technique to attenuate the BCG artefact in EEG–fMRI, and also to recover meaningful task‐based neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond accurately. We analysed EEG–fMRI data in 20 healthy participants during the ANT, and compared four different BCG corrections (non‐BCG corrected, AAS BCG corrected, beamforming + AAS BCG corrected, beamforming BCG corrected). We demonstrated that the beamforming approach did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task‐based brain activity when compared to the standard AAS correction. This data‐driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG–fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI‐related artefacts.

Thalamocortical Spectral Transmission Relies on Balanced Input Strengths

The thalamus is a key element of sensory transmission in the brain, as it gates and selects sensory streams through a modulation of its internal activity. A preponderant role in these functions is played by its internal activity in the alpha range ([8–14] Hz), but the mechanism underlying this process is not completely understood. In particular, how do thalamocortical connections convey stimulus driven information selectively over the back-ground of thalamic internally generated activity? Here we investigate this issue with a spiking network model of feedforward connectivity between thalamus and primary sensory cortex reproducing the local field potential of both areas. We found that in a feedforward network, thalamic oscillations in the alpha range do not entrain cortical activity for two reasons: (i) alpha range oscillations are weaker in neurons projecting to the cortex, (ii) the gamma resonance dynamics of cortical networks hampers oscillations over the 10–20 Hz range thus weakening alpha range oscillations. This latter mechanism depends on the balance of the strength of thalamocortical connections toward excitatory and inhibitory neurons in the cortex. Our results highlight the relevance of corticothalamic feedback to sustain alpha range oscillations and pave the way toward an integrated understanding of the sensory streams traveling between the periphery and the cortex.

EEG Patterns Prior to Motor Activations of Parasomnias: A Systematic Review

INTRODUCTION

Non-rapid eye movement (NREM) parasomnias are defined as abnormal nocturnal behaviors that typically arise from the NREM sleep stage 3 during the first sleep cycle. The polysomnographic studies showed an increase in sleep fragmentation and an atypical slow wave activity (SWA) in participants with NREM parasomnias compared to healthy controls. To date, the pathophysiology of NREM parasomnias is still poorly understood. The recent investigation of the EEG patterns immediately before parasomnia events could shed light on the motor activations' processes. This systematic review aims to summarize empirical evidence about these studies and provide an overview of the methodological issues.

METHODS

A systematic literature search was carried out in PubMed, Web of Science, and Scopus, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The documents obtained were evaluated using the Newcastle-Ottawa Scale (NOS).

RESULTS

Nine studies were included in the qualitative synthesis. The major evidence revealed an increased slow frequency EEG activity immediately before the motor activations in frontal and central areas and increased beta activity in the anterior cingulate cortices.

DISCUSSION

The investigation of EEG patterns before parasomniac episodes could provide new insight into the study of NREM parasomnia pathophysiology. The high- and low-frequency EEG increase before the episodes could represent a predictive electrophysiological pattern of the motor activations' onset. Overall, identifying specific sleep markers before parasomnias might also help differentiate between NREM parasomnias and other motor sleep disorders. Different methodological protocols should be integrated for overcoming the lack of consistent empirical findings. Thus, future studies should focus on the topographical examination of canonical EEG frequency bands to better understand spatial and time dynamics before the episodes and identify the networks underlying the onset of activations.

Cortical and subcortical hemodynamic changes during sleep slow waves in human light sleep

EEG slow waves, the hallmarks of NREM sleep are thought to be crucial for the regulation of several important processes, including learning, sensory disconnection and the removal of brain metabolic wastes. Animal research indicates that slow waves may involve complex interactions within and between cortical and subcortical structures. Conventional EEG in humans, however, has a low spatial resolution and is unable to accurately describe changes in the activity of subcortical and deep cortical structures. To overcome these limitations, here we took advantage of simultaneous EEG-fMRI recordings to map cortical and subcortical hemodynamic (BOLD) fluctuations time-locked to slow waves of light sleep. Recordings were performed in twenty healthy adults during an afternoon nap. Slow waves were associated with BOLD-signal increases in the posterior brainstem and in portions of thalamus and cerebellum characterized by preferential functional connectivity with limbic and somatomotor areas, respectively. At the cortical level, significant BOLD-signal decreases were instead found in several areas, including insula and somatomotor cortex. Specifically, a slow signal increase preceded slow-wave onset and was followed by a delayed, stronger signal decrease. Similar hemodynamic changes were found to occur at different delays across most cortical brain areas, mirroring the propagation of electrophysiological slow waves, from centro-frontal to inferior temporo-occipital cortices. Finally, we found that the amplitude of electrophysiological slow waves was positively related to the magnitude and inversely related to the delay of cortical and subcortical BOLD-signal changes. These regional patterns of brain activity are consistent with theoretical accounts of the functions of sleep slow waves.

Slow oscillation density and amplitude decrease across development in pediatric Duchenne and Becker muscular dystrophy

STUDY OBJECTIVES

From childhood through adolescence, brain rhythms during non-rapid eye movement (NREM) sleep show dramatic development that mirror underlying brain maturation. For example, the function and characteristics of slow oscillations (SOs, <1 Hz) in healthy children are linked to brain development, motor skill, and cognition. However, little is known of possible changes in pediatric populations with neurologic abnormalities.

METHODS

We measured slow oscillations in 28 Duchenne and Becker muscular dystrophy male patients from age 4 to 20 years old during overnight in-lab clinical sleep studies. We compared our pediatric patients by age to evaluate the developmental changes of SOs from childhood to early and late adolescence.

RESULTS

Consistent with the current neuro- and physically typical literature, we found greater slow oscillation density (count of SOs per minute of each sleep stage) in NREM N3 than N2, and significantly greater slow oscillation density in frontal compared to central and occipital regions. However, separating patients into age-defined groups (child, early adolescent, and late adolescent) revealed a significant age effect, with a specific decline in the rate and amplitude of SOs.

CONCLUSIONS

We found that with age, pediatric patients with Duchenne muscular dystrophy show a significant decline in slow oscillation density. Given the role that slow oscillations play in memory formation and retention, it is critical to developmentally characterize these brain rhythms in medically complex populations. Our work converges with previous pediatric sleep literature that promotes the use of sleep electroencephalographic markers as prognostic tools and identifies potential targets to promote our patients' quality of life.

