Classifying attentional vulnerability to total sleep deprivation using baseline features of Psychomotor Vigilance Test performance

Individualised prediction of resilience and vulnerability to sleep loss using EEG features

It is well established that individuals differ in their response to sleep loss. However, existing methods to predict an individual's sleep-loss phenotype are not scalable or involve effort-dependent neurobehavioural tests. To overcome these limitations, we sought to predict an individual's level of resilience or vulnerability to sleep loss using electroencephalographic (EEG) features obtained from routine night sleep. To this end, we retrospectively analysed five studies in which 96 healthy young adults (41 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge. After classifying subjects into sleep-loss phenotypic groups, we extracted two EEG features from the first sleep cycle (median duration: 1.6 h), slow-wave activity (SWA) power and SWA rise rate, from four channels during the baseline nights. Using these data, we developed two sets of logistic regression classifiers (resilient versus not-resilient and vulnerable versus not-vulnerable) to predict the probability of sleep-loss resilience or vulnerability, respectively, and evaluated model performance using test datasets not used in model development. Consistently, the most predictive features came from the left cerebral hemisphere. For the resilient versus not-resilient classifiers, we obtained an average testing performance of 0.68 for the area under the receiver operating characteristic curve, 0.72 for accuracy, 0.50 for sensitivity, 0.84 for specificity, 0.61 for positive predictive value, and 3.59 for likelihood ratio. We obtained similar performance for the vulnerable versus not-vulnerable classifiers. These results indicate that logistic regression classifiers based on SWA power and SWA rise rate from routine night sleep can largely predict an individual's sleep-loss phenotype.

Cognitive performance during exposure to moderate normobaric hypoxia after sleep restriction: Relationship to physiological and stress biomarkers

INTRODUCTION

Exposure to moderate levels of simulated hypoxia has subtle cognitive effects relative to ground level, in healthy individuals. However, there are few data on the cognitive consequences of the combination of hypoxia and partial sleep deprivation, which is a classic military or civilian operational context. In this study, we tested the hypothesis that exposure to moderate hypoxia while sleep-restricted impairs several domains of cognition, and we also assessed physiological parameters and salivary concentrations of cortisol and alpha-amylase.

METHOD

Seventeen healthy males completed two sessions of cognitive tests (sustained attention using the PVT psychomotor vigilance task and executive functions using the Go-NoGo inhibition task and N-Back working memory task) after 30 min (T + 30') and 4 h (T + 240') of exposure in a normobaric hypoxic tent (FIO2 = 13.6 %, ≃ 3,500 m) (HY). This was completed after one night of sleep restriction (3 a.m. to 6 a.m. bedtime, SRHY) and one night of habitual sleep (10 p.m. to 6 a.m. bedtime, HSHY) (with cross-over randomization). The two nights sleep architecture and physiological parameters (oxygen saturation (SpO2) and heart rate (HR) during T + 30' and T + 240'sessions were analyzed. Salivary cortisol and alpha-amylase (sAA) concentrations were analyzed before hypoxia, after the T + 30' and T + 240' cognitive sessions, and after leaving the hypoxic tent.

RESULTS

Sustained attention (RT and number of lapses in the PVT) and executive functions (Go-NoGo and 1-Back and 2-Back parameters, as inhibition and working memory signatures) were impaired in the SRHY condition compared to HSHY. SpO2 and HR were higher after 4 h compared with 30 min of hypoxia in the HSHY condition, while only HR was statistically higher in the SRHY condition. In SRHY, salivary AA concentration was lower and cortisol was higher than in HSHY. A significant increase in sAA concentration is observed after the cognitive session at 4 h of hypoxia exposure compared to that at 30 min, only in the SRHY condition. There are significant positive correlations between reaction time and the corresponding heart rate (a non-invasive marker of physiological stress) for the executive tasks in the two sleep conditions. This was not observed for salivary levels of sAA and cortisol, respective reliable indicators of the sympathoadrenomedullary system and the hypothalamic-pituitary adrenocortical system.

CONCLUSION

Exposure to moderate normobaric hypoxia (≃ 3500 m / ≃ 11,500 ft simulated) after a single night of 3-hour sleep impairs cognitive performance after 30 min and 4 h of exposure. The key determinants and/or mechanism(s) responsible for cognitive impairment when exposed to moderate hypoxia with sleep restriction, particularly on the executive function, have yet to be elucidated.

