Sleep efficiency and neurophysiological patterns in middle‐aged men are associated with cognitive change over their adult life course. (12th November 2018)

• Record Type: Journal Article
• Authors: Waser, Markus; Lauritzen, Martin J.; Fagerlund, Birgitte; Osler, Merete; Mortensen, Erik L.; Sørensen, Helge B. D.; Jennum, Poul
• Is Part Of: Journal of sleep research. Volume 28:Number 4(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-11-12
• Subjects: cognitive ageing -- electroencephalogram -- functional connectivity -- neurodegeneration -- sleep efficiency; Sleep -- Periodicals; Sleep disorders -- Periodicals; 612.821
• DOI: 10.1111/jsr.12793
• Languages: English
• ISSNs: 0962-1105

Abstract

Summary: Disrupted sleep is a contributing factor to cognitive ageing, while also being associated with neurodegenerative disorders. Little is known, however, about the relation of sleep and the gradual cognitive changes over the adult life course. Sleep electroencephalogram (EEG) patterns are potential markers of the cognitive progress. To test this hypothesis, we assessed sleep architecture and EEG of 167 men born in the Copenhagen Metropolitan Area in 1953, who, based on individual cognitive testing from early (~18 years) to late adulthood (~58 years), were divided into 85 subjects with negative and 82 with positive cognitive change over their adult life. Participants underwent standard polysomnography, including manual sleep scoring at age ~58 years. Features of sleep macrostructure were combined with a number of EEG features to distinguish between the two groups. EEG rhythmicity was assessed by spectral power analysis in frontal, central and occipital sites. Functional connectivity was measured by inter‐hemispheric EEG coherence. Group differences were assessed by analysis of covariance ( p < 0.05), including education and severity of depression as potential covariates. Subjects with cognitive decline exhibited lower sleep efficiency, reduced inter‐hemispheric connectivity during rapid eye movement (REM) sleep, and slower EEG rhythms during stage 2 non‐REM sleep. Individually, none of these tendencies remained significant after multiple test correction; however, by combining them in a machine learning approach, the groups were separated with 72% accuracy (75% sensitivity, 67% specificity). Ongoing medical screenings are required to confirm the potential of sleep efficiency and sleep EEG patterns as signs of individual cognitive progress.