Modeling autosomal dominant Alzheimer's disease with machine learning

for the Dominantly Inherited Alzheimer Network (DIAN)

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1005-1016
Number of pages12
JournalAlzheimer's and Dementia
Volume17
Issue number6
DOIs
StatePublished - Jun 2021

Keywords

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

ASJC Scopus subject areas

  • Epidemiology
  • Health Policy
  • Developmental Neuroscience
  • Clinical Neurology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience

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