A local, bottom-up perspective on sleep deprivation and neurobehavioral performance

Biomathematical modeling of fatigue due to sleep inertia

Biomathematical models of fatigue capture the physiology of sleep/wake regulation and circadian rhythmicity to predict changes in neurobehavioral functioning over time. We used a biomathematical model of fatigue linked to the adenosinergic neuromodulator/receptor system in the brain as a framework to predict sleep inertia, that is, the transient neurobehavioral impairment experienced immediately after awakening. Based on evidence of an adenosinergic basis for sleep inertia, we expanded the biomathematical model with novel differential equations to predict the propensity for sleep inertia during sleep and its manifestation after awakening. Using datasets from large laboratory studies of sleep loss and circadian misalignment, we calibrated the model by fitting just two new parameters and then validated the model's predictions against independent data. The expanded model was found to predict the magnitude and time course of sleep inertia with generally high accuracy. Analysis of the model's dynamics revealed a bifurcation in the predicted manifestation of sleep inertia in sustained sleep restriction paradigms, which reflects the observed escalation of the magnitude of sleep inertia in scenarios with sleep restriction to less than ∼ 4 h per day. Another emergent property of the model involves a rapid increase in the predicted propensity for sleep inertia in the early part of sleep followed by a gradual decline in the later part of the sleep period, which matches what would be expected based on the adenosinergic regulation of non-rapid eye movement (NREM) sleep and its known influence on sleep inertia. These dynamic behaviors provide confidence in the validity of our approach and underscore the predictive potential of the model. The expanded model provides a useful tool for predicting sleep inertia and managing impairment in 24/7 settings where people may need to perform critical tasks immediately after awakening, such as on-demand operations in safety and security, emergency response, and health care.

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration

Objective. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC.Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials.Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed an increased frontal theta power and increasing trend in the central alpha and central beta power after the unexpected robot stopping compared to when the robot stopped correctly at the target. We also demonstrate that a popular convolution neural network model, EEGNet, could predict the amplitudes of PEN and Pe from the EEG data prior to the error.Significance. This prediction model could be instrumental in developing an online prediction model that could forewarn the system and operators of the diminished error awareness of the user, alluding to a potential safety breach in error-related potential-based BCI system for pHRC. Therefore, our work paves the way for embracing BCI technology in pHRC to optimally adapt the robot behavior for personalized user experience using real-time brain activity, enriching the quality of the interaction.

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.

Effects of total sleep deprivation on components of top-down attentional control using a flexible attentional control task

Sleep deprivation consistently decreases vigilant attention, which can lead to difficulty in performing a variety of cognitive tasks. However, sleep-deprived individuals may be able to compensate for degraded vigilant attention by means of top-down attentional control. We employed a novel task to measure the degree to which individuals overcome impairments in vigilant attention by using top-down attentional control, the Flexible Attentional Control Task (FACT). The FACT is a two-choice task that has trials with valid, invalid, and neutral cues, along with an unexpected switch in the probability of cue validity about halfway in the task. The task provides indices that isolate performance components reflecting vigilant attention and top-down attentional control. Twelve healthy young adults completed an in-laboratory study. After a baseline day, the subjects underwent 39 hours of total sleep deprivation (TSD), followed by a recovery day. The FACT was administered at 03:00, 11:00, and 19:00 during sleep deprivation (TSD condition) and at 11:00 and 19:00 after baseline sleep and at 11:00 after recovery sleep (rested condition). When rested, the subjects demonstrated both facilitation and interference effects on cued trials. While sleep deprived, the subjects showed vigilant attention deficits on neutral cue trials, and an impaired ability to reduce these deficits by using predictive contextual cues. Our results indicate that the FACT can dissociate vigilant attention from top-down attentional control. Furthermore, they show that during sleep deprivation, contextual cues help individuals to compensate partially for impairments in vigilant attention, but the effectiveness of top-down attentional control is diminished.

The Effect of Partial Sleep Deprivation and Time-on-Task on Young Drivers' Subjective and Objective Sleepiness

Despite sleepiness being considered one of the main factors contributing to road crashes, and even though extensive efforts have been made in the identification of techniques able to detect it, the assessment of fitness-to-drive regarding driving fatigue and sleepiness is still an open issue. In the literature on driver sleepiness, both vehicle-based measures and behavioral measures are used. Concerning the former, the one considered more reliable is the Standard Deviation of Lateral Position (SDLP) while the PERcent of eye CLOSure over a defined period of time (PERCLOS) seems to be the most informative behavioral measure. In the present study, using a within-subject design, we assessed the effect of a single night of partial sleep deprivation (PSD, less than 5 h sleeping time) compared to a control condition (full night of sleep, 8 h sleeping time) on SDLP and PERCLOS, in young adults driving in a dynamic car simulator. Results show that time-on-task and PSD affect both subjective and objective sleepiness measures. Moreover, our data confirm that both objective and subjective sleepiness increase through a monotonous driving scenario. Considering that SDLP and PERCLOS were often used separately in studies on driver sleepiness and fatigue detection, the present results have potential implications for fitness-to-drive assessment in that they provide useful information allowing to combine the advantages of the two measures for drowsiness detection while driving.

Neural fatigue by passive induction: repeated stimulus exposure results in cognitive fatigue and altered representations in task-relevant networks

Cognitive fatigue is defined by a reduced capacity to perform mental tasks. Despite its pervasiveness, the underlying neural mechanisms remain elusive. Specifically, it is unclear whether prolonged effort affects performance through alterations in over-worked task-relevant neuronal assemblies. Our paradigm based on repeated passive visual stimulation discerns fatigue effects from the influence of motivation, skill and boredom. We induced performance loss and observed parallel alterations in the neural blueprint of the task, by mirroring behavioral performance with multivariate neuroimaging techniques (MVPA) that afford a subject-specific approach. Crucially, functional areas that responded the most to repeated stimulation were also the most affected. Finally, univariate analysis revealed clusters displaying significant disruption within the extrastriate visual cortex. In sum, here we show that repeated stimulation impacts the implicated brain areas' activity and causes tangible behavioral repercussions, providing evidence that cognitive fatigue can result from local, functional, disruptions in the neural signal induced by protracted recruitment.

