Predicting lapses of attention with sleep-like slow waves

Multiband task related components enhance rapid cognition decoding for both small and similar objects

The cortically-coupled target recognition system based on rapid serial visual presentation (RSVP) has a wide range of applications in brain computer interface (BCI) fields such as medical and military. However, in the complex natural environment backgrounds, the identification of event-related potentials (ERP) of both small and similar objects that are quickly presented is a research challenge. Therefore, we designed corresponding experimental paradigms and proposed a multi-band task related components matching (MTRCM) method to improve the rapid cognitive decoding of both small and similar objects. We compared the areas under the receiver operating characteristic curve (AUC) between MTRCM and other 9 methods under different numbers of training sample using RSVP-ERP data from 50 subjects. The results showed that MTRCM maintained an overall superiority and achieved the highest average AUC (0.6562 ± 0.0091). We also optimized the frequency band and the time parameters of the method. The verification on public data sets further showed the necessity of designing MTRCM method. The MTRCM method provides a new approach for neural decoding of both small and similar RSVP objects, which is conducive to promote the further development of RSVP-BCI.

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.

Assessing the effects of an 8-week mindfulness training program on neural oscillations and self-reports during meditation practice

Previous literature suggests that mindfulness meditation can have positive effects on mental health, however, its mechanisms of action are still unclear. In this pre-registered study, we investigate the effects of mindfulness training on lapses of attention (and their associated neural correlates) during meditation practice. For this purpose, we recorded Electroencephalogram (EEG) during meditation practice before and after 8 weeks of mindfulness training (or waitlist) in 41 participants (21 treatment and 20 controls). In order to detect lapses of attention and characterize their EEG correlates, we interrupted participants during meditation to report their level of focus and drowsiness. First, we show that self-reported lapses of attention during meditation practice were associated to an increased occurrence of theta oscillations (3-6 Hz), which were slower in frequency and more spatially widespread than theta oscillations occurring during focused attention states. Then, we show that mindfulness training did not reduce the occurrence of lapses of attention nor their associated EEG correlate (i.e. theta oscillations) during meditation. Instead, we find that mindfulness training was associated with a significant slowing of alpha oscillations in frontal electrodes during meditation. Crucially, frontal alpha slowing during meditation practice has been reported in experienced meditators and is thought to reflect relative decreases in arousal levels. Together, our findings provide insights into the EEG correlates of mindfulness meditation, which could have important implications for the identification of its mechanisms of action and/or the development of neuromodulation protocols aimed at facilitating meditation practice.

Hippocampal sharp-wave ripples correlate with periods of naturally occurring self-generated thoughts in humans

Core features of human cognition highlight the importance of the capacity to focus on information distinct from events in the here and now, such as mind wandering. However, the brain mechanisms that underpin these self-generated states remain unclear. An emerging hypothesis is that self-generated states depend on the process of memory replay, which is linked to sharp-wave ripples (SWRs), which are transient high-frequency oscillations originating in the hippocampus. Local field potentials were recorded from the hippocampus of 10 patients with epilepsy for up to 15 days, and experience sampling was used to describe their association with ongoing thought patterns. The SWR rates were higher during extended periods of time when participants' ongoing thoughts were more vivid, less desirable, had more imaginable properties, and exhibited fewer correlations with an external task. These data suggest a role for SWR in the patterns of ongoing thoughts that humans experience in daily life.

Extended wakefulness alters the relationship between EEG oscillations and performance in a sustained attention task

During drowsiness, maintaining consistent attention becomes difficult, leading to behavioural lapses. Bursts of oscillations in the electroencephalogram (EEG) might predict such lapses, given that alpha bursts increase during inattention and theta bursts increase with time spent awake. Paradoxically, however, alpha bursts decrease with time awake and theta bursts increase during focussed attention and cognitive tasks. Therefore, we investigated to what extent theta and alpha bursts predicted performance in a sustained attention task, either when well rested (baseline, BL) or following 20 h of extended wakefulness (EW). High-density EEG was measured in 18 young adults, and the timing of bursts was related to trial outcomes (fast, slow, and lapse trials). To increase the likelihood of lapses, the task was performed under soporific conditions. Against expectations, alpha bursts were more likely before fast trials and less likely before lapses at baseline, although the effect was substantially reduced during extended wakefulness. Theta bursts showed no significant relationship to behavioural outcome either at baseline or extended wakefulness. However, following exploratory analyses, we found that large-amplitude theta and alpha bursts were more likely to be followed by lapse trials during extended wakefulness but not baseline. In summary, alpha bursts during baseline anticipated better trial outcomes, whereas large-amplitude theta and alpha bursts during extended wakefulness anticipated worse outcomes. Therefore, neither theta nor alpha bursts maintain a consistent relationship with behaviour under different levels of overall vigilance.

SVD-Based Mind-Wandering Prediction from Facial Videos in Online Learning

This paper presents a novel approach to mind-wandering prediction in the context of webcam-based online learning. We implemented a Singular Value Decomposition (SVD)-based 1D temporal eye-signal extraction method, which relies solely on eye landmark detection and eliminates the need for gaze tracking or specialized hardware, then extract suitable features from the signals to train the prediction model. Our thorough experimental framework facilitates the evaluation of our approach alongside baseline models, particularly in the analysis of temporal eye signals and the prediction of attentional states. Notably, our SVD-based signal captures both subtle and major eye movements, including changes in the eye boundary and pupil, surpassing the limited capabilities of eye aspect ratio (EAR)-based signals. Our proposed model exhibits a 2% improvement in the overall Area Under the Receiver Operating Characteristics curve (AUROC) metric and 7% in the F1-score metric for 'not-focus' prediction, compared to the combination of EAR-based and computationally intensive gaze-based models used in the baseline study These contributions have potential implications for enhancing the field of attentional state prediction in online learning, offering a practical and effective solution to benefit educational experiences.

