Local experience-dependent changes in the wake EEG after prolonged wakefulness

Timing and cortical region matter: theta power differences between teenagers affected by Major Depression and healthy controls

In adults affected by Major Depressive Disorder (MDD), most findings point to higher electroencephalographic (EEG) theta power during wake compared to healthy controls (HC) as a potential biomarker aiding the diagnostic process or subgrouping for stratified treatment. Besides these group differences, theta power is modulated by time of day, sleep/wake history, and age. Thus, we aimed at assessing if the time of recording alters theta power in teenagers affected by MDD or HC. Standardized wake EEG power was assessed with high-density EEG in 15 children and adolescents with MDD and in 15 age- and sex-matched HC in the evening and morning. Using a two-way ANOVA, group, time, and their interaction were tested. In patients, the current severity of depression was rated using the Children's Depression Rating Scale. Broadband EEG power was lower in the morning after sleep, with a significant interaction (group x time) in central regions in the 4-6 Hz range. In MDD relative to HC, theta power was decreased over occipital areas in the evening and increased over frontal areas in the morning. A higher frontal theta power was correlated with more severe depressive mood in the morning but not in the evening. This was a cross-sectional study design, including patients on antidepressant medication. In conclusion, depending on time of recording, region-specific opposite differences of theta power were found between teenagers with MDD and HC. These findings stress the importance of the time of the recording when investigating theta power's relationship to psychopathology.

Conscious but not thinking-Mind-blanks during visuomotor tracking: An fMRI study of endogenous attention lapses

Attention lapses (ALs) are complete lapses of responsiveness in which performance is briefly but completely disrupted and during which, as opposed to microsleeps, the eyes remain open. Although the phenomenon of ALs has been investigated by behavioural and physiological means, the underlying cause of an AL has largely remained elusive. This study aimed to investigate the underlying physiological substrates of behaviourally identified endogenous ALs during a continuous visuomotor task, primarily to answer the question: Were the ALs during this task due to extreme mind-wandering or mind-blanks? The data from two studies were combined, resulting in data from 40 healthy non-sleep-deprived subjects (20M/20F; mean age 27.1 years, 20-45). Only 17 of the 40 subjects were used in the analysis due to a need for a minimum of two ALs per subject. Subjects performed a random 2-D continuous visuomotor tracking task for 50 and 20 min in Studies 1 and 2, respectively. Tracking performance, eye-video, and functional magnetic resonance imaging (fMRI) were recorded simultaneously. A human expert visually inspected the tracking performance and eye-video recordings to identify and categorise lapses of responsiveness as microsleeps or ALs. Changes in neural activity during 85 ALs (17 subjects) relative to responsive tracking were estimated by whole-brain voxel-wise fMRI and by haemodynamic response (HR) analysis in regions of interest (ROIs) from seven key networks to reveal the neural signature of ALs. Changes in functional connectivity (FC) within and between the key ROIs were also estimated. Networks explored were the default mode network, dorsal attention network, frontoparietal network, sensorimotor network, salience network, visual network, and working memory network. Voxel-wise analysis revealed a significant increase in blood-oxygen-level-dependent activity in the overlapping dorsal anterior cingulate cortex and supplementary motor area region but no significant decreases in activity; the increased activity is considered to represent a recovery-of-responsiveness process following an AL. This increased activity was also seen in the HR of the corresponding ROI. Importantly, HR analysis revealed no trend of increased activity in the posterior cingulate of the default mode network, which has been repeatedly demonstrated to be a strong biomarker of mind-wandering. FC analysis showed decoupling of external attention, which supports the involuntary nature of ALs, in addition to the neural recovery processes. Other findings were a decrease in HR in the frontoparietal network before the onset of ALs, and a decrease in FC between default mode network and working memory network. These findings converge to our conclusion that the ALs observed during our task were involuntary mind-blanks. This is further supported behaviourally by the short duration of the ALs (mean 1.7 s), which is considered too brief to be instances of extreme mind-wandering. This is the first study to demonstrate that at least the majority of complete losses of responsiveness on a continuous visuomotor task are, if not due to microsleeps, due to involuntary mind-blanks.

Resting-state electroencephalography (EEG) microstates of healthy individuals following mild sleep deprivation

Mild sleep deprivation is widespread in many societies worldwide. Electroencephalography (EEG) microstate analysis provides information on spatial and temporal characteristics of resting brain network, serving as an indicator of neurophysiological activities at rest. This study seeks to investigate potential neural markers in EEG following mild sleep deprivation of a single night using EEG microstate analysis. Six-minute resting EEG was conducted on thirty healthy adults within 6 hours of waking in the morning and after at least 18 h of sleep deprivation. Translated and validated Malay language Karolinska Sleepiness Scale was used to assess the participants' degree of sleepiness. Microstate characteristics analysis was conducted on the final 24 subjects based on four standard microstate maps. Microstate C shows a significant increase in mean duration, coverage and occurrence, while microstate D has significantly higher occurrence after sleep deprivation. This study demonstrates notable changes in resting state EEG microstates following mild sleep deprivation. Present findings deepen our understanding of the brain's spatiotemporal dynamics under this condition and suggest the potential utility of neural markers in this domain as components of composite markers for sleep deprivation.

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.

Fluctuation in cortical excitation/inhibition modulates capability of attention across time scales ranging from hours to seconds

Sustained attention, as the basis of general cognitive ability, naturally varies across different time scales, spanning from hours, e.g. from wakefulness to drowsiness state, to seconds, e.g. trial-by-trail fluctuation in a task session. Whether there is a unified mechanism underneath such trans-scale variability remains unclear. Here we show that fluctuation of cortical excitation/inhibition (E/I) is a strong modulator to sustained attention in humans across time scales. First, we observed the ability to attend varied across different brain states (wakefulness, postprandial somnolence, sleep deprived), as well as within any single state with larger swings. Second, regardless of the time scale involved, we found highly attentive state was always linked to more balanced cortical E/I characterized by electroencephalography (EEG) features, while deviations from the balanced state led to temporal decline in attention, suggesting the fluctuation of cortical E/I as a common mechanism underneath trans-scale attentional variability. Furthermore, we found the variations of both sustained attention and cortical E/I indices exhibited fractal structure in the temporal domain, exhibiting features of self-similarity. Taken together, these results demonstrate that sustained attention naturally varies across different time scales in a more complex way than previously appreciated, with the cortical E/I as a shared neurophysiological modulator.