The effect of time on task, sleep deprivation, and time of day on simulated driving performance

Sleep deprivation and time of day have been shown to play a critical role in decreasing ability to sustain attention, such as when driving long distances. However, a gap in the literature exists regarding external factors, such as workload. One way to examine workload is via modulating time on task. This study investigated the combined effect of sleep deprivation, time of day, and time on task as a workload factor on driving performance. Twenty-one participants (18–34 years, 10 females) underwent 62 h of sleep deprivation within a controlled laboratory environment. Participants received an 8-h baseline and 9.5-h recovery sleep. Every 8 h, participants completed a Psychomotor Vigilance Task (PVT), Karolinska Sleepiness Scale (KSS), 30-min monotonous driving task and NASA-Task Load Index (TLX). Driving variables examined were lane deviation, number of crashes, speed deviation and time outside the safe zone. Workload was measured by comparing two 15-min loops of the driving track. A mixed model ANOVA revealed significant main effects of day and time of day on all driving performance measures (p < .001). There was a significant main effect of workload on lane deviation (p < .05), indicating that a longer time on task resulted in greater lane deviation. A significant main effect of day (p < .001) but not time of day for the NASA-TLX, PVT and KSS was found. Time on task has a significant further impact on driving performance and should be considered alongside sleep deprivation and time of day when implementing strategies for long-distance driving.

Unravelling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks

Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just "when" but also "what" to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments.

Working around the Clock: Is a Person’s Endogenous Circadian Timing for Optimal Neurobehavioral Functioning Inherently Task-Dependent?

Neurobehavioral task performance is modulated by the circadian and homeostatic processes of sleep/wake regulation. Biomathematical modeling of the temporal dynamics of these processes and their interaction allows for prospective prediction of performance impairment in shift-workers and provides a basis for fatigue risk management in 24/7 operations. It has been reported, however, that the impact of the circadian rhythm—and in particular its timing—is inherently task-dependent, which would have profound implications for our understanding of the temporal dynamics of neurobehavioral functioning and the accuracy of biomathematical model predictions. We investigated this issue in a laboratory study designed to unambiguously dissociate the influences of the circadian and homeostatic processes on neurobehavioral performance, as measured during a constant routine protocol preceded by three days on either a simulated night shift or a simulated day shift schedule. Neurobehavioral functions were measured every 3 h using three functionally distinct assays: a digit symbol substitution test, a psychomotor vigilance test, and the Karolinska Sleepiness Scale. After dissociating the circadian and homeostatic influences and accounting for inter-individual variability, peak circadian performance occurred in the late biological afternoon (in the “wake maintenance zone”) for all three neurobehavioral assays. Our results are incongruent with the idea of inherent task-dependent differences in the endogenous circadian impact on performance. Rather, our results suggest that neurobehavioral functions are under top-down circadian control, consistent with the way they are accounted for in extant biomathematical models.

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.

Sleep in Normal Aging, Homeostatic and Circadian Regulation and Vulnerability to Sleep Deprivation

In the context of geriatric research, a growing body of evidence links normal age-related changes in sleep with many adverse health outcomes, especially a decline in cognition in older adults. The most important sleep alterations that continue to worsen after 60 years involve sleep timing, (especially early wake time, phase advance), sleep maintenance (continuity of sleep interrupted by numerous awakenings) and reduced amount of sigma activity (during non-rapid eye movement (NREM) sleep) associated with modifications of sleep spindle characteristics (density, amplitude, frequency) and spindle-Slow Wave coupling. After 60 years, there is a very clear gender-dependent deterioration in sleep. Even if there are degradations of sleep after 60 years, daytime wake level and especially daytime sleepiness is not modified with age. On the other hand, under sleep deprivation condition, older adults show smaller cognitive impairments than younger adults, suggesting an age-related lower vulnerability to extended wakefulness. These sleep and cognitive age-related modifications would be due to a reduced homeostatic drive and consequently a reduced sleep need, an attenuation of circadian drive (reduction of sleep forbidden zone in late afternoon and wake forbidden zone in early morning), a modification of the interaction of the circadian and homeostatic processes and/or an alteration of subcortical structures involved in generation of circadian and homeostatic drive, or connections to the cerebral cortex with age. The modifications and interactions of these two processes with age are still uncertain, and still require further investigation. The understanding of the respective contribution of circadian and homeostatic processes in the regulation of neurobehavioral function with aging present a challenge for improving health, management of cognitive decline and potential early chronobiological or sleep-wake interventions.

New insights into the cognitive effects of sleep deprivation by decomposition of a cognitive throughput task

STUDY OBJECTIVES

A cognitive throughput task known as the Digit Symbol Substitution Test (DSST) (or Symbol Digit Modalities Test) has been used as an assay of general cognitive slowing during sleep deprivation. Here, the effects of total sleep deprivation (TSD) on specific cognitive processes involved in DSST performance, including visual search, spatial memory, paired-associate learning, and motor response, were investigated through targeted task manipulations.

METHODS

A total of 12 DSST variants, designed to manipulate the use of specific cognitive processes, were implemented in two laboratory-based TSD studies with N = 59 and N = 26 subjects, respectively. In each study, the Psychomotor Vigilance Test (PVT) was administered alongside the DSST variants.

RESULTS

TSD reduced cognitive throughput on all DSST variants, with response time distributions exhibiting rightward skewing. All DSST variants showed practice effects, which were however minimized by inclusion of a pause between trials. Importantly, TSD-induced impairment on the DSST variants was not uniform, with a principal component analysis revealing three factors. Diffusion model decomposition of cognitive processes revealed that inter-individual differences during TSD on a two-alternative forced choice DSST variant were different from those on the PVT.

CONCLUSIONS

While reduced cognitive throughput has been interpreted to reflect general cognitive slowing, such TSD-induced impairment appears to reflect cognitive instability, like on the PVT, rather than general slowing. Further, comparisons between task variants revealed not one, but three distinct underlying processes impacted by sleep deprivation. Moreover, the practice effect on the task was found to be independent of the TSD effect and minimized by a task pacing manipulation.

Effects of partially automated driving on the development of driver sleepiness

The objective of this study was to compare the development of sleepiness during manual driving versus level 2 partially automated driving, when driving on a motorway in Sweden. The hypothesis was that partially automated driving will lead to higher levels of fatigue due to underload. Eighty-nine drivers were included in the study using a 2 × 2 design with the conditions manual versus partially automated driving and daytime (full sleep) versus night-time (sleep deprived). The results showed that night-time driving led to markedly increased levels of sleepiness in terms of subjective sleepiness ratings, blink durations, PERCLOS, pupil diameter and heart rate. Partially automated driving led to slightly higher subjective sleepiness ratings, longer blink durations, decreased pupil diameter, slower heart rate, and higher EEG alpha and theta activity. However, elevated levels of sleepiness mainly arose from the night-time drives when the sleep pressure was high. During daytime, when the drivers were alert, partially automated driving had little or no detrimental effects on driver fatigue. Whether the negative effects of increased sleepiness during partially automated driving can be compensated by the positive effects of lateral and longitudinal driving support needs to be investigated in further studies.