Are age and sex effects on sleep slow waves only a matter of electroencephalogram amplitude?

Aging is associated with reduced slow wave (SW) density (number SW/min in nonrapid-eye movement sleep) and amplitude. It has been proposed that an age-related decrease in SW density may be due to a reduction in electroencephalogram (EEG) amplitude instead of a decline in the capacity to generate SW. Here, we propose a data-driven approach to adapt SW amplitude criteria to age and sex. We predicted that the adapted criteria would reduce age and sex differences in SW density and SW characteristics but would not abolish them. A total of 284 healthy younger and older adults participated in one night of sleep EEG recording. We defined age- and sex-adapted SW criteria in a first cohort of younger (n = 97) and older (n = 110) individuals using a signal-to-noise ratio approach. We then used these age- and sex-specific criteria in an independent second cohort (n = 77, 38 younger and 39 older adults) to evaluate age and sex differences on SW density and SW characteristics. After adapting SW amplitude criteria, we showed maintenance of an age-related difference for SW density whereas the sex-related difference vanished. Indeed, older adults produced less SW compared with younger adults. Specifically, the adapted SW amplitude criteria increased the probability of occurrence of low amplitude SW (<80 µV) for older men especially. Our results thereby confirm an age-related decline in SW generation rather than an artifact in the detection amplitude criteria. As for the SW characteristics, the age- and sex-adapted criteria display reproducible effects across the two independent cohorts suggesting a more reliable inventory of the SW.

The effect of zolpidem on memory consolidation over a night of sleep

STUDY OBJECTIVES

Nonrapid eye movement sleep boosts hippocampus-dependent, long-term memory formation more so than wake. Studies have pointed to several electrophysiological events that likely play a role in this process, including thalamocortical sleep spindles (12-15 Hz). However, interventional studies that directly probe the causal role of spindles in consolidation are scarce. Previous studies have used zolpidem, a GABA-A agonist, to increase sleep spindles during a daytime nap and promote hippocampal-dependent episodic memory. The current study investigated the effect of zolpidem on nighttime sleep and overnight improvement of episodic memories.

METHODS

We used a double-blind, placebo-controlled within-subject design to test the a priori hypothesis that zolpidem would lead to increased memory performance on a word-paired associates task by boosting spindle activity. We also explored the impact of zolpidem across a range of other spectral sleep features, including slow oscillations (0-1 Hz), delta (1-4 Hz), theta (4-8 Hz), sigma (12-15 Hz), as well as spindle-SO coupling.

RESULTS

We showed greater memory improvement after a night of sleep with zolpidem, compared to placebo, replicating a prior nap study. Additionally, zolpidem increased sigma power, decreased theta and delta power, and altered the phase angle of spindle-SO coupling, compared to placebo. Spindle density, theta power, and spindle-SO coupling were associated with next-day memory performance.

CONCLUSIONS

These results are consistent with the hypothesis that sleep, specifically the timing and amount of sleep spindles, plays a causal role in the long-term formation of episodic memories. Furthermore, our results emphasize the role of nonrapid eye movement theta activity in human memory consolidation.

Cortical astrocytes independently regulate sleep depth and duration via separate GPCR pathways

Non-rapid eye movement (NREM) sleep, characterized by slow-wave electrophysiological activity, underlies several critical functions, including learning and memory. However, NREM sleep is heterogeneous, varying in duration, depth, and spatially across the cortex. While these NREM sleep features are thought to be largely independently regulated, there is also evidence that they are mechanistically coupled. To investigate how cortical NREM sleep features are controlled, we examined the astrocytic network, comprising a cortex-wide syncytium that influences population-level neuronal activity. We quantified endogenous astrocyte activity in mice over natural sleep and wake, then manipulated specific astrocytic G-protein-coupled receptor (GPCR) signaling pathways in vivo. We find that astrocytic Gi- and Gq-coupled GPCR signaling separately control NREM sleep depth and duration, respectively, and that astrocytic signaling causes differential changes in local and remote cortex. These data support a model in which the cortical astrocyte network serves as a hub for regulating distinct NREM sleep features.

Sleep has many roles, from strengthening new memories to regulating mood and appetite. While we might instinctively think of sleep as a uniform state of reduced brain activity, the reality is more complex. First, over the course of the night, we cycle between a number of different sleep stages, which reflect different levels of sleep depth. Second, the amount of sleep depth is not necessarily even across the brain but can vary between regions.

These sleep stages consist of either rapid eye movement (REM) sleep or non-REM (NREM) sleep. REM sleep is when most dreaming occurs, whereas NREM sleep is particularly important for learning and memory and can vary in duration and depth. During NREM sleep, large groups of neurons synchronize their firing to create rhythmic waves of activity known as slow waves. The more synchronous the activity, the deeper the sleep.

Vaidyanathan et al. now show that brain cells called astrocytes help regulate NREM sleep. Astrocytes are not neurons but belong to a group of specialized cells called glia. They are the largest glia cell type in the brain and display an array of proteins on their surfaces called G-protein-coupled receptors (GPCRs). These enable them to sense sleep-wake signals from other parts of the brain and to generate their own signals. In fact, each astrocyte can communicate with thousands of neurons at once. They are therefore well-poised to coordinate brain activity during NREM sleep.

Using innovative tools, Vaidyanathan et al. visualized astrocyte activity in mice as the animals woke up or fell asleep. The results showed that astrocytes change their activity just before each sleep–wake transition. They also revealed that astrocytes control both the depth and duration of NREM sleep via two different types of GPCR signals. Increasing one of these signals (Gi-GPCR) made the mice sleep more deeply but did not change sleep duration. Decreasing the other (Gq-GPCR) made the mice sleep for longer but did not affect sleep depth.

Sleep problems affect many people at some point in their lives, and often co-exist with other conditions such as mental health disorders. Understanding how the brain regulates different features of sleep could help us develop better – and perhaps more specific – treatments for sleep disorders. The current study suggests that manipulating GPCRs on astrocytes might increase sleep depth, for example. But before work to test this idea can begin, we must first determine whether findings from sleeping mice also apply to people.

Cerebral functional networks during sleep in young and older individuals

Even though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.

