Cognitive and neural basis of vigilance advantage in soccer players: Evidence from the drift-diffusion model and magnetic resonance imaging

Soccer is a sport that requires athletes to be constantly aware of rapidly changing and unpredictable environments and to react adaptively. Previous studies have found that soccer players typically exhibit a vigilance advantage, but the underlying cognitive and neural basis for this is unclear. In this study, 27 soccer players, 17 age-matched artistic gymnasts, and 57 college students were recruited to participate in a psychomotor vigilance task. Compared to the college students, the soccer players demonstrated higher vigilance, whereas the artistic gymnasts did not. Drift-Diffusion Modeling revealed that soccer players' non-decision time was significantly lower than that of college students, while drift rate and boundary were not significantly different between the two groups. This suggests that the vigilance advantage of soccer players stems from their shorter information encoding and action generation time. Vigilance was not only correlated with Right Ventral lateral (rtVL), Left Intralaminar (ltIL), Left Mediodorsal medial magnocellular (ltMDm) and Right Mediodorsal medial mag-no-cellular (rtMDm) thalamic subregions, and also correlates with the functional connectivity be-tween the thalamic subregions of rtVL and Right Intralaminar (rtIL), and rtVL and Left Ventral anterior (ltVA). And, rtVL may be an important region of vigilance dominance in soccer players. This finding not only helps to deepen the understanding of the computational process of vigilance in players, but also provides a reference for subsequent more in-depth studies of neural computational mechanisms.

Resting-State EEG Microstates Dynamics Associated with Interindividual Vulnerability to Sleep Deprivation

Purpose

Sleep deprivation can induce severe deficits in vigilant maintenance and alternation in large-scale networks. However, differences in the dynamic brain networks after sleep deprivation across individuals have rarely been investigated. In the present study, we used EEG microstate analysis to investigate the effects of sleep deprivation and how it differentially affects resting-state brain activity in different individuals.

Participants and Methods

A total of 44 healthy adults participated in a within-participant design study involving baseline sleep and 24-hour sleep deprivation, with resting-state EEG recorded during wakefulness. The psychomotor vigilance task (PVT) was used to measure vigilant attention. Participants were median split as vulnerable or resilient according to their changes in the number of lapses between the baseline sleep and sleep deprivation conditions.

Results

Sleep deprivation caused decreases in microstates A, B, and D, and increases in microstate C. We also found increased transition probabilities of microstates C and D between each other, lower transition probabilities from microstates C and D to microstate B, and higher transition probabilities from microstates A and B to microstate C. Sleep-deprived vulnerable individuals showed decreased occurrence of microstate B and transition probability from microstate C to B after sleep deprivation, but not in resilient individuals.

Conclusion

The findings suggest that sleep deprivation critically affects dynamic brain-state properties and the differences in time parameters of microstates might be the underlying neural basis of interindividual vulnerability to sleep deprivation.

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.

The effect of total sleep deprivation on working memory: evidence from diffusion model

STUDY OBJECTIVES

Working memory is crucial in human daily life and is vulnerable to sleep loss. The current study investigated the impact of sleep deprivation on working memory from the information processing perspective, to explore whether sleep deprivation affects the working memory via impairing information manipulation.

METHODS

Thirty-seven healthy adults attended two counterbalanced protocols: a normal sleep night and a total sleep deprivation (TSD). The N-back and the psychomotor vigilance task (PVT) assessed working memory and sustained attention. Response time distribution and drift-diffusion model analyses were applied to explore cognitive process alterations.

RESULTS

TSD increased the loading effect of accuracy, but not the loading effect of response time in the N-back task. TSD reduced the speed of information accumulation, increased the variability of the speed of accumulation, and elevated the decision threshold only in 1-back task. Moreover, the slow responses of PVT and N-back were severely impaired after TSD, mainly due to increased information accumulation variability.

CONCLUSIONS

The present study provides a new perspective to investigate behavioral performance by using response time distribution and drift-diffusion models, revealing that sleep deprivation affected multicognitive processes underlying working memory, especially information accumulation processes.

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.

