| Literature DB >> 33199858 |
Ina Djonlagic1,2, Sara Mariani2,3, Annette L Fitzpatrick4, Veerle M G T H Van Der Klei3, Dayna A Johnson5, Alexis C Wood6, Teresa Seeman7, Ha T Nguyen8, Michael J Prerau2,3, José A Luchsinger9, Joseph M Dzierzewski10, Stephen R Rapp11, Gregory J Tranah12, Kristine Yaffe13, Katherine E Burdick2,3, Katie L Stone12, Susan Redline1,2,3, Shaun M Purcell14,15.
Abstract
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.Entities:
Mesh：
Year: 2020 PMID： 33199858 DOI： 10.1038/s41562-020-00964-y
Source DB: PubMed Journal: Nat Hum Behav ISSN： 2397-3374