Longitudinal single-subject neuroimaging study reveals effects of daily environmental, physiological, and lifestyle factors on functional brain connectivity

Our behavior and mental states are constantly shaped by our environment and experiences. However, little is known about the response of brain functional connectivity to environmental, physiological, and behavioral changes on different timescales, from days to months. This gives rise to an urgent need for longitudinal studies that collect high-frequency data. To this end, for a single subject, we collected 133 days of behavioral data with smartphones and wearables and performed 30 functional magnetic resonance imaging (fMRI) scans measuring attention, memory, resting state, and the effects of naturalistic stimuli. We find traces of past behavior and physiology in brain connectivity that extend up as far as 15 days. While sleep and physical activity relate to brain connectivity during cognitively demanding tasks, heart rate variability and respiration rate are more relevant for resting-state connectivity and movie-watching. This unique data set is openly accessible, offering an exceptional opportunity for further discoveries. Our results demonstrate that we should not study brain connectivity in isolation, but rather acknowledge its interdependence with the dynamics of the environment, changes in lifestyle, and short-term fluctuations such as transient illnesses or restless sleep. These results reflect a prolonged and sustained relationship between external factors and neural processes. Overall, precision mapping designs such as the one employed here can help to better understand intraindividual variability, which may explain some of the observed heterogeneity in fMRI findings. The integration of brain connectivity, physiology data and environmental cues will propel future environmental neuroscience research and support precision healthcare.

Electroencephalographic markers from routine sleep discriminate individuals who are vulnerable or resilient to sleep loss

Sleep loss impairs cognition; however, individuals differ in their response to sleep loss. Current methods to identify an individual's vulnerability to sleep loss involve time-consuming sleep-loss challenges and neurobehavioural tests. Here, we sought to identify electroencephalographic markers of sleep-loss vulnerability obtained from routine night sleep. We retrospectively analysed four studies in which 50 healthy young adults (21 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge. After classifying subjects as resilient or vulnerable to sleep loss, we extracted three electroencephalographic features from four channels during the baseline nights, evaluated the discriminatory power of these features using the first two studies (discovery), and assessed reproducibility of the results using the remaining two studies (reproducibility). In the discovery analysis, we found that, compared to resilient subjects, vulnerable subjects exhibited: (1) higher slow-wave activity power in channel O1 (p < 0.0042, corrected for multiple comparisons) and in channels O2 and C3 (p < 0.05, uncorrected); (2) higher slow-wave activity rise rate in channels O1 and O2 (p < 0.05, uncorrected); and (3) lower sleep spindle frequency in channels C3 and C4 (p < 0.05, uncorrected). Our reproducibility analysis confirmed the discovery results on slow-wave activity power and slow-wave activity rise rate, and for these two electroencephalographic features we observed consistent group-difference trends across all four channels in both analyses. The higher slow-wave activity power and slow-wave activity rise rate in vulnerable individuals suggest that they have a persistently higher sleep pressure under normal rested conditions.

A resting-state EEG dataset for sleep deprivation

To investigate the impact of sleep deprivation (SD) on mood, alertness, and resting-state electroencephalogram (EEG), we present an eyes-open resting-state EEG dataset. The dataset comprises EEG recordings and cognitive data from 71 participants undergoing two testing sessions: one involving SD and the other normal sleep. In each session, participants engaged in eyes-open resting-state EEG. The Psychomotor Vigilance Task (PVT) was employed for alertness measurement. Emotional and sleepiness were measured using Positive and Negative Affect Scale (PANAS) and Stanford Sleepiness Scale (SSS). Additionally, to examine the influence of individual sleep quality and traits on SD, Pittsburgh Sleep Quality Index (PSQI) and Buss-Perry Aggression Questionnaire (BPAQ) were utilized. This dataset's sharing may contribute to open EEG measurements in the field of SD.

