Modeling autosomal dominant Alzheimer's disease with machine learning. Issue 6 (21st January 2021)

• Record Type: Journal Article
• Authors: 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.; Cruchaga, Carlos; Fagan, Anne M.; Farlow, Martin R.; Fox, Nick C.; Jucker, Mathias; Levin, Johannes; Masters, Colin L.; Mori, Hiroshi; Noble, James M.; Salloway, Stephen; Schofield, Peter R.; Brickman, Adam M.; Brooks, William S.; Cash, David M.; Fulham, Michael J.; Ghetti, Bernardino; Jack, Clifford R.; Vöglein, Jonathan; Klunk, William; Koeppe, Robert; Oh, Hwamee; Su, Yi; Weiner, Michael; Wang, Qing; Swisher, Laura; Marcus, Dan; Koudelis, Deborah; Joseph‐Mathurin, Nelly; Cash, Lisa; Hornbeck, Russ; Xiong, Chengjie; Perrin, Richard J.; Karch, Celeste M.; Hassenstab, Jason; McDade, Eric; Morris, John C.; Benzinger, Tammie L.S.; Bateman, Randall J.; Ances, Beau M.;
• Is Part Of: Alzheimer's & dementia. Volume 17:Issue 6(2021)
• Page Start: 1005
• Page End: 1016
• Publication Date: 2021-01-21
• Subjects: autosomal dominant Alzheimer's disease (ADAD) -- fluorodeoxyglucose (FDG) -- machine learning -- magnetic resonance imaging (MRI) -- Pittsburgh compound B (PiB); Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.12259
• Languages: English
• ISSNs: 1552-5260

Abstract

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.