Multivariate prediction of cognitive performance from the sleep electroencephalogram

Area of Science:

• Neuroscience
• Sleep Science
• Cognitive Aging

Background:

• Human cognitive performance is influenced by biological factors, with genetics and brain imaging providing partial insights.
• The sleep electroencephalogram (EEG) is a promising biomarker, closely related to central nervous system structure and function.

Purpose of the Study:

• To investigate the association between sleep EEG features and cognitive performance in older men.
• To determine the extent to which sleep EEG can predict cognitive function and whether this association is independent of demographic and health variables.

Main Methods:

• Utilized data from the MrOS cohort of older men.
• Employed cross-validated regularized regression to link sleep EEG features with cognitive performance.
• Validated findings in independent samples.

Main Results:

• Sleep EEG features accounted for 2.5-10% of the variance in cognitive performance.
• Sigma power in Non-Rapid Eye Movement (NREM) sleep and beta power in Rapid Eye Movement (REM) sleep were associated with better cognitive performance.
• Theta power in REM sleep was linked to worse cognitive performance.
• The association between sleep EEG and cognition remained significant after adjusting for demographic and health covariates.

Conclusions:

• Cognitive performance is significantly associated with sleep EEG patterns, particularly spindle-frequency activity.
• This association partially reflects trait-like differences in cognitive ability, persisting even after covariate adjustment.
• Sleep EEG serves as a valuable, albeit partial, predictor of cognitive function in older adults.