Brain age from the electroencephalogram of sleep

NEUROBIOLOGY OF AGING

卷:74

Article

Sun, Haoqi¹  Paixao, Luis¹  Oliva, Jefferson T.^1,2  Goparaju, Balaji¹  Carvalho, Diego Z.¹  van Leeuwen, Kicky G.^1,3  Akeju, Oluwaseun⁴  Thomas, Robert J.⁵  Cash, Sydney S.¹  Bianchi, Matt T.¹  Westover, M. Brandon¹ 

[1] Massachusetts Gen Hosp, Dept Neurol, 55 Fruit St, Boston, MA 02114 USA

[2] Univ Sao Paulo, Comp Sci Dept, Bioinspired Comp Lab, Sao Paulo, Brazil

[3] Univ Twente, Enschede, Netherlands

[4] Massachusetts Gen Hosp, Dept Anesthesiol Crit Care & Pain Med, Boston, MA 02114 USA

[5] Beth Israel Deaconess Med Ctr, Dept Med, Div Pulm Crit Care & Sleep, Boston, MA 02215 USA

关键词: Brain age;    Sleep;    EEG;    Machine learning;   

DOI  :  10.1016/j.neurobiolaging.2018.10.016

来源: Elsevier

【 摘 要 】

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. (C) 2018 Elsevier Inc. All rights reserved.

【 授权许可】

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