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

Joseph Giorgio, William J. Jagust, Suzanne Baker, Susan M. Landau, Peter Tino and Zoe Kourtzi ()
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Joseph Giorgio: University of Cambridge
William J. Jagust: University of California
Suzanne Baker: Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory
Susan M. Landau: University of California
Peter Tino: University of Birmingham
Zoe Kourtzi: University of Cambridge

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-28795-7

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