A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation
Publication Date
2022-12Journal Title
Nature Communications
Publisher
Springer Science and Business Media LLC
Volume
13
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
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
Description
Funder: Global Alliance
Abstract
<jats:title>Abstract</jats:title><jats:p>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.</jats:p>
Keywords
Article, /631/378/116/2396, /692/617/375/132/1283, /59, /59/78, /123, /129, article
Sponsorship
RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) (H012508, BB/P021255/1)
Alan Turing Institute (TU/B/000095)
Wellcome Trust (Wellcome) (205067/Z/16/Z, 221633/Z/20/Z)
Royal Society (INF/R2/202107)
U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (AG034570, AG062542, AG024904)
Identifiers
s41467-022-28795-7, 28795
External DOI: https://doi.org/10.1038/s41467-022-28795-7
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335879
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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