How the time-of-day affects the EEG signatures of vigilance fluctuation

Previous research suggested the homeostatic effect on the top-down control system as a major factor for daytime vigilance decrement, yet how it alters the cognitive processes of vigilance remains unclear. Using EEG, the current study measured the vigilance of 28 participants under three states: the morning, the midafternoon after napping and no-nap. The drift-diffusion model was applied to decompose vigilant reaction time into decision and non-decision components. From morning to midafternoon, vigilance declined during sustained wakefulness, but remained stable after midday napping. Increased sleep pressure negatively affected decision time and drift rate, but did not significantly alter the non-decision process. Frontocentral N2 amplitude decreased from morning to no-nap afternoon, associated with slowing decision time. In contrast, parietal P3 had no diurnal alterations during sustained wakefulness, but enhanced after napping. Pre-stimulus parietooccipital alpha power enhanced under high sleep pressure relative to low, accompanied by more lapses in no-nap vs. post-napping conditions. The homeostasis effect is a major contributor to daily vigilance fluctuation, specifically targeting top-down control processes during the pre-stimulus and decision-making stages. Under the influence of sleep homeostasis, the speed of decision-making declines with degradation in target monitoring from morning to afternoon, leading to post-noon vigilance decrement.

Isolation of subjectively reported sleepiness and objectively measured vigilance during sleep deprivation: a resting-state fMRI study

Subjectively reported sleepiness and objectively measured vigilance are often used to assess and monitor operating performance. Evidence suggests that the response patterns of the two measures are independent of each other. However, the neural mechanism underlying this phenomenon remains unclear. This study aimed to investigate whether subjective sleepiness and objective vigilance were associated with each other. Thirty-three participants were subjected to 34 h of acute sleep deprivation. We collected sleepiness, vigilance, and resting-state fMRI data. We also located the neural mechanism of isolation of object and subject parameters. Firstly, the correlation analysis showed that there was no statistically significant correlation between the changes in vigilance and sleepiness during the sleep deprivation period. Then, implementing the support vector machine algorithm through functional connectivities as features, we found that different functional connectivity patterns underline the isolation of these two factors during sleep deprivation. The functional connectivities involved in characterizing the vulnerability of objective vigilance are more extensive, involving the connectivities within the sensorimotor network, between the subcortical and cortical network, and among multiple cortical networks. The functional connectivity involved in characterizing the vulnerability of subjective sleepiness is limited to the communication between the subcortical thalamus and the somatosensory cortex. In addition, we found that implementing global signal regression would reduce the model's power to predict vigilance and sleepiness. This work contributes to our understanding of how sleep deprivation affects individual cognition and behavior, and will be of use in the evaluation and prediction of cognitive performance during sleep loss.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-021-09772-0.

Inter-Individual Differences in Cognitive Tasks: Focusing on the Shaping of Decision-Making Strategies

In this paper, we review recent (published and novel) data showing inter-individual variation in decision-making strategies established by mice in a gambling task (MGT for Mouse Gambling Task). It may look intriguing, at first, that congenic animals develop divergent behaviors. However, using large groups of mice, we show that individualities emerge in the MGT, with about 30% of healthy mice displaying risk-averse choices while about 20-25% of mice make risk-prone choices. These strategies are accompanied by different brain network mobilization and individual levels of regional -prefrontal and striatal- monoamines. We further illustrate three ecological ways that influence drastically cognitive strategies in healthy adult mice: sleep deprivation, sucrose or artificial sweetener exposure, and regular exposure to stimulating environments. Questioning how to unmask individual strategies, what are their neural/neurochemical bases and whether we can shape or reshape them with different environmental manipulations is of great value, first to understand how the brain may build flexible decisions, and second to study behavioral plasticity, in healthy adult, as well as in developing brains. The latter may open new avenues for the identification of vulnerability traits to adverse events, before the emergence of mental pathologies.

A systematic review of sleep deprivation and neurobehavioral function in young adults

AIM

To examine the effect of sleep deprivation (total and partial) on neurobehavioral function compared to a healthy sleep opportunity (7-9 h) in young adults 18-30 years.