Macro and micro sleep architecture and cognitive performance in older adults

We sought to determine which facets of sleep neurophysiology were most strongly linked to cognitive performance in 3,819 older adults from two independent cohorts, using whole-night electroencephalography. From over 150 objective sleep metrics, we identified 23 that predicted cognitive performance, and processing speed in particular, with effects that were broadly independent of gross changes in sleep quality and quantity. These metrics included rapid eye movement duration, features of the electroencephalography power spectra derived from multivariate analysis, and spindle and slow oscillation morphology and coupling. These metrics were further embedded within broader associative networks linking sleep with aging and cardiometabolic disease: individuals who, compared with similarly aged peers, had better cognitive performance tended to have profiles of sleep metrics more often seen in younger, healthier individuals. Taken together, our results point to multiple facets of sleep neurophysiology that track coherently with underlying, age-dependent determinants of cognitive and physical health trajectories in older adults.

Hallucination, imagery, dreaming: reassembling stimulus-independent perceptions based on Edmund Parish's classic misperception framework

Within the broad field of human perception lies the category of stimulus-independent perceptions, which draws together experiences such as hallucinations, mental imagery and dreams. Traditional divisions between medical and psychological sciences have contributed to these experiences being investigated separately. This review aims to examine their similarities and differences at the levels of phenomenology and underlying brain function and thus reassemble them within a common framework. Using Edmund Parish's historical work as a guiding tool and the latest research findings in the cognitive, clinical and computational sciences, we consider how different perspectives may be reconciled and help generate novel hypotheses for future research. This article is part of the theme issue 'Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.

NREM sleep stages specifically alter dynamical integration of large-scale brain networks

Functional dissociations in the brain observed during non-rapid eye movement (NREM) sleep have been associated with reduced information integration and impaired consciousness that accompany increasing sleep depth. Here, we explored the dynamical properties of large-scale functional brain networks derived from transient brain activity using functional magnetic resonance imaging. Spatial brain maps generally display significant modifications in terms of their tendency to occur across wakefulness and NREM sleep. Unexpectedly, almost all networks predominated in activity during NREM stage 2 before an abrupt loss of activity is observed in NREM stage 3. Yet, functional connectivity and mutual dependencies between these networks progressively broke down with increasing sleep depth. Thus, the efficiency of information transfer during NREM stage 2 is low despite the high attempt to communicate. Critically, our approach provides relevant data for evaluating functional brain network integrity and our findings robustly support a significant advance in our neural models of human sleep and consciousness.

Distinct effects of low-intensity physical activity in the evening on sleep quality in older women: A comparison of exercise and housework

BACKGROUND

The effects of intensity, type, and time of day of physical activity on sleep are not well understood. An appropriate increase in core body temperature, due to physical activity during daytime, aids sleep in older adults. Our previous study showed that evening exercise has more positive effects on sleep quality in older adults when compared with morning exercise. However, older adults who do not exercise form a large part of the population. This study aimed to examine the distinct effects of low-intensity exercise and housework during the evening on sleep quality in older women.

METHODS

This randomized crossover study included 10 healthy older women aged 65-79 years. The participants engaged in low-intensity physical activity for 30 min, either aerobic exercise (70 steps per minute), housework (at the same intensity), or remained sedentary (control) 3 h before bedtime. Sleep was recorded polysomnographically, and self-reported sleep quality was assessed the next morning using the Oguri-Shirakawa-Azumi sleep inventory, Middle-Aged and Aged version (OSA-MA) questionnaire.

RESULTS

Compared with the control trial, core body temperature was significantly elevated in participants after each activity trial (0.5 ± 0.4 and 0.4 ± 0.4 °C for housework and aerobic exercise, respectively). There was a significant difference in sleep latency (14.2 ± 19.1, 9.9 ± 15.6, and 4.2 ± 3.5 min for control, housework, and aerobic exercise, respectively; ANOVA P = 0.011) among the trials. The delta power density after aerobic exercise was significantly higher than that after the control trial. We observed an increase of 53% and 15% in delta power during the 1st hour of sleep as a result of aerobic exercise and housework, respectively. The total score on OSA-MA was significantly higher after aerobic exercise (91.0 ± 5.4, 88.1 ± 6.9, and 108.6 ± 5.9 points for control, housework, and exercise, respectively).

CONCLUSIONS

Engaging in low-intensity aerobic exercise in the evening improved polysomnographic and self-reported sleep quality. Although housework increased core body temperature to the same level as that observed after aerobic exercise, self-reported sleep quality after housework was lower than that after aerobic exercise.

Obstructive Sleep Apnea and Its Treatment in Aging: Effects on Alzheimer's disease Biomarkers, Cognition, Brain Structure and Neurophysiology

Here we review the impact of obstructive sleep apnea (OSA) on biomarkers of Alzheimer's disease (AD) pathogenesis, neuroanatomy, cognition and neurophysiology, and present the research investigating the effects of continuous positive airway pressure (CPAP) therapy. OSA is associated with an increase in AD markers amyloid-β and tau measured in cerebrospinal fluid (CSF), by Positron Emission Tomography (PET) and in blood serum. There is some evidence suggesting CPAP therapy normalizes AD biomarkers in CSF but since mechanisms for amyloid-β and tau production/clearance in humans are not completely understood, these findings remain preliminary. Deficits in the cognitive domains of attention, vigilance, memory and executive functioning are observed in OSA patients with the magnitude of impairment appearing stronger in younger people from clinical settings than in older community samples. Cognition improves with varying degrees after CPAP use, with the greatest effect seen for attention in middle age adults with more severe OSA and sleepiness. Paradigms in which encoding and retrieval of information are separated by periods of sleep with or without OSA have been done only rarely, but perhaps offer a better chance to understand cognitive effects of OSA than isolated daytime testing. In cognitively normal individuals, changes in EEG microstructure during sleep, particularly slow oscillations and spindles, are associated with biomarkers of AD, and measures of cognition and memory. Similar changes in EEG activity are reported in AD and OSA, such as "EEG slowing" during wake and REM sleep, and a degradation of NREM EEG microstructure. There is evidence that CPAP therapy partially reverses these changes but large longitudinal studies demonstrating this are lacking. A diagnostic definition of OSA relying solely on the Apnea Hypopnea Index (AHI) does not assist in understanding the high degree of inter-individual variation in daytime impairments related to OSA or response to CPAP therapy. We conclude by discussing conceptual challenges to a clinical trial of OSA treatment for AD prevention, including inclusion criteria for age, OSA severity, and associated symptoms, the need for a potentially long trial, defining relevant primary outcomes, and which treatments to target to optimize treatment adherence.