Guidelines for the assessment and management of residual sleepiness in obstructive apnea-hypopnea syndrome: Endorsed by the French Sleep Research and Medicine Society (SFRMS) and the French Speaking Society of Respiratory Diseases (SPLF)

Excessive daytime sleepiness (EDS) is frequent among patients with obstructive sleep apnea hypopnea syndrome (OSAHS) and can persist despite the optimal correction of respiratory events (apnea, hypopnea and respiratory efforts), using continuous positive airway pressure (CPAP) or mandibular advancement device. Symptoms like apathy and fatigue may be mistaken for EDS. In addition, EDS has multi-factorial origin, which makes its evaluation complex. The marketing authorization [Autorisation de Mise sur le Marché (AMM)] for two wake-promoting agents (solriamfetol and pitolisant) raises several practical issues for clinicians. This consensus paper presents recommendations of good clinical practice to identify and evaluate EDS in this context, and to manage and follow-up the patients. It was conducted under the mandate of the French Societies for sleep medicine and for pneumology [Société Française de Recherche et de Médecine du Sommeil (SFRMS) and Société de Pneumologie de Langue Française (SPLF)]. A management algorithm is suggested, as well as a list of conditions during which the patient should be referred to a sleep center or a sleep specialist. The benefit/risk balance of a wake-promoting drug in residual EDS in OSAHS patients must be regularly reevaluated, especially in elderly patients with increased cardiovascular and psychiatric disorders risks. This consensus is based on the scientific knowledge at the time of the publication and may be revised according to their evolution.

Embracing sleep-onset complexity

Sleep is crucial for many vital functions and has been extensively studied. By contrast, the sleep-onset period (SOP), often portrayed as a mere prelude to sleep, has been largely overlooked and remains poorly characterized. Recent findings, however, have reignited interest in this transitional period and have shed light on its neural mechanisms, cognitive dynamics, and clinical implications. This review synthesizes the existing knowledge about the SOP in humans. We first examine the current definition of the SOP and its limits, and consider the dynamic and complex electrophysiological changes that accompany the descent to sleep. We then describe the interplay between internal and external processing during the wake-to-sleep transition. Finally, we discuss the putative cognitive benefits of the SOP and identify novel directions to better diagnose sleep-onset disorders.

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.

Two models of mind blanking

Mind blanking is a mental state in which attention does not bring any perceptual input into conscious awareness. As this state is still largely unexplored, we suggest that a comprehensive understanding of mind blanking can be achieved through a multifaceted approach combining self-assessment methods, neuroimaging and neuromodulation. In this article, we explain how electroencephalography and transcranial magnetic stimulation could be combined to help determine whether mind blanking is associated with a lack of mental content or a lack of linguistically or conceptually determinable mental content. We also question whether mind blanking occurs spontaneously or intentionally and whether these two forms are instantiated by the same or different neural correlates.

Inactivation of hypocretin receptor-2 signaling in dopaminergic neurons induces hyperarousal and enhanced cognition but impaired inhibitory control

Hypocretin/Orexin (HCRT/OX) and dopamine (DA) are both key effectors of salience processing, reward and stress-related behaviors and motivational states, yet their respective roles and interactions are poorly delineated. We inactivated HCRT-to-DA connectivity by genetic disruption of Hypocretin receptor-1 (Hcrtr1), Hypocretin receptor-2 (Hcrtr2), or both receptors (Hcrtr1&2) in DA neurons and analyzed the consequences on vigilance states, brain oscillations and cognitive performance in freely behaving mice. Unexpectedly, loss of Hcrtr2, but not Hcrtr1 or Hcrtr1&2, induced a dramatic increase in theta (7-11 Hz) electroencephalographic (EEG) activity in both wakefulness and rapid-eye-movement sleep (REMS). DAHcrtr2-deficient mice spent more time in an active (or theta activity-enriched) substate of wakefulness, and exhibited prolonged REMS. Additionally, both wake and REMS displayed enhanced theta-gamma phase-amplitude coupling. The baseline waking EEG of DAHcrtr2-deficient mice exhibited diminished infra-theta, but increased theta power, two hallmarks of EEG hyperarousal, that were however uncoupled from locomotor activity. Upon exposure to novel, either rewarding or stress-inducing environments, DAHcrtr2-deficient mice featured more pronounced waking theta and fast-gamma (52-80 Hz) EEG activity surges compared to littermate controls, further suggesting increased alertness. Cognitive performance was evaluated in an operant conditioning paradigm, which revealed that DAHcrtr2-ablated mice manifest faster task acquisition and higher choice accuracy under increasingly demanding task contingencies. However, the mice concurrently displayed maladaptive patterns of reward-seeking, with behavioral indices of enhanced impulsivity and compulsivity. None of the EEG changes observed in DAHcrtr2-deficient mice were seen in DAHcrtr1-ablated mice, which tended to show opposite EEG phenotypes. Our findings establish a clear genetically-defined link between monosynaptic HCRT-to-DA neurotransmission and theta oscillations, with a differential and novel role of HCRTR2 in theta-gamma cross-frequency coupling, attentional processes, and executive functions, relevant to disorders including narcolepsy, attention-deficit/hyperactivity disorder, and Parkinson's disease.

Reduced REM and N2 sleep, and lower dream intensity predict increased mind-wandering

Mind-wandering is a mental state in which attention shifts from the present environment or current task to internally driven, self-referent mental content. Homeostatic sleep pressure seems to facilitate mind-wandering as indicated by studies observing links between increased mind-wandering and impaired sleep. Nevertheless, previous studies mostly relied on cross-sectional measurements and self-reports. We aimed to combine the accuracy of objective sleep measures with the use of self-reports in a naturalistic setting in order to examine if objective sleep parameters predict the tendency for increased mind-wandering on the following day. We used mobile sleep electroencephalographic (EEG) headbands and self-report scales over 7 consecutive nights in a group of 67 healthy participants yielding ~400 analyzable nights. Nights with more wakefulness and shorter REM and slow wave sleep were associated with poorer subjective sleep quality at the intraindividual level. Reduced REM and N2 sleep, as well as less intense dream experiences, predicted more mind-wandering the following day. Our micro-longitudinal study indicates that intraindividual fluctuations in the duration of specific sleep stages predict the perception of sleep quality as assessed in the morning, as well as the intensity of daytime mind-wandering the following hours. The combined application of sleep EEG assessments and self-reports over repeated assessments provides new insights into the subtle intraindividual, night-to-day associations between nighttime sleep and the next day's subjective experiences.

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.

