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dc.contributor.authorGiorgio, Joseph
dc.contributor.authorJagust, William J
dc.contributor.authorBaker, Suzanne
dc.contributor.authorLandau, Susan M
dc.contributor.authorTino, Peter
dc.contributor.authorKourtzi, Zoe
dc.contributor.authorAlzheimer’s Disease Neuroimaging Initiative
dc.date.accessioned2022-02-15T00:30:20Z
dc.date.available2022-02-15T00:30:20Z
dc.date.issued2022-04-07
dc.identifier.issn2041-1723
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334023
dc.description.abstractThe 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.
dc.description.sponsorshipThis 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).
dc.publisherNature Research
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleA robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation.
dc.typeArticle
dc.publisher.departmentDepartment of Psychology
dc.date.updated2022-02-11T16:53:32Z
prism.publicationNameNat Commun
dc.identifier.doi10.17863/CAM.81435
dcterms.dateAccepted2022-02-11
rioxxterms.versionofrecord10.1038/s41467-022-28795-7
rioxxterms.versionAM
dc.contributor.orcidJagust, William J [0000-0002-4458-113X]
dc.contributor.orcidBaker, Suzanne [0000-0003-0209-3127]
dc.contributor.orcidKourtzi, Zoe [0000-0001-9441-7832]
dc.identifier.eissn2041-1723
rioxxterms.typeJournal Article/Review
pubs.funder-project-idWellcome Trust (205067/Z/16/Z)
pubs.funder-project-idBiotechnology and Biological Sciences Research Council (BB/P021255/1)
pubs.funder-project-idAlan Turing Institute (EP/N510129/1)
pubs.funder-project-idAlan Turing Institute (SF\087)
pubs.funder-project-idAlan Turing Institute (Unknown)
pubs.funder-project-idWellcome Trust (221633/Z/20/Z)
pubs.funder-project-idAlzheimer's Research UK (ARUK-EDoN2021-002)
datacite.ispreviousversionof.handlehttps://www.repository.cam.ac.uk/handle/1810/336967
datacite.issupplementedby.urlhttps://doi.org/10.17863/CAM.80891
cam.orpheus.success2022-07-06: VoR is JISC record
cam.orpheus.counter3
cam.depositDate2022-02-11
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2100-01-01


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International