Modeling autosomal dominant Alzheimer's disease with machine learning

Abstract

Introduction Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. Methods Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non‐carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. Results The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R² = 0.95), fluorodeoxyglucose (R² = 0.93), and atrophy (R² = 0.95) in mutation carriers compared to non‐carriers. Discussion Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.