Article
Modeling autosomal dominant Alzheimer's disease with machine learning.
Alzheimer's & dementia : the journal of the Alzheimer's Association - 1 Jun 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, 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
Abstract excerpt
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,...
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