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

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

Intracortical mechanisms of single pulse electrical stimulation (SPES) evoked excitations and inhibitions in humans

Cortico-cortical evoked potentials (CCEPs) elicited by single-pulse electric stimulation (SPES) are widely used to assess effective connectivity between cortical areas and are also implemented in the presurgical evaluation of epileptic patients. Nevertheless, the cortical generators underlying the various components of CCEPs in humans have not yet been elucidated. Our aim was to describe the laminar pattern arising under SPES evoked CCEP components (P1, N1, P2, N2, P3) and to evaluate the similarities between N2 and the downstate of sleep slow waves. We used intra-cortical laminar microelectrodes (LMEs) to record CCEPs evoked by 10 mA bipolar 0.5 Hz electric pulses in seven patients with medically intractable epilepsy implanted with subdural grids. Based on the laminar profile of CCEPs, the latency of components is not layer-dependent, however their rate of appearance varies across cortical depth and stimulation distance, while the seizure onset zone does not seem to affect the emergence of components. Early neural excitation primarily engages middle and deep layers, propagating to the superficial layers, followed by mainly superficial inhibition, concluding in a sleep slow wave-like inhibition and excitation sequence.

Considerations towards a neurobiologically-informed EEG measurement of sleepiness

Sleep is a daily experience across humans and other species, yet our understanding of how and why we sleep is presently incomplete. This is particularly prevalent in research examining the neurophysiological measurement of sleepiness in humans, where several electroencephalogram (EEG) phenomena have been linked with prolonged wakefulness. This leaves researchers without a solid basis for the measurement of homeostatic sleep need and complicates our understanding of the nature of sleep. Recent theoretical and technical advances may allow for a greater understanding of the neurobiological basis of homeostatic sleep need: this may result from increases in neuronal excitability and shifts in excitation/inhibition balance in neuronal circuits and can potentially be directly measured via the aperiodic component of the EEG. Here, we review the literature on EEG-derived markers of sleepiness in humans and argue that changes in these electrophysiological markers may actually result from neuronal activity represented by changes in aperiodic markers. We argue for the use of aperiodic markers derived from the EEG in predicting sleepiness and suggest areas for future research based on these.

Alteration in neural oscillatory activity and phase-amplitude coupling after sleep deprivation: Evidence for impairment and compensation effects

Insufficient sleep can significantly affect vigilance and increase slow-wave electroencephalographic power as homeostatic sleep pressure accumulates. Phase-amplitude coupling is involved in regulating the spatiotemporal integration of physiological processes. This study aimed to examine the functional associations of resting-state electroencephalographic power and delta/theta-gamma phase-amplitude coupling from the prefrontal cortex (PFC) to posterior regions with vigilance performance after sleep deprivation. Forty-six healthy adults underwent 24-hr sleep deprivation with resting-state electroencephalographic recordings, and vigilant attention was measured using the Psychomotor Vigilance Task. Power spectral and phase-amplitude coupling analyses were conducted, and correlation analysis was utilized to reveal the relationship between electroencephalographic patterns and changes in vigilance resulting from sleep deprivation. Sleep deprivation significantly declined vigilance performance, accompanied by increased resting-state electroencephalographic power in all bands and delta/theta-gamma phase-amplitude coupling. The increased theta activity in centro-parieto-occipital areas significantly correlated with decreased mean and slowest response speed. Conversely, the increased delta-low gamma and theta-high gamma phase-amplitude couplings negatively correlated with the deceleration of the fastest Psychomotor Vigilance Task reaction times. These findings suggest that sleep deprivation affects vigilance by altering electroencephalographic spectral power and information communication across frequency bands in different brain regions. The distinct effects of increased theta power and delta/theta-gamma phase-amplitude coupling might reflect the impairment and compensation of sleep deprivation on vigilance performance, respectively.

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

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

Consciousness and sleep

Sleep is a universal, essential biological process. It is also an invaluable window on consciousness. It tells us that consciousness can be lost but also that it can be regained, in all its richness, when we are disconnected from the environment and unable to reflect. By considering the neurophysiological differences between dreaming and dreamless sleep, we can learn about the substrate of consciousness and understand why it vanishes. We also learn that the ongoing state of the substrate of consciousness determines the way each experience feels regardless of how it is triggered-endogenously or exogenously. Dreaming consciousness is also a window on sleep and its functions. Dreams tell us that the sleeping brain is remarkably lively, recombining intrinsic activation patterns from a vast repertoire, freed from the requirements of ongoing behavior and cognitive control.

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.

Objective evaluation of excessive daytime sleepiness

Excessive daytime sleepiness (EDS) is multifactorial. It combines, among other things, an excessive propensity to fall asleep ("physiological sleepiness") and a continuous non-imperative sleepiness (or drowsiness/hypo-arousal) leading to difficulties remaining awake and maintaining sustained attention and vigilance over the long term ("manifest sleepiness"). There is no stand-alone biological measure of EDS. EDS measures can either capture the severity of physiological sleepiness, which corresponds to the propensity to fall asleep, or the severity of manifest sleepiness, which corresponds to behavioral consequences of sleepiness and reduced vigilance. Neuropsychological tests (The psychomotor vigilance task (PVT), Oxford Sleep Resistance Test (OSLeR), Sustained Attention to Response Task (SART)) explore manifest sleepiness through several sustained attention tests but the lack of normative values and standardized protocols make the results difficult to interpret and use in clinical practice. Neurophysiological tests explore the two main aspects of EDS, i.e. the propensity to fall asleep (Multiple sleep latency test, MSLT) and the capacity to remain awake (Maintenance of wakefulness test, MWT). The MSLT and the MWT are widely used in clinical practice. The MSLT is recognized as the "gold standard" test for measuring the severity of the propensity to fall asleep and it is a diagnostic criterion for narcolepsy. The MWT measures the ability to stay awake. The MWT is not a diagnostic test as it is recommended only to evaluate the evolution of EDS and efficacy of EDS treatment. Even if some efforts to standardize the protocols for administration of these tests have been ongoing, MSLT and MWT have numerous limitations: age effect, floor or ceiling effects, binding protocol, no normal or cutoff value (or determined in small samples), and no or low test-retest values in some pathologies. Moreover, the recommended electrophysiological set-up and the determination of sleep onset using the 30‑sec epochs scoring rule show some limitations. New, more precise neurophysiological techniques should aim to detect very brief periods of physiological sleepiness and, in the future, the brain local phenomenon of sleepiness likely to underpin drowsiness, which could be called "physiological drowsiness".

Sleep deprivation detected by voice analysis

Sleep deprivation has an ever-increasing impact on individuals and societies. Yet, to date, there is no quick and objective test for sleep deprivation. Here, we used automated acoustic analyses of the voice to detect sleep deprivation. Building on current machine-learning approaches, we focused on interpretability by introducing two novel ideas: the use of a fully generic auditory representation as input feature space, combined with an interpretation technique based on reverse correlation. The auditory representation consisted of a spectro-temporal modulation analysis derived from neurophysiology. The interpretation method aimed to reveal the regions of the auditory representation that supported the classifiers' decisions. Results showed that generic auditory features could be used to detect sleep deprivation successfully, with an accuracy comparable to state-of-the-art speech features. Furthermore, the interpretation revealed two distinct effects of sleep deprivation on the voice: changes in slow temporal modulations related to prosody and changes in spectral features related to voice quality. Importantly, the relative balance of the two effects varied widely across individuals, even though the amount of sleep deprivation was controlled, thus confirming the need to characterize sleep deprivation at the individual level. Moreover, while the prosody factor correlated with subjective sleepiness reports, the voice quality factor did not, consistent with the presence of both explicit and implicit consequences of sleep deprivation. Overall, the findings show that individual effects of sleep deprivation may be observed in vocal biomarkers. Future investigations correlating such markers with objective physiological measures of sleep deprivation could enable "sleep stethoscopes" for the cost-effective diagnosis of the individual effects of sleep deprivation.