The association between eating frequency with alertness and gastrointestinal complaints in nurses during the night shift

We investigated the association of the number of eating occasions and energy intake with alertness and gastrointestinal (GI) complaints in nurses during their night shift. During this observational study we collected data on anthropometrics and demographics, eating frequency, energy intake, alertness and GI complaints in 118 healthy female nurses, aged 20 to 61 years. Nurses completed an alertness test (psychomotor vigilance task) during the night shift and a 24‐hr dietary recall and a questionnaire about GI complaints after the night shift. This was repeated three times, always on the first night shift in a night shift series. The number of eating occasions during the night shift was negatively associated with reaction times (β = −4.81 ms, 95% confidence interval [CI] −9.14 to −0.48; p = .030), and number of lapses (β = −0.04, 95% CI −0.07 to −0.00; p = .030). However, the number of eating occasions was not associated with subjective alertness and GI complaints. Energy intake during the night shift was not associated with objective or subjective alertness or with GI complaints. These associations were independent of caffeine intake, age, body mass index and dependence among the repeated measurements. The present study showed that eating frequency was positively associated with objectively measured alertness levels in female nurses during the night shift. The results need to be confirmed in an intervention study, where also timing, size and composition of the meal will be taken into account. In practice, optimising nutritional guidelines on these aspects could lead to faster responses, less (medical) errors, and a better wellbeing of night shift workers.

Fidelity of Information Processing on a Psychomotor Vigilance Task Predicts Changes in Self-Reported Sleepiness Ratings

INTRODUCTION

Daytime sleepiness is a common problem. Although sleepiness is primarily assessed at the self-report unit of analysis, factors that contribute to an individual's experience and report of sleepiness remain poorly understood. While sleepiness is known to impact vigilance performance, the impact of vigilance performance on sleepiness reports is less well understood. We aimed to explore how performance on a psychomotor vigilance task (PVT) relates to changes in self-reported sleepiness in a rested condition.

METHODS

Participants were 66 adults (Mdn=23, range 18-49 years old), 47% female, 88% white, with a wide range of insomnia symptoms. Participants rated their sleepiness on a scale from 1 (not sleepy) to 10 (extremely sleepy) at the start (pre) and the end (post) of a 10-minute computerized PVT. Ordinal regression determined whether mean reciprocal reaction time, a measure of overall performance, or the log-transformed signal-to-noise ratio (LSNR), a measure of fidelity of information processing, predicted post-sleepiness, adjusting for pre-sleepiness, insomnia, and potential confounds.

RESULTS

Lower LSNR predicted greater change in sleepiness (pre-to-post PVT) and higher post-sleepiness even after adjusting for pre-sleepiness, mean reciprocal reaction time, insomnia, and other potential confounds (p<0.05).

DISCUSSION

When adjusting for insomnia symptoms and potential confounds, participants with lower fidelity of information processing reported higher sleepiness than they had reported at the start of the PVT. Possible mechanisms and explanations are discussed in relation to a 3-factor model of sleep-wake states. This line of research may contribute to innovative approaches to assessing and treating sleepiness.

Local Sleep and Alzheimer's Disease Pathophysiology

Even prior to the onset of the prodromal stages of Alzheimer's disease (AD), a constellation of sleep disturbances are apparent. A series of epidemiological studies indicate that multiple forms of these sleep disturbances are associated with increased risk for developing mild cognitive impairment (MCI) and AD, even triggering disease onset at an earlier age. Through the combination of causal manipulation studies in humans and rodents, as well as targeted examination of sleep disturbance with respect to AD biomarkers, mechanisms linking sleep disturbance to AD are beginning to emerge. In this review, we explore recent evidence linking local deficits in brain oscillatory function during sleep with local AD pathological burden and circuit-level dysfunction and degeneration. In short, three deficits in the local expression of sleep oscillations have been identified in relation to AD pathophysiology: (1) frequency-specific frontal deficits in slow wave expression during non-rapid eye movement (NREM) sleep, (2) deficits in parietal sleep spindle expression, and (3) deficits in the quality of electroencephalographic (EEG) desynchrony characteristic of REM sleep. These deficits are noteworthy since they differ from that seen in normal aging, indicating the potential presence of an abnormal aging process. How each of these are associated with β-amyloid (Aβ) and tau pathology, as well as neurodegeneration of circuits sensitive to AD pathophysiology, are examined in the present review, with a focus on the role of dysfunction within fronto-hippocampal and subcortical sleep-wake circuits. It is hypothesized that each of these local sleep deficits arise from distinct network-specific dysfunctions driven by regionally-specific accumulation of AD pathologies, as well as their associated neurodegeneration. Overall, the evolution of these local sleep deficits offer unique windows into the circuit-specific progression of distinct AD pathophysiological processes prior to AD onset, as well as their impact on brain function. This includes the potential erosion of sleep-dependent memory mechanisms, which may contribute to memory decline in AD. This review closes with a discussion of the remaining critical knowledge gaps and implications of this work for future mechanistic studies and studies implementing sleep-based treatment interventions.

Sleep and circadian rhythms: Evolutionary entanglement and local regulation

Circadian rhythms evolved within single cell organisms and serve to regulate rest-activity cycles in most single-cell and multiple-cell organisms. In contrast, sleep is a network emergent property found in animals with a nervous system. Rhythms and sleep are much entangled involving shared regulatory molecules such as adenosine, ATP, cytokines, neurotrophins, and nitric oxide. These molecules are activity-dependent and act locally to initiate regulatory events involved in rhythms, sleep, and plasticity.

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

The assessment and prediction of cognitive performance is a key issue for any discipline concerned with human operators in the context of safety-critical behavior. Most of the research has focused on the measurement of mental workload but this construct remains difficult to operationalize despite decades of research on the topic. Recent advances in Neuroergonomics have expanded our understanding of neurocognitive processes across different operational domains. We provide a framework to disentangle those neural mechanisms that underpin the relationship between task demand, arousal, mental workload and human performance. This approach advocates targeting those specific mental states that precede a reduction of performance efficacy. A number of undesirable neurocognitive states (mind wandering, effort withdrawal, perseveration, inattentional phenomena) are identified and mapped within a two-dimensional conceptual space encompassing task engagement and arousal. We argue that monitoring the prefrontal cortex and its deactivation can index a generic shift from a nominal operational state to an impaired one where performance is likely to degrade. Neurophysiological, physiological and behavioral markers that specifically account for these states are identified. We then propose a typology of neuroadaptive countermeasures to mitigate these undesirable mental states.