Wake slow waves in focal human epilepsy impact network activity and cognition

Slow waves of neuronal activity are a fundamental component of sleep that are proposed to have homeostatic and restorative functions. Despite this, their interaction with pathology is unclear and there is only indirect evidence of their presence during wakefulness. Using intracortical recordings from the temporal lobe of 25 patients with epilepsy, we demonstrate the existence of local wake slow waves (LoWS) with key features of sleep slow waves, including a down-state of neuronal firing. Consistent with a reduction in neuronal activity, LoWS were associated with slowed cognitive processing. However, we also found that LoWS showed signatures of a homeostatic relationship with interictal epileptiform discharges (IEDs): exhibiting progressive adaptation during the build-up of network excitability before an IED and reducing the impact of subsequent IEDs on network excitability. We therefore propose an epilepsy homeostasis hypothesis: that slow waves in epilepsy reduce aberrant activity at the price of transient cognitive impairment.

Behavioral and brain responses to verbal stimuli reveal transient periods of cognitive integration of the external world during sleep

Sleep has long been considered as a state of behavioral disconnection from the environment, without reactivity to external stimuli. Here we questioned this 'sleep disconnection' dogma by directly investigating behavioral responsiveness in 49 napping participants (27 with narcolepsy and 22 healthy volunteers) engaged in a lexical decision task. Participants were instructed to frown or smile depending on the stimulus type. We found accurate behavioral responses, visible via contractions of the corrugator or zygomatic muscles, in most sleep stages in both groups (except slow-wave sleep in healthy volunteers). Across sleep stages, responses occurred more frequently when stimuli were presented during high cognitive states than during low cognitive states, as indexed by prestimulus electroencephalography. Our findings suggest that transient windows of reactivity to external stimuli exist during bona fide sleep, even in healthy individuals. Such windows of reactivity could pave the way for real-time communication with sleepers to probe sleep-related mental and cognitive processes.

Recent advances in the neuroscience of spontaneous and off-task thought: implications for mental health

People spend a remarkable 30-50% of awake life thinking about something other than what they are currently doing. These experiences of being "off-task" can be described as spontaneous thought when mental dynamics are relatively flexible. Here we review recent neuroscience developments in this area and consider implications for mental wellbeing and illness. We provide updated overviews of the roles of the default mode network and large-scale network dynamics, and we discuss emerging candidate mechanisms involving hippocampal memory (sharp-wave ripples, replay) and neuromodulatory (noradrenergic and serotonergic) systems. We explore how distinct brain states can be associated with or give rise to adaptive and maladaptive forms of thought linked to distinguishable mental health outcomes. We conclude by outlining new directions in the neuroscience of spontaneous and off-task thought that may clarify mechanisms, lead to personalized biomarkers, and facilitate therapy developments toward the goals of better understanding and improving mental health.

Whole-Brain Deactivations Precede Uninduced Mind-Blanking Reports

Mind-blanking (MB) is termed as the inability to report our immediate-past mental content. In contrast to mental states with reportable content, such as mind-wandering or sensory perceptions, the neural correlates of MB started getting elucidated only recently. A notable particularity that pertains to MB studies is the way MB is instructed for reporting, like by deliberately asking participants to "empty their minds." Such instructions were shown to induce fMRI activations in frontal brain regions, typically associated with metacognition and self-evaluative processes, suggesting that MB may be a result of intentional mental content suppression. Here, we aim at examining this hypothesis by determining the neural correlates of MB without induction. Using fMRI combined with experience-sampling in 31 participants (22 female), univariate analysis of MB reports revealed deactivations in occipital, frontal, parietal, and thalamic areas, but no activations in prefrontal regions. These findings were confirmed using Bayesian region-of-interest analysis on areas previously shown to be implicated in induced MB, where we report evidence for frontal deactivations during MB reports compared with other mental states. Contrast analysis between reports of MB and content-oriented mental states also revealed deactivations in the left angular gyrus. We propose that these effects characterize a neuronal profile of MB, where key thalamocortical nodes are unable to communicate and formulate reportable content. Collectively, we show that study instructions for MB lead to differential neural activation. These results provide mechanistic insights linked to the phenomenology of MB and point to the possibility of MB being expressed in different forms.SIGNIFICANCE STATEMENT This study explores how brain activity changes when individuals report unidentifiable thoughts, a phenomenon known as mind-blanking (MB). It aims to detect changes in brain activations and deactivations when MB is reported spontaneously, as opposed to the neural responses that have been previously reported when MB is induced. By means of brain imaging and experience-sampling, the study points to reduced brain activity in a wide number of regions, including those mesio-frontally which were previously detected as activated during induced MB. These results enhance our understanding of the complexity of spontaneous thinking and contribute to broader discussions on consciousness and reportable experience.

Predicting the effect of CRISPR-Cas9-based epigenome editing

Epigenetic regulation orchestrates mammalian transcription, but functional links between them remain elusive. To tackle this problem, we here use epigenomic and transcriptomic data from 13 ENCODE cell types to train machine learning models to predict gene expression from histone post-translational modifications (PTMs), achieving transcriptome-wide correlations of ~ 0.70 - 0.79 for most samples. In addition to recapitulating known associations between histone PTMs and expression patterns, our models predict that acetylation of histone subunit H3 lysine residue 27 (H3K27ac) near the transcription start site (TSS) significantly increases expression levels. To validate this prediction experimentally and investigate how engineered vs. natural deposition of H3K27ac might differentially affect expression, we apply the synthetic dCas9-p300 histone acetyltransferase system to 8 genes in the HEK293T cell line. Further, to facilitate model building, we perform MNase-seq to map genome-wide nucleosome occupancy levels in HEK293T. We observe that our models perform well in accurately ranking relative fold changes among genes in response to the dCas9-p300 system; however, their ability to rank fold changes within individual genes is noticeably diminished compared to predicting expression across cell types from their native epigenetic signatures. Our findings highlight the need for more comprehensive genome-scale epigenome editing datasets, better understanding of the actual modifications made by epigenome editing tools, and improved causal models that transfer better from endogenous cellular measurements to perturbation experiments. Together these improvements would facilitate the ability to understand and predictably control the dynamic human epigenome with consequences for human health.

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.

