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Resting-state subjective experience and EEG biomarkers are associated with sleep-onset latency

By B. Alexander Diaz, B. Alexander Diaz, Richard eHardstone, Richard eHardstone, Huibert D. Mansvelder, Huibert D. Mansvelder, Eus J.W. Van Someren, Eus J.W. Van Someren, Eus J.W. Van Someren, Eus J.W. Van Someren and Klaus eLinkenkaer-Hansen and Klaus eLinkenkaer-Hansen

Abstract

Difficulties initiating sleep are common in several disorders, including insomnia and attention deficit hyperactivity disorder. These disorders are prevalent, bearing significant societal and financial costs which require the consideration of new treatment strategies and a better understanding of the physiological and cognitive processes surrounding the time of preparing for sleep or falling asleep. Here, we search for neuro-cognitive associations in the resting state and examine their relevance for predicting sleep-onset latency using multi-level mixed models. Multiple EEG recordings were obtained from healthy male participants (N = 13) during a series of 5 minutes eyes-closed resting-state trials (in total, n = 223) followed by a period—varying in length up to 30 minutes—that either allowed subjects to transition into sleep (sleep trials, n¬sleep = 144) or was ended while they were still awake (wake trials, n¬wake = 79). After both eyes-closed rest, sleep and wake trials, subjective experience was assessed using the Amsterdam Resting-State Questionnaire (ARSQ). Our data revealed multiple associations between eyes-closed rest alpha and theta oscillations and ARSQ-dimensions Discontinuity of Mind, Self, Theory of Mind, Planning and Sleepiness. The sleep trials showed that the transition towards the first sleep stage exclusively affected subjective experiences related to Theory of Mind, Planning and Sleepiness. Importantly, sleep-onset latency was negatively associated both with eyes-closed rest ratings on the ARSQ dimension of Sleepiness and with the long-range temporal correlations of parietal theta oscillations derived by detrended fluctuation analysis (DFA). These results could be relevant to the development of personalized tools that help evaluate the success of falling asleep based on measures of resting-state cognition and EEG biomarkers

Topics: Consciousness, Sleep, mind wandering, Amsterdam Resting-State Questionnaire (ARSQ), Multilevel modelling, Psychology, BF1-990
Publisher: Frontiers Media S.A.
Year: 2016
DOI identifier: 10.3389/fpsyg.2016.00492
OAI identifier: oai:doaj.org/article:e65f46c156a34797bd2b533f97439665
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