Increased Automaticity and Altered Temporal Preparation Following Sleep Deprivation

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.

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.

Pupillary fluctuation amplitude preceding target presentation is linked to the variable foreperiod effect on reaction time in Psychomotor Vigilance Tasks

Understanding temporally attention fluctuations can benefit scientific knowledge and real-life applications. Temporal attention studies have typically used the reaction time (RT), which can be measured only after a target presentation, as an index of attention level. We have proposed the Micro-Pupillary Unrest Index (M-PUI) based on pupillary fluctuation amplitude to estimate RT before the target presentation. However, the kind of temporal attention effects that the M-PUI reflects remains unclear. We examined if the M-PUI shows two types of temporal attention effects initially reported for RTs in the variable foreperiod tasks: the variable foreperiod effect (FP effect) and the sequential effect (SE effect). The FP effect refers to a decrease in the RT due to an increase in the foreperiod of the current trial, whereas the SE effect refers to an increase in the RT in the early part of the foreperiod of the current trial due to an increase in the foreperiod of the previous trial. We used a simple reaction task with the medium-term variable foreperiods (Psychomotor Vigilance Task) and found that the M-PUI primarily reflects the FP effect. Inter-individual analyses showed that the FP effect on the M-PUI, unlike other eye movement indices, is correlated with the FP effect on RT. These results suggest that the M-PUI is a potentially powerful tool for investigating temporal attention fluctuations for a partly unpredictable target.

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.

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.

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.

Dissociable influences of implicit temporal expectation on attentional performance and mind wandering

Mind wandering at critical moments during a cognitive task degrades performance. At other moments, mind wandering could serve to conserve task-relevant resources, allowing a brief mental respite. Recent research has shown that, if target timing is predictable, mind wandering episodes coincide with moments of low target likelihood. Conversely, mind wandering can be avoided at moments when targets are expected. In the current study, we tested whether mind wandering can be guided by implicit temporal expectations when target timing is less predictable. In two experiments (Experiment 1: N = 37, Experiment 2: N = 61), participants performed a sustained attention task in which target events were preceded by a variable pre-target interval (foreperiod). As time passes over the foreperiod duration, implicit target expectation increases, given that it has not yet appeared. In Experiment 1, all foreperiod durations were equally probable (uniform distribution: 2-10 s). This resulted in faster responses when targets were preceded by long compared to short foreperiods (foreperiod-effect). In contrast, mind wandering, assessed by thought probes inserted following short or long foreperiods, did not follow this pattern. In Experiment 2, alterations in the foreperiod distribution (left or right-skewed) resulted in changes in the behavioral foreperiod-effect, but mind wandering was unaffected. Our findings indicate that implicit timing strongly affects behavioral response to target events, but has no bearing on the mind wandering. Contrastingly, mind wandering did correlate with performance deterioration due to fatigue (time-on-task), suggesting that the thought probe method was sufficiently sensitive to behaviorally relevant changes in mental state.

Motivation alters implicit temporal attention through sustained and transient mechanisms: A behavioral and pupillometric study

Temporal expectations aid performance by allowing the optimization of attentional readiness at moment of highest target probability. Reward enhances cognitive performance through its action on preparatory and reactive attentional processes. To elucidate how motivation interacts with mechanisms of implicit temporal attention, we studied healthy young adult participants (N = 73) performing a sustained attention task with simultaneous pupillometric recording, under different reward conditions (baseline: 0 c; reward: 10 c/fast response). Target timing was temporally unpredictable (variable foreperiod: 2-10 s, uniformly distributed), in which case implicitly formed timing expectations. Trials were binned according to current foreperiod (FP_n ; short: 2-6 s; long: 6-10 s) and preceding foreperiod (FP_n-1 ; short: 2-6 s; long: 6-10 s). Overall, performance data showed the expected temporal attention effects, with slower responses after shorter FP_n s, particularly when they followed longer FP_n-1 s. Moreover, these temporal effects were significantly reduced in the reward condition. While performance improved in all trial types, the largest benefit appeared in trials that were normally most disadvantaged by invalid temporal expectation. Furthermore, reward motivation was accompanied by an increase in sustained (prestimulus) and transient (poststimulus response) pupil diameter. The latter effect was particularly evident following short FP_n s. The current findings suggest that reward motivation can improve overall attentional performance and reduce implicit temporal bias, both through preparatory and reactive attentional mechanisms.