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Macro and micro sleep architecture and cognitive performance in older adults

by Wood, Alexis C. , Luchsinger, José A. , Yaffe, Kristine , Johnson, Dayna A. , Purcell, Shaun M. , Prerau, Michael J. , Rapp, Stephen R. , Redline, Susan , Fitzpatrick, Annette L. , Tranah, Gregory J. , Stone, Katie L. , Mariani, Sara , Seeman, Teresa , Djonlagic, Ina , Nguyen, Ha T. , Burdick, Katherine E. , Dzierzewski, Joseph M. , Van Der Klei, Veerle M. G. T. H.

in 631/378/2612 / 692/308/174 / 692/53/2423 / 692/617/375/1816 / Adults / Age Factors / Aged / Aged, 80 and over / Aging / Aging - physiology / Behavioral Sciences / Biomedical and Life Sciences / Case-Control Studies / Cognition / Cognition - physiology / Cognitive ability / Electroencephalography / Experimental Psychology / Eye movements / Health status / Humans / Life Sciences / Male / Metabolic Syndrome - physiopathology / Microeconomics / Middle Aged / Morphology / Multivariate analysis / Neurophysiology / Neurosciences / Older people / Oscillation / Personality and Social Psychology / Processing speed / Sleep / Sleep - physiology / Sleep, REM - physiology

2021

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2021

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2021

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Journal Article

Macro and micro sleep architecture and cognitive performance in older adults

Wood, Alexis C.,

Luchsinger, José A.,

Yaffe, Kristine,

Johnson, Dayna A.,

Purcell, Shaun M.,

Prerau, Michael J.,

Rapp, Stephen R.,

Redline, Susan,

Fitzpatrick, Annette L.,

Tranah, Gregory J.,

Stone, Katie L.,

Mariani, Sara,

Seeman, Teresa,

Djonlagic, Ina,

Nguyen, Ha T.,

Burdick, Katherine E.,

Dzierzewski, Joseph M.,

Van Der Klei, Veerle M. G. T. H.

2021

Overview

We sought to determine which facets of sleep neurophysiology were most strongly linked to cognitive performance in 3,819 older adults from two independent cohorts, using whole-night electroencephalography. From over 150 objective sleep metrics, we identified 23 that predicted cognitive performance, and processing speed in particular, with effects that were broadly independent of gross changes in sleep quality and quantity. These metrics included rapid eye movement duration, features of the electroencephalography power spectra derived from multivariate analysis, and spindle and slow oscillation morphology and coupling. These metrics were further embedded within broader associative networks linking sleep with aging and cardiometabolic disease: individuals who, compared with similarly aged peers, had better cognitive performance tended to have profiles of sleep metrics more often seen in younger, healthier individuals. Taken together, our results point to multiple facets of sleep neurophysiology that track coherently with underlying, age-dependent determinants of cognitive and physical health trajectories in older adults. In their analysis of two datasets, Djonlagic et al. identify 23 objective sleep metrics that predict cognitive performance and processing speed in older adults.

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Publisher

Nature Publishing Group UK,Nature Publishing Group

Subject

/ Adults

/ Aged