Idiopathic hypersomnia and Kleine-Levin syndrome

Idiopathic hypersomnia (IH) and Kleine-Levin syndrome (KLS) are rare disorders of central hypersomnolence of unknown cause, affecting young people. However, increased sleep time and excessive daytime sleepiness (EDS) occur daily for years in IH, whereas they occur as relapsing/remitting episodes associated with cognitive and behavioural disturbances in KLS. Idiopathic hypersomnia is characterized by EDS, prolonged, unrefreshing sleep at night and during naps, and frequent morning sleep inertia, but rare sleep attacks, no cataplexy and sleep onset in REM periods as in narcolepsy. The diagnosis requires: (i) ruling out common causes of hypersomnolence, including mostly sleep apnea, insufficient sleep syndrome, psychiatric hypersomnia and narcolepsy; and (ii) obtaining objective EDS measures (mean latency at the multiple sleep latency test≤8min) or increased sleep time (sleep time>11h during a 18-24h bed rest). Treatment is similar to narcolepsy (except for preventive naps), including adapted work schedules, and off label use (after agreement from reference/competence centres) of modafinil, sodium oxybate, pitolisant, methylphenidate and solriamfetol. The diagnosis of KLS requires: (i) a reliable history of distinct episodes of one to several weeks; (ii) episodes contain severe hypersomnia (sleep>15h/d) associated with cognitive impairment (mental confusion and slowness, amnesia), derealisation, major apathy or disinhibited behaviour (hypersexuality, megaphagia, rudeness); and (iii) return to baseline sleep, cognition, behaviour and mood after episodes. EEG may contain slow rhythms during episodes, and rules out epilepsy. Functional brain imaging indicates hypoactivity of posterior associative cortex and hippocampus during symptomatic and asymptomatic periods. KLS attenuates with time when starting during teenage, including less frequent and less severe episodes. Adequate sleep habits, avoidance of alcohol and infections, as well as lithium and sometimes valproate (off label, after agreement from reference centres) help reducing the frequency and severity of episodes, and IV methylprednisolone helps reducing long (>30d) episode duration.

Sleepiness in neurological disorders

Sleepiness is a frequent and underrecognized symptom in neurological disorders, that impacts functional outcomes and quality of life. Multiple and potentially additive factors might contribute to sleepiness in neurological disorders, including sleep quality alterations, circadian rhythm disorders, drugs, and sleep disorders including sleep apnea or central disorders of hypersomnolence. Physician awareness of the possible symptoms of hypersomnolence, and associated causes is of crucial importance to allow proper identification and treatment of underlying causes. This review first provides a brief overview on clinical aspects of excessive daytime sleepiness, and diagnosis tools, then examines its frequency and mechanisms in various neurological disorders, including neurodegenerative disorders, multiple sclerosis, autoimmune encephalitis, epilepsy, and stroke.

Evaluation of hypersomnolence: From symptoms to diagnosis, a multidimensional approach

Hypersomnolence is a major public health issue given its high frequency, its impact on academic/occupational functioning and on accidentology, as well as its heavy socio-economic burden. The positive and aetiological diagnosis is crucial, as it determines the therapeutic strategy. It must consider the following aspects: i) hypersomnolence is a complex concept referring to symptoms as varied as excessive daytime sleepiness, excessive need for sleep, sleep inertia, or drowsiness, all of which warrant specific dedicated investigations; ii) the boundary between physiological and abnormal hypersomnolence is blurred, since most symptoms can be encountered in the general population to varying degrees without being considered as pathological, meaning that their severity, frequency, context of occurrence and related impairment need to be carefully assessed; iii) investigation of hypersomnolence relies on scales/questionnaires as well as behavioural and neurophysiological tests, which measure one or more dimensions, keeping in mind the possible discrepancy between objective and subjective assessment; iv) aetiological reasoning is driven by knowledge of the main sleep regulation mechanisms, epidemiology, and associated symptoms. The need to assess hypersomnolence is growing, both for its management, and for assessing the efficacy of treatments. The landscape of tools available for investigating hypersomnolence is constantly evolving, in parallel with research into sleep physiology and technical advances. These investigations face the challenges of reconciling subjective perception and objective data, making tools accessible to as many people as possible and predicting the risk of accidents.

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.

An EEG-based attention recognition method: fusion of time domain, frequency domain, and non-linear dynamics features

Introduction

Attention is a complex cognitive function of human brain that plays a vital role in our daily lives. Electroencephalogram (EEG) is used to measure and analyze attention due to its high temporal resolution. Although several attention recognition brain-computer interfaces (BCIs) have been proposed, there is a scarcity of studies with a sufficient number of subjects, valid paradigms, and reliable recognition analysis across subjects.

Methods

In this study, we proposed a novel attention paradigm and feature fusion method to extract features, which fused time domain features, frequency domain features and nonlinear dynamics features. We then constructed an attention recognition framework for 85 subjects.

Results and discussion

We achieved an intra-subject average classification accuracy of 85.05% ± 6.87% and an inter-subject average classification accuracy of 81.60% ± 9.93%, respectively. We further explored the neural patterns in attention recognition, where attention states showed less activation than non-attention states in the prefrontal and occipital areas in α, β and θ bands. The research explores, for the first time, the fusion of time domain features, frequency domain features and nonlinear dynamics features for attention recognition, providing a new understanding of attention recognition.

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.

Spotlight on Sleep Stage Classification Based on EEG

The recommendations for identifying sleep stages based on the interpretation of electrophysiological signals (electroencephalography [EEG], electro-oculography [EOG], and electromyography [EMG]), derived from the Rechtschaffen and Kales manual, were published in 2007 at the initiative of the American Academy of Sleep Medicine, and regularly updated over years. They offer an important tool to assess objective markers in different types of sleep/wake subjective complaints. With the aims and advantages of simplicity, reproducibility and standardization of practices in research and, most of all, in sleep medicine, they have overall changed little in the way they describe sleep. However, our knowledge on sleep/wake physiology and sleep disorders has evolved since then. High-density electroencephalography and intracranial electroencephalography studies have highlighted local regulation of sleep mechanisms, with spatio-temporal heterogeneity in vigilance states. Progress in the understanding of sleep disorders has allowed the identification of electrophysiological biomarkers better correlated with clinical symptoms and outcomes than standard sleep parameters. Finally, the huge development of sleep medicine, with a demand for explorations far exceeding the supply, has led to the development of alternative studies, which can be carried out at home, based on a smaller number of electrophysiological signals and on their automatic analysis. In this perspective article, we aim to examine how our description of sleep has been constructed, has evolved, and may still be reshaped in the light of advances in knowledge of sleep physiology and the development of technical recording and analysis tools. After presenting the strengths and limitations of the classification of sleep stages, we propose to challenge the "EEG-EOG-EMG" paradigm by discussing the physiological signals required for sleep stages identification, provide an overview of new tools and automatic analysis methods and propose avenues for the development of new approaches to describe and understand sleep/wake states.

