Modeling autosomal dominant Alzheimer's disease with machine learning. Issue 6 (21st January 2021) - Luckett, Patrick H.; McCullough, Austin; Gordon, Brian A.; Strain, Jeremy; Flores, Shaney; Dincer, Aylin; McCarthy, John; Kuffner, Todd; Stern, Ari; Meeker, Karin L.; Berman, Sarah B.; Chhatwal, Jasmeer P.; et al.
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 (R 2  = 0.95), fluorodeoxyglucose (R 2  = 0.93), and atrophy (R 2  = 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.