A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation

Alzheimer’s Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

Abstract

The early stages of Alzheimer's disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal lobe atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: (a) stratifies patients based on future pathological tau accumulation, (b) predicts individualised regional future rate of tau accumulation, and (c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD.

Original languageEnglish
Pages (from-to)1887
JournalNature Communications
Volume13
Issue number1
DOIs
Publication statusPublished - 7 Apr 2022

Bibliographical note

© 2022. The Author(s).

Keywords

  • Alzheimer Disease/pathology
  • Amyloid beta-Peptides
  • Apolipoprotein E4
  • Biomarkers
  • Cognitive Dysfunction
  • Humans
  • Machine Learning
  • Magnetic Resonance Imaging/methods
  • Positron-Emission Tomography/methods
  • tau Proteins

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