Comparison of effects of modafinil and caffeine on fatigue-vulnerable and fatigue-resistant aircrew after a limited period of sleep deprivation

Introduction: Literature suggests pilots experience fatigue differently. So-called fatigue-resistant or -vulnerable individuals might also respond differently to countermeasures or stimulants. This study, which is part of a larger randomized controlled clinical trial, aims to investigate the effect of caffeine and modafinil on fatigue-resistant and -vulnerable pilots. Methods: This study included 32 healthy employees of the Royal Netherlands Air Force, who completed three test days, separated by at least 7 days. After a regular work day, the subjects were randomly administered either 300 mg caffeine, 200 mg modafinil or placebo at midnight. Hereafter the subjects performed the psychomotor vigilance test (PVT), vigilance and tracking test (VigTrack) and Stanford sleepiness scale (SSS) six times until 8 a.m. the next day. Subjects were ranked on the average number of lapses on the PVT during the placebo night and divided into three groups: fatigue-vulnerable (FVUL), -intermediate (FINT) and -resistant (FRES), with 11, 10 and 11 subjects in each group, respectively. Area under the curve (AUC) of the PVT, VigTrack and SSS during the test nights were calculated, which were used in univariate factorial analysis of variance (ANOVA). Tukey's HSD post hoc tests were used to differentiate between the groups. Results: A significant effect of treatment was found in the ANOVA of both PVT parameters, VigTrack mean reaction time and SSS. There was a statistically significant effect of fatigue group on all PVT parameters and VigTrack mean percentage omissions, where FINT and FRES scored better than FVUL. There was a significant interaction effect between treatment and fatigue group for PVT number of lapses. This is congruent for the AUC analyses in which for all parameters (except for the SSS) the performance of the FVUL group was consistently worse than that of the FINT and FRES groups. Discussion: This study demonstrates that the performance of individuals with different fatigue tolerances are differently affected by simulants after a limited period of sleep deprivation. The classification of fatigue tolerance through PVT lapses when sleep deprived seems to be able to predict this.

Psychomotor Vigilance Performance in Participants with Excessive Daytime Sleepiness in Obstructive Sleep Apnea or Narcolepsy Compared with SAFTE-FAST Model Predictions

INTRODUCTION

Excessive daytime sleepiness (EDS) associated with narcolepsy or obstructive sleep apnea (OSA) can impair vigilance/attention. Solriamfetol, a dopamine/norepinephrine reuptake inhibitor, is approved to treat EDS associated with narcolepsy (75-150 mg/day) or OSA (37.5-150 mg/day). The analysis reported here explored the use of the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model (used in transport industries to model performance based on accumulated sleep and circadian variability) as a substitute for healthy controls using psychomotor vigilance task (PVT) data collected during clinical studies.

METHODS

Data were analyzed from two phase 2 studies of solriamfetol in adults with OSA (NCT02806895, EudraCT 2015-003930-28) or narcolepsy (NCT02806908, EudraCT 2015-003931-36). Participants were randomly assigned 1:1 to solriamfetol 150 mg/day (3 days) followed by 300 mg/day (4 days), or placebo (7 days), then crossed over to the other treatment. Actual task effectiveness scores were calculated from average PVT inverse reaction time (pre-dose; 2 h post-dose; 6 h post-dose). Actigraphy-derived sleep intervals were used in SAFTE to determine modeled healthy control task effectiveness scores.

RESULTS

In participants with OSA (N = 31) on placebo or solriamfetol, actual and modeled healthy control task effectiveness did not differ at any time point. In participants with narcolepsy (N = 20) on placebo, actual task effectiveness at 2 h post-dose was lower than modeled healthy control task effectiveness (nominal P = 0.03), a difference not present with solriamfetol. There was no main effect of solriamfetol on actual or modeled healthy control task effectiveness across time points.

CONCLUSION

This study represents a novel application of the SAFTE biomathematical model to approximate healthy controls in sleep disorder research and provides valuable lessons that may optimize future research. Future studies should perform a priori power analyses for model-tested outcomes and use sleep measures that capture sleep fragmentation characteristic of sleep disorders for sleep input (e.g., total sleep time rather than time in bed).

TRIAL REGISTRATION

NCT02806895, EudraCT 2015-003930-28: A Randomized, Double-Blind, Placebo-Controlled, Crossover On-Road Driving Study Assessing the Effect of JZP-110 on Driving Performance in Subjects With Excessive Sleepiness Due to Obstructive Sleep Apnea. NCT02806908, EudraCT 2015-003931-36: A Randomized, Double-Blind, Placebo-Controlled, Crossover On-Road Driving Study Assessing the Effect of JZP-110 on Driving Performance in Subjects With Excessive Sleepiness Due to Narcolepsy.

