Sleep and vigilance states: Embracing spatiotemporal dynamics

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

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

Monoaminergic signaling during mammalian NREM sleep - Recent insights and next-level questions

Subcortical neuromodulatory activity in the mammalian brain enables flexible wake behaviors, which are essential for survival in an ever-changing external environment. With the suppression of such behaviors in sleep, this activity is, on average, much reduced. Recent discoveries, enabled by unprecedented technical advancements, challenge the long-standing view that monoaminergic systems-noradrenaline (NA), dopamine (DA), and serotonin (5-HT)-remain largely inactive during sleep. This review highlights recent technological and scientific progress in this field, summarizing evidence that monoaminergic signaling in the brain supplements sleep with essential wake-related functions. Stress and/or neuropsychiatric conditions negatively impact on monoaminergic signaling, which can lead to sleep disruptions. Furthermore, subcortical neuromodulatory systems are vulnerable to neurodegenerative pathologies, which implies them in sleep disruptions at early stages of disease. We propose that future research will be well-invested in elucidating the spatiotemporal organization, cellular mechanisms, and functional relevance of neuromodulatory dynamics across species, and in identifying the molecular and physiological processes that sustain their integrity throughout the lifespan.

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

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

Sleep pressure causes birds to trade asymmetric sleep for symmetric sleep

Sleep is a dangerous part of an animal's life.1,2,3 Nonetheless, following sleep loss, mammals and birds sleep longer and deeper, as reflected by increased electroencephalogram (EEG) slow-wave activity (SWA; ≈1-5 Hz spectral power) during non-rapid eye movement (NREM) sleep.4,5 Stimulating a brain region during wakefulness also causes that region to sleep deeper afterwards,6,7,8,9 indicating that NREM sleep is a local, homeostatically regulated process.10,11 Birds and some marine mammals can keep one eye open during NREM sleep,12,13 a behavior associated with lighter sleep or wakefulness in the hemisphere opposite the open eye-states called asymmetric and unihemispheric NREM sleep, respectively.13,14,15,16,17,18,19,20,21,22,23 Closure of both eyes is associated with symmetric NREM or REM sleep. Birds rely on asymmetric and unihemispheric sleep to stay safe.17,24,25 However, as sleeping deeply with only one hemisphere at a time increases the time required for both hemispheres to fulfill their need for NREM sleep, increased sleep pressure might cause birds to engage in symmetric sleep at the expense of asymmetric sleep.26,27 Using high-density EEG recordings of European jackdaws (Coloeus monedula), we investigated intra- and inter-hemispheric asymmetries during normal sleep and following sleep deprivation (SD). The proportion of asymmetric sleep was lower early in the sleep period and following SD-periods of increased sleep pressure. Our findings demonstrate a trade-off between the benefits of sleep and vigilance and indicate that a bird's utilization of asymmetric sleep is constrained by temporal dynamics in their need for sleep.

SleepInvestigatoR: a flexible R function for analyzing scored sleep in rodents

Analyzing scored sleep is a fundamental prerequisite to understanding how sleep changes between health and disease. Classically, this is accomplished by manually calculating various measures (e.g. percent of non-rapid eye movement sleep) from a collection of scored sleep files. This process can be tedious and error-prone, especially when studies include large animal numbers or involve long recording sessions. To address this issue, we present SleepInvestigatoR, a versatile tool that can quickly organize and analyze multiple scored sleep files into a single output. The function is written in the open-source statistical language R and has a total of 25 parameters that can be set to match a wide variety of experimental needs. SleepInvestigatoR delivers a total of 23 unique measures of sleep, including all measures commonly reported in the rodent literature. A simple plotting function is also provided to quickly graph and visualize the scored data. All code is designed to be implemented with little formal coding knowledge, and step-by-step instructions are provided on the corresponding GitHub page. Overall, SleepInvestigatoR provides the sleep researcher a critical tool to increase efficiency, interpretation, and reproducibility in analyzing scored rodent sleep.

Anything but small: Microarousals stand at the crossroad between noradrenaline signaling and key sleep functions

Continuous sleep restores the brain and body, whereas fragmented sleep harms cognition and health. Microarousals (MAs), brief (3- to 15-s-long) wake intrusions into sleep, are clinical markers for various sleep disorders. Recent rodent studies show that MAs during healthy non-rapid eye movement (NREM) sleep are driven by infraslow fluctuations of noradrenaline (NA) in coordination with electrophysiological rhythms, vasomotor activity, cerebral blood volume, and glymphatic flow. MAs are hence part of healthy sleep dynamics, raising questions about their biological roles. We propose that MAs bolster NREM sleep's benefits associated with NA fluctuations, according to an inverted U-shaped curve. Weakened noradrenergic fluctuations, as may occur in neurodegenerative diseases or with sleep aids, reduce MAs, whereas exacerbated fluctuations caused by stress fragment NREM sleep and collapse NA signaling. We suggest that MAs are crucial for the restorative and plasticity-promoting functions of sleep and advance our insight into normal and pathological arousal dynamics from sleep.

