A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation.
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Giorgio, J., Jagust, W. J., Baker, S., Landau, S. M., Tino, P., & Kourtzi, Z. (2022). A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation.. Nature communications, 13 (1) https://doi.org/10.1038/s41467-022-28795-7
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.
Humans, Alzheimer Disease, tau Proteins, Positron-Emission Tomography, Magnetic Resonance Imaging, Apolipoprotein E4, Amyloid beta-Peptides, Biomarkers, Machine Learning, Cognitive Dysfunction
Biotechnology and Biological Sciences Research Council (H012508, BB/P021255/1, BB/P021255/1)
Wellcome Trust (221633/Z/20/Z, 205067/Z/16/Z, 205067/Z/16/Z, 221633/Z/20/Z)
NIA NIH HHS (U01 AG024904, R01 AG034570, R01 AG062542, U19 AG024904)
Royal Society (INF/R2/202107)
Alan Turing Institute (TU/B/000095)
U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (AG034570, AG062542, AG024904)
External DOI: https://doi.org/10.1038/s41467-022-28795-7
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336967
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/