Tracing diurnal differences in brain anatomy with voxel-based morphometry - associations with sleep characteristics

Multiple aspects of brain functioning, including arousal, motivation, and cognitive performance, are governed by circadian rhythmicity. Although the recent rise in the use of magnetic resonance imaging (MRI) has enabled investigations into the macroscopic correlates of the diurnal brain processes, neuroanatomical studies are scarce. The current work investigated how time-of-day (TOD) impacts white (WM) and grey matter (GM) volumes using voxel-based morphometry (VBM) in a large dataset (N = 72) divided into two equal, comparable subsamples to assess the replicability of effects. Furthermore, we aimed to assess how the magnitude of these diurnal differences was related to actigraphy-derived indices of sleep health. The results extend the current knowledge by reporting that TOD is predominantly associated with regional WM volume decreases. Additionally, alongside corroborating previously observed volumetric GM decreases, we provide the first evidence for positive TOD effects. Higher replicability was observed for WM, with the only two replicated GM clusters being volumetric increases in the amygdala and hippocampus, and decreases in the retrosplenial cortex, with the latter more pronounced in individuals with shorter sleep times. These findings implicate the existence of region-specific mechanisms behind GM effects, which might be related to cognitive processes taking place during wakefulness and homeostatic sleep pressure.

Maturation-dependent changes in cortical and thalamic activity during sleep slow waves: Insights from a combined EEG-fMRI study

INTRODUCTION

Studies using scalp EEG have shown that slow waves (0.5-4 Hz), the most prominent hallmark of NREM sleep, undergo relevant changes from childhood to adulthood, mirroring brain structural modifications and the acquisition of cognitive skills. Here we used simultaneous EEG-fMRI to investigate the cortical and subcortical correlates of slow waves in school-age children and determine their relative developmental changes.

METHODS

We analyzed data from 14 school-age children with self-limited focal epilepsy of childhood who fell asleep during EEG-fMRI recordings. Brain regions associated with slow-wave occurrence were identified using a voxel-wise regression that also modelled interictal epileptic discharges and sleep spindles. At the group level, a mixed-effects linear model was used. The results were qualitatively compared with those obtained from 2 adolescents with epilepsy and 17 healthy adults.

RESULTS

Slow waves were associated with hemodynamic-signal decreases in bilateral somatomotor areas. Such changes extended more posteriorly relative to those in adults. Moreover, the involvement of areas belonging to the default mode network changes as a function of age. No significant hemodynamic responses were observed in subcortical structures. However, we identified a significant correlation between age and thalamic hemodynamic changes.

CONCLUSIONS

Present findings indicate that the somatomotor cortex may have a key role in slow-wave expression throughout the lifespan. At the same time, they are consistent with a posterior-to-anterior shift in slow-wave distribution mirroring brain maturational changes. Finally, our results suggest that slow-wave changes may not reflect only neocortical modifications but also the maturation of subcortical structures, including the thalamus.

Disrupted synaptic homeostasis and partial occlusion of associative long-term potentiation in the human cortex during social isolation

Social isolation often occurs in the military mission of soldiers but has increased in the general population since the COVID-19 epidemic. Overall synaptic homeostasis along with associative plasticity for the activity-dependent refinement of transmission across single synapses represent basic neural network function and adaptive behavior mechanisms. Here, we use electrophysiological and behavioral indices to non-invasively study the net synaptic strength and long-term potentiation (LTP)-like plasticity of humans in social isolation environments. The theta activity of electroencephalography (EEG) signals and transcranial magnetic stimulation (TMS) intensity to elicit a predefined amplitude of motor-evoked potential (MEP) demonstrate the disrupted synaptic homeostasis in the human cortex during social isolation. Furthermore, the induced MEP change by paired associative stimulation (PAS) demonstrates the partial occlusion of LTP-like plasticity, further behavior performances in a word-pair task are also identified as a potential index. Our study indicates that social isolation disrupts synaptic homeostasis and occludes associative LTP-like plasticity in the human cortex, decreasing behavior performance related to declarative memory.

What is sleep exactly? Global and local modulations of sleep oscillations all around the clock

Wakefulness, non-rapid eye-movement (NREM) and rapid eye-movement (REM) sleep differ from each other along three dimensions: behavioral, phenomenological, physiological. Although these dimensions often fluctuate in step, they can also dissociate. The current paradigm that views sleep as made of global NREM and REM states fail to account for these dissociations. This conundrum can be dissolved by stressing the existence and significance of the local regulation of sleep. We will review the evidence in animals and humans, healthy and pathological brains, showing different forms of local sleep and the consequences on behavior, cognition, and subjective experience. Altogether, we argue that the notion of local sleep provides a unified account for a host of phenomena: dreaming in REM and NREM sleep, NREM and REM parasomnias, intrasleep responsiveness, inattention and mind wandering in wakefulness. Yet, the physiological origins of local sleep or its putative functions remain unclear. Exploring further local sleep could provide a unique and novel perspective on how and why we sleep.

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.

Effect of homeostatic pressure on daytime vigilance performance: Evidence from behaviour and resting-state EEG

Vigilance is highly sensitive to the time-of-day effect and goes through the daytime trough during the period of the post-noon dip. A midday nap could maintain individuals' vigilance at an optimal level. Thus, homeostatic sleep pressure is one of the main reasons for the post-noon dip in daytime vigilance. The current study focussed on the role of homeostatic sleep pressure in the diurnal variation of vigilance performance with normal circadian rhythms and the corresponding neural basis. With 34 healthy adults, we recorded the resting-state electroencephalogram activities and the following vigilance performance measured by psychomotor vigilance test in the morning, the no-nap mid afternoon, and the nap mid afternoon. The circadian process was controlled by measuring vigilance and resting-state electroencephalogram activities at the same time point in the nap and no-nap conditions. Homeostatic sleep pressure accumulated from morning to mid afternoon induced the declined vigilance performance and a global increase in resting-state delta, theta, alpha, and beta1 bands power, and a local increase in beta2 band power in the central region. Furthermore, the more the spontaneous beta2 power increased, the less vigilance declined from morning to mid afternoon. The current findings suggest that homeostatic sleep pressure increased cortical excitability but decreased cortical communication efficiency from morning to mid afternoon. In addition, the activity of the high beta waves probably reflected the compensatory effort to counteract the negative impact of the low arousal state on the following vigilance task by performing more action preparation in the no-nap afternoon.

