Altering temporal dynamics of sleepiness and mood during sleep deprivation: evidence from resting-state EEG microstates

bai, duo; lei, xu · 2024 · Crossref

DOI: 10.21203/rs.3.rs-3856018/v1

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Summary

This study investigates the neural mechanisms underlying sleep deprivation-induced impairments in sleepiness and mood by analyzing the temporal dynamics of resting-state electroencephalography (EEG) microstates. While previous research has established that sleep deprivation negatively affects subjective and objective measures of alertness and emotional state, the specific relationship between abnormal brain dynamic activity and these behavioral deficits remains unclear. The authors utilized EEG microstate analysis, a method that captures large-scale brain network dynamics on a millisecond scale, to explore how sleep loss alters the temporal characteristics of distinct brain states and how these changes correlate with behavioral outcomes. The experimental design involved 71 participants who underwent two conditions: a sleep control (SC) session and a total sleep deprivation (SD) session involving 24 hours of continuous wakefulness. The sessions were separated by approximately one week and randomized. Resting-state EEG data were recorded for five minutes with eyes open, alongside assessments of subjective sleepiness using the Karolinska Sleepiness Scale (KSS), objective vigilance via the Psychomotor Vigilance Task (PVT), and mood using the Positive and Negative Affect Scale (PANAS). After excluding six participants due to excessive artifacts, data from 65 participants were analyzed. The researchers applied standard preprocessing techniques, including band-pass filtering and independent component analysis, and utilized the MICROSTATELAB toolbox to identify four canonical microstate classes (A, B, C, and D). Statistical analyses included paired sample t-tests to compare microstate parameters between conditions and Pearson correlation analyses to link microstate alterations with behavioral metrics. The results demonstrated significant alterations in microstate temporal dynamics following sleep deprivation. Specifically, the mean duration and occurrence of Microstate B, which is associated with visual processing and self-related cognition, significantly decreased. Conversely, the duration and occurrence of Microstate C, linked to the default mode network and self-reflection, significantly increased. Behavioral correlations revealed that the decrease in Microstate B occurrence was significantly negatively correlated with increased subjective sleepiness (KSS scores), while the reduction in Microstate B duration was negatively correlated with increased reaction times on the PVT, indicating impaired objective vigilance. Although no direct correlation was found between microstate changes and the overall decline in positive mood between conditions, further analysis showed that under sleep-deprived conditions, the mean duration of Microstate B was significantly positively correlated with positive affect scores. This suggests that preserved Microstate B dynamics may help maintain positive mood despite sleep loss. These findings indicate that sleep deprivation compromises the neural dynamics of the visual network, as reflected by the reduction in Microstate B, which serves as a neural marker for both subjective and objective sleepiness. The increase in Microstate C may represent a compensatory mechanism involving the default mode network. The study highlights the utility of EEG microstate analysis in elucidating the neurophysiological basis of sleep deprivation effects, suggesting that Microstate B parameters could serve as potential biomarkers for identifying individuals sensitive to sleep loss and for monitoring the efficacy of sleep interventions.

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Topics

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• drowsiness
• microsleep
• sleep deprivation
• drowsiness detection algorithms

Information type

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• Empirical Findings: physiological data