Neurovascular coupling and bilateral connectivity during NREM and REM sleep

To understand how arousal state impacts cerebral hemodynamics and neurovascular coupling, we monitored neural activity, behavior, and hemodynamic signals in un-anesthetized, head-fixed mice. Mice frequently fell asleep during imaging, and these sleep events were interspersed with periods of wake. During both NREM and REM sleep, mice showed large increases in cerebral blood volume ([HbT]) and arteriole diameter relative to the awake state, two to five times larger than those evoked by sensory stimulation. During NREM, the amplitude of bilateral low-frequency oscillations in [HbT] increased markedly, and coherency between neural activity and hemodynamic signals was higher than the awake resting and REM states. Bilateral correlations in neural activity and [HbT] were highest during NREM, and lowest in the awake state. Hemodynamic signals in the cortex are strongly modulated by arousal state, and changes during sleep are substantially larger than sensory-evoked responses.

Inhibiting Epileptiform Activity in Cognitive Disorders: Possibilities for a Novel Therapeutic Approach

Cognitive impairment is a common and seriously debilitating symptom of various mental and neurological disorders including autism, attention deficit hyperactivity disorder, multiple sclerosis, epilepsy, and neurodegenerative diseases, like Alzheimer's disease. In these conditions, high prevalence of epileptiform activity emerges as a common pathophysiological hallmark. Growing body of evidence suggests that this discrete but abnormal activity might have a long-term negative impact on cognitive performance due to neuronal circuitries' remodeling, altered sleep structure, pathological hippocampo-cortical coupling, and even progressive neuronal loss. In animal models, epileptiform activity was shown to enhance the formation of pathological amyloid and tau proteins that in turn trigger network hyperexcitability. Abolishing epileptiform discharges might slow down the cognitive deterioration. These findings might provide basis for therapeutic use of antiepileptic drugs in neurodegenerative cognitive disorders. The aim of our review is to describe the data on the prevalence of epileptiform activity in various cognitive disorders, to summarize the current knowledge of the mechanisms of epileptic activity in relation to cognitive impairment, and to explore the utility of antiepileptic drugs in the therapy of cognitive disorders. We also propose future directions for drug development and novel therapeutic interventions targeting epileptiform discharges in these disorders.

EEG microstates of dreams

Why do people sometimes report that they remember dreams, while at other times they recall no experience? Despite the interest in dreams that may happen during the night, it has remained unclear which brain states determine whether these conscious experiences will occur and what prevents us from waking up during these episodes. Here we address this issue by comparing the EEG activity preceding awakenings with recalled vs. no recall of dreams using the EEG microstate approach. This approach characterizes transiently stable brain states of sub-second duration that involve neural networks with nearly synchronous dynamics. We found that two microstates (3 and 4) dominated during NREM sleep compared to resting wake. Further, within NREM sleep, microstate 3 was more expressed during periods followed by dream recall, whereas microstate 4 was less expressed. Source localization showed that microstate 3 encompassed the medial frontal lobe, whereas microstate 4 involved the occipital cortex, as well as thalamic and brainstem structures. Since NREM sleep is characterized by low-frequency synchronization, indicative of neuronal bistability, we interpret the increased presence of the “frontal” microstate 3 as a sign of deeper local deactivation, and the reduced presence of the “occipital” microstate 4 as a sign of local activation. The latter may account for the occurrence of dreaming with rich perceptual content, while the former may account for why the dreaming brain may undergo executive disconnection and remain asleep. This study demonstrates that NREM sleep consists of alternating brain states whose temporal dynamics determine whether conscious experience arises.

Neural Oscillations: Understanding a Neural Code of Pain

Neural oscillations play an important role in the integration and segregation of brain regions that are important for brain functions, including pain. Disturbances in oscillatory activity are associated with several disease states, including chronic pain. Studies of neural oscillations related to pain have identified several functional bands, especially alpha, beta, and gamma bands, implicated in nociceptive processing. In this review, we introduce several properties of neural oscillations that are important to understand the role of brain oscillations in nociceptive processing. We also discuss the role of neural oscillations in the maintenance of efficient communication in the brain. Finally, we discuss the role of neural oscillations in healthy and chronic pain nociceptive processing. These data and concepts illustrate the key role of regional and interregional neural oscillations in nociceptive processing underlying acute and chronic pains.

An electrophysiological marker of arousal level in humans

Deep non-rapid eye movement sleep (NREM) and general anesthesia with propofol are prominent states of reduced arousal linked to the occurrence of synchronized oscillations in the electroencephalogram (EEG). Although rapid eye movement (REM) sleep is also associated with diminished arousal levels, it is characterized by a desynchronized, 'wake-like' EEG. This observation implies that reduced arousal states are not necessarily only defined by synchronous oscillatory activity. Using intracranial and surface EEG recordings in four independent data sets, we demonstrate that the 1/f spectral slope of the electrophysiological power spectrum, which reflects the non-oscillatory, scale-free component of neural activity, delineates wakefulness from propofol anesthesia, NREM and REM sleep. Critically, the spectral slope discriminates wakefulness from REM sleep solely based on the neurophysiological brain state. Taken together, our findings describe a common electrophysiological marker that tracks states of reduced arousal, including different sleep stages as well as anesthesia in humans.