Where do we look for the next breakthrough in sleep research?

The wakefulness-sleep cycle sets the pace of our life. Sleep research examines the transition between wakefulness and sleep, its hormonal regulation, and its pathological disruption. Understanding sleep mechanisms would improve quality-of-life well beyond sleep itself. To this aim, we invite contributions for the Collection "sleep physiology and circadian rhythms".

How and when EEG reflects changes in neuronal connectivity due to time awake

Being awake means forming new memories, primarily by strengthening neuronal synapses. The increase in synaptic strength results in increasing neuronal synchronicity, which should result in higher amplitude electroencephalography (EEG) oscillations. This is observed for slow waves during sleep but has not been found for wake oscillations. We hypothesized that this was due to a limitation of spectral power analysis, which does not distinguish between changes in amplitudes from changes in number of occurrences of oscillations. By using cycle-by-cycle analysis instead, we found that theta and alpha oscillation amplitudes increase as much as 30% following 24 h of extended wake. These increases were interrupted during the wake maintenance zone (WMZ), a window just before bedtime when it is difficult to fall asleep. We found that pupil diameter increased during this window, suggesting the ascending arousal system is responsible. In conclusion, wake oscillation amplitudes reflect increased synaptic strength, except during the WMZ.

Sleep and vigilance states: Embracing spatiotemporal dynamics

The classic view of sleep and vigilance states is a global stationary perspective driven by the interaction between neuromodulators and thalamocortical systems. However, recent data are challenging this view by demonstrating that vigilance states are highly dynamic and regionally complex. Spatially, sleep- and wake-like states often co-occur across distinct brain regions, as in unihemispheric sleep, local sleep in wakefulness, and during development. Temporally, dynamic switching prevails around state transitions, during extended wakefulness, and in fragmented sleep. This knowledge, together with methods monitoring brain activity across multiple regions simultaneously at millisecond resolution with cell-type specificity, is rapidly shifting how we consider vigilance states. A new perspective incorporating multiple spatial and temporal scales may have important implications for considering the governing neuromodulatory mechanisms, the functional roles of vigilance states, and their behavioral manifestations. A modular and dynamic view highlights novel avenues for finer spatiotemporal interventions to improve sleep function.

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.

The effect of TENS on sleep: A pilot study

BACKGROUND

Insomnia is the second most common neuropsychiatric disorder, but the current treatments are not very effective. There is therefore an urgent need to develop better treatments. Transcutaneous electrical nerve stimulation (TENS) may be a promising means of treating insomnia.

OBJECTIVE

This work aims to explore whether and how TENS modulate sleep and the effect of stimulation waveforms on sleep.

METHODS

Forty-five healthy subjects participated in this study. Electroencephalography (EEG) data were recorded before and after four mode low-frequency (1 Hz) TENS with different waveforms, which were formed by superimposing sine waves of different high frequencies (60-210 Hz) and low frequencies (1-6 Hz). The four waveform modes are formed by combining sine waves of varying frequencies. Mode 1 (M1) consists of a combination of high frequencies (60-110 Hz) and low frequencies (1-6 Hz). Mode 2 (M2) is made up of high frequencies (60-210 Hz) and low frequencies (1-6 Hz). Mode 3 (M3) consists of high frequencies (110-160 Hz) and low frequencies (1-6 Hz), while mode 4 (M4) is composed of high frequencies (160-210 Hz) and low frequencies (1-6 Hz). For M1, M3 and M4, the high frequency portions of the stimulus waveforms account for 50%, while for M2, the high frequency portion of the waveform accounts for 65%. For each mode, the current intensities ranged from 4 mA to 7 mA, with values for each participant adjusted according to individual tolerance. During stimulation, the subjects were stimulated at the greater occipital nerve by the four mode TENS.

RESULTS

M1, M3, and M4 slowed down the frequency of neural activity, broadened the distribution of theta waves, and caused a decrease in activity in wakefulness-related regions and an increase in activity in sleep-related regions. However, M2 has the opposite modulation effect.

CONCLUSION

These results indicated that low-frequency TENS (1 Hz) may facilitate sleep in a waveform-specific manner. Our findings provide new insights into the mechanisms of sleep modulation by TENS and the design of effective insomnia treatments.

Comparative analysis of acoustic therapies for tinnitus treatment based on auditory event-related potentials

Introduction

So far, Auditory Event-Related Potential (AERP) features have been used to characterize neural activity of patients with tinnitus. However, these EEG patterns could be used to evaluate tinnitus evolution as well. The aim of the present study is to propose a methodology based on AERPs to evaluate the effectiveness of four acoustic therapies for tinnitus treatment.

Methods

The acoustic therapies were: (1) Tinnitus Retraining Therapy (TRT), (2) Auditory Discrimination Therapy (ADT), (3) Therapy for Enriched Acoustic Environment (TEAE), and (4) Binaural Beats Therapy (BBT). In addition, relaxing music was included as a placebo for both: tinnitus sufferers and healthy individuals. To meet this aim, 103 participants were recruited, 53% were females and 47% were males. All the participants were treated for 8 weeks with one of these five sounds, which were moreover tuned in accordance with the acoustic features of their tinnitus (if applied) and hearing loss. They were electroencephalographically monitored before and after their acoustic therapy, and wherefrom AERPs were estimated. The sound effect of acoustic therapies was evaluated by examining the area under the curve of those AERPs. Two parameters were obtained: (1) amplitude and (2) topographical distribution.

Results

The findings of the investigation showed that after an 8-week treatment, TRT and ADT, respectively achieved significant neurophysiological changes over somatosensory and occipital regions. On one hand, TRT increased the tinnitus perception. On the other hand, ADT redirected the tinnitus attention, what in turn diminished the tinnitus perception. Tinnitus handicapped inventory outcomes verified these neurophysiological findings, revealing that 31% of patients in each group reported that TRT increased tinnitus perception, but ADT diminished it.

Discussion

Tinnitus has been identified as a multifactorial condition highly associated with hearing loss, age, sex, marital status, education, and even, employment. However, no conclusive evidence has been found yet. In this study, a significant (but low) correlation was found between tinnitus intensity and right ear hearing loss, left ear hearing loss, heart rate, area under the curve of AERPs, and acoustic therapy. This study raises the possibility to assign acoustic therapies by neurophysiological response of patient.