BACKGROUND

More than one-third of young adults are sleep deprived, which negatively affects a range of neurobehavioral functions, including psychomotor vigilance performance (cognitive), affect, and daytime sleepiness.

METHODS

A systematic review of randomized controlled trials (RCTs) on sleep deprivation and neurobehavioral function. Multiple electronic databases (Cochrane Central Registry of Controlled Trials [CENTRAL], PubMed, PsycINFO, CINAHL, and Web of Science) were searched for relevant RCTs published in English from the establishment of each database to December 31, 2020.

RESULTS

Nineteen RCTs were selected (N = 766, mean age = 23.7 ± 3.1 years; 44.8% female). Seven were between-person (5 were parallel-group designs and 2 had multiple arms), and 12 were within-person designs (9 were cross over and 3 used a Latin square approach). Total sleep deprivation had the strongest detrimental effect on psychomotor vigilance performance, with the largest effects on vigilance tasks in young adults in the included studies.

CONCLUSION

Acute sleep deprivation degrades multiple dimensions of neurobehavioral function including psychomotor vigilance performance, affect, and daytime sleepiness in young adults. The effect of chronic sleep deprivation on the developing brain and associated neurobehavioral functions in young adults remains unclear.

Predicting Attentional Vulnerability to Sleep Deprivation: A Multivariate Pattern Analysis of DTI Data

BACKGROUND

Large individual differences exist in sleep deprivation (SD) induced sustained attention deterioration. Several brain imaging studies have suggested that the activities within frontal-parietal network, cortico-thalamic connections, and inter-hemispheric connectivity might underlie the neural correlates of vulnerability/resistance to SD. However, those traditional approaches are based on average estimates of differences at the group level. Currently, a neuroimaging marker that can reliably predict this vulnerability at the individual level is lacking.

METHODS

Efficient transfer of information relies on the integrity of white matter (WM) tracts in the human brain, we therefore applied machine learning approach to investigate whether the WM diffusion metrics can predict vulnerability to SD. Forty-nine participants completed the psychomotor vigilance task (PVT) both after resting wakefulness (RW) and after 24 h of sleep deprivation (SD). The number of PVT lapse (reaction time > 500 ms) was calculated for both RW condition and SD condition and participants were categorized as vulnerable (24 participants) or resistant (25 participants) to SD according to the change in the number of PVT lapses between the two conditions. Diffusion tensor imaging were acquired to extract four multitype WM features at a regional level: fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) learning approach using leave-one-out cross-validation (LOOCV) was performed to assess the discriminative power of WM features in SD-vulnerable and SD-resistant participants.

RESULTS

LSVM analysis achieved a correct classification rate of 83.67% (sensitivity: 87.50%; specificity: 80.00%; and area under the receiver operating characteristic curve: 0.85) for differentiating SD-vulnerable from SD-resistant participants. WM fiber tracts that contributed most to the classification model were primarily commissural pathways (superior longitudinal fasciculus), projection pathways (posterior corona radiata, anterior limb of internal capsule) and association pathways (body and genu of corpus callosum). Furthermore, we found a significantly negative correlation between changes in PVT lapses and the LSVM decision value.

CONCLUSION

These findings suggest that WM fibers connecting (1) regions within frontal-parietal attention network, (2) the thalamus to the prefrontal cortex, and (3) the left and right hemispheres contributed the most to classification accuracy.

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.

Classifying Vulnerability to Sleep Deprivation Using Resting-State Functional MRI Graph Theory Metrics