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.

High-Definition Transcranial Direct Current Stimulation in the Right Ventrolateral Prefrontal Cortex Lengthens Sustained Attention in Virtual Reality

Due to the current limitations of three-dimensional (3D) simulation graphics technology, mind wandering commonly occurs in virtual reality tasks, which has impeded it being applied more extensively. The right ventrolateral prefrontal cortex (rVLPFC) plays a vital role in executing continuous two-dimensional (2D) mental paradigms, and transcranial direct current stimulation (tDCS) over this cortical region has been shown to successfully modulate sustained 2D attention. Accordingly, we further explored the effects of electrical activation of the rVLPFC on 3D attentional tasks using anodal high-definition (HD)-tDCS. A 3D Go/No-go (GNG) task was developed to compare the after effects of real and sham brain stimulation. Specifically, GNG tasks were periodically interrupted to assess the subjective perception of attentional level, behavioral reactions were tracked and decomposed into an underlying decision cognition process, and electroencephalography data were recorded to calculate event-related potentials (ERPs) in rVLPFC. The p-values statistically indicated that HD-tDCS improved the subjective mentality, led to more cautious decisions, and enhanced neuronal discharging in rVLPFC. Additionally, the neurophysiological P300 ERP component and stimulation being active or sham could effectively predict several objective outcomes. These findings indicate that the comprehensive approach including brain stimulation, 3D mental paradigm, and cross-examined performance could significantly lengthen and robustly compare sustained 3D attention.

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.

Context independent reductions in external processing during self-generated episodic social cognition

Ongoing cognition supports behavioral flexibility by facilitating behavior in the moment, and through the consideration of future actions. These different modes of cognition are hypothesized to vary with the correlation between brain activity and external input, since evoked responses are reduced when cognition switches to topics unrelated to the current task. This study examined whether these reduced evoked responses change as a consequence of the task environment in which the experience emerges. We combined electroencephalography (EEG) recording with multidimensional experience sampling (MDES) to assess the electrophysiological correlates of ongoing thought in task contexts which vary on their need to maintain continuous representations of task information for satisfactory performance. We focused on an event-related potential (ERP) known as the parietal P3 that had a greater amplitude in our tasks relying on greater external attention. A principal component analysis (PCA) of the MDES data revealed four patterns of ongoing thought: off-task episodic social cognition, deliberate on-task thought, imagery, and emotion. Participants reported more off-task episodic social cognition and mental imagery under low external demands and more deliberate on-task thought under high external task demands. Importantly, the occurrence of off-task episodic social cognition was linked to similar reductions in the amplitude of the P3 regardless of external task. These data suggest the amplitude of the P3 may often be a general feature of external task-related content and suggest attentional decoupling from sensory inputs are necessary for certain types of perceptually-decoupled, self-generated thoughts.

Mind wandering and sleep in daily life: A combined actigraphy and experience sampling study

Individuals who sleep poorly report spending more time mind wandering during the day. However, past research has relied on self-report measures of sleep or measured mind wandering during laboratory tasks, which prevents generalization to everyday contexts. We used ambulatory assessments to examine the relations between several features of sleep (duration, fragmentation, and disturbances) and mind wandering (task-unrelated, stimulus-independent, and unguided thoughts). Participants wore a wristband device that collected actigraphy and experience-sampling data across 7 days and 8 nights. Contrary to our expectations, task-unrelated and stimulus-independent thoughts were not associated with sleep either within- or between-persons (n = 164). Instead, individual differences in unguided thoughts were associated with sleep disturbances and duration, suggesting that individuals who more often experience unguided train-of-thoughts have greater sleep disturbances and sleep longer. These results highlight the need to consider the context and features of mind wandering when relating it to sleep.

Detecting inattentiveness caused by mind-wandering during a driving task: A behavioral study

This study aims to investigate whether behavioral variability and participants' self-ratings can be used to detect mind-wandering while driving and to examine their effects on braking performance during a driving task. We created a novel driving task and added a sustained attention response task (SART). We examined the effects of mind-wandering on braking performance and whether mind-wandering could be detected from SART response variability. The within-subjects results showed that self-reports of inattentiveness during driving correlated significantly with SART response variability. Multiple regression analysis with brake reaction time as the dependent variable revealed a significant relationship between self-reports of inattentiveness and mind-wandering. However, there were no other consistent linear associations between mind-wandering and SART response variability. Our results not only suggest that inattentiveness to driving caused by mind-wandering impairs braking performance but also emphasize the importance and difficulty of detecting this state from behavioral data alone.

Deterministic and Stochastic Components of Cortical Down States: Dynamics and Modulation

Slow oscillations are an emergent activity of the cerebral cortex network consisting of alternating periods of activity (Up states) and silence (Down states). Up states are periods of persistent cortical activity that share properties with that of underlying wakefulness. However, the occurrence of Down states is almost invariably associated with unconsciousness, both in animal models and clinical studies. Down states have been attributed relevant functions, such as being a resetting mechanism or breaking causal interactions between cortical areas. But what do Down states consist of? Here, we explored in detail the network dynamics (e.g., synchronization and phase) during these silent periods in vivo (male mice), in vitro (ferrets, either sex), and in silico, investigating various experimental conditions that modulate them: anesthesia levels, excitability (electric fields), and excitation/inhibition balance. We identified metastability as two complementary phases composing such quiescence states: a highly synchronized "deterministic" period followed by a low-synchronization "stochastic" period. The balance between these two phases determines the dynamical properties of the resulting rhythm, as well as the responsiveness to incoming inputs or refractoriness. We propose detailed Up and Down state cycle dynamics that bridge cortical properties emerging at the mesoscale with their underlying mechanisms at the microscale, providing a key to understanding unconscious states.SIGNIFICANCE STATEMENT The cerebral cortex expresses slow oscillations consisting of Up (active) and Down (silent) states. Such activity emerges not only in slow wave sleep, but also under anesthesia and in brain lesions. Down states functionally disconnect the network, and are associated with unconsciousness. Based on a large collection of data, novel data analysis approaches and computational modeling, we thoroughly investigate the nature of Down states. We identify two phases: a highly synchronized "deterministic" period, followed by a low-synchronization "stochastic" period. The balance between these two phases determines the dynamic properties of the resulting rhythm and responsiveness to incoming inputs. This finding reconciles different theories of slow rhythm generation and provides clues about how the brain switches from conscious to unconscious brain states.