Sleep and dreaming in the light of reactive and predictive homeostasis

Dreams are often viewed as fascinating but irrelevant mental epihenomena of the sleeping mind with questionable functional relevance. Despite long hours of oneiric activity, and high individual differences in dream recall, dreams are lost into oblivion. Here, we conceptualize dreaming and dream amnesia as inherent aspects of the reactive and predictive homeostatic functions of sleep. Mental activity during sleep conforms to the interplay of restorative processes and future anticipation, and particularly during the second half of the night, it unfolds as a special form of non-constrained, self-referent, and future-oriented cognitive process. Awakening facilitates constrained, goal-directed prospection that competes for shared neural resources with dream production and dream recall, and contributes to dream amnesia. We present the neurophysiological aspects of reactive and predictive homeostasis during sleep, highlighting the putative role of cortisol in predictive homeostasis and forgetting dreams. The theoretical and methodological aspects of our proposal are discussed in relation to the study of dreaming, dream recall, and sleep-related cognitive processes.

N-acetylcysteine treatment mitigates loss of cortical parvalbumin-positive interneuron and perineuronal net integrity resulting from persistent oxidative stress in a rat TBI model

Traumatic brain injury (TBI) increases cerebral reactive oxygen species production, which leads to continuing secondary neuronal injury after the initial insult. Cortical parvalbumin-positive interneurons (PVIs; neurons responsible for maintaining cortical inhibitory tone) are particularly vulnerable to oxidative stress and are thus disproportionately affected by TBI. Systemic N-acetylcysteine (NAC) treatment may restore cerebral glutathione equilibrium, thus preventing post-traumatic cortical PVI loss. We therefore tested whether weeks-long post-traumatic NAC treatment mitigates cortical oxidative stress, and whether such treatment preserves PVI counts and related markers of PVI integrity and prevents pathologic electroencephalographic (EEG) changes, 3 and 6 weeks after fluid percussion injury in rats. We find that moderate TBI results in persistent oxidative stress for at least 6 weeks after injury and leads to the loss of PVIs and the perineuronal net (PNN) that surrounds them as well as of per-cell parvalbumin expression. Prolonged post-TBI NAC treatment normalizes the cortical redox state, mitigates PVI and PNN loss, and - in surviving PVIs - increases per-cell parvalbumin expression. NAC treatment also preserves normal spectral EEG measures after TBI. We cautiously conclude that weeks-long NAC treatment after TBI may be a practical and well-tolerated treatment strategy to preserve cortical inhibitory tone post-TBI.

Lateralized tactile stimulation during NREM sleep globally increases both slow and fast frequency activities

Slow frequency activity during non-rapid eye movement (NREM) sleep emerges from synchronized activity of widely distributed thalamo-cortical and cortico-cortical networks, reflecting homeostatic and restorative properties of sleep. Slow frequency activity exhibits a reactive nature, and can be increased by acoustic stimulation. Although non-invasive brain stimulation is a promising technique in basic and clinical sleep research, sensory stimulation studies focusing on modalities other than the acoustic are scarce. We explored here the potential of lateralized vibro-tactile stimulation (VTS) of the finger to locally modify electroencephalographic activity during nocturnal NREM sleep. Eight seconds-long sequences of vibro-tactile pulses were delivered at a rate of 1 Hz either to the left or to the right index finger, in addition to a sham condition, in fourteen healthy participants. VTS markedly increased slow frequency activity that peaked between 1-4 Hz but extended to higher (~13 Hz) frequencies, with fronto-central dominance. Enhanced slow frequency activity was accompanied by increased (14-22 Hz) fast frequency power peaking over central and posterior locations. VTS increased the amplitude of slow waves, especially during the first 3-4 s of stimulation. Noticeably, we did not observe local-hemispheric effects, that is, VTS resulted in a global cortical response regardless of stimulation laterality. VTS moderately increased slow and fast frequency activities in resting wakefulness, to a much lower extent compared to NREM sleep. The concomitant increase in slow and fast frequency activities in response to VTS indicates an instant homeostatic response coupled with wake-like, high-frequency activity potentially reflecting transient periods of increased environmental processing.

Novel Objective Measures of Hypersomnolence

Purpose of review

To provide a brief overview of current objective measures of hypersomnolence, discuss proposed measure modifications, and review emerging measures.

Recent findings

There is potential to optimize current tools using novel metrics. High-density and quantitative EEG-based measures may provide discriminative informative. Cognitive testing may quantify cognitive dysfunction common to hypersomnia disorders, particularly in attention, and objectively measure pathologic sleep inertia. Structural and functional neuroimaging studies in narcolepsy type 1 have shown considerable variability but so far implicate both hypothalamic and extra-hypothalamic regions; fewer studies of other CDH have been performed. There is recent renewed interest in pupillometry as a measure of alertness in the evaluation of hypersomnolence.

Summary

No single test captures the full spectrum of disorders and use of multiple measures will likely improve diagnostic precision. Research is needed to identify novel measures and disease-specific biomarkers, and to define combinations of measures optimal for CDH diagnosis.

Sleep Deprivation-Induced Changes in Baseline Brain Activity and Vigilant Attention Performance

Sleep deprivation (SD) negatively affects several aspects of cognitive performance, and one of the most widely-used tools to evaluate these effects is the Psychomotor Vigilance Test (PVT). The present study investigated the possibility of predicting changes induced by SD in vigilant attention performance by evaluating the baseline electroencephalographic (EEG) activity immediately preceding the PVT stimuli onset. All participants (n = 10) underwent EEG recordings during 10 min of PVT before and after a night of SD. For each participant, the root mean square (RMS) of the baseline EEG signal was evaluated for each 1 s time window, and the respective average value was computed. After SD, participants showed slower (and less accurate) performance in the PVT task. Moreover, a close relationship between the changes in the baseline activity with those in cognitive performance was identified at several electrodes (Fp2, F7, F8, P3, T6, O1, Oz, O2), with the highest predictive power at the occipital derivations. These results indicate that vigilant attention impairments induced by SD can be predicted by the pre-stimulus baseline activity changes.

The Theta Paradox: 4-8 Hz EEG Oscillations Reflect Both Sleep Pressure and Cognitive Control

Human electroencephalographic (EEG) oscillations characterize specific behavioral and vigilance states. The frequency of these oscillations is typically sufficient to distinguish a given state; however, theta oscillations (4-8 Hz) have instead been found in near-opposite conditions of drowsiness during sleep deprivation and alert cognitive control. While the latter has been extensively studied and is often referred to as "frontal midline theta," (fmTheta) the former has been investigated far less but is considered a marker for sleep pressure during wake. In this study we investigated to what extent theta oscillations differed during cognitive tasks and sleep deprivation. We measured high-density EEG in 18 young healthy adults (nine female) performing six tasks under three levels of sleep deprivation. We found both cognitive load and sleep deprivation increased theta power in medial prefrontal cortical areas; however, sleep deprivation caused additional increases in theta in many other, predominantly frontal, areas. The sources of sleep deprivation theta (sdTheta) were task dependent, with a visual-spatial task and short-term memory (STM) task showing the most widespread effects. Notably, theta was highest in supplementary motor areas during passive music listening, and highest in the inferior temporal cortex (responsible for object recognition) during a spatial game. Furthermore, while changes in task performance were correlated with increases in theta during sleep deprivation, this relationship was not specific to the EEG of the same task and did not survive correction for multiple comparisons. Altogether, these results suggest that both during sleep deprivation and cognition theta oscillations may preferentially occur in cortical areas not involved in ongoing behavior.SIGNIFICANCE STATEMENT Electroencephalographic (EEG) research in sleep has often remained separate from research in cognition. This has led to two incompatible interpretations of the function of theta brain oscillations (4-8 Hz): that they reflect local sleep events during sleep deprivation, or that they reflect cognitive processing during tasks. With this study, we found no fundamental differences between theta oscillations during cognition and theta during sleep deprivation that would suggest different functions. Instead, our results indicate that in both cases, theta oscillations are generated by cortical areas not required for ongoing behavior. Therefore, at least in humans, theta may reflect either cortical disengagement or inhibition.