Understanding the Need for Sleep to Improve Cognition

The restorative function of sleep is shaped by its duration, timing, continuity, subjective quality, and efficiency. Current sleep recommendations specify only nocturnal duration and have been largely derived from sleep self-reports that can be imprecise and miss relevant details. Sleep duration, preferred timing, and ability to withstand sleep deprivation are heritable traits whose expression may change with age and affect the optimal sleep prescription for an individual. Prevailing societal norms and circumstances related to work and relationships interact to influence sleep opportunity and quality. The value of allocating time for sleep is revealed by the impact of its restriction on behavior, functional brain imaging, sleep macrostructure, and late-life cognition. Augmentation of sleep slow oscillations and spindles have been proposed for enhancing sleep quality, but they inconsistently achieve their goal. Crafting bespoke sleep recommendations could benefit from large-scale, longitudinal collection of objective sleep data integrated with behavioral and self-reported data.

A brief assessment of eye blink drowsiness immediately prior to or following driving detects drowsiness related driving impairment

Drowsy driving is a major cause of fatal and serious injury motor vehicle accidents. The inability objectively to assess drowsiness has hindered the assessment of fitness to drive and the development of drowsy driving regulations. This study evaluated whether spontaneous eye blink parameters measured briefly pre- and post-drive could be used to detect drowsy driving impairment. Twelve healthy participants (6 female) drove an instrumented vehicle for 2 h on a closed-loop track during a rested (8-10 h awake) and an extended wake condition (32-34 h awake). Pre- and post-drive, the participants completed a 5 min eye blink task, a psychomotor vigilance task (PVT), and the Karolinska sleepiness scale (KSS). Whole drive impairment was defined as >3.5 lane departures per hour. Severe end of drive impairment was defined as ≥2 lane departures in the last 15 min. The pre-drive % of time with eyes closed best predicted the whole drive impairment (area under the curve [AUC] 0.87). KSS had similar prediction ability (AUC 0.85), while PVT reaction time (AUC 0.72) was less accurate. The composite eye blink parameter, the Johns drowsiness scale was the best retrospective detector of severe end of drive impairment (AUC 0.99). The PVT reaction time (AUC 0.92) and the KSS (AUC 0.93) were less accurate. Eye blink parameters detected drowsy driving impairment with an accuracy that was similar to, or marginally better than, PVT and KSS. As eye blink measures are simple to measure, are objective and have high accuracy, they present an ideal option for the assessment of fitness for duty and roadside drowsiness.

Altered effective connectivity of thalamus with vigilance impairments after sleep deprivation

The thalamus is an essential gating hub to relay brainstem ascending arousal signals to attention-related networks, including the frontal-parietal attention network and default mode network, which plays an important role in attentional maintenance. Research has proved that sleep loss leads to impairment of attentional performance by affecting neural connectivity between thalamic and attention-related cortical regions. However, the effective connectivity between thalamic and cortical areas in the resting state remains unclear after sleep deprivation. The present study aimed to investigate the effect of sleep deprivation on the effective connectivity between thalamic and cortical areas, and explored whether the alteration of the effective connectivity can predict vigilance impairment after sleep deprivation. We implemented resting-state functional magnetic resonance imaging with 31 participants under both normally-rested and sleep-deprivation conditions. The Granger causality analysis was used to investigate the alteration of effective connectivity between thalamic and cortical areas, and the psychomotor vigilance task was used to measure vigilance. Correlation analysis investigated the relationship between the alteration in effective connectivity and vigilance performance. Sleep deprivation significantly decreased the effective connectivity from the thalamus to the nodes in the default mode network, and significantly increased in the effective connectivity from the thalamus to the nodes in the frontal-parietal attention network. Critically, increased thalamus-parietal effective connectivity was correlated with decreased lapses. The findings indicated sleep deprivation induced a robust alteration of the communication from the sub-cortical to cortical regions. The alteration of thalamus-parietal effective connectivity was anti-correlated with sustained attentional impairment after sleep deprivation.

