Enriched resting-state EEG prediction of cognitive decline in prodromal Alzheimer's disease: a machine-learning approach.

OBJECTIVE

We evaluated the accuracy of standard machine learning (ML) algorithms in predicting 1-year cognitive decline in Alzheimer's disease patients with mild cognitive impairment (ADMCI) using resting-state electroencephalographic (rsEEG) biomarkers enriched with APOE genotype, sex, age, and educational attainment data.

METHODS

The study analyzed datasets from 63 ADMCI patients obtained from an international archive. The ML algorithms included Simple Logistic Regression, Model Trees, Logistic Regression, K-nearest neighbor, and Support Vector Machine. Input features comprised lobar rsEEG source activities across delta (<4 Hz) to alpha (≈10-12 Hz) bands, cerebrospinal fluid (CSF Aβ1-42/p-tau), and structural magnetic resonance imaging (sMRI) biomarkers. Cognitive decline was assessed over a 1-year follow-up ("stable" vs. "decliner") based on Mini-Mental State Examination (MMSE) scores.

RESULTS

The four independent ML algorithms accurately predicted changes in the MMSE score over a 1-year follow-up, with accuracies of 77-78% in ADMCI participants aged ≥ 70 years and 74-77% in those aged < 70 years.

CONCLUSIONS AND SIGNIFICANCE: These findings suggest that rsEEG biomarkers in ADMCI patients may not only reveal underlying pathophysiological mechanisms affecting cortical arousal and vigilance but also hold predictive value for cognitive outcomes.