Neuronal-spiking-based closed-loop stimulation during cortical ON- and OFF-states in freely moving mice

The slow oscillation is a central neuronal dynamic during sleep, and is generated by alternating periods of high and low neuronal activity (ON- and OFF-states). Mounting evidence causally links the slow oscillation to sleep's functions, and it has recently become possible to manipulate the slow oscillation non-invasively and phase-specifically. These developments represent promising clinical avenues, but they also highlight the importance of improving our understanding of how ON/OFF-states affect incoming stimuli and what role they play in neuronal plasticity. Most studies using closed-loop stimulation rely on the electroencephalogram and local field potential signals, which reflect neuronal ON- and OFF-states only indirectly. Here we develop an online detection algorithm based on spiking activity recorded from laminar arrays in mouse motor cortex. We find that online detection of ON- and OFF-states reflects specific phases of spontaneous local field potential slow oscillation. Our neuronal-spiking-based closed-loop procedure offers a novel opportunity for testing the functional role of slow oscillation in sleep-related restorative processes and neural plasticity.

Closed-loop modulation of local slow oscillations in human NREM sleep

Slow-wave sleep is the deep non-rapid eye-movement (NREM) sleep stage that is most relevant for the recuperative function of sleep. Its defining property is the presence of slow oscillations (<2 Hz) in the scalp electroencephalogram (EEG). Slow oscillations are generated by a synchronous back and forth between highly active UP-states and silent DOWN-states in neocortical neurons. Growing evidence suggests that closed-loop sensory stimulation targeted at UP-states of EEG-defined slow oscillations can enhance the slow oscillatory activity, increase sleep depth, and boost sleep's recuperative functions. However, several studies failed to replicate such findings. Failed replications might be due to the use of conventional closed-loop stimulation algorithms that analyze the signal from one single electrode and thereby neglect the fact that slow oscillations vary with respect to their origins, distributions, and trajectories on the scalp. In particular, conventional algorithms nonspecifically target functionally heterogeneous UP-states of distinct origins. After all, slow oscillations at distinct sites of the scalp have been associated with distinct functions. Here we present a novel EEG-based closed-loop stimulation algorithm that allows targeting UP- and DOWN-states of distinct cerebral origins based on topographic analyses of the EEG: the topographic targeting of slow oscillations (TOPOSO) algorithm. We present evidence that the TOPOSO algorithm can detect and target local slow oscillations with specific, predefined voltage maps on the scalp in real-time. When compared to a more conventional, single-channel-based approach, TOPOSO leads to fewer but locally more specific stimulations in a simulation study. In a validation study with napping participants, TOPOSO targets auditory stimulation reliably at local UP-states over frontal, sensorimotor, and centro-parietal regions. Importantly, auditory stimulation temporarily enhanced the targeted local state. However, stimulation then elicited a standard frontal slow oscillation rather than local slow oscillations. The TOPOSO algorithm is suitable for the modulation and the study of the functions of local slow oscillations.

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.

Mind blanking is a distinct mental state linked to a recurrent brain profile of globally positive connectivity during ongoing mentation

Mind blanking (MB) is a waking state during which we do not report any mental content. The phenomenology of MB challenges the view of a constantly thinking mind. Here, we comprehensively characterize the MB's neurobehavioral profile with the aim to delineate its role during ongoing mentation. Using functional MRI experience sampling, we show that the reportability of MB is less frequent, faster, and with lower transitional dynamics than other mental states, pointing to its role as a transient mental relay. Regarding its neural underpinnings, we observed higher global signal amplitude during MB reports, indicating a distinct physiological state. Using the time-varying functional connectome, we show that MB reports can be classified with high accuracy, suggesting that MB has a unique neural composition. Indeed, a pattern of global positive-phase coherence shows the highest similarity to the connectivity patterns associated with MB reports. We interpret this pattern's rigid signal architecture as hindering content reportability due to the brain's inability to differentiate signals in an informative way. Collectively, we show that MB has a unique neurobehavioral profile, indicating that nonreportable mental events can happen during wakefulness. Our results add to the characterization of spontaneous mentation and pave the way for more mechanistic investigations of MB's phenomenology.

Visual working memory recruits two functionally distinct alpha rhythms in posterior cortex

Oscillatory activity in the human brain is dominated by posterior alpha oscillations (8-14 Hz), which have been shown to be functionally relevant in a wide variety of cognitive tasks. Although posterior alpha oscillations are commonly considered a single oscillator anchored at an individual alpha frequency (IAF; ∼10 Hz), previous work suggests that IAF reflects a spatial mixture of different brain rhythms. In this study, we assess whether Independent Component Analysis (ICA) can disentangle functionally distinct posterior alpha rhythms in the context of visual short-term memory retention. Magnetoencephalography (MEG) was recorded in 33 subjects while performing a visual working memory task. Group analysis at sensor level suggested the existence of a single posterior alpha oscillator that increases in power and decreases in frequency during memory retention. Conversely, single-subject analysis of independent components revealed the existence of two dissociable alpha rhythms: one that increases in power during memory retention (Alpha1) and another one that decreases in power (Alpha2). Alpha1 and Alpha2 rhythms were differentially modulated by the presence of visual distractors (Alpha1 increased in power while Alpha2 decreased) and had an opposite relationship with accuracy (positive for Alpha1 and negative for Alpha2). In addition, Alpha1 rhythms showed a lower peak frequency, a narrower peak width, a greater relative peak amplitude and a more central source than Alpha2 rhythms. Together, our results demonstrate that modulations in posterior alpha oscillations during short-term memory retention reflect the dynamics of at least two distinct brain rhythms with different functions and spatiospectral characteristics.Significance statementAlpha oscillations are the most prominent feature of the human brain's electrical activity, and consist of rhythmic neuronal activity in posterior parts of the cortex. Alpha is usually considered a single brain rhythm that changes in power and frequency to support cognitive operations. We here show that posterior alpha entails at least two dissociable rhythms with distinct functions and characteristics. These findings could solve previous inconsistencies in the literature regarding the direction of task-related alpha power/frequency modulations and their relation to cognitive performance. In addition, the existence of two distinct posterior alpha rhythms could have important consequences for the design of neurostimulation protocols aimed at modulating alpha oscillations and subsequently cognition.

