Better Sleep Now, Better Cognition Later? Predicting Cognitive Function Using A Machine Learning-Based Sleep EEG Brain Health Score.

STUDY OBJECTIVES: Sleep state electrocortical activity measured using electroencephalograms (EEG) is linked with cognitive function and dementia risk. The rich information in overnight EEG can be condensed into integrated scores using machine learning. The Brain Health Score (BHS) was derived in prior work using an end-to-end deep learning model trained to jointly optimize higher cognitive function and lower disease risk from raw EEG. However, it is unknown whether the BHS predicts future neuropsychological (NP) performance. METHODS: This study included 426 Framingham Heart Study (FHS) Generation 2 or Omni 1 participants with BHS values from in-home polysomnography in mid-to-late life, and subsequent digital clock drawing (dCDT) and NP testing an average of 12.6 years later. Linear regression models estimated associations between BHS and dCDT, memory, language, or executive function scores, adjusting for age, sex, race, education, smoking, body mass index, and FHS cohort. To enable comparisons across outcomes, the independent variables were centered and rescaled, and then the model was refitted to generate standardized estimates. RESULTS: Participants were on average 56 years at sleep assessment, 55% female, and 86% non-Hispanic White. Each 1-SD higher BHS was associated with higher dCDT, memory, language, and executive function scores (dCDT: β=0.16, 95% CI=0.06-0.26; memory: β=0.13, 95% CI=0.03-0.23; language: β=0.13, 95% CI=0.03-0.23; executive: β=0.10, 95% CI=0.01-0.19). CONCLUSIONS: Higher BHS in mid-to-late life was associated with better digital and traditional NP performance more than a decade later. These findings support the potential of EEG-derived, data-driven scores as a biomarker of future cognitive health.