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Resting-State Subjective Experience and EEG Biomarkers Are Associated with Sleep-Onset Latency

Identifieur interne : 000494 ( Pmc/Corpus ); précédent : 000493; suivant : 000495

Resting-State Subjective Experience and EEG Biomarkers Are Associated with Sleep-Onset Latency

Auteurs : B. Alexander Diaz ; Richard Hardstone ; Huibert D. Mansvelder ; Eus J. W. Van Someren ; Klaus Linkenkaer-Hansen

Source :

RBID : PMC:4828461

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 min eyes-closed resting-state trials (in total, n = 223) followed by a period–varying in length up to 30 min–that either allowed subjects to transition into sleep (“sleep trials,” nsleep = 144) or was ended while they were still awake (“wake trials,” nwake = 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 toward 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.


Url:
DOI: 10.3389/fpsyg.2016.00492
PubMed: 27148107
PubMed Central: 4828461

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