Signals of Decline: Machine Learning driven Biomarkers for Alzheimer’s Disease

Alzheimer’s disease (AD) is a growing global health challenge, with pathological changes beginning decades before clinical symptoms. Identifying non-invasive and interpretable biomarkers is critical for early intervention. Magnetoencephalography (MEG) provides access to brain oscillatory dynamics and connectivity patterns that are disrupted in mild cognitive impairment (MCI), a prodromal stage of AD. We evaluate five families of MEG-derived features and train an ensemble of 200 feature models, achieving MCI classification with F1 72.43%. We use Shapley Additive Explanations (SHAP) to highlight discriminative regions and connections, offering interpretable insights and pointing to potential new markers. Beyond binary detection, model scores correlate with Mini-Mental State Examination (MMSE) scores, suggesting potential for continuous disease staging. Together, these results establish MEG-based machine learning as a promising avenue for robust and clinically meaningful biomarkers.