Left Ventricular Ejection Time Measured by Echocardiography Differentiates Neurobehavioral Resilience and Vulnerability to Sleep Loss and Stress

There are substantial individual differences (resilience and vulnerability) in performance resulting from sleep loss and psychosocial stress, but predictive potential biomarkers remain elusive. Similarly, marked changes in the cardiovascular system from sleep loss and stress include an increased risk for cardiovascular disease. It remains unknown whether key hemodynamic markers, including left ventricular ejection time (LVET), stroke volume (SV), heart rate (HR), cardiac index (CI), blood pressure (BP), and systemic vascular resistance index (SVRI), differ in resilient vs. vulnerable individuals and predict differential performance resilience with sleep loss and stress. We investigated for the first time whether the combination of total sleep deprivation (TSD) and psychological stress affected a comprehensive set of hemodynamic measures in healthy adults, and whether these measures differentiated neurobehavioral performance in resilient and vulnerable individuals. Thirty-two healthy adults (ages 27-53; 14 females) participated in a 5-day experiment in the Human Exploration Research Analog (HERA), a high-fidelity National Aeronautics and Space Administration (NASA) space analog isolation facility, consisting of two baseline nights, 39 h TSD, and two recovery nights. A modified Trier Social Stress Test induced psychological stress during TSD. Cardiovascular measure collection [SV, HR, CI, LVET, BP, and SVRI] and neurobehavioral performance testing (including a behavioral attention task and a rating of subjective sleepiness) occurred at six and 11 timepoints, respectively. Individuals with longer pre-study LVET (determined by a median split on pre-study LVET) tended to have poorer performance during TSD and stress. Resilient and vulnerable groups (determined by a median split on average TSD performance) showed significantly different profiles of SV, HR, CI, and LVET. Importantly, LVET at pre-study, but not other hemodynamic measures, reliably differentiated neurobehavioral performance during TSD and stress, and therefore may be a biomarker. Future studies should investigate whether the non-invasive marker, LVET, determines risk for adverse health outcomes.

Evaluation of Changes in Psychophysical Performance during the Afternoon Drop off in Work Capacity after the Exposure to Specific Color of Light

The aim of the study was to define whether changes in psychophysical performance will occur after the exposure to light of a specific color during the early afternoon decrease in work capacity. The evaluation of psychophysical performance was carried out on a group of 50 subjects using the following tools: Grandjean Scale, Attention and Perceptiveness Test (TUS), and GONOGO test. The study was performed for exposure to reference light, white light enriched by blue light (WBL), and white light enriched by red light (WRL). The analysis of psychophysical performance results indicates the positive influence of a specific color of light on different factors of psychophysical performance. Exposure to WRL among participants from the 22–34 subgroup contributed to an increase in the number of correct tests and the speed of work as well as a decrease in the number of mistakes, less boredom, and higher performance. The exposure to WBL among participants from the 55+ subgroup decreased the number of mistakes and reduced the response time. The results are consistent with the outcomes of previous research carried out on an international level, confirming that blue and red light are effective at increasing psychophysical performance. It was demonstrated that the psychophysical performance increases also when blue or red light is a significant component in the spectrum of white light.

Predictors of interindividual differences in vulnerability to neurobehavioral consequences of chronic partial sleep restriction

Interindividual differences in the neurobehavioral response to sleep loss are largely unexplained and phenotypic in nature. Numerous factors have been examined as predictors of differential response to sleep loss, but none have yielded a comprehensive view of the phenomenon. The present study examines the impact of baseline factors, habitual sleep-wake patterns, and homeostatic response to sleep loss on accrued deficits in psychomotor vigilance during chronic partial sleep restriction (SR), in a total of 306 healthy adults that participated in one of three independent laboratory studies. Findings indicate no significant impact of personality, academic intelligence, subjective reports of chronotype, sleepiness and fatigue, performance on working memory, and demographic factors such as sex, ethnicity, and body mass index, on neurobehavioral vulnerability to the negative effects of sleep loss. Only superior baseline performance on the psychomotor vigilance test and ability to sustain wakefulness on the maintenance of wakefulness test were associated with relative resilience to decrements in vigilant attention during SR. Interindividual differences in vulnerability to the effects of sleep loss were not accounted for by prior sleep history, habitual sleep patterns outside of the laboratory, baseline sleep architecture, or homeostatic sleep response during chronic partial SR. A recent theoretical model proposed that sleep-wake modulation may be influenced by competing internal and external demands which may promote wakefulness despite homeostatic and circadian signals for sleep under the right circumstances. Further research is warranted to examine the possibility of interindividual differences in the ability to prioritize external demands for wakefulness in the face of mounting pressure to sleep.