Sleep deprivation, vigilant attention, and brain function: a review

Vigilant attention is a major component of a wide range of cognitive performance tasks. Vigilant attention is impaired by sleep deprivation and restored after rest breaks and (more enduringly) after sleep. The temporal dynamics of vigilant attention deficits across hours and days are driven by physiologic, sleep regulatory processes-a sleep homeostatic process and a circadian process. There is also evidence of a slower, allostatic process, which modulates the sleep homeostatic setpoint across days and weeks and is responsible for cumulative deficits in vigilant attention across consecutive days of sleep restriction. There are large inter-individual differences in vulnerability to sleep loss, and these inter-individual differences constitute a pronounced human phenotype. However, this phenotype is multi-dimensional; vulnerability in terms of vigilant attention impairment can be dissociated from vulnerability in terms of other cognitive processes such as attentional control. The vigilance decrement, or time-on-task effect-a decline in performance across the duration of a vigilant attention task-is characterized by progressively increasing response variability, which is exacerbated by sleep loss. This variability, while crucial to understanding the impact of sleep deprivation on performance in safety-critical tasks, is not well explained by top-down regulatory mechanisms, such as the homeostatic and circadian processes. A bottom-up, neuronal pathway-dependent mechanism involving use-dependent, local sleep may be the main driver of response variability. This bottom-up mechanism may also explain the dissociation between cognitive processes with regard to trait vulnerability to sleep loss.

Drowsiness measures for commercial motor vehicle operations

Timely detection of drowsiness in Commercial Motor Vehicle (C MV) operations is necessary to reduce drowsiness-related CMV crashes. This is relevant for manual driving and, paradoxically, even more so with increasing levels of driving automation. Measures available for drowsiness detection vary in reliability, validity, usability, and effectiveness. Passively recorded physiologic measures such as electroencephalography (EEG) and a variety of ocular parameters tend to accurately identify states of considerable drowsiness, but are limited in their potential to detect lower levels of drowsiness. They also do not correlate well with measures of driver performance. Objective measures of vigilant attention performance capture drowsiness reliably, but they require active driver involvement in a performance task and are prone to confounds from distraction and (lack of) motivation. Embedded performance measures of actual driving, such as lane deviation, have been found to correlate with physiologic and vigilance performance measures, yet to what extent drowsiness levels can be derived from them reliably remains a topic of investigation. Transient effects from external circumstances and behaviors - such as task load, light exposure, physical activity, and caffeine intake - may mask a driver's underlying state of drowsiness. Also, drivers differ in the degree to which drowsiness affects their driving performance, based on trait vulnerability as well as age. This paper provides a broad overview of the current science pertinent to a range of drowsiness measures, with an emphasis on those that may be most relevant for CMV operations. There is a need for smart technologies that in a transparent manner combine different measurement modalities with mathematical representations of the neurobiological processes driving drowsiness, that account for various mediators and confounds, and that are appropriately adapted to the individual driver. The research for and development of such technologies requires a multi-disciplinary approach and significant resources, but is technically within reach.

Mind-wandering, or the allocation of attentional resources, is sleep-driven across childhood

Mind-wandering or the spontaneous, uncontrolled changes in the allocation of attention resources (lapses) may cause variability in performance. In childhood, the relationship between the activation state of the brain, such as in attentional performance, and sleep has not been explored in detail. We investigated the role of sleep in attentional performance, and explored the most important parameters of their relationship. We objectively measured momentary lapses of attention of 522 children and correlated them with sleep schedules. In the subgroup of young children (age 7.1 ± 0.6 years; 60.8% girls), increasing age, long sleep duration and assessment closer to the previous night’s sleep period was associated with impaired performance speed and consistency. From pre-adolescence (age 9.4 ± 0.8 years; 50.5% girls) onwards somno-typologies may develop. As a result, in adolescence (age 13.4 ± 1.2 years; 51.3% girls) not only sleep duration but also sleep midpoint and sleep regularity influence the individual speed and stability of attention. Across development, regularity of sleep, individual sleep midpoint and bedtime become increasingly important for optimal performance throughout the day. Attentional performance and sleep shared almost half of their variance, and performance was sleep-driven across childhood. Future studies should focus on intra- and inter-individual differences in sleep-wake behavior to improve performance or decrease mind-wandering in youth by targeting sleep habits.

Time-on-Task Effect During Sleep Deprivation in Healthy Young Adults Is Modulated by Dopamine Transporter Genotype

Study Objectives

The time-on-task (TOT) effect and total sleep deprivation (TSD) have similar effects on neurobehavioral functioning, including increased performance instability during tasks requiring sustained attention. The TOT effect is exacerbated by TSD, suggesting potentially overlapping mechanisms. We probed these mechanisms by investigating genotype-phenotype relationships on psychomotor vigilance test (PVT) performance for 3 a-priori selected genes previously linked to the TOT effect and/or TSD: dopamine active transporter 1 (DAT1), catechol-O-methyltransferase (COMT), and tumor necrosis factor alpha (TNFα).

Methods

N = 82 healthy adults participated in 1 of 3 laboratory studies. A 10-min PVT was administered repeatedly during 38 h of TSD. We assessed changes in response time (RT) across each minute of the PVT as a function of time awake and genotype. Additionally, cumulative relative RT frequency distributions were constructed to examine changes in performance from the first to the second 5 min of the PVT as a function of genotype.

Results

DAT1, COMT, and TNFα were associated with differences in the build-up of the TOT effect across the 10-min PVT. DAT1 additionally modulated the interaction between TSD and the TOT effect. Subjects homozygous for the DAT1 10-repeat allele were relatively protected against TOT deficits on the PVT during TSD compared to carriers of the 9-repeat allele.

Conclusions

DAT1 is known to regulate dopamine reuptake and is highly expressed in the striatum. Our results implicate striatal dopamine in mechanisms involved in performance instability that appear to be common to TSD and the TOT effect. Furthermore, DAT1 may be a candidate biomarker of resilience to the build-up of performance impairment across TOT due to TSD.

