Decoding information about cognitive health from the brainwaves of sleep
Noor Adra, Lisa Dummer, Luis Gustavo Porto Paixão, Ryan A. Tesh, Haoqi Sun, Wolfgang Ganglberger, Mike Westmeijer, Madalena Da Silva Cardoso, Anagha Kumar, Elissa Ye, Jonathan J. Henry, Sydney S. Cash, Erin Kitchener, Catherine L. Leveroni, Rhoda Au, Jonathan Rosand, Joel Salinas, Alice Lam, Robert J. Thomas, M. Brandon Westover
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.