Multifractal and Entropy-Based Analysis of Delta Band Neural Activity Reveals Altered Functional Connectivity Dynamics in Schizophrenia

Dynamic functional connectivity (DFC) was established in the past decade as a potent approach to reveal non-trivial, time-varying properties of neural interactions – such as their multifractality or information content –, that otherwise remain hidden from conventional static methods. Several neuropsychiatric disorders were shown to be associated with altered DFC, with schizophrenia (SZ) being one of the most intensely studied among such conditions. Here we analyzed resting-state electroencephalography recordings of 14 SZ patients and 14 age- and gender-matched healthy controls (HC). We reconstructed dynamic functional networks from delta band (0.5–4 Hz) neural activity and captured their spatiotemporal dynamics in various global network topological measures. The acquired network measure time series were made subject to dynamic analyses including multifractal analysis and entropy estimation. Besides group-level comparisons, we built a classifier to explore the potential of DFC features in classifying individual cases. We found stronger delta-band connectivity, as well as increased variance of DFC in SZ patients. Surrogate data testing verified the true multifractal nature of DFC in SZ, with patients expressing stronger long-range autocorrelation and degree of multifractality when compared to controls. Entropy analysis indicated reduced temporal complexity of DFC in SZ. When using these indices as features, an overall cross-validation accuracy surpassing 89% could be achieved in classifying individual cases. Our results imply that dynamic features of DFC such as its multifractal properties and entropy are potent markers of altered neural dynamics in SZ and carry significant potential not only in better understanding its pathophysiology but also in improving its diagnosis. The proposed framework is readily applicable for neuropsychiatric disorders other than schizophrenia.

Sleep slow oscillations favour local cortical plasticity underlying the consolidation of reinforced procedural learning in human sleep

We investigated changes of slow-wave activity and sleep slow oscillations in the night following procedural learning boosted by reinforcement learning, and how these changes correlate with behavioural output. In the Task session, participants had to reach a visual target adapting cursor's movements to compensate an angular deviation introduced experimentally, while in the Control session no deviation was applied. The task was repeated at 13:00 hours, 17:00 hours and 23:00 hours before sleep, and at 08:00 hours after sleep. The deviation angle was set at 15° (13:00 hours and 17:00 hours) and increased to 45° (reinforcement) at 23:00 hours and 08:00 hours. Both for Task and Control nights, high-density electroencephalogram sleep recordings were carried out (23:30-19:30 hours). The Task night as compared with the Control night showed increases of: (a) slow-wave activity (absolute power) over the whole scalp; (b) slow-wave activity (relative power) in left centro-parietal areas; (c) sleep slow oscillations rate in sensorimotor and premotor areas; (d) amplitude of pre-down and up states in premotor regions, left sensorimotor and right parietal regions; (e) sigma crowning the up state in right parietal regions. After Task night, we found an improvement of task performance showing correlations with sleep slow oscillations rate in right premotor, sensorimotor and parietal regions. These findings suggest a key role of sleep slow oscillations in procedural memories consolidation. The diverse components of sleep slow oscillations selectively reflect the network activations related to the reinforced learning of a procedural visuomotor task. Indeed, areas specifically involved in the task stand out as those with a significant association between sleep slow oscillations rate and overnight improvement in task performance.

The power of children's sleep - Improved declarative memory consolidation in children compared with adults

Post-learning slow wave sleep (SWS) is known to support declarative memory consolidation. As SWS is more abundant in young population, we suggested that sleep-dependent memory consolidation processes could occur at a faster pace in school-aged children. After learning new associations between non-objects and their functions, retrieval performance was tested in 30 children (7–12 years) and 34 adults (20–30 years) during an immediate (IR) and a delayed retrieval (DR) session separated by either a Sleep or a Wake condition. Sleep led to stabilized memory retrieval performance only in children, not in adults, whereas no age-related difference was observed after a similar period of wakefulness. Hence, our results suggest more efficient sleep-dependent declarative memory consolidation processes in children compared with adults, an effect potentially ascribed to more abundant and deeper SWS during childhood.

Competing Roles of Slow Oscillations and Delta Waves in Memory Consolidation versus Forgetting

Sleep has been implicated in both memory consolidation and forgetting of experiences. However, it is unclear what governs the balance between consolidation and forgetting. Here, we tested how activity-dependent processing during sleep might differentially regulate these two processes. We specifically examined how neural reactivations during non-rapid eye movement (NREM) sleep were causally linked to consolidation versus weakening of the neural correlates of neuroprosthetic skill. Strikingly, we found that slow oscillations (SOs) and delta (δ) waves have dissociable and competing roles in consolidation versus forgetting. By modulating cortical spiking linked to SOs or δ waves using closed-loop optogenetic methods, we could, respectively, weaken or strengthen consolidation and thereby bidirectionally modulate sleep-dependent performance gains. We further found that changes in the temporal coupling of spindles to SOs relative to δ waves could account for such effects. Thus, our results indicate that neural activity driven by SOs and δ waves have competing roles in sleep-dependent memory consolidation.

Sleep Circuits and Physiology in Non-Mammalian Systems

Research over the last 20 years has firmly established the existence of sleep states across the animal kingdom. Work in non-mammalian animal models such as nematodes, fruit flies, and zebrafish has now uncovered many evolutionarily conserved aspects of sleep physiology and regulation, including shared circuit architecture, homeostatic and circadian control elements, and principles linking sleep physiology to function. Non-mammalian sleep research is now shedding light on fundamental aspects of the genetic and neuronal circuit regulation of sleep, with direct implications for the understanding of how sleep is regulated in mammals.

EEG microstates are correlated with brain functional networks during slow-wave sleep

Electroencephalography (EEG) microstates have been extensively studied in wakefulness and have been described as the "atoms of thought". Previous studies of EEG have found four microstates, i.e., microstates A, B, C and D, that are consistent among participants across the lifespan during the resting state. Studies using simultaneous EEG and functional magnetic resonance imaging (fMRI) have provided evidence for correlations between EEG microstates and fMRI networks during the resting state. Microstates have also been found during non-rapid eye movement (NREM) sleep. Slow-wave sleep (SWS) is considered the most restorative sleep stage and has been associated with the maintenance of sleep. However, the relationship between EEG microstates and brain functional networks during SWS has not yet been investigated. In this study, simultaneous EEG-fMRI data were collected during SWS to test the correspondence between EEG microstates and fMRI networks. EEG microstate-informed fMRI analysis revealed that three out of the four microstates showed significant correlations with fMRI data: 1) fMRI fluctuations in the insula and posterior temporal gyrus positively correlated with microstate B, 2) fMRI signals in the middle temporal gyrus and fusiform gyrus negatively correlated with microstate C, and 3) fMRI fluctuations in the occipital lobe negatively correlated with microstate D, while fMRI signals in the anterior cingulate and cingulate gyrus positively correlated with this microstate. Functional brain networks were then assessed using group independent component analysis based on the fMRI data. The group-level spatial correlation analysis showed that the fMRI auditory network overlapped the fMRI activation map of microstate B, the executive control network overlapped the fMRI deactivation of microstate C, and the visual and salience networks overlapped the fMRI deactivation and activation maps of microstate D. In addition, the subject-level spatial correlations between the general linear model (GLM) beta map of each microstate and the individual maps of each component yielded by dual regression also showed that EEG microstates were closely associated with brain functional networks measured using fMRI during SWS. Overall, the results showed that EEG microstates were closely related to brain functional networks during SWS, which suggested that EEG microstates provide an important electrophysiological basis underlying brain functional networks.

