Sleep Fragmentation Modulates the Neurophysiological Correlates of Cognitive Fatigue

Abstract

Cognitive fatigue (CF) is a critical factor affecting performance and well-being. It can be altered in suboptimal sleep quality conditions, e.g., in patients suffering from obstructive sleep apnea who experience both intermittent hypoxia and sleep fragmentation (SF). Understanding the neurophysiological basis of SF in healthy individuals can provide insights to improve cognitive functioning in disrupted sleep conditions. In this electroencephalographical (EEG) study, we investigated in 16 healthy young participants the impact of experimentally induced SF on the neurophysiological correlates of CF measured before, during, and after practice on the TloadDback, a working memory task tailored to each individual's maximal cognitive resources. The participants spent three consecutive nights in the laboratory two times, once in an undisrupted sleep (UdS) condition and once in an SF condition induced by non-awakening auditory stimulations, counterbalanced and performed the TloadDback task both in a high (HCL) and a low (LCL) cognitive load condition. EEG activity was recorded during wakefulness in the 5 min resting state immediately before and after, as well as during the 16 min of the TloadDback task practice. In the high cognitive load under a sleep-fragmentation (HCL/SF) condition, high beta power increased during the TloadDback, indicating heightened cognitive effort, and the beta and alpha power increased in the post- vs. pre-task resting state, suggesting a relaxation rebound. In the low cognitive load/undisturbed sleep (LCL/UdS) condition, low beta activity increased, suggesting a relaxed focus, as well as mid beta activity associated with active thinking. These findings highlight the dynamic impact of SF on the neurophysiological correlates of CF and underscore the importance of sleep quality and continuity to maintain optimal cognitive functioning.

Keywords: EEG; cognitive fatigue; sleep fragmentation.

Figure 1

Visualization of absolute power spectra during resting states across contrasted conditions. Panels highlight the comparison between resting-state EEG power before the TloadDback task (RS1) and after the TloadDback task (RS2) under high (HCL) and low (LCL) cognitive load conditions, under sleep fragmentation (SF) and undisturbed sleep (UdS) conditions. Inset heatmaps show channel-wise changes in the power spectra across frequencies for the specified conditions. Each heatmap displays the EEG channels (F3, F4, C3, C4, P3, P4, and Cz) on the y-axis and frequency on the x-axis. Greyscale intensity indicates statistical significance of power change, with scale on the right-side axis (0 to 0.05). Significant differences at each frequency bin are marked with * (p < 0.05). To address multiple comparisons, spectral power differences along frequency ranges were considered significant only if they spanned along at least three adjacent bins.

Figure 2

Visualization of absolute power spectra during the first and last segment of the TloadDback task across conditions. Figure panels illustrate power spectra in the first 4 min segment (Tload1) and the last 4 min segment (Tload4) of the TloadDback task under different cognitive load (HCL and LCL) and sleep (SF and UdS) conditions. Inset heatmaps show channel-wise changes in the power spectra across frequencies for the specified conditions. Each heatmap displays the EEG channels (F3, F4, C3, C4, P3, P4, and Cz) on the y-axis and frequency on the x-axis. Greyscale intensity indicates statistical significance of power change, with a legend on the right showing the scale (0 to 0.05). Significant differences per frequency bin are marked with * (p < 0.05). However, to address for multiple comparisons, spectral power differences along frequency ranges were considered significant if they spanned along a minimum of three adjacent bins.

Figure 3

Experimental protocol (see general experimental procedure). Note: PSG = polysomnography. VAS = visual analog scales (fatigue, sleepiness, stress, and motivation). LCL = low cognitive load condition. HCL = high cognitive load condition. Participants spent three consecutive nights both in a sleep fragmentation (SF) and in a sleep undisturbed (UdS) condition at a one-week interval, counterbalanced. Cognitive fatigue (CF) calibration (pretest) was performed after the first SF or UdS night, and CF assessment was conducted in HCL and LCL conditions after the second and third nights, counterbalanced.

Figure 4

Timeline of resting states before and after the TloadDback task, performed under EEG. The TloadDback task was administered in both cognitive load conditions, counterbalanced, after SF/UdS nights 2 and 3.