Arousal as a universal embedding for spatiotemporal brain dynamics

Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, multidimensional arousal-related process that regulates brain-wide physiology on the timescale of seconds. By framing this interpretation within dynamical systems theory, we arrive at a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multidimensional, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and brain blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology-widely interpreted to reflect regionally-specific neural communication-are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.

Can Neuromodulation Improve Sleep and Psychiatric Symptoms?

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

Defining slow wave sleep without slow waves

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

Multiple Intrinsic Timescales Govern Distinct Brain States in Human Sleep

Human sleep exhibits multiple, recurrent temporal regularities, ranging from circadian rhythms to sleep stage cycles and neuronal oscillations during nonrapid eye movement sleep. Moreover, recent evidence revealed a functional role of aperiodic activity, which reliably discriminates different sleep stages. Aperiodic activity is commonly defined as the spectral slope χ of the 1/frequency (1/fχ) decay function of the electrophysiological power spectrum. However, several lines of inquiry now indicate that the aperiodic component of the power spectrum might be better characterized by a superposition of several decay processes with associated timescales. Here, we determined multiple timescales, which jointly shape aperiodic activity using human intracranial electroencephalography. Across three independent studies (47 participants, 23 female), our results reveal that aperiodic activity reliably dissociated sleep stage-dependent dynamics in a regionally specific manner. A principled approach to parametrize aperiodic activity delineated several, spatially and state-specific timescales. Lastly, we employed pharmacological modulation by means of propofol anesthesia to disentangle state-invariant timescales that may reflect physical properties of the underlying neural population from state-specific timescales that likely constitute functional interactions. Collectively, these results establish the presence of multiple intrinsic timescales that define the electrophysiological power spectrum during distinct brain states.

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

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

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

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

Claustrum neurons projecting to the anterior cingulate restrict engagement during sleep and behavior

The claustrum has been linked to attention and sleep. We hypothesized that this reflects a shared function, determining responsiveness to stimuli, which spans the axis of engagement. To test this hypothesis, we recorded claustrum population dynamics from male mice during both sleep and an attentional task ('ENGAGE'). Heightened activity in claustrum neurons projecting to the anterior cingulate cortex (ACCp) corresponded to reduced sensory responsiveness during sleep. Similarly, in the ENGAGE task, heightened ACCp activity correlated with disengagement and behavioral lapses, while low ACCp activity correlated with hyper-engagement and impulsive errors. Chemogenetic elevation of ACCp activity reduced both awakenings during sleep and impulsive errors in the ENGAGE task. Furthermore, mice employing an exploration strategy in the task showed a stronger correlation between ACCp activity and performance compared to mice employing an exploitation strategy which reduced task complexity. Our results implicate ACCp claustrum neurons in restricting engagement during sleep and goal-directed behavior.

SleepInvestigatoR: A flexible R function for analyzing scored sleep in rodents

Analyzing scored sleep is a fundamental prerequisite to understanding how sleep changes between health and disease. Classically, this is accomplished by manually calculating various measures (e.g., percent of non-rapid eye movement sleep) from a collection of scored sleep files. This process can be tedious and error prone especially when studies include a large number of animals or involve long recording sessions. To address this issue, we present SleepInvestigatoR, a versatile tool that can quickly organize and analyze multiple scored sleep files into a single output. The function is written in the open-source statistical language R and has a total of 25 parameters that can be set to match a wide variety of experimenter needs. SleepInvestigatoR delivers a total of 22 unique measures of sleep, including all measures commonly reported in the rodent literature. A simple plotting function is also provided to quickly graph and visualize the scored data. All code is designed to be implemented with little formal coding knowledge and step-by-step instructions are provided on the corresponding GitHub page. Overall, SleepInvestigatoR provides the sleep researcher a critical tool to increase efficiency, interpretation, and reproducibility in analyzing scored rodent sleep.