Cross-Scale Dynamicity of Entropy and Connectivity in the Sleeping Brain

Introduction: The concept of local sleep refers to the phenomenon of local brain activity that modifies neural networks during unresponsive global sleep. Such network rewiring may differ across spatial scales; however, the global and local alterations in brain systems remain elusive in human sleep. Materials and Methods: We examined cross-scale changes of brain networks in sleep. Functional magnetic resonance imaging data were acquired from 28 healthy participants during nocturnal sleep. We adopted both metrics of connectivity (functional connectivity [FC] and regional homogeneity [ReHo]) and complexity (multiscale entropy) to explore the global and local functionality of the neural assembly across nonrapid eye movement sleep stages. Results: Long-range FC decreased with sleep depth, whereas local ReHo peaked at the N2 stage and reached its lowest level at the N3 stage. Entropy exhibited a general decline at the local scale (Scale 1) as sleep deepened, whereas the coarse-scale entropy (Scale 3) was consistent across stages. Discussion: The negative correlation between Scale-1 entropy and ReHo reflects the enhanced signal regularity and synchronization in sleep, identifying the information exchange at the local scale. The N2 stage showed a distinctive pattern toward local information processing with scrambled long-distance information exchange, indicating a specific time window for network reorganization. Collectively, the multidimensional metrics indicated an imbalanced global-local relationship among brain functional networks across sleep-wake stages.

Pharmacological Manipulations of Physiological Arousal and Sleep-Like Slow Waves Modulate Sustained Attention

Sustained attention describes our ability to keep a constant focus on a given task. This ability is modulated by our physiological state of arousal. Although lapses of sustained attention have been linked with dysregulations of arousal, the underlying physiological mechanisms remain unclear. An emerging body of work proposes that the intrusion during wakefulness of sleep-like slow waves, a marker of the transition toward sleep, could mechanistically account for attentional lapses. This study aimed to expose, via pharmacological manipulations of the monoamine system, the relationship between the occurrence of sleep-like slow waves and the behavioral consequences of sustained attention failures. In a double-blind, randomized-control trial, 32 healthy human male participants received methylphenidate, atomoxetine, citalopram or placebo during four separate experimental sessions. During each session, electroencephalography (EEG) was used to measure neural activity while participants completed a visual task requiring sustained attention. Methylphenidate, which increases wake-promoting dopamine and noradrenaline across cortical and subcortical areas, improved behavioral performance whereas atomoxetine, which increases dopamine and noradrenaline predominantly over frontal cortices, led to more impulsive responses. Additionally, citalopram, which increases sleep-promoting serotonin, led to more missed trials. Based on EEG recording, citalopram was also associated with an increase in sleep-like slow waves. Importantly, compared with a classical marker of arousal such as α power, only slow waves differentially predicted both misses and faster responses in a region-specific fashion. These results suggest that a decrease in arousal can lead to local sleep intrusions during wakefulness which could be mechanistically linked to impulsivity and sluggishness.SIGNIFICANCE STATEMENT We investigated whether the modulation of attention and arousal could not only share the same neuromodulatory pathways but also rely on similar neuronal mechanisms; for example, the intrusion of sleep-like activity within wakefulness. To do so, we pharmacologically manipulated noradrenaline, dopamine, and serotonin in a four-arm, randomized, placebo-controlled trial and examined the consequences on behavioral and electroencephalography (EEG) indices of attention and arousal. We showed that sleep-like slow waves can predict opposite behavioral signatures: impulsivity and sluggishness. Slow waves may be a candidate mechanism for the occurrence of attentional lapses since the relationship between slow-wave occurrence and performance is region-specific and the consequences of these local sleep intrusions are in line with the cognitive functions carried by the underlying brain regions.

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

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

Electrophysiological Evidence of Local Sleep During Yoga Nidra Practice

Background and Objectives

Yoga nidra is a technique sages use to self-induce sleep. Classically, sleep is characterized by three cardinal electrophysiological features, namely, electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG). As the literature on electrophysiological characterization of Yoga nidra is lacking, it is not known whether it is a sleep or awake state. The objective of the study was to electrophysiologically characterize yoga nidra practice.

Materials and Methods

Thirty subjects underwent five initial supervised yoga nidra sessions and then continued practice on their own. The subjects completed their sleep diaries for 2 weeks before and during the intervention. The electrophysiological characterization was done after 2 weeks of yoga nidra practice using 19 EEG channels polysomnography for pre-yoga nidra, yoga nidra practice and post-yoga nidra. Polysomnographic data were scored for sleep-wake stages as per standard criteria. Power spectral density (PSD) was calculated from various frequency bands in different time bins. EEG data were grouped by areas, namely, central, frontal, prefrontal, parietal, temporal, and occipital in time bins. Sleep diary parameters were also compared for pre-post-yoga nidra training.

Results

After 2 weeks of yoga nidra practice, awake was scored throughout the session (n = 26). PSD results (mean difference in dB between different time bins; P value) showed significant changes. When compared to pre-yoga nidra, there was an increase in delta power in the central area (1.953; P = 0.033) and a decrease in the prefrontal area (2.713; P = 0.041) during yoga nidra. Sleep diary showed improvement in sleep duration (P = 0.0001), efficiency (P = 0.0005), quality (P = 0.0005), and total wake duration (P = 0.00005) after 2 weeks of practice.

Interpretations and Conclusions

Yoga nidra practice in novices is electrophysiologically an awake state with signs of slow waves locally, often referred to as local sleep.

Clinical Trial

Clinical Trial Registry of India, http://www.ctri.nic.in/Clinicaltrials/pmaindet2.php? trialid = 6253, 2013/05/003682.

