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.

Keywords: Pittsburgh compound B (PiB); autosomal dominant Alzheimer's disease (ADAD); fluorodeoxyglucose (FDG); machine learning; magnetic resonance imaging (MRI).