Sensory Neuromodulation

We describe a model of neurological disease based on dysfunctional brain oscillators. This is not a new model, but it is not one that is widely appreciated by clinicians. The value of this model lies in the predictions it makes and the utility it provides in translational applications, in particular for neuromodulation devices. Specifically, we provide a perspective on devices that provide input to sensory receptors and thus stimulate endogenous sensory networks. Current forms of clinically applied neuromodulation, including devices such as (implanted) deep brain stimulators (DBS) and various, noninvasive methods such as transcranial magnetic stimulation (TMS) and transcranial current methods (tACS, tDCS), have been studied extensively. The potential strength of neuromodulation of a sensory organ is access to the same pathways that natural environmental stimuli use and, importantly, the modulatory signal will be transformed as it travels through the brain, allowing the modulation input to be consistent with regional neuronal dynamics. We present specific examples of devices that rely on sensory neuromodulation and evaluate the translational potential of these approaches. We argue that sensory neuromodulation is well suited to, ideally, repair dysfunctional brain oscillators, thus providing a broad therapeutic approach for neurological diseases.

Sleeping with Hippocampal Damage

The hippocampus plays a critical role in sleep-related memory processes [1-3], but it is unclear which specific sleep features are dependent upon this brain structure. The examination of sleep physiology in patients with focal bilateral hippocampal damage and amnesia could supply important evidence regarding these links. However, there is a dearth of such studies, despite these patients providing compelling insights into awake cognition [4, 5]. Here, we sought to identify the contribution of the hippocampus to the sleep phenotype by characterizing sleep via comprehensive qualitative and quantitative analyses in memory-impaired patients with selective bilateral hippocampal damage and matched control participants using in-home polysomnography on 4 nights. We found that, compared to control participants, patients had significantly reduced slow-wave sleep-likely due to decreased density of slow waves-as well as slow-wave activity. In contrast, slow and fast spindles were indistinguishable from those of control participants. Moreover, patients expressed slow oscillations (SOs), and SO-fast spindle coupling was observed. However, on closer scrutiny, we noted that the timing of spindles within the SO cycle was delayed in the patients. The shift of patients' spindles into the later phase of the up-state within the SO cycle may indicate a mismatch in timing across the SO-spindle-ripple events that are associated with memory consolidation [6, 7]. The substantial effect of selective bilateral hippocampal damage on large-scale oscillatory activity in the cortex suggests that, as with awake cognition, the hippocampus plays a significant role in sleep physiology, which may, in turn, be necessary for efficacious episodic memory.

Integrating sleep, neuroimaging, and computational approaches for precision psychiatry

In advancing precision psychiatry, we focus on what imaging technology and computational approaches offer for the future of diagnostic subtyping and personalized tailoring of interventions for sleep impairment in mood and anxiety disorders. Current diagnostic criteria for mood and anxiety tend to lump different forms of sleep disturbance together. Parsing the biological features of sleep impairment and brain circuit dysfunction is one approach to identifying subtypes within these disorders that are mechanistically coherent and offer targets for intervention. We focus on two large-scale neural circuits implicated in sleep impairment and in mood and anxiety disorders: the default mode network and negative affective network. Through a synthesis of existing knowledge about these networks, we pose a testable framework for understanding how hyper- versus hypo-engagement of these networks may underlie distinct features of mood and sleep impairment. Within this framework we consider whether poor sleep quality may have an explanatory role in previously observed associations between network dysfunction and mood symptoms. We expand this framework to future directions including the potential for connecting circuit-defined subtypes to more distal features derived from digital phenotyping and wearable technologies, and how new discovery may be advanced through machine learning approaches.

Physiological changes in sleep that affect fMRI inference

fMRI relies on a localized cerebral blood flow (CBF) response to changes in cortical neuronal activity. An underappreciated aspect however is its sensitivity to contributions from autonomic physiology that may affect CBF through changes in vascular resistance and blood pressure. As is reviewed here, this is crucial to consider in fMRI studies of sleep, given the close linkage between the regulation of arousal state and autonomic physiology. Typical methods for separating these effects are based on the use of reference signals that may include physiological parameters such as heart rate and respiration; however, the use of time-invariant models may not be adequate due to the possibly changing relationship between reference and fMRI signals with arousal state. In addition, recent research indicates that additional physiological reference signals may be needed to accurately describe changes in systemic physiology, including sympathetic indicators such as finger skin vascular tone and blood pressure.

Coupled electrophysiological, hemodynamic, and cerebrospinal fluid oscillations in human sleep

Sleep is essential for both cognition and maintenance of healthy brain function. Slow waves in neural activity contribute to memory consolidation, whereas cerebrospinal fluid (CSF) clears metabolic waste products from the brain. Whether these two processes are related is not known. We used accelerated neuroimaging to measure physiological and neural dynamics in the human brain. We discovered a coherent pattern of oscillating electrophysiological, hemodynamic, and CSF dynamics that appears during non-rapid eye movement sleep. Neural slow waves are followed by hemodynamic oscillations, which in turn are coupled to CSF flow. These results demonstrate that the sleeping brain exhibits waves of CSF flow on a macroscopic scale, and these CSF dynamics are interlinked with neural and hemodynamic rhythms.

Timing between Cortical Slow Oscillations and Heart Rate Bursts during Sleep Predicts Temporal Processing Speed, but Not Offline Consolidation

Central and autonomic nervous system activities are coupled during sleep. Cortical slow oscillations (SOs; <1 Hz) coincide with brief bursts in heart rate (HR), but the functional consequence of this coupling in cognition remains elusive. We measured SO-HR temporal coupling (i.e., the peak-to-peak interval between downstate of SO event and HR burst) during a daytime nap and asked whether this SO-HR timing measure was associated with temporal processing speed and learning on a texture discrimination task by testing participants before and after a nap. The coherence of SO-HR events during sleep strongly correlated with an individual's temporal processing speed in the morning and evening test sessions, but not with their change in performance after the nap (i.e., consolidation). We confirmed this result in two additional experimental visits and also discovered that this association was visit-specific, indicating a state (not trait) marker. Thus, we introduce a novel physiological index that may be a useful marker of state-dependent processing speed of an individual.