Abnormal timing of slow wave synchronization processes in non-rapid eye movement sleep parasomnias

STUDY OBJECTIVES

Sleepwalking, confusional arousals, and sleep terrors are parasomnias occurring out of non-rapid eye movement (NREM) sleep. Several previous studies have described EEG changes associated with NREM parasomnia episodes, but it remains unclear whether these changes are specific to parasomnia episodes or whether they are part of the normal awakening process. Here we directly compared regional brain activity, measured with high-density (hd-) EEG, between parasomnia episodes and normal awakenings (without behavioral manifestations of parasomnia).

METHODS

Twenty adult patients with non-rapid eye movement parasomnias underwent a baseline hd-EEG recording (256 electrodes) followed by a recovery sleep recording after 25 h of total sleep deprivation, during which auditory stimuli were administered to provoke parasomnia episodes.

RESULTS

Both normal awakenings (n = 25) and parasomnia episodes (n = 96) were preceded by large, steep, and "K-complex-like" slow waves in frontal and central brain regions, and by a concomitant increase in high-frequency EEG (beta) activity. Compared to normal awakenings, parasomnia episodes occurred on a less activated EEG background and displayed higher slow wave activity (SWA) and lower beta activity in frontal and central brain regions after movement onset.

CONCLUSIONS

Our results suggest that non-rapid eye movement awakenings, irrespective of behavioral manifestations of parasomnia episodes, involve an arousal-related slow wave synchronization process that predominantly recruits frontal and central brain areas. In parasomnia episodes, this synchronization process comes into play abnormally during periods of high SWA and is associated with higher SWA after movement onset. Thus, an abnormal timing of arousal-related slow wave synchronization processes could underlie the occurrence of NREM parasomnias.

Local sleep: A new concept in brain plasticity

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

EEG as a marker of brain plasticity in clinical applications

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

Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning

BACKGROUND

Wide-field calcium imaging (WFCI) allows for monitoring of cortex-wide neural dynamics in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming and often suffers from low inter- and intra-rater reliability and invasiveness. Therefore, an automated sleep state classification method that operates on WFCI data alone is needed.

NEW METHOD

A hybrid, two-step method is proposed. In the first step, spatial-temporal WFCI data is mapped to multiplex visibility graphs (MVGs). Subsequently, a two-dimensional convolutional neural network (2D CNN) is employed on the MVGs to be classified as wakefulness, NREM and REM.

RESULTS

Sleep states were classified with an accuracy of 84% and Cohen's κ of 0.67. The method was also effectively applied on a binary classification of wakefulness/sleep (accuracy=0.82, κ = 0.62) and a four-class wakefulness/sleep/anesthesia/movement classification (accuracy=0.74, κ = 0.66). Gradient-weighted class activation maps revealed that the CNN focused on short- and long-term temporal connections of MVGs in a sleep state-specific manner. Sleep state classification performance when using individual brain regions was highest for the posterior area of the cortex and when cortex-wide activity was considered.

COMPARISON WITH EXISTING METHOD

On a 3-hour WFCI recording, the MVG-CNN achieved a κ of 0.65, comparable to a κ of 0.60 corresponding to the human EEG/EMG-based scoring.

CONCLUSIONS

The hybrid MVG-CNN method accurately classifies sleep states from WFCI data and will enable future sleep-focused studies with WFCI.

Sleep Instability Correlates with Attentional Impairment in Boys with Attention Deficit Hyperactivity Disorder

Theoretical models of sleep and attention deficit hyperactivity disorder (ADHD) suggest that symptoms of ADHD are associated with daytime sleepiness, but it has received little support. The present study aimed at testing an alternative model involving the association of attentional instability with sleep instability, i.e., sleep stage transitions and arousals. Twelve ADHD and 15 healthy control (HC) boys aged between 8 and 12 years old underwent polysomnography recording and attentional testing. The microarousal index, the number of awakenings, and the number of stage shifts between stages 1, 2, 3, 4 and REM sleep throughout the night were computed as sleep stability parameters. Attentional functioning was assessed using the Continuous Performance Test-II. We found significantly higher sleep instability in ADHD compared to HC. Sleep arousals and stage transitions (micro arousal index, stage 4/3 and 2/4 transitions) in ADHD significantly correlated with lower attentional scores. No association whatsoever was found between sleep instability and attentional functioning in HC. The results show that sleep instability is associated with lower attentional performance in boys with ADHD, but not in HC. This could be compatible with a model according to which attention and sleep stability share a common neural substrate in ADHD.

Emotion Regulation Failures Are Preceded by Local Increases in Sleep-like Activity

Emotion self-regulation relies both on cognitive and behavioral strategies implemented to modulate the subjective experience and/or the behavioral expression of a given emotion. Although it is known that a network encompassing fronto-cingulate and parietal brain areas is engaged during successful emotion regulation, the functional mechanisms underlying failures in emotion suppression (ES) are still unclear. In order to investigate this issue, we analyzed video and high-density EEG recordings of 20 healthy adult participants during an ES and a free expression task performed on two consecutive days. Changes in facial expression during ES, but not free expression, were preceded by local increases in sleep-like activity (1-4 Hz) in brain areas responsible for emotional suppression, including bilateral anterior insula and anterior cingulate cortex, and in right middle/inferior frontal gyrus (p < .05, corrected). Moreover, shorter sleep duration the night before the ES experiment correlated with the number of behavioral errors (p = .03) and tended to be associated with higher frontal sleep-like activity during ES failures (p = .09). These results indicate that local sleep-like activity may represent the cause of ES failures in humans and may offer a functional explanation for previous observations linking lack of sleep, changes in frontal activity, and emotional dysregulation.

Heterogenous electrophysiological responses of functionally distinct striatal subregions to circadian and sleep-related homeostatic processes

Basal Ganglia (BG) are a set of subcortical nuclei that are involved in the control of a wide variety of motor, cognitive and affective behaviors. Although many behavioral abnormalities associated with BG dysfunction overlap with the clinical picture precipitated by the lack of sleep, the impact of sleep alterations on neuronal activity in BG is unknown. Using wildtype C57BI mice, we investigated the circadian and sleep-related homeostatic modulation of neuronal activity in the 3 functional subdivisions of the striatum (i.e. sensorimotor, associative and limbic striatum). We found no circadian modulation of activity in both ventral and dorso-medial striatum while the dorso-lateral striatum displayed a significant circadian rhythm with increased firing rates during the subjective dark, active phase. By combining neuronal activity recordings with electroencephalogram (EEG) recordings, we found a strong modulation of neuronal activity by the nature of vigilance states with increased activity during wakefulness and rapid eye movement sleep relative to non-rapid eye movement sleep in all striatal subregions. Depriving animals of sleep for 6 hours induced significant, but heterogenous alterations in the neuronal activity across striatal subregions. Notably, these alterations lasted for up to 48 hours in the sensorimotor striatum and persisted even after the normalization of cortical EEG power densities. Our results show that vigilance and sleep states as well as their disturbances significantly affect neuronal activity within the striatum. We propose that these changes in neuronal activity underlie both the well-established links between sleep alterations and several disorders involving BG dysfunction as well as the maladaptive changes in behavior induced in healthy subjects following sleep loss.

