Sleep Electroencaphalography-Based Brain Age Index

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.

人类睡眠脑电图（EEG）随年龄增长而发生深刻变化，这些变化可以被视为‘脑龄（BA）’，该指标可与生理年龄相比较，以反映与正常衰老程度的偏差程度。在本研究中，我们开发了一种可解释的机器学习模型，基于两个大型睡眠EEG数据集预测脑龄：马萨诸塞州总医院（MGH）睡眠实验室数据集（N = 2532；年龄18-80岁）；以及睡眠心脏健康研究（SHHS，N = 1974；年龄40-80岁）。该模型在MGH数据集中健康参与者的脑龄（BA）与生理年龄（CA）之间平均绝对偏差为7.6年。为验证，SHHS数据集中纵向EEG数据相隔5.2年的子集显示脑龄平均增加5.4年。患有显著神经或精神疾病的参与者，相对于健康对照组，其脑龄指数（BAI = BA-CA）平均超出4年。患有高血压和糖尿病的参与者，其脑龄平均超出3.5年。这些发现提出了将睡眠EEG作为评估健康大脑衰老的生物标志物的潜在可能性。