The Emergence of Spindles and K-Complexes and the Role of the Dorsal Caudal Part of the Anterior Cingulate as the Generator of K-Complexes

The large multicomponent K-complex (KC) and the rhythmic spindle are the hallmarks of non-rapid eye movement (NREM)-2 sleep stage. We studied with magnetoencephalography (MEG) the progress of light sleep (NREM-1 and NREM-2) and emergence of KCs and spindles. Seven periods of interest (POI) were analyzed: wakefulness, the two quiet "core" periods of light sleep (periods free from any prominent phasic or oscillatory events) and four periods before and during spindles and KCs. For each POI, eight 2-s (1250 time slices) segments were used. We employed magnetic field tomography (MFT) to extract an independent tomographic estimate of brain activity from each MEG data sample. The spectral power was then computed for each voxel in the brain for each segment of each POI. The sets of eight maps from two POIs were contrasted using a voxel-by-voxel t-test. Only increased spectral power was identified in the four key contrasts between POIs before and during spindles and KCs versus the NREM2 core. Common increases were identified for all four subjects, especially within and close to the anterior cingulate cortex (ACC). These common increases were widespread for low frequencies, while for higher frequencies they were focal, confined to specific brain areas. For the pre-KC POI, only one prominent increase was identified, confined to the theta/alpha bands in a small area in the dorsal caudal part of ACC (dcACC). During KCs, the activity in this area grows in intensity and extent (in space and frequency), filling the space between the areas that expanded their low frequency activity (in the delta band) during NREM2 compared to NREM1. Our main finding is that prominent spectral power increases before NREM2 graphoelements are confined to the dcACC, and only for KCs, sharing common features with changes of activity in dcACC of the well-studied error related negativity (ERN). ERN is seen in awake state, in perceptual conflict and situations where there is a difference between expected and actual environmental or internal events. These results suggest that a KC is the sleep side of the awake state ERN, both serving their putative sentinel roles in the frame of the saliency network.

Electroencephalogram Microstate Abnormalities in Early-Course Psychosis

BACKGROUND

Microstates are periods of characteristic electroencephalographic signal topography that are related to activity in brain networks. Previous work has identified abnormal microstate parameters in individuals with psychotic disorders. We combined microstate analysis with sample entropy analysis to study the dynamics of resting-state networks in patients with early-course psychosis.

METHODS

We used microstate analysis to transform resting-state high-density electroencephalography data from 22 patients with early-course psychosis and 22 healthy control subjects into sequences of characteristic scalp topographies. Sample entropy was used to calculate the complexity of microstate sequences across a range of template lengths.

RESULTS

Patients and control subjects produced similar sets of 4 microstates that agree with a widely reported canonical set (A, B, C, and D). Relative to control subjects, patients had decreased frequency of microstate A. In control subjects, sample entropy decreased as template length increased, suggesting that sequence of microstate transitions is self-similar across multiple transitions. In patients, sample entropy did not decrease, suggesting a lack of self-similarity in transition sequences. This finding was unrelated to data length or microstate topography. Entropy was elevated in unmedicated patients, and it decreased in patients who were administered medication. We identified patterns of transitions between microstates that were overrepresented in control data compared with representation in patient data.

CONCLUSIONS

Our findings suggest that patients with early-course psychosis have abnormally chaotic transitions between brain networks. This chaos may reflect an underlying abnormality in allocating neural resources and effecting appropriate transitions between distinct activity states in psychosis.

Altered brain perfusion patterns in wakefulness and slow-wave sleep in sleepwalkers

Study Objectives

The present study assessed brain perfusion patterns with single-photon emission computed tomography (SPECT) during sleepwalkers' post-sleep deprivation slow-wave sleep (SWS) and resting-state wakefulness.

Methods

Following a 24 hr period of sleep deprivation, 10 sleepwalkers and 10 sex- and age-matched controls were scanned with a high-resolution SPECT scanner. Participants were injected with 99mTc-ethylene cysteinate dimer after 2 min of stable SWS within their first sleep cycle as well as during resting-state wakefulness, both after a subsequent 24 hr period of sleep deprivation.

Results

When compared with controls' brain perfusion patterns during both SWS and resting-state wakefulness, sleepwalkers showed reduced regional cerebral perfusion in several bilateral frontal regions, including the superior frontal, middle frontal, and medial frontal gyri. Moreover, reduced regional cerebral perfusion was also found in sleepwalkers' left postcentral gyrus, insula, and superior temporal gyrus during SWS compared with controls. During resting-state wakefulness compared with controls, reduced cerebral perfusion was also found in parietal and temporal regions of sleepwalkers' left hemisphere, whereas the right parahippocampal gyrus showed increased regional cerebral perfusion.

Conclusions

Our results reveal patterns of reduced regional cerebral perfusion in sleepwalkers' frontal and parietal areas when compared with controls, regions previously associated with SWS generation and episode occurrence. Additionally, reduced perfusion in the dorsolateral prefrontal cortex and insula during recovery SWS is consistent with the clinical features of somnambulistic episodes, including impaired awareness and reduced pain perception. Altered regional cerebral perfusion patterns during sleepwalkers' resting-state wakefulness may be related to daytime functional anomalies in this population.

Apparent diffusion coefficient changes in human brain during sleep - Does it inform on the existence of a glymphatic system?