Prediction of shiftworker alertness, sleep, and circadian phase using a model of arousal dynamics constrained by shift schedules and light exposure

STUDY OBJECTIVES

The study aimed to, for the first time, (1) compare sleep, circadian phase, and alertness of intensive care unit (ICU) nurses working rotating shifts with those predicted by a model of arousal dynamics; and (2) investigate how different environmental constraints affect predictions and agreement with data.

METHODS

The model was used to simulate individual sleep-wake cycles, urinary 6-sulphatoxymelatonin (aMT6s) profiles, subjective sleepiness on the Karolinska Sleepiness Scale (KSS), and performance on a Psychomotor Vigilance Task (PVT) of 21 ICU nurses working day, evening, and night shifts. Combinations of individual shift schedules, forced wake time before/after work and lighting, were used as inputs to the model. Predictions were compared to empirical data. Simulations with self-reported sleep as an input were performed for comparison.

RESULTS

All input constraints produced similar prediction for KSS, with 56%-60% of KSS scores predicted within ±1 on a day and 48%-52% on a night shift. Accurate prediction of an individual's circadian phase required individualized light input. Combinations including light information predicted aMT6s acrophase within ±1 h of the study data for 65% and 35%-47% of nurses on diurnal and nocturnal schedules. Minute-by-minute sleep-wake state overlap between the model and the data was between 81 ± 6% and 87 ± 5% depending on choice of input constraint.

CONCLUSIONS

The use of individualized environmental constraints in the model of arousal dynamics allowed for accurate prediction of alertness, circadian phase, and sleep for more than half of the nurses. Individual differences in physiological parameters will need to be accounted for in the future to further improve predictions.

Concordance of multiple methods to define resiliency and vulnerability to sleep loss depends on Psychomotor Vigilance Test metric

STUDY OBJECTIVES

Sleep restriction (SR) and total sleep deprivation (TSD) reveal well-established individual differences in Psychomotor Vigilance Test (PVT) performance. While prior studies have used different methods to categorize such resiliency/vulnerability, none have systematically investigated whether these methods categorize individuals similarly.

METHODS

41 adults participated in a 13-day laboratory study consisting of 2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The PVT was administered every 2h during wakefulness. Three approaches (Raw Score [average SR performance], Change from Baseline [average SR minus average baseline performance], and Variance [intraindividual variance of SR performance]), and within each approach, six thresholds (±1 standard deviation and the best/worst performing 12.5%, 20%, 25%, 33%, and 50%) classified Resilient/Vulnerable groups. Kendall's tau-b correlations examined the concordance of group categorizations of approaches within and between PVT lapses and 1/reaction time (RT). Bias-corrected and accelerated bootstrapped t-tests compared group performance.

RESULTS

Correlations comparing the approaches ranged from moderate to perfect for lapses and zero to moderate for 1/RT. Defined by all approaches, the Resilient groups had significantly fewer lapses on nearly all study days. Defined by the Raw Score approach only, the Resilient groups had significantly faster 1/RT on all study days. Between-measures comparisons revealed significant correlations between the Raw Score approach for 1/RT and all approaches for lapses.

CONCLUSION

The three approaches defining vigilant attention resiliency/vulnerability to sleep loss resulted in groups comprised of similar individuals for PVT lapses but not for 1/RT. Thus, both method and metric selection for defining vigilant attention resiliency/vulnerability to sleep loss is critical.