Observing changes in human functioning during induced sleep deficiency and recovery periods

Prolonged periods of sleep restriction seem to be common in the contemporary world. Sleep loss causes perturbations of circadian rhythmicity and degradation of waking alertness as reflected in attention, cognitive efficiency and memory. Understanding whether and how the human brain recovers from chronic sleep loss is important not only from a scientific but also from a public health perspective. In this work we report on behavioral, motor, and neurophysiological correlates of sleep loss in healthy adults in an unprecedented study conducted in natural conditions and comprising 21 consecutive days divided into periods of 4 days of regular life (a baseline), 10 days of chronic partial sleep restriction (30% reduction relative to individual sleep need) and 7 days of recovery. Throughout the whole experiment we continuously measured the spontaneous locomotor activity by means of actigraphy with 1-minute resolution. On a daily basis the subjects were undergoing EEG measurements (64-electrodes with 500 Hz sampling frequency): resting state with eyes open and closed (8 minutes long each) followed by Stroop task lasting 22 minutes. Altogether we analyzed actigraphy (distributions of rest and activity durations), behavioral measures (reaction times and accuracy from Stroop task) and EEG (amplitudes, latencies and scalp maps of event-related potentials from Stroop task and power spectra from resting states). We observed unanimous deterioration in all the measures during sleep restriction. Further results indicate that a week of recovery subsequent to prolonged periods of sleep restriction is insufficient to recover fully. Only one measure (mean reaction time in Stroop task) reverted to baseline values, while the others did not.

Mind-wandering Is Accompanied by Both Local Sleep and Enhanced Processes of Spatial Attention Allocation

Mind-wandering (MW) is a subjective, cognitive phenomenon, in which thoughts move away from the task toward an internal train of thoughts, possibly during phases of neuronal sleep-like activity (local sleep, LS). MW decreases cortical processing of external stimuli and is assumed to decouple attention from the external world. Here, we directly tested how indicators of LS, cortical processing, and attentional selection change in a pop-out visual search task during phases of MW. Participants’ brain activity was recorded using magnetoencephalography, MW was assessed via self-report using randomly interspersed probes. As expected, the performance decreased under MW. Consistent with the occurrence of LS, MW was accompanied by a decrease in high-frequency activity (HFA, 80–150 Hz) and an increase in slow wave activity (SWA, 1–6 Hz). In contrast, visual attentional selection as indexed by the N2pc component was enhanced during MW with the N2pc amplitude being directly linked to participants’ performance. This observation clearly contradicts accounts of attentional decoupling that would predict a decrease in attention-related responses to external stimuli during MW. Together, our results suggest that MW occurs during phases of LS with processes of attentional target selection being upregulated, potentially to compensate for the mental distraction during MW.

Extended Visual Sequence Learning Leaves a Local Trace in the Spontaneous EEG

We have previously demonstrated that, in rested subjects, extensive practice in a motor learning task increased both electroencephalographic (EEG) theta power in the areas involved in learning and improved the error rate in a motor test that shared similarities with the task. A nap normalized both EEG and performance changes. We now ascertain whether extensive visual declarative learning produces results similar to motor learning. Thus, during the morning, we recorded high-density EEG in well rested young healthy subjects that learned the order of different visual sequence task (VSEQ) for three one-hour blocks. Afterward, a group of subjects took a nap and another rested quietly. Between each VSEQ block, we recorded spontaneous EEG (sEEG) at rest and assessed performance in a motor test and a visual working memory test that shares similarities with VSEQ. We found that after the third block, VSEQ induced local theta power increases in the sEEG over a right temporo-parietal area that was engaged during the task. This local theta increase was preceded by increases in alpha and beta power over the same area and was paralleled by performance decline in the visual working memory test. Only after the nap, VSEQ learning rate improved and performance in the visual working memory test was restored, together with partial normalization of the local sEEG changes. These results suggest that intensive learning, like motor learning, produces local theta power increases, possibly reflecting local neuronal fatigue. Sleep may be necessary to resolve neuronal fatigue and its effects on learning and performance.

Predicting lapses of attention with sleep-like slow waves

Attentional lapses occur commonly and are associated with mind wandering, where focus is turned to thoughts unrelated to ongoing tasks and environmental demands, or mind blanking, where the stream of consciousness itself comes to a halt. To understand the neural mechanisms underlying attentional lapses, we studied the behaviour, subjective experience and neural activity of healthy participants performing a task. Random interruptions prompted participants to indicate their mental states as task-focused, mind-wandering or mind-blanking. Using high-density electroencephalography, we report here that spatially and temporally localized slow waves, a pattern of neural activity characteristic of the transition toward sleep, accompany behavioural markers of lapses and preceded reports of mind wandering and mind blanking. The location of slow waves could distinguish between sluggish and impulsive behaviours, and between mind wandering and mind blanking. Our results suggest attentional lapses share a common physiological origin: the emergence of local sleep-like activity within the awake brain.

The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again

Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both "Cartesian theaters" and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due to their initial and ongoing significance for solutions to inference problems in cognitive (and affective) development. Embodied experiences provide foundational lessons in learning curriculums in which agents explore increasingly challenging problem spaces, so answering an unresolved question in Bayesian cognitive science: what are biologically plausible mechanisms for equipping learners with sufficiently powerful inductive biases to adequately constrain inference spaces? Drawing on models from neurophysiology, psychology, and developmental robotics, I describe how embodiment provides fundamental sources of empirical priors (as reliably learnable posterior expectations). If ESMs play this kind of foundational role in cognitive development, then bidirectional linkages will be found between all sensory modalities and frontal-parietal control hierarchies, so infusing all senses with somatic-motoric properties, thereby structuring all perception by relevant affordances, so solving frame problems for embodied agents. Drawing upon the Free Energy Principle and Active Inference framework, I describe a particular mechanism for intentional action selection via consciously imagined (and explicitly represented) goal realization, where contrasts between desired and present states influence ongoing policy selection via predictive coding mechanisms and backward-chained imaginings (as self-realizing predictions). This embodied developmental legacy suggests a mechanism by which imaginings can be intentionally shaped by (internalized) partially-expressed motor acts, so providing means of agentic control for attention, working memory, imagination, and behavior. I further describe the nature(s) of mental causation and self-control, and also provide an account of readiness potentials in Libet paradigms wherein conscious intentions shape causal streams leading to enaction. Finally, I provide neurophenomenological handlings of prototypical qualia including pleasure, pain, and desire in terms of self-annihilating free energy gradients via quasi-synesthetic interoceptive active inference. In brief, this manuscript is intended to illustrate how radically embodied minds may create foundations for intelligence (as capacity for learning and inference), consciousness (as somatically-grounded self-world modeling), and will (as deployment of predictive models for enacting valued goals).