Local sleep

The historic sleep regulatory paradigm invokes "top-down" imposition of sleep on the brain by sleep regulatory circuits. While remaining conceptually useful, many sleep phenomena are difficult to explain using that paradigm, including, unilateral sleep, sleep-walking, and poor performance after sleep deprivation. Further, all animals sleep after non-lethal brain lesions, regardless of whether the lesion includes sleep regulatory circuits, suggesting that sleep is a fundamental property of small viable neuronal/glial networks. That small areas of the brain can exhibit non-rapid eye movement sleep-like states is summarized. Further, sleep-like states in neuronal/glial cultures are described. The local sleep states, whether in vivo or in vitro, share electrophysiological properties and molecular regulatory components with whole animal sleep and exhibit sleep homeostasis. The molecular regulatory components of sleep are also involved in plasticity and inflammation. Like sleep, these processes, are initiated by local cell-activity dependent events, yet have at higher levels of tissue organization whole body functions. While there are large literatures dealing with local initiation and regulation of plasticity and inflammation, the literature surrounding local sleep is in its infancy and clinical applications of the local sleep concept are absent. Regardless, the local use-dependent sleep paradigm can advise and advance future research and clinical applications.

Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data

The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.

An improved model to predict performance under mental fatigue

Fatigue has become an increasing problem in our modern society. Using MATLAB as a generic modelling tool, a fatigue model was developed based on an existing one and compared with a commercial fatigue software for prediction of cognitive performance under total and partial sleep deprivation. The flexibility of our fatigue model allowed additions of new algorithms and mechanisms for non-sleep factors and countermeasures and thus improved model predictions and usability for both civilian and military applications. This was demonstrated by model simulations of various scenarios and comparison with experimental studies. Our future work will be focused on model validation and integration with other modelling tools. Practitioner Summary: Mental fatigue affects health, safety and quality of life in our modern society. In this paper, we reported a cognitive fatigue model based on existing models with newly incorporated components taking both the operator's state of alertness and task demand into account. The model provided the additional capability for prediction of cognitive performance in scenarios involving pharmaceutical countermeasures, different task demands and shift work.

Computational cognitive modeling of the temporal dynamics of fatigue from sleep loss

Computational models have become common tools in psychology. They provide quantitative instantiations of theories that seek to explain the functioning of the human mind. In this paper, we focus on identifying deep theoretical similarities between two very different models. Both models are concerned with how fatigue from sleep loss impacts cognitive processing. The first is based on the diffusion model and posits that fatigue decreases the drift rate of the diffusion process. The second is based on the Adaptive Control of Thought - Rational (ACT-R) cognitive architecture and posits that fatigue decreases the utility of candidate actions leading to microlapses in cognitive processing. A biomathematical model of fatigue is used to control drift rate in the first account and utility in the second. We investigated the predicted response time distributions of these two integrated computational cognitive models for performance on a psychomotor vigilance test under conditions of total sleep deprivation, simulated shift work, and sustained sleep restriction. The models generated equivalent predictions of response time distributions with excellent goodness-of-fit to the human data. More importantly, although the accounts involve different modeling approaches and levels of abstraction, they represent the effects of fatigue in a functionally equivalent way: in both, fatigue decreases the signal-to-noise ratio in decision processes and decreases response inhibition. This convergence suggests that sleep loss impairs psychomotor vigilance performance through degradation of the quality of cognitive processing, which provides a foundation for systematic investigation of the effects of sleep loss on other aspects of cognition. Our findings illustrate the value of treating different modeling formalisms as vehicles for discovery.

Circadian and Homeostatic Modulation of Multi-Unit Activity in Midbrain Dopaminergic Structures

Although the link between sleep disturbances and dopamine (DA)-related neurological and neuropsychiatric disorders is well established, the impact of sleep alterations on neuronal activity of midbrain DA-ergic structures is currently unknown. Here, using wildtype C57Bl mice, we investigated the circadian- and sleep-related modulation of electrical neuronal activity in midbrain ventral-tegmental-area (VTA) and substantia nigra (SN). We found no significant circadian modulation of activity in SN while VTA displayed a low amplitude but significant circadian modulation with increased firing rates during the active phase. Combining neural activity recordings with electroencephalogram (EEG) recordings revealed a strong vigilance state dependent modulation of neuronal activity with increased activity during wakefulness and rapid eye movement sleep relative to non-rapid eye movement sleep in both SN and VTA. Six-hours of sleep deprivation induced a significant depression of neuronal activity in both areas. Surprisingly, these alterations lasted for up to 48 hours and persisted even after the normalization of cortical EEG waves. Our results show that sleep and sleep disturbances significantly affect neuronal activity in midbrain DA structures. We propose that these changes in neuronal activity underlie the well-known relationship between sleep alterations and several disorders involving dysfunction of the DA circuitry such as addiction and depression.

Exploring gene expression biomarker candidates for neurobehavioral impairment from total sleep deprivation

Background

Although sleep deprivation is associated with neurobehavioral impairment that may underlie significant risks to performance and safety, there is no reliable biomarker test to detect dangerous levels of impairment from sleep loss in humans. This study employs microarrays and bioinformatics analyses to explore candidate gene expression biomarkers associated with total sleep deprivation (TSD), and more specifically, the phenotype of neurobehavioral impairment from TSD. Healthy adult volunteers were recruited to a sleep laboratory for seven consecutive days (six nights). After two Baseline nights of 10 h time in bed, 11 subjects underwent an Experimental phase of 62 h of continuous wakefulness, followed by two Recovery nights of 10 h time in bed. Another six subjects underwent a well-rested Control condition of 10 h time in bed for all six nights. Blood was drawn for measuring gene expression on days two, four, and six at 4 h intervals from 08:00 to 20:00 h, corresponding to 12 timepoints across one Baseline, one Experimental, and one Recovery day.

Results

Altogether 212 genes changed expression in response to the TSD Treatment, with most genes exhibiting down-regulation during TSD. Also, 28 genes were associated with neurobehavioral impairment as measured by the Psychomotor Vigilance Test. The results support previous findings associating TSD with the immune response and ion signaling, and reveal novel candidate biomarkers such as the Speedy/RINGO family of cell cycle regulators.

Conclusions

This study serves as an important step toward understanding gene expression changes during sleep deprivation. In addition to exploring potential biomarkers for TSD, this report presents novel candidate biomarkers associated with lapses of attention during TSD. Although further work is required for biomarker validation, analysis of these genes may aid fundamental understanding of the impact of TSD on neurobehavioral performance.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4664-3) contains supplementary material, which is available to authorized users.