Altered frontal connectivity after sleep deprivation predicts sustained attentional impairment: A resting-state functional magnetic resonance imaging study

A series of studies have shown that sleep loss impairs one's capability for sustained attention. However, the underlying neurobiological mechanism linking sleep loss with sustained attention has not been elucidated. The present study aimed to investigate the effect of sleep deprivation on the resting-state brain and explored whether the magnitude of vigilance impairment after acute sleep deprivation can be predicted by measures of spontaneous fluctuations and functional connectivity. We implemented resting-state functional magnetic resonance imaging with 42 participants under both normal sleep and 24-hr sleep-deprivation conditions. The amplitude of low-frequency fluctuations (ALFF) and functional connectivity was used to investigate the neurobiological change caused by sleep deprivation, and the psychomotor vigilance task (PVT) was used to measure sustained attention in each state. Correlation analysis was used to investigate the relationship between the change in ALFF/functional connectivity and vigilance performance. Sleep deprivation induced significant reductions in ALFF in default mode network nodes and frontal-parietal network nodes, while inducing significant increments of ALFF in the bilateral thalamus, motor cortex, and visual cortex. The increased ALFF in the visual cortex was correlated with increased PVT lapses. Critically, decreased frontal-thalamus connectivity was correlated with increased PVT lapses, while increased frontal-visual connectivity was correlated with increased PVT lapses. The findings indicated that acute sleep deprivation induced a robust alteration in the resting brain, and sustained attentional impairment after sleep deprivation could be predicted by altered frontal connectivity with crucial neural nodes of stimulus input, such as the thalamus and visual cortex.

Raw scores on subjective sleepiness, fatigue, and vigor metrics consistently define resilience and vulnerability to sleep loss

STUDY OBJECTIVES

Although trait-like individual differences in subjective responses to sleep restriction (SR) and total sleep deprivation (TSD) exist, reliable characterizations remain elusive. We comprehensively compared multiple methods for defining resilience and vulnerability by subjective metrics.

METHODS

41 adults participated in a 13-day experiment:2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue (POMS-F) and Vigor (POMS-V) were administered every 2h. Three approaches (Raw Score [average SR score], Change from Baseline [average SR minus average baseline score], and Variance [intraindividual SR score variance]), and six thresholds (±1 standard deviation, and the highest/lowest scoring 12.5%, 20%, 25%, 33%, 50%) categorized Resilient/Vulnerable groups. Kendall's tau-b correlations compared the group categorization's concordance within and between KSS, POMS-F, and POMS-V scores. Bias-corrected and accelerated bootstrapped t-tests compared group scores.

RESULTS

There were significant correlations between all approaches at all thresholds for POMS-F, between Raw Score and Change from Baseline approaches for KSS, and between Raw Score and Variance approaches for POMS-V. All Resilient groups defined by the Raw Score approach had significantly better scores throughout the study, notably including during baseline and recovery, whereas the two other approaches differed by measure, threshold, or day. Between-measure correlations varied in strength by measure, approach, or threshold.

CONCLUSION

Only the Raw Score approach consistently distinguished Resilient/Vulnerable groups at baseline, during sleep loss, and during recovery‒‒we recommend this approach as an effective method for subjective resilience/vulnerability categorization. All approaches created comparable categorizations for fatigue, some were comparable for sleepiness, and none were comparable for vigor. Fatigue and vigor captured resilience/vulnerability similarly to sleepiness but not each other.

Genetic Markers of Differential Vulnerability to Sleep Loss in Adults

In this review, we discuss reports of genotype-dependent interindividual differences in phenotypic neurobehavioral responses to total sleep deprivation or sleep restriction. We highlight the importance of using the candidate gene approach to further elucidate differential resilience and vulnerability to sleep deprivation in humans, although we acknowledge that other omics techniques and genome-wide association studies can also offer insights into biomarkers of such vulnerability. Specifically, we discuss polymorphisms in adenosinergic genes (ADA and ADORA2A), core circadian clock genes (BHLHE41/DEC2 and PER3), genes related to cognitive development and functioning (BDNF and COMT), dopaminergic genes (DRD2 and DAT), and immune and clearance genes (AQP4, DQB1*0602, and TNFα) as potential genetic indicators of differential vulnerability to deficits induced by sleep loss. Additionally, we review the efficacy of several countermeasures for the neurobehavioral impairments induced by sleep loss, including banking sleep, recovery sleep, caffeine, and naps. The discovery of reliable, novel genetic markers of differential vulnerability to sleep loss has critical implications for future research involving predictors, countermeasures, and treatments in the field of sleep and circadian science.

