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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.; [...]

Modeling autosomal dominant Alzheimer's disease with machine learning

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  • Medientyp: E-Artikel
  • Titel: Modeling autosomal dominant Alzheimer's disease with machine learning
  • Beteiligte: 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.; [...]
  • Erschienen: Wiley, 2021
  • Erschienen in: Alzheimer's & Dementia
  • Sprache: Englisch
  • DOI: 10.1002/alz.12259
  • ISSN: 1552-5260; 1552-5279
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: <jats:title>Abstract</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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 (<jats:italic>APOE</jats:italic> ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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<jats:sup>2</jats:sup> = 0.95), fluorodeoxyglucose (R<jats:sup>2</jats:sup> = 0.93), and atrophy (R<jats:sup>2</jats:sup> = 0.95) in mutation carriers compared to non‐carriers.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>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.</jats:p></jats:sec>