Interest of the BLAST paradigm and salivary markers for the evaluation of sleepiness in drivers

Objectives

Sleepiness is associated with decreased cognitive abilities and remains one of the main causes of fatal road accidents. The tools currently available to assess sleepiness, such as questionnaires, are subject to intra- and inter-individual variability, while multiple sleep latency tests are only feasible in few sleep laboratories. The main objective of this study was to explore new potential markers (neurocognitive, biological) to objectively assess sleepiness in drivers.

Methods

A total of 186 drivers (median age 44 years, range 20–74 years, 73% men, 14% obese) were included during a break at a highway service area, in the morning, while on the road for vacation. Questionnaires on sleepiness and sleep characteristics (habitual and on the night before travel), the Bron-Lyon Attention Stability Test (BLAST), and two salivary samples (α-amylase and oxalate) were collected. Associations between measures of sleepiness [Epworth Sleepiness Scale (ESS), and Stanford Sleepiness Scale (SSS)], sleep characteristics, neurocognitive, and biological markers were tested using regression models adjusted for confounding factors.

Results

The night before travel, 83% of the drivers reduced their sleep time and 30% slept 5 h or less. The higher the number of miles to be traveled, the higher the decrease, and the shorter the sleep time. The night before travel, 18 and 24% of the drivers complained of poor sleep quality and difficulty falling asleep. The sleep characteristics on the night before travel were associated with the habitual sleep characteristics. At the time of the test, 47% of the drivers scored pathologically on the SSS. Poor sleep quality and difficulty falling asleep the night before travel were associated with increased sleepiness as assessed by the SSS and decreased attentional ability as assessed by the BLAST. No association between salivary markers and acute sleepiness was observed.

Conclusions

The sleep characteristics of the night before travel were associated with sleepiness and attentional performance. The SSS and the BLAST could be used by individual drivers in a self-evaluation context. Biological markers showed a high variability and limited association with sleep parameters across subjects, emphasizing the need for within-subject designs to assess their usefulness.

Readiness to remember: predicting variability in episodic memory

Learning and remembering are fundamental to our lives, so what causes us to forget? Answers often highlight preparatory processes that precede learning, as well as mnemonic processes during the act of encoding or retrieval. Importantly, evidence now indicates that preparatory processes that precede retrieval attempts also have powerful influences on memory success or failure. Here, we review recent work from neuroimaging, electroencephalography, pupillometry, and behavioral science to propose an integrative framework of retrieval-period dynamics that explains variance in remembering in the moment and across individuals as a function of interactions among preparatory attention, goal coding, and mnemonic processes. Extending this approach, we consider how a 'readiness to remember' (R2R) framework explains variance in high-level functions of memory and mnemonic disruptions in aging.

What is mind blanking: a conceptual clarification

The investigation of so-called mind blanking has been recently established in the context of mind wandering research. Unfortunately, the description of the term "mind blanking" and its experimental assessment are often ambiguous. More specifically, two distinct phenomena have been conceptually blended and both characterized using the term "mind blanking" in experimental studies: the absence of task-focus and thought, versus complete lack of conscious experiences. At least in part, this confusion can be traced back to the writings of William James, who referred to "thought" as a superordinate term to address different conscious experiences. Applying the technique of so-called experience sampling, experimental studies, up to now, probably assessed the absence of thought, but not a complete lack of conscious experiences. There is no clear evidence yet for instances of the latter phenomenon occurring during non-pathological waking state. Possibly, such evidence could be revealed in the future using a conceptually more refined experience sampling.

Sleep well, mind wander less: A systematic review of the relationship between sleep outcomes and spontaneous cognition

Despite an upsurge of research on spontaneous cognition, little is known about its associations with sleep-related outcomes. This systematic review, following PRISMA guidelines, examined the relationship between sleep and spontaneous thoughts, across different definitions and measurements of sleep outcomes and spontaneous cognition, and a diversity of methodologies. Twenty-one articles with survey and/or experimental designs were identified. Self-reported disturbed sleep-comprising poor sleep quality, more insomnia symptoms, more daytime sleepiness and a tendency towards eveningness-and experimentally induced sleep deprivation were associated with a tendency to engage in disruptive mind wandering and daydreaming, but not positive-constructive daydreaming. Findings regarding circadian fluctuation in spontaneous thoughts were mixed and inconclusive. This systematic review bridges the gap between the sleep and spontaneous cognition research by contributing to the understanding of potential psychological and cognitive mechanisms of spontaneous cognition, as well as by elucidating the emotional and cognitive consequences of disturbed sleep.

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

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

Electrophysiological markers of mind wandering: A systematic review

The ability to mentally wander away from the external environment is a remarkable feature of the human mind. Although recent years have witnessed a surge of interest in examining mind wandering using EEG, there is no comprehensive review that summarizes and accounts for the variable findings. Accordingly, we conducted a systematic review that synthesizes evidence from EEG studies that examined the electrophysiological measures of mind wandering. Our search yielded 42 studies that met eligibility criteria. The reviewed literature converges on a reduction in the amplitude of canonical ERP components (i.e., P1, N1 and P3) as the most reliable markers of mind wandering. Spectral findings were less robust, but point towards greater activity in lower frequency bands, (i.e., delta, theta, and alpha), as well as a decrease in beta band activity, during mind wandering compared to on-task states. The variability in these findings appears to be modulated by the task context. To integrate these findings, we propose an electrophysiological account of mind wandering that explains how the brain supports this inner experience. Conclusions drawn from this work will inform future endeavours in basic science to map out electrophysiological patterns underlying mind wandering and in translational science using EEG to predict the occurrence of this phenomenon.

A novel EEG marker predicts perceived sleepiness and poor sleep quality

STUDY OBJECTIVES

To determine if a novel EEG-derived continuous index of sleep depth/alertness, the odds ratio product (ORP), predicts self-reported daytime sleepiness and poor sleep quality in two large population-based cohorts.

METHODS

ORP values which range from 0 (deep sleep) to 2.5 (fully alert) were calculated in 3s intervals during awake periods (ORPwake) and NREM sleep (ORPNREM) determined from home sleep studies in the HypnoLaus (N = 2162: 1106 females, 1056 males) and men androgen inflammation lifestyle environment and stress (MAILES) cohorts (N = 754 males). Logistic regression was used to examine associations between ORPwake, ORPNREM, and traditional polysomnography measures (as comparators) with excessive sleepiness (Epworth sleepiness scale >10) and poor sleep quality (Pittsburgh sleep quality index >5) and insomnia symptoms.

