A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation.
Landau, Susan M
Alzheimer’s Disease Neuroimaging Initiative
MetadataShow full item record
Giorgio, J., Jagust, W. J., Baker, S., Landau, S. M., Tino, P., Kourtzi, Z., & Alzheimer’s Disease Neuroimaging Initiative. (2022). A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation.. Nat Commun 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.
Is supplemented by: https://doi.org/10.17863/CAM.80891
This work was supported by grants to: Z.K. from the Biotechnology and Biological Sciences Research Council (H012508 and BB/P021255/1), Alan Turing Institute (TU/B/000095), Wellcome Trust (205067/Z/16/Z, 221633/Z/20/Z), Royal Society (INF\R2\202107); Z.K. and W.J.J. from the Global Alliance; W.J.J. US National Institute on Aging (AG034570, AG062542, AG024904). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012).
Wellcome Trust (205067/Z/16/Z)
Biotechnology and Biological Sciences Research Council (BB/P021255/1)
Alan Turing Institute (EP/N510129/1)
Alan Turing Institute (SF\087)
Alan Turing Institute (Unknown)
Wellcome Trust (221633/Z/20/Z)
Alzheimer's Research UK (ARUK-EDoN2021-002)
Embargo Lift Date
External DOI: https://doi.org/10.1038/s41467-022-28795-7
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334023
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/