Sleep deprivation (SD) has become very common in contemporary society, where people work around the clock. SD-induced cognitive deficits show large inter-individual differences and are trait-like with known neural correlates. However, few studies have used neuroimaging to predict vulnerability to SD. Here, resting state functional magnetic resonance imaging (fMRI) data and psychomotor vigilance task (PVT) data were collected from 60 healthy subjects after resting wakefulness and after one night of SD. The number of PVT lapses was then used to classify participants on the basis of whether they were vulnerable or resilient to SD. We explored the viability of graph-theory-based degree centrality to accurately classify vulnerability to SD. Compared with during resting wakefulness, widespread changes in degree centrality (DC) were found after SD, indicating significant reorganization of sleep homeostasis with respect to activity in resting state brain network architecture. Support vector machine (SVM) analysis using leave-one-out cross-validation achieved a correct classification rate of 84.75% [sensitivity 82.76%, specificity 86.67%, and area under the receiver operating characteristic curve (AUC) 0.94] for differentiating vulnerable subjects from resilient subjects. Brain areas that contributed most to the classification model were mainly located within the sensorimotor network, default mode network, and thalamus. Furthermore, we found a significantly negative correlation between changes in PVT lapses and DC in the thalamus after SD. These findings suggest that resting-state network measures combined with a machine learning algorithm could have broad potential applications in screening vulnerability to SD.

Age-related emotional bias in associative memory consolidation: The role of sleep

Sleep plays a crucial role in memory consolidation. However, the influence of sleep on emotional memory consolidation in older adults, especially in the context of associative memory, which is more cognitively demanding than item memory, remains elusive. For this study we recruited young and older adults, and randomly assigned them into the sleep or wake condition. They were administrated a visual-spatial associative memory task, which required them to remember a picture and its location. We measured memory performance for positive, neutral, and negative stimuli before and after a 12-h interval of being awake or asleep. An accuracy analysis indicated a beneficial effect of sleep on location memory regardless of age and valence. In addition, in a more fine-grained analysis, the drift rate from diffusion modeling showed that sleep facilitated the consolidation of negative stimuli in young adults, while this emotion bias shifted to positive stimuli in older adults. Moreover, negative correlations were observed between the change of memory performance and sleep characteristics in older adults, indicating that more sleep results in fewer negative memories. Our results provide a relatively weak support for an age-related emotional bias in the context of associative memory, manifested in the absence of an age-by-valence interaction in accuracy, whilst a modeling parameter in consideration of both accuracy and response time yielded evidence consistent with the predictions of the socioemotional selectivity theory.

Vulnerability to mood degradation during sleep deprivation is influenced by white-matter compactness of the triple-network model

Sleep deprivation (SD) is often associated with significant shifts in mood state relative to baseline functioning. Prior work suggests that there are consistent trait-like differences among individuals in the degree to which their mood and performances are affected by sleep loss. The goal of this study was to determine the extent to which trait-like individual differences in vulnerability/resistance to mood degradation during a night of SD are dependent upon region-specific white and grey matter (WM/GM) characteristics of a triple-network model, including the default-mode network (DMN), control-execution network (CEN) and salience network (SN). Diffusion-weighted and anatomical brain data were collected from 45 healthy individuals several days prior to a 28-h overnight SD protocol. During SD, a visual analog mood scale was administered every hour from 19:15 (time point1; TP1) to 11:15 (TP17) the following morning to measure two positive and six negative mood states. Four core regions within the DMN, five within the CEN, and seven within the SN were used as regions of interest (ROIs). An index of mood resistance (IMR) was defined as the averaged differences between positive and negative mood states over 12 TPs (TP5 to TP16) relative to baseline (TP1 to TP4). For each ROI, characteristics of WM - quantitative anisotropy (QA) and mean curvature index (WM-MCI), and GM - cortical volume (CV) and GM-MCI were estimated, and used to predict IMR. WM characteristics, particularly QA, of all of regions within the DMN, and most of the regions within the CEN and SN predicted IMR during SD. In contrast, most ROIs did not show significant association between IMR and any of the GM characteristics (CV and MCI) or WM MCI. Our findings suggest that greater resilience to mood degradation induced by total SD appears to be associated with more compact axonal pathways within the DMN, CEN and SN.

