Brain age from the electroencephalogram of sleep

Haoqi Sun, Luis Paixao, Jefferson T. Oliva, Balaji Goparaju, Diego Z. Carvalho, Kicky G. van Leeuwen, Oluwaseun Akeju, Robert J. Thomas, Sydney S. Cash, Matt T. Bianchi, M. Brandon Westover*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

23 Citations (Scopus)
6 Downloads (Pure)

Abstract

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as “brain age (BA),” which can be compared to chronological age to reflect the degree of deviation from normal aging. Here, we develop an interpretable machine learning model to predict BA based on 2 large sleep EEG data sets: the Massachusetts General Hospital (MGH) sleep lab data set (N = 2532; ages 18–80); and the Sleep Heart Health Study (SHHS, N = 1974; ages 40–80). The model obtains a mean absolute deviation of 7.6 years between BA and chronological age (CA) in healthy participants in the MGH data set. As validation, a subset of SHHS containing longitudinal EEGs 5.2 years apart shows an average of 5.4 years increase in BA. Participants with significant neurological or psychiatric disease exhibit a mean excess BA, or “brain age index” (BAI = BA-CA) of 4 years relative to healthy controls. Participants with hypertension and diabetes have a mean excess BA of 3.5 years. The findings raise the prospect of using the sleep EEG as a potential biomarker for healthy brain aging.

Original languageEnglish
Pages (from-to)112-120
Number of pages9
JournalNeurobiology of aging
Volume74
DOIs
Publication statusPublished - Feb 2019

Keywords

  • Brain age
  • EEG
  • Machine learning
  • Sleep

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