Intraindividual Increase of Homeostatic Sleep Pressure Across Acute and Chronic Sleep Loss: A High-Density EEG Study

Study Objectives

To compare intraindividually the effects of acute sleep deprivation (ASD) and chronic sleep restriction (CSR) on the homeostatic increase in slow wave activity (SWA) and to relate it to impairments in basic cognitive functioning, that is, vigilance.

Methods

The increase in SWA after ASD (40 hours of wakefulness) and after CSR (seven nights with time in bed restricted to 5 hours per night) relative to baseline sleep was assessed in nine healthy, male participants (age = 18-26 years) by high-density electroencephalography. The SWA increase during the initial part of sleep was compared between the two conditions of sleep loss. The increase in SWA was related to the increase in lapses of vigilance in the psychomotor vigilance task (PVT) during the preceding days.

Results

While ASD induced a stronger increase in initial SWA than CSR, the increase was globally correlated across the two conditions in most electrodes. The increase in initial SWA was positively associated with the increase in PVT lapses.

Conclusions

The individual homeostatic response in SWA is globally preserved across acute and chronic sleep loss, that is, individuals showing a larger increase after ASD also do so after CSR and vice versa. Furthermore, the increase in SWA is globally correlated to vigilance impairments after sleep loss over both conditions. Thus, the increase in SWA might therefore provide a physiological marker for individual differences in performance impairments after sleep loss.

Local Use-Dependent Sleep in Wakefulness Links Performance Errors to Learning

Sleep and wakefulness are no longer to be considered as discrete states. During wakefulness brain regions can enter a sleep-like state (off-periods) in response to a prolonged period of activity (local use-dependent sleep). Similarly, during nonREM sleep the slow-wave activity, the hallmark of sleep plasticity, increases locally in brain regions previously involved in a learning task. Recent studies have demonstrated that behavioral performance may be impaired by off-periods in wake in task-related regions. However, the relation between off-periods in wake, related performance errors and learning is still untested in humans. Here, by employing high density electroencephalographic (hd-EEG) recordings, we investigated local use-dependent sleep in wake, asking participants to repeat continuously two intensive spatial navigation tasks. Critically, one task relied on previous map learning (Wayfinding) while the other did not (Control). Behaviorally awake participants, who were not sleep deprived, showed progressive increments of delta activity only during the learning-based spatial navigation task. As shown by source localization, delta activity was mainly localized in the left parietal and bilateral frontal cortices, all regions known to be engaged in spatial navigation tasks. Moreover, during the Wayfinding task, these increments of delta power were specifically associated with errors, whose probability of occurrence was significantly higher compared to the Control task. Unlike the Wayfinding task, during the Control task neither delta activity nor the number of errors increased progressively. Furthermore, during the Wayfinding task, both the number and the amplitude of individual delta waves, as indexes of neuronal silence in wake (off-periods), were significantly higher during errors than hits. Finally, a path analysis linked the use of the spatial navigation circuits undergone to learning plasticity to off periods in wake. In conclusion, local sleep regulation in wakefulness, associated with performance failures, could be functionally linked to learning-related cortical plasticity.

The effects of sleep deprivation on item and associative recognition memory

Sleep deprivation adversely affects the ability to perform cognitive tasks, but theories range from predicting an overall decline in cognitive functioning because of reduced stability in attentional networks to specific deficits in various cognitive domains or processes. We measured the effects of sleep deprivation on two memory tasks, item recognition ("was this word in the list studied") and associative recognition ("were these two words studied in the same pair"). These tasks test memory for information encoded a few minutes earlier and so do not address effects of sleep deprivation on working memory or consolidation after sleep. A diffusion model was used to decompose accuracy and response time distributions to produce parameter estimates of components of cognitive processing. The model assumes that over time, noisy evidence from the task stimulus is accumulated to one of two decision criteria, and parameters governing this process are extracted and interpreted in terms of distinct cognitive processes. Results showed that sleep deprivation reduces drift rate (evidence used in the decision process), with little effect on the other components of the decision process. These results contrast with the effects of aging, which show little decline in item recognition but large declines in associative recognition. The results suggest that sleep deprivation degrades the quality of information stored in memory and that this may occur through degraded attentional processes. (PsycINFO Database Record

3-minute smartphone-based and tablet-based psychomotor vigilance tests for the assessment of reduced alertness due to sleep deprivation

The psychomotor vigilance test (PVT) is widely used to measure reduced alertness due to sleep loss. Here, two newly developed, 3-min versions of the psychomotor vigilance test, one smartphone-based and the other tablet-based, were validated against a conventional 10-min laptop-based PVT. Sixteen healthy participants (ages 22-40; seven males, nine females) completed a laboratory study, which included a practice and a baseline day, a 38-h total sleep deprivation (TSD) period, and a recovery day, during which they performed the three different versions of the PVT every 3 h. For each version of the PVT, the number of lapses, mean response time (RT), and number of false starts showed statistically significant changes across the sleep deprivation and recovery days. The number of lapses on the laptop was significantly correlated with the numbers of lapses on the smartphone and tablet. The mean RTs were generally faster on the smartphone and tablet than on the laptop. All three versions of the PVT exhibited a time-on-task effect in RTs, modulated by time awake and time of day. False starts were relatively rare on all three PVTs. For the number of lapses, the effect sizes across 38 h of TSD were large for the laptop PVT and medium for the smartphone and tablet PVTs. These results indicate that the 3-min smartphone and tablet PVTs are valid instruments for measuring reduced alertness due to sleep deprivation and restored alertness following recovery sleep. The results also indicate that the loss of sensitivity on the 3-min PVTs may be mitigated by modifying the threshold defining lapses.

Further Evidence That Sleep Deprivation Effects and the Vigilance Decrement Are Functionally Equivalent: Comment on Altmann (2018)

Veksler and Gunzelmann (2018) argue that the vigilance decrement and the deleterious effects of sleep loss reflect functionally equivalent degradations in cognitive processing and performance. Our account is implemented in a cognitive architecture, where these factors produce breakdowns in goal-directed cognitive processing that we refer to as microlapses. Altmann (2018) raises a number of challenges to microlapses as a unified account of these deficits. Under scrutiny, however, the challenges do little to discredit the theory or conclusions in the original paper. In our response, we address the most serious challenges. In so doing, we provide additional support for the theory and mechanisms, and we highlight opportunities for extending their explanatory breadth.