RESULTS

High ORPwake was associated with a ~30% increase in poor sleep quality in both HypnoLaus (odds ratio, OR, and 95% CI) 1.28 (1.09, 1.51), and MAILES 1.36 (1.10, 1.68). High ORPwake was also associated with a ~28% decrease in excessive daytime sleepiness in the MAILES dataset. ORPNREM was associated with a ~30% increase in poor sleep quality in HypnoLaus but not in MAILES. No consistent associations across cohorts were detected using traditional polysomnography markers.

CONCLUSIONS

ORP, a novel EEG-derived metric, measured during wake periods predicts poor sleep quality in two independent cohorts. Consistent with insomnia symptomatology of poor perceived sleep in the absence of excessive daytime sleepiness, ORPwake may provide valuable objective mechanistic insight into physiological hyperarousal.

Revisiting consciousness: Distinguishing between states of conscious focused attention and mind wandering with EEG

Fluctuating between external conscious processing and mind wandering is inherent to the human condition. Past research showed that in tasks requiring sustained attention, mind wandering episodes in which attention is directed internally constrain conscious processing of external stimuli. Conversely, conscious processing of internal stimuli is enhanced during mind wandering. To investigate this, we developed and administered a visuomotor tracking task in which participants were instructed to track the path of a stimulus on a screen with a mouse while responding to rare targets. Prior to reports of mind wandering we found the following: The P3 event-related potential component for targets, indicative of conscious stimulus processing, was attenuated at electrodes Cz and Pz. Moreover, alpha power, indicative of internal mental states, increased globally. Theta power increased along the centroparietal area, and beta decreased along right frontal and right centroparietal areas. Interestingly, trait mind wandering was positively correlated with delta power and gamma power, but negatively correlated with the theta-beta ratio. These results demonstrate that mind wandering is characterized by distinct neural signatures at both a state and trait level.

Predicting the loss of responsiveness when falling asleep in humans

Falling asleep is a dynamical process that is poorly defined. The period preceding sleep, characterized by the progressive alteration of behavioral responses to the environment, which may last several minutes, has no electrophysiological definition, and is embedded in the first stage of sleep (N1). We aimed at better characterizing this drowsiness period looking for neurophysiological predictors of responsiveness using electro and magnetoencephalography. Healthy participants were recorded when falling asleep, while they were presented with continuous auditory stimulations and asked to respond to deviant sounds. We analysed brain responses to sounds and markers of ongoing activity, such as information and connectivity measures, in relation to rapid fluctuations of brain rhythms observed at brain onset and participants' capabilities to respond. Results reveal a drowsiness period distinct from wakefulness and sleep, from alpha rhythms to the first sleep spindles, characterized by diverse and transient brain states that come on and off at the scale of a few seconds and closely reflects, mainly through neural processes in alpha and theta bands, decreasing probabilities to be responsive to external stimuli. Results also show that the global P300 was only present in responsive trials, regardless of vigilance states. A better consideration of the drowsiness period through a formalized classification and its specific brain markers such as described here should lead to significant advances in vigilance assessment in the future, in medicine and ecological environments.

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.

Consciousness as a multidimensional phenomenon: implications for the assessment of disorders of consciousness

Disorders of consciousness (DoCs) pose a significant clinical and ethical challenge because they allow for complex forms of conscious experience in patients where intentional behaviour and communication are highly limited or non-existent. There is a pressing need for brain-based assessments that can precisely and accurately characterize the conscious state of individual DoC patients. There has been an ongoing research effort to develop neural measures of consciousness. However, these measures are challenging to validate not only due to our lack of ground truth about consciousness in many DoC patients but also because there is an open ontological question about consciousness. There is a growing, well-supported view that consciousness is a multidimensional phenomenon that cannot be fully described in terms of the theoretical construct of hierarchical, easily ordered conscious levels. The multidimensional view of consciousness challenges the utility of levels-based neural measures in the context of DoC assessment. To examine how these measures may map onto consciousness as a multidimensional phenomenon, this article will investigate a range of studies where they have been applied in states other than DoC and where more is known about conscious experience. This comparative evidence suggests that measures of conscious level are more sensitive to some dimensions of consciousness than others and cannot be assumed to provide a straightforward hierarchical characterization of conscious states. Elevated levels of brain complexity, for example, are associated with conscious states characterized by a high degree of sensory richness and minimal attentional constraints, but are suboptimal for goal-directed behaviour and external responsiveness. Overall, this comparative analysis indicates that there are currently limitations to the use of these measures as tools to evaluate consciousness as a multidimensional phenomenon and that the relationship between these neural signatures and phenomenology requires closer scrutiny.

RoWDI: rolling window detection of sleep intrusions in the awake brain using fMRI

Objective.Brief episodes of sleep can intrude into the awake human brain due to lack of sleep or fatigue-compromising the safety of critical daily tasks (i.e. driving). These intrusions can also introduce artefactual activity within functional magnetic resonance imaging (fMRI) experiments, prompting the need for an objective and effective method of removing them.Approach.We have developed a method to track sleep-like events in awake humans via rolling window detection of intrusions (RoWDI) of fMRI signal template. These events can then be used in voxel-wise event-related analysis of fMRI data. To test this approach, we generated a template of fMRI activity associated with transition to sleep via simultaneous fMRI and electroencephalogram (EEG) (N= 10). RoWDI was then used to identify sleep-like events in 20 individuals performing a cognitive task during fMRI after a night of partial sleep deprivation. This approach was further validated in an independent fMRI dataset (N= 56).Main results.Our method (RoWDI) was able to infer frequent sleep-like events during the cognitive task performed after sleep deprivation. The sleep-like events were associated with on average of 20% reduction in pupil size and prolonged response time. The blood-oxygen-level-dependent activity during the sleep-like events covered thalami-cortical regions, which although spatially distinct, co-existed with, task-related activity. These key findings were validated in the independent dataset.Significance.RoWDI can reliably detect spontaneous sleep-like events in the human brain. Thus, it may also be used as a tool to delineate and account for neural activity associated with wake-sleep transitions in both resting-state and task-related fMRI studies.