S141. Predicting brain age from the electroencephalogram of sleep

Abstract

Introduction The human electroencephalogram (EEG) of sleep undergoes profound changes with age, such as decreased sleep spindle amplitude and density in non-rapid eye movement stage 2 (NREM2). However, it is unknown how accurately a patient’s age can be predicted from EEG activity during sleep. A quantitative characterization of age-related EEG provides important insights into healthy aging. Moreover, the ability to detect deviations of EEG patterns from those typical for age could provide insights into age-related neurological disorders, and might provide a way to gauge the effects of interventions designed to preserve or improve brain health. Here we develop a model to predict a patient’s age based on large-scale and heterogeneous sleep EEG datasets. The prediction is called “brain age” (BA). Methods Datasets: (1) MGH sleep dataset: 3100 patients aged 18–80 years. (2) sleep-heart health study (SHHS): 3680 paired recordings aged 18–80 years, where each pair is recorded approximately 5 years apart from the same subject. This dataset is used to verify the longitudinal reliability of the brain age. EEG features: 102 features are extracted from 30s-epochs, and then averaged separately for the 5 sleep stages, yielding 510 features to summarize each patient’s overnight sleep. We also analyze medications and clinical variables to identify factors that help account for brain age being older or younger than chronological age. Results For 1000 testing patients from MGH dataset, the Pearson’s correlation between EEG-based brain age and chronological age is 0.86 (95% CI 0.84–0.87). The mean absolute prediction error (MAE) is 6.6 years. For SHHS dataset, training the model on a subset of 2000 records and testing on the other 1680 records achieves correlation at 0.71 (95% CI 0.69–0.73) and MAE 5.9 yrs. The average difference of BA between each pair is 4.8yrs. Training the entire MGH dataset and testing on SHHS achieves correlation at 0.61 (95% CI 0.59–0.63) and MAE 8.6 yrs. The average difference of BA between each pair is 3.7 yrs. In the MGH dataset, older brain age (predicted age greater than chronological age) is associated diabetes (Kruskal-Wallis test p-value 0.01) and weakly associated with wake time after sleep onset (Pearson’s correlation p-value 0.07). Conclusion Our results indicate that, at the population level, chronological age can be accurately predicted from overnight sleep EEG. Moreover, brain age accurately tracks chronological age. Further research is needed to characterize how EEG-based brain age relates to cognitive function and to what degree and by what means brain age is modifiable.