Human brain patterns underlying vigilant attention: impact of sleep debt, circadian phase and attentional engagement

Sleepiness and cognitive function vary over the 24-h day due to circadian and sleep-wake-dependent mechanisms. However, the underlying cerebral hallmarks associated with these variations remain to be fully established. Using functional magnetic resonance imaging (fMRI), we investigated brain responses associated with circadian and homeostatic sleep-wake-driven dynamics of subjective sleepiness throughout day and night. Healthy volunteers regularly performed a psychomotor vigilance task (PVT) in the MR-scanner during a 40-h sleep deprivation (high sleep pressure) and a 40-h multiple nap protocol (low sleep pressure). When sleep deprived, arousal-promoting thalamic activation during optimal PVT performance paralleled the time course of subjective sleepiness with peaks at night and troughs on the subsequent day. Conversely, task-related cortical activation decreased when sleepiness increased as a consequence of higher sleep debt. Under low sleep pressure, we did not observe any significant temporal association between PVT-related brain activation and subjective sleepiness. Thus, a circadian modulation in brain correlates of vigilant attention was only detectable under high sleep pressure conditions. Our data indicate that circadian and sleep homeostatic processes impact on vigilant attention via specific mechanisms; mirrored in a decline of cortical resources under high sleep pressure, opposed by a subcortical “rescuing” at adverse circadian times.

Sleep- and Wake-Like States in Small Networks In Vivo and In Vitro

Wakefulness and sleep are highly complex and heterogeneous processes, involving multiple neurotransmitter systems and a sophisticated interplay between global and local networks of neurons and non-neuronal cells. Macroscopic approaches applied at the level of the whole organism, view sleep as a global behaviour and allow for investigation into aspects such as the effects of insufficient or disrupted sleep on cognitive function, metabolism, thermoregulation and sensory processing. While significant progress has been achieved using such large-scale approaches, the inherent complexity of sleep-wake regulation has necessitated the development of methods which tackle specific aspects of sleep in isolation. One way this may be achieved is by investigating specific cellular or molecular phenomena in the whole organism in situ, either during spontaneous or induced sleep-wake states. This approach has greatly advanced our knowledge about the electrophysiology and pharmacology of ion channels, specific receptors, intracellular pathways and the small networks implicated in the control and regulation of the sleep-wake cycle. Importantly though, there are a variety of external and internal factors that influence global behavioural states which are difficult to control for using these approaches. For this reason, over the last few decades, ex vivo experimental models have become increasingly popular and have greatly advanced our understanding of many fundamental aspects of sleep, including the neuroanatomy and neurochemistry of sleep states, sleep regulation, the origin and dynamics of specific sleep oscillations, network homeostasis as well as the functional roles of sleep. This chapter will focus on the use of small neuronal networks as experimental models and will highlight the most significant and novel insights these approaches have provided.

Catechol-O-methyltransferase (COMT) genotype affects cognitive control during total sleep deprivation

Adaptive decision making is profoundly impaired by total sleep deprivation (TSD). This suggests that TSD impacts fronto-striatal pathways involved in cognitive control, where dopamine is a key neuromodulator. In the prefrontal cortex (PFC), dopamine is catabolized by the enzyme catechol-O-methyltransferase (COMT). A functional polymorphism (Val158Met) influences COMT's enzymatic activity, resulting in markedly different levels of prefrontal dopamine. We investigated the effect of this polymorphism on adaptive decision making during TSD. Sixty-six healthy young adults participated in one of two in-laboratory studies. After a baseline day, subjects were randomized to either a TSD group (n = 32) with 38 h or 62 h of extended wakefulness or a well-rested control group (n = 34) with 10 h nighttime sleep opportunities. Subjects performed a go/no-go reversal learning (GNGr) task at well-rested baseline and again during TSD or equivalent control. During the task, subjects were required to learn stimulus-response relationships from accuracy feedback. The stimulus-response relationships were reversed halfway through the task, which required subjects to learn the new stimulus-response relationships from accuracy feedback. Performance on the GNGr task was quantified by discriminability (d') between go and no-go stimuli before and after the stimulus-response reversal. GNGr performance did not differ between COMT genotypes when subjects were well-rested. However, TSD exposed a significant vulnerability to adaptive decision making impairment in subjects with the Val allele. Our results indicate that sleep deprivation degrades cognitive control through a fronto-striatal, dopaminergic mechanism.

Modeling the adenosine system as a modulator of cognitive performance and sleep patterns during sleep restriction and recovery

Sleep loss causes profound cognitive impairments and increases the concentrations of adenosine and adenosine A1 receptors in specific regions of the brain. Time courses for performance impairment and recovery differ between acute and chronic sleep loss, but the physiological basis for these time courses is unknown. Adenosine has been implicated in pathways that generate sleepiness and cognitive impairments, but existing mathematical models of sleep and cognitive performance do not explicitly include adenosine. Here, we developed a novel receptor-ligand model of the adenosine system to test the hypothesis that changes in both adenosine and A1 receptor concentrations can capture changes in cognitive performance during acute sleep deprivation (one prolonged wake episode), chronic sleep restriction (multiple nights with insufficient sleep), and subsequent recovery. Parameter values were estimated using biochemical data and reaction time performance on the psychomotor vigilance test (PVT). The model closely fit group-average PVT data during acute sleep deprivation, chronic sleep restriction, and recovery. We tested the model's ability to reproduce timing and duration of sleep in a separate experiment where individuals were permitted to sleep for up to 14 hours per day for 28 days. The model accurately reproduced these data, and also correctly predicted the possible emergence of a split sleep pattern (two distinct sleep episodes) under these experimental conditions. Our findings provide a physiologically plausible explanation for observed changes in cognitive performance and sleep during sleep loss and recovery, as well as a new approach for predicting sleep and cognitive performance under planned schedules.

Long-term effects of sleep deprivation on neuronal activity in four hypothalamic areas

Lack of adequate sleep has become increasingly common in our 24/7 society. Unfortunately diminished sleep has significant health consequences including metabolic and cardiovascular disease and mental disorders including depression. The pathways by which reduced sleep adversely affects physiology and behavior are unknown. We found that 6h of sleep deprivation in adult male rats induces changes in neuronal activity in the lateral hypothalamus, the paraventricular nucleus, the arcuate nucleus and the mammillary bodies. Surprisingly, these alterations last for up to 48h. The data show that sleep loss has prolonged effects on the activity of multiple hypothalamic areas. Our data indicate also that measuring electroencephalographic slow wave activity underestimates the amount of time that the hypothalamus requires to recover from episodes of sleep deprivation. We propose that these hypothalamic changes underlie the well-established relationship between sleep loss and several diseases such as metabolic disorders, stress and depression and that sufficient sleep is vital for autonomic functions controlled by the hypothalamus.

