Brain Age from the Electroencephalogram of Sleep

By Haoqi Sun, L. Paixão, J. T. Oliva, B. Goparaju, Diego Z. Carvalho, K. V. van Leeuwen, Akeju Oluwaseun, R. Thomas, S. Cash, M. Bianchi and 1 more. 2018

131 citations recorded by the source corpus.

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as “brain age”, 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 brain age based on two large sleep EEG datasets: the Massachusetts General Hospital sleep lab dataset (MGH, N = 2,532; ages 18 to 80); and the Sleep Hearth Health Study (SHHS, N = 1,974; ages 40 to 80). The model obtains a mean absolute deviation of 7.6 years between brain age (BA) and chronological age (CA) in healthy participants in the MGH dataset. 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 brain age, or “brain age index” (BAI = BA - CA) of 4 years relative to healthy controls. Participants with hypertension and diabetes have a mean excess brain age of 3.5 years. The findings raise the prospect of using the sleep EEG as a potential biomarker for healthy brain aging.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Physiology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Cuntai Guan
2. Erico Tjoa
3. Fabien Lotte
4. Dongrui Wu

In Physiology

1. Cuntai Guan
2. Erico Tjoa

Citations in the corpus

• A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “from mean values of sleep EEG features of healthy subjects [156].”
• Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso (2025, unknown): “Sun et al. (2019) adopted the temporal statistical indicators and band powers, PR and RP, as upstream features and an interpretable machne learning model for the prediction of BA based on sleep EEG.”