Bayesian latent time joint mixed-effects model of progression in the Alzheimer's Disease Neuroimaging Initiative

Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Introduction: We characterize long-term disease dynamics from cognitively healthy to dementia using data from the Alzheimer's Disease Neuroimaging Initiative. Methods: We apply a latent time joint mixed-effects model to 16 cognitive, functional, biomarker, and imaging outcomes in Alzheimer's Disease Neuroimaging Initiative. Markov chain Monte Carlo methods are used for estimation and inference. Results: We find good concordance between latent time and diagnosis. Change in amyloid positron emission tomography shows a moderate correlation with change in cerebrospinal fluid tau (ρ = 0.310) and phosphorylated tau (ρ = 0.294) and weaker correlation with amyloid-β 42 (ρ = 0.176). In comparison to amyloid positron emission tomography, change in volumetric magnetic resonance imaging summaries is more strongly correlated with cognitive measures (e.g., ρ = 0.731 for ventricles and Alzheimer's Disease Assessment Scale). The average disease trends are consistent with the amyloid cascade hypothesis. Discussion: The latent time joint mixed-effects model can (1) uncover long-term disease trends; (2) estimate the sequence of pathological abnormalities; and (3) provide subject-specific prognostic estimates of the time until onset of symptoms.

Original languageEnglish
Pages (from-to)657-668
Number of pages12
JournalAlzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volume10
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • Alzheimer's disease
  • Hierarchical Bayesian models
  • Joint mixed-effects models
  • Latent disease time
  • Multicohort longitudinal data
  • Multiple outcomes

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