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

There are strong individual differences in performance during sleep deprivation. We assessed whether baseline features of Psychomotor Vigilance Test (PVT) performance can be used for classifying participants’ relative attentional vulnerability to total sleep deprivation. In a laboratory, healthy adults (n = 160, aged 18–30 years) completed a 10-min PVT every 2 h while being kept awake for ≥24 hours. Participants were categorized as vulnerable (n = 40), intermediate (n = 80), or resilient (n = 40) based on their number of PVT lapses during one night of sleep deprivation. For each baseline PVT (taken 4–14 h after wake-up time), a linear discriminant model with wrapper-based feature selection was used to classify participants’ vulnerability to subsequent sleep deprivation. Across models, classification accuracy was about 70% (range 65–76%) using stratified 5-fold cross validation. The models provided about 78% sensitivity and 86% specificity for classifying resilient participants, and about 70% sensitivity and 89% specificity for classifying vulnerable participants. These results suggest features derived from a single 10-min PVT at baseline can provide substantial, but incomplete information about a person’s relative attentional vulnerability to total sleep deprivation. In the long term, modeling approaches that incorporate baseline performance characteristics can potentially improve personalized predictions of attentional performance when sleep deprivation cannot be avoided.

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.

Interindividual variability in neurobehavioral response to sleep loss: A comprehensive review

Stable trait-like responding is well established for neurobehavioral performance measures across repeated exposures to total sleep deprivation and partial chronic sleep restriction. These observed phenotypes are task-dependent, suggesting that there are distinct cognitive profiles of responding with differential vulnerability to sleep loss within the same individual. Numerous factors have been investigated as potential markers of phenotypic vulnerability to the effects of sleep loss but none fully account for this phenomenon. Observed interindividual differences in performance during extended wakefulness may be driven by underlying deficits in the wake-promoting system resulting in greater performance instability due to failure to counteract increased homeostatic pressure. Further work would benefit from a systems approach to the study of interindividual vulnerability in which behavioral, neurobiological, and genetic data are integrated in a larger framework delineating the relationships between genes, proteins, neurobiology, and behavior.

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

Disrupted Sleep: From Molecules to Cognition

Although the functions of sleep remain to be fully elucidated, it is clear that there are far-reaching effects of its disruption, whether by curtailment for a single night, by a few hours each night over a long period, or by disruption in sleep continuity. Epidemiological and experimental studies of these different forms of sleep disruption show deranged physiology from subcellular levels to complex affective behavior. In keeping with the multifaceted influence of sleep on health and well-being, we illustrate how the duration of sleep, its timing, and continuity can affect cellular ultrastructure, gene expression, metabolic and hormone regulation, mood, and vigilance. Recent brain imaging studies provide some clues on mechanisms underlying the most common cause of disrupted sleep (insomnia). These insights should ultimately result in adequate interventions to prevent and treat sleep disruption because of their high relevance to our most prevalent health problems.

SIGNIFICANCE STATEMENT

Disruption of the duration, timing, and continuity of sleep affects cellular ultrastructure, gene expression, appetite regulation, hormone production, vigilance, and reward functions.

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.

Classifying vulnerability to sleep deprivation using baseline measures of psychomotor vigilance

OBJECTIVES

To identify measures derived from baseline psychomotor vigilance task (PVT) performance that can reliably predict vulnerability to sleep deprivation.

DESIGN

Subjects underwent total sleep deprivation and completed a 10-min PVT every 1-2 h in a controlled laboratory setting. Participants were categorized as vulnerable or resistant to sleep deprivation, based on a median split of lapses that occurred following sleep deprivation. Standard reaction time, drift diffusion model (DDM), and wavelet metrics were derived from PVT response times collected at baseline. A support vector machine model that incorporated maximum relevance and minimum redundancy feature selection and wrapper-based heuristics was used to classify subjects as vulnerable or resistant using rested data.

SETTING

Two academic sleep laboratories.

PARTICIPANTS

Independent samples of 135 (69 women, age 18 to 25 y), and 45 (3 women, age 22 to 32 y) healthy adults.

INTERVENTIONS

In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively.

MEASUREMENTS AND RESULTS

In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively.

CONCLUSIONS

Despite differences in experimental conditions across studies, drift diffusion model parameters associated reliably with individual differences in performance during total sleep deprivation. These results demonstrate the utility of drift diffusion modeling of baseline performance in estimating vulnerability to psychomotor vigilance decline following sleep deprivation.