Linking activity dyshomeostasis and sleep disturbances in Alzheimer disease

The presymptomatic phase of Alzheimer disease (AD) starts with the deposition of amyloid-β in the cortex and begins a decade or more before the emergence of cognitive decline. The trajectory towards dementia and neurodegeneration is shaped by the pathological load and the resilience of neural circuits to the effects of this pathology. In this Perspective, I focus on recent advances that have uncovered the vulnerability of neural circuits at early stages of AD to hyperexcitability, particularly when the brain is in a low-arousal states (such as sleep and anaesthesia). Notably, this hyperexcitability manifests before overt symptoms such as sleep and memory deficits. Using the principles of control theory, I analyse the bidirectional relationship between homeostasis of neuronal activity and sleep and propose that impaired activity homeostasis during sleep leads to hyperexcitability and subsequent sleep disturbances, whereas sleep disturbances mitigate hyperexcitability via negative feedback. Understanding the interplay among activity homeostasis, neuronal excitability and sleep is crucial for elucidating the mechanisms of vulnerability to and resilience against AD pathology and for identifying new therapeutic avenues.

Sleepiness and the transition from wakefulness to sleep

The transition from wakefulness to sleep is a progressive process that is reflected in the gradual loss of responsiveness, an alteration of cognitive functions, and a drastic shift in brain dynamics. These changes do not occur all at once. The sleep onset period (SOP) refers here to this period of transition between wakefulness and sleep. For example, although transitions of brain activity at sleep onset can occur within seconds in a given brain region, these changes occur at different time points across the brain, resulting in a SOP that can last several minutes. Likewise, the transition to sleep impacts cognitive and behavioral levels in a graded and staged fashion. It is often accompanied and preceded by a sensation of drowsiness and the subjective feeling of a need for sleep, also associated with specific physiological and behavioral signatures. To better characterize fluctuations in vigilance and the SOP, a multidimensional approach is thus warranted. Such a multidimensional approach could mitigate important limitations in the current classification of sleep, leading ultimately to better diagnoses and treatments of individuals with sleep and/or vigilance disorders. These insights could also be translated in real-life settings to either facilitate sleep onset in individuals with sleep difficulties or, on the contrary, prevent or control inappropriate sleep onsets.

Sleep and the hypothalamus

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

How we sleep: From brain states to processes

All our lives, we alternate between wakefulness and sleep with direct consequences on our ability to interact with our environment, the dynamics and contents of our subjective experience, and our brain activity. Consequently, sleep has been extensively characterised in terms of behavioural, phenomenological, and physiological changes, the latter constituting the gold standard of sleep research. The common view is thus that sleep represents a collection of discrete states with distinct neurophysiological signatures. However, recent findings challenge such a monolithic view of sleep. Indeed, there can be sharp discrepancies in time and space in the activity displayed by different brain regions or networks, making it difficult to assign a global vigilance state to such a mosaic of contrasted dynamics. Viewing sleep as a multidimensional continuum rather than a succession of non-overlapping and mutually exclusive states could account for these local aspects of sleep. Moving away from the focus on sleep states, sleep can also be investigated through the brain processes that are present in sleep, if not necessarily specific to sleep. This focus on processes rather than states allows to see sleep for what it does rather than what it is, avoiding some of the limitations of the state perspective and providing a powerful heuristic to understand sleep. Indeed, what is sleep if not a process itself that makes up wake up every morning with a brain cleaner, leaner and less cluttered.

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

Sleep and wake are understood to be slow, long-lasting processes that span the entire brain. Brain states correlate with many neurophysiological changes, yet the most robust and reliable signature of state is enriched in rhythms between 0.1 and 20 Hz. The possibility that the fundamental unit of brain state could be a reliable structure at the scale of milliseconds and microns has not been addressed due to the physical limits associated with oscillation-based definitions. Here, by analyzing high resolution neural activity recorded in 10 anatomically and functionally diverse regions of the murine brain over 24 h, we reveal a mechanistically distinct embedding of state in the brain. Sleep and wake states can be accurately classified from on the order of 100 to 101 ms of neuronal activity sampled from 100 μm of brain tissue. In contrast to canonical rhythms, this embedding persists above 1,000 Hz. This high frequency embedding is robust to substates and rapid events such as sharp wave ripples and cortical ON/OFF states. To ascertain whether such fast and local structure is meaningful, we leveraged our observation that individual circuits intermittently switch states independently of the rest of the brain. Brief state discontinuities in subsets of circuits correspond with brief behavioral discontinuities during both sleep and wake. Our results suggest that the fundamental unit of state in the brain is consistent with the spatial and temporal scale of neuronal computation, and that this resolution can contribute to an understanding of cognition and behavior.