Cognitive throughput and working memory raw scores consistently differentiate resilient and vulnerable groups to sleep loss

STUDY OBJECTIVES

Substantial individual differences exist in cognitive deficits due to sleep restriction (SR) and total sleep deprivation (TSD), with various methods used to define such neurobehavioral differences. We comprehensively compared numerous methods for defining cognitive throughput and working memory resiliency and vulnerability.

METHODS

41 adults participated in a 13-day experiment: 2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The Digit Symbol Substitution Test (DSST) and Digit Span Test (DS) were administered every 2h. Three approaches (Raw Score [average SR performance], Change from Baseline [average SR minus average baseline performance], and Variance [intraindividual variance of SR performance]), and six thresholds (±1 standard deviation, and the best/worst performing 12.5%, 20%, 25%, 33%, 50%) classified Resilient/Vulnerable groups. Kendall's tau-b correlations compared the group categorizations' concordance within and between DSST number correct and DS total number correct. Bias-corrected and accelerated bootstrapped t-tests compared group performance. .

RESULTS

The approaches generally did not categorize the same participants into Resilient/Vulnerable groups within or between measures. The Resilient groups categorized by the Raw Score approach had significantly better DSST and DS performance across all thresholds on all study days, while the Resilient groups categorized by the Change from Baseline approach had significantly better DSST and DS performance for several thresholds on most study days. By contrast, the Variance approach showed no significant DSST and DS performance group differences.

CONCLUSION

Various approaches to define cognitive throughput and working memory resilience/vulnerability to sleep loss are not synonymous. The Raw Score approach can be reliably used to differentiate resilient and vulnerable groups using DSST and DS performance during sleep loss.

Neural Mechanism Underlying the Sleep Deprivation-Induced Abnormal Bistable Perception

Quality sleep is vital for physical and mental health. No matter whether sleep problems are a consequence of or contributory factor to mental disorders, people with psychosis often suffer from severe sleep disturbances. Previous research has shown that acute sleep deprivation (SD) can cause transient brain dysfunction and lead to various cognitive impairments in healthy individuals. However, the relationship between sleep disturbance and bistable perception remains unclear. Here, we investigated whether the bistable perception could be affected by SD and elucidated the functional brain changes accompanying SD effects on bistable perception using functional magnetic resonance imaging. We found that the 28-h SD resulted in slower perceptual transitions in healthy individuals. The reduced perceptual transition was accompanied by the decreased activations in rivalry-related frontoparietal areas, including the right superior parietal lobule, right frontal eye field, and right temporoparietal junction. We speculated that SD might disrupt the normal function of these regions crucial for bistable perception, which mediated the slower rivalry-related perceptual transitions in behavior. Our findings revealed the neural changes underlying the abnormal bistable perception following the SD. It also suggested that SD might offer a new window to understand the neural mechanisms underlying the bistable perception.

The impact of the wake maintenance zone on attentional capacity, physiological drowsiness, and subjective task demands during sleep deprivation

We aimed to investigate the impact of the Wake Maintenance Zone (WMZ) on measures of drowsiness, attention, and subjective performance under rested and sleep deprived conditions. We studied 23 healthy young adults (18 males; mean age = 25.41 ± 5.73 years) during 40 hr of total sleep deprivation under constant routine conditions. Participants completed assessments of physiological drowsiness (EEG-scored slow eye movements and microsleeps), sustained attention (PVT), and subjective task demands every two hours, and four-hourly ocular motor assessment of inhibitory control (inhibition of reflexive saccades on an anti-saccade task). Tests were analyzed relative to dim light melatonin onset (DLMO); the WMZ was defined as the 3 hr prior to DLMO, and the preceding 3 hr window was deemed the pre-WMZ. The WMZ did not mitigate the adverse impact of ~37 hr sleep deprivation on drowsiness, sustained attention, response inhibition, and self-rated concentration and difficulty, relative to rested WMZ performance (~13 hr of wakefulness). Compared to the pre-WMZ, though, the WMZ improved measures of sustained attention, and subjective concentration and task difficulty, during sleep deprivation. Cumulatively, these results expand on previous work by characterizing the beneficial effects of the WMZ on operationally-relevant indices of drowsiness, inhibitory attention control, and self-rated concentration and task difficulty relative to the pre-WMZ during sleep deprivation. These results may inform scheduling safety-critical tasks at more optimal circadian times to improve workplace performance and safety.