학술논문
Decoding information about cognitive health from the brainwaves of sleep.
Document Type
Article
Author
Adra, Noor; Dümmer, Lisa W.; Paixao, Luis; Tesh, Ryan A.; Sun, Haoqi; Ganglberger, Wolfgang; Westmeijer, Mike; Da Silva Cardoso, Madalena; Kumar, Anagha; Ye, Elissa; Henry, Jonathan; Cash, Sydney S.; Kitchener, Erin; Leveroni, Catherine L.; Au, Rhoda; Rosand, Jonathan; Salinas, Joel; Lam, Alice D.; Thomas, Robert J.; Westover, M. Brandon
Source
Subject
*COGNITIVE testing
*PROBLEM solving
*COGNITIVE computing
*COGNITION
*SLEEP deprivation
*BRAIN diseases
*MACHINE learning
*SLEEP
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Language
ISSN
2045-2322
Abstract
Sleep electroencephalogram (EEG) signals likely encode brain health information that may identify individuals at high risk for age-related brain diseases. Here, we evaluate the correlation of a previously proposed brain age biomarker, the "brain age index" (BAI), with cognitive test scores and use machine learning to develop and validate a series of new sleep EEG-based indices, termed "sleep cognitive indices" (SCIs), that are directly optimized to correlate with specific cognitive scores. Three overarching cognitive processes were examined: total, fluid (a measure of cognitive processes involved in reasoning-based problem solving and susceptible to aging and neuropathology), and crystallized cognition (a measure of cognitive processes involved in applying acquired knowledge toward problem-solving). We show that SCI decoded information about total cognition (Pearson's r = 0.37) and fluid cognition (Pearson's r = 0.56), while BAI correlated only with crystallized cognition (Pearson's r = − 0.25). Overall, these sleep EEG-derived biomarkers may provide accessible and clinically meaningful indicators of neurocognitive health. [ABSTRACT FROM AUTHOR]