The role of sleep in brain physiology is poorly understood. Recently rodent studies have shown that the glymphatic system clears waste products from brain more efficiently during sleep compared to wakefulness due to the expansion of the interstitial fluid space facilitating entry of cerebrospinal fluid (CSF) into the brain. Here, we studied water diffusivity in the brain during sleep and awake conditions, hypothesizing that an increase in water diffusivity during sleep would occur concomitantly with an expansion of CSF volume - an effect that we predicted based on preclinical findings would be most prominent in cerebellum. We used MRI to measure slow and fast components of the apparent diffusion coefficient (ADC) of water in the brain in 50 healthy participants, in 30 of whom we compared awake versus sleep conditions and in 20 of whom we compared rested-wakefulness versus wakefulness following one night of sleep-deprivation. Sleep compared to wakefulness was associated with increases in slow-ADC in cerebellum and left temporal pole and with decreases in fast-ADC in thalamus, insula, parahippocampus and striatal regions, and the density of sleep arousals was inversely associated with ADC changes. The CSF volume was also increased during sleep and was associated with sleep-induced changes in ADCs in cerebellum. There were no differences in ADCs with wakefulness following sleep deprivation compared to rested-wakefulness. Although we hypothesized increases in ADC with sleep, our findings uncovered both increases in slow ADC (mostly in cerebellum) as well as decreases in fast ADC, which could reflect the distinct biological significance of fast- and slow-ADC values in relation to sleep. While preliminary, our findings suggest a more complex sleep-related glymphatic function in the human brain compared to rodents. On the other hand, our findings of sleep-induced changes in CSF volume provide preliminary evidence that is consistent with a glymphatic transport process in the human brain.

Spatio-temporal structure of sleep slow oscillations on the electrode manifold and its relation to spindles

Electrophysiological sleep rhythms have been shown to impact human waking cognition, but their spatio-temporal dynamics are not understood. We investigated how slow oscillations (SOs; 0.5-4 Hz) are organized during a night of polysomnographically-recorded sleep, focusing on the scalp electrode manifold. We detected troughs of SOs at all electrodes independently and analyzed the concurrent SO troughs found in every other electrode within ±400 ms. We used a k-clustering algorithm to categorize the spatial patterns of SO trough co-occurrence into three types (Global, Local or Frontal) depending on their footprint on the electrode manifold during the considered time window. When comparing the clusters across non-rapid eye movement (NREM) sleep stages, we found a relatively larger fraction of Local SOs in slow wave sleep (SWS) compared to stage 2, and larger fraction of Global SOs in stage 2 compared to SWS. The probability of SO detection in time between two electrodes showed that SO troughs of all types co-occurred at some nearby electrodes, but only Global troughs had traveling wave profiles, moving anteriorly to posteriorly. Global SOs also had larger amplitudes at frontal electrodes and stronger coupling with fast spindles (12.5-16 Hz). Indeed, SO-spindle complexes were more likely to be detected following a Global SO trough compared to SOs in other clusters. Also, the phase-amplitude modulation of SOs over spindles (modulation vector) was higher for Global SOs across the electrode manifold. Given the recent evidence of a link between thalamocortical coupling and cognition, our findings suggest stronger cognitive relevance of Global SOs as compared to other SO types in sleep memory processing.

Clinical Trials

No clinical trial is related to this study.

Coupling of autonomic and central events during sleep benefits declarative memory consolidation

While anatomical pathways between forebrain cognitive and brainstem autonomic nervous centers are well-defined, autonomic-central interactions during sleep and their contribution to waking performance are not understood. Here, we analyzed simultaneous central activity via electroencephalography (EEG) and autonomic heart beat-to-beat intervals (RR intervals) from electrocardiography (ECG) during wake and daytime sleep. We identified bursts of ECG activity that lasted 4-5 s and predominated in non-rapid-eye-movement sleep (NREM). Using event-based analysis of NREM sleep, we found an increase in delta (0.5-4 Hz) and sigma (12-15 Hz) power and an elevated density of slow oscillations (0.5-1 Hz) about 5 s prior to peak of the heart rate burst, as well as a surge in vagal activity, assessed by high-frequency (HF) component of RR intervals. Using regression framework, we show that these Autonomic/Central Events (ACE) positively predicted post-nap improvement in a declarative memory task after controlling for the effects of spindles and slow oscillations from sleep periods without ACE. No such relation was found between memory performance and a control nap. Additionally, NREM ACE negatively correlated with REM sleep and learning in a non-declarative memory task. These results provide the first evidence that coordinated autonomic and central events play a significant role in declarative memory consolidation.

Dreaming in NREM Sleep: A High-Density EEG Study of Slow Waves and Spindles

Dreaming can occur in both rapid eye movement (REM) and non-REM (NREM) sleep. We recently showed that in both REM and NREM sleep, dreaming is associated with local decreases in slow wave activity (SWA) in posterior brain regions. To expand these findings, here we asked how specific features of slow waves and spindles, the hallmarks of NREM sleep, relate to dream experiences. Fourteen healthy human subjects (10 females) underwent nocturnal high-density EEG recordings combined with a serial awakening paradigm. Reports of dreaming, compared with reports of no experience, were preceded by fewer, smaller, and shallower slow waves, and faster spindles, especially in central and posterior cortical areas. We also identified a minority of very steep and large slow waves in frontal regions, which occurred on a background of reduced SWA and were associated with high-frequency power increases (local "microarousals") heralding the successful recall of dream content. These results suggest that the capacity of the brain to generate experiences during sleep is reduced in the presence of neuronal off-states in posterior and central brain regions, and that dream recall may be facilitated by the intermittent activation of arousal systems during NREM sleep.SIGNIFICANCE STATEMENT By combining high-density EEG recordings with a serial awakening paradigm in healthy subjects, we show that dreaming in non-rapid eye movement sleep occurs when slow waves in central and posterior regions are sparse, small, and shallow. We also identified a small subset of very large and steep frontal slow waves that are associated with high-frequency activity increases (local "microarousals") heralding successful recall of dream content. These results provide noninvasive measures that could represent a useful tool to infer the state of consciousness during sleep.

Sleep-Wake Neurochemistry

The regulated alternations between wakefulness and sleep states reflect complex behavioral processes, orchestrated by distinct neurochemical changes in brain parenchyma. No single neurotransmitter or neuromodulator controls the sleep-wake states in isolation. Rather, fine-tuned interactions within organized neuronal circuits regulate waking and sleep states and drive their transitions. Structural or functional dysregulation and medications interfering with these ensembles can lead to sleep-wake disorders and exert wanted or unwanted pharmacological actions on sleep-wake states. Knowledge of the neurochemical bases of sleep-wake states, which will be discussed in this article, provides the conceptual framework for understanding pharmacological effects on sleep and wake.