Sleep homeostasis, habits and habituation

The importance of sleep for behavioural performance during waking is long-established, but the underlying reasons and mechanisms remain elusive. Waking and sleep are associated with changes in the levels of GluA1 AMPAR subunit in synaptic membranes, while studies using genetically-modified mice have identified an important role for GluA1-dependent synaptic plasticity in a non-associative form of memory that underlies short-term habituation to recently experienced stimuli. Here we posit that sleep may play a role in dishabituation, which restores attentional capacity and maximises the readiness of the animal for learning and goal-directed behaviour during subsequent wakefulness. Furthermore we suggest that sleep disturbance may fundamentally change the nature of behaviour, making it more model-free and habitual as a result of reduced attentional capacity.

The sleep-deprived human brain

How does a lack of sleep affect our brains? In contrast to the benefits of sleep, frameworks exploring the impact of sleep loss are relatively lacking. Importantly, the effects of sleep deprivation (SD) do not simply reflect the absence of sleep and the benefits attributed to it; rather, they reflect the consequences of several additional factors, including extended wakefulness. With a focus on neuroimaging studies, we review the consequences of SD on attention and working memory, positive and negative emotion, and hippocampal learning. We explore how this evidence informs our mechanistic understanding of the known changes in cognition and emotion associated with SD, and the insights it provides regarding clinical conditions associated with sleep disruption.

Sleep function: Toward elucidating an enigma

Sleep function remains controversial. Individual perspectives frame the issue of sleep function differently. We briefly illustrate how sleep measurement and the evolution, tissue organization levels, molecular mechanisms, and regulation of sleep could influence one's view of sleep function. Then we discuss six viable theories of sleep function. Sleep serves host-defense mechanisms and conserves caloric expenditures, but these functions likely are opportunistic functions evolving later in evolution. That sleep replenishes brain energy stores and that sleep serves a glymphatic function by removing toxic byproducts of waking activity are attractive ideas, but lack extensive supporting experimental evidence. That sleep restores performance is experimentally demonstrated and has obvious evolutionary value. However, this hypothesis lacks experimentally verified mechanisms although ideas relating to this issue are presented. Finally, the ideas surrounding the broad hypothesis that sleep serves a connectivity/plasticity function are many and attractive. There is experimental evidence that connectivity changes with sleep, sleep loss, and with changing afferent input, and that those changes are linked to sleep regulatory mechanisms. In our view, this is the leading contender for the primordial function of sleep. However, much refinement of ideas and innovative experimental approaches are needed to clarify the sleep-connectivity relationship.

The Slow Oscillation in Cortical and Thalamic Networks: Mechanisms and Functions

During even the most quiescent behavioral periods, the cortex and thalamus express rich spontaneous activity in the form of slow (<1 Hz), synchronous network state transitions. Throughout this so-called slow oscillation, cortical and thalamic neurons fluctuate between periods of intense synaptic activity (Up states) and almost complete silence (Down states). The two decades since the original characterization of the slow oscillation in the cortex and thalamus have seen considerable advances in deciphering the cellular and network mechanisms associated with this pervasive phenomenon. There are, nevertheless, many questions regarding the slow oscillation that await more thorough illumination, particularly the mechanisms by which Up states initiate and terminate, the functional role of the rhythmic activity cycles in unconscious or minimally conscious states, and the precise relation between Up states and the activated states associated with waking behavior. Given the substantial advances in multineuronal recording and imaging methods in both in vivo and in vitro preparations, the time is ripe to take stock of our current understanding of the slow oscillation and pave the way for future investigations of its mechanisms and functions. My aim in this Review is to provide a comprehensive account of the mechanisms and functions of the slow oscillation, and to suggest avenues for further exploration.

Prolonged wakefulness alters neuronal responsiveness to local electrical stimulation of the neocortex in awake rats

Prolonged wakefulness or a lack of sleep lead to cognitive deficits, but little is known about the underlying cellular mechanisms. We recently found that sleep deprivation affects spontaneous neuronal activity in the neocortex of sleeping and awake rats. While it is well known that synaptic responses are modulated by ongoing cortical activity, it remains unclear whether prolonged waking affects responsiveness of cortical neurons to incoming stimuli. By applying local electrical microstimulation to the frontal area of the neocortex, we found that after a 4 h period of waking the initial neuronal response in the contralateral frontal cortex was stronger and more synchronous, and was followed by a more profound inhibition of neuronal spiking as compared with the control condition. These changes in evoked activity suggest increased neuronal excitability and indicate that, after staying awake, cortical neurons become transiently bistable. We propose that some of the detrimental effects of sleep deprivation may be a result of altered neuronal responsiveness to incoming intrinsic and extrinsic inputs.

TNFα G308A polymorphism is associated with resilience to sleep deprivation-induced psychomotor vigilance performance impairment in healthy young adults

Cytokines such as TNFα play an integral role in sleep/wake regulation and have recently been hypothesized to be involved in cognitive impairment due to sleep deprivation. We examined the effect of a guanine to adenine substitution at position 308 in the TNFα gene (TNFα G308A) on psychomotor vigilance performance impairment during total sleep deprivation. A total of 88 healthy women and men (ages 22-40) participated in one of five laboratory total sleep deprivation experiments. Performance on a psychomotor vigilance test (PVT) was measured every 2-3h. The TNFα 308A allele, which is less common than the 308G allele, was associated with greater resilience to psychomotor vigilance performance impairment during total sleep deprivation (regardless of time of day), and also provided a small performance benefit at baseline. The effect of genotype on resilience persisted when controlling for between-subjects differences in age, gender, race/ethnicity, and baseline sleep duration. The TNFα G308A polymorphism predicted less than 10% of the overall between-subjects variance in performance impairment during sleep deprivation. Nonetheless, the differential effect of the polymorphism at the peak of performance impairment was more than 50% of median performance impairment at that time, which is sizeable compared to the effects of other genotypes reported in the literature. Our findings provided evidence for a role of TNFα in the effects of sleep deprivation on psychomotor vigilance performance. Furthermore, the TNFα G308A polymorphism may have predictive potential in a biomarker panel for the assessment of resilience to psychomotor vigilance performance impairment due to sleep deprivation.