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Automated classification of depression from structural brain measures across two independent community‐based cohorts

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dc.contributor.authorStolicyn, Aleks
dc.contributor.authorHarris, Mathew A.
dc.contributor.authorShen, Xueyi
dc.contributor.authorBarbu, Miruna C.
dc.contributor.authorAdams, Mark J.
dc.contributor.authorHawkins, Emma L.
dc.contributor.authorde Nooij, Laura
dc.contributor.authorYeung, Hon Wah
dc.contributor.authorMurray, Alison D
dc.contributor.authorLawrie, Stephen M.
dc.contributor.authorSteele, J. Douglas
dc.contributor.authorMcIntosh, Andrew M.
dc.contributor.authorWhalley, Heather C.
dc.contributor.institutionUniversity of Aberdeen.Aberdeen Biomedical Imaging Centreen
dc.contributor.institutionUniversity of Aberdeen.Applied Medicineen
dc.date.accessioned2020-09-07T12:30:03Z
dc.date.available2020-09-07T12:30:03Z
dc.date.issued2020-10-01
dc.descriptionACKNOWLEDGEMENTS: This study was supported and funded by the Wellcome Trust Strategic Award ‘Stratifying Resilience and Depression Longitudinally’ (STRADL) (Reference 104036/Z/14/Z), and the Medical Research Council Mental Health Pathfinder Award ‘Leveraging routinely collected and linked research data to study the causes and consequences of common mental disorders’ (Reference MRC-MC_PC_17209). MAH is supported by research funding from the Dr Mortimer and Theresa Sackler Foundation. The research was conducted using the UK Biobank resource, with application number 4844. Structural brain imaging data from the UK Biobank was processed at the University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE) http://www.ccace.ed.ac.uk/), which is a part of the crosscouncil Lifelong Health and Wellbeing Initiative (MR/K026992/1). CCACE received funding from Biotechnology and Biological Sciences Research Council (BBSRC), Medical Research Council (MRC), and was also supported by Age UK as part of The Disconnected Mind project. This work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF) (http://www.ecdf.ed.ac.uk/)en
dc.description.statusPeer revieweden
dc.format.extent16
dc.format.extent1887316
dc.identifier175425723
dc.identifier4d5b9736-218a-410d-b637-ee1dc30a91b6
dc.identifier85087179913
dc.identifier000541158200001
dc.identifier.citationStolicyn, A, Harris, M A, Shen, X, Barbu, M C, Adams, M J, Hawkins, E L, de Nooij, L, Yeung, H W, Murray, A D, Lawrie, S M, Steele, J D, McIntosh, A M & Whalley, H C 2020, 'Automated classification of depression from structural brain measures across two independent community‐based cohorts', Human Brain Mapping, vol. 41, no. 14, pp. 3922-3937. https://doi.org/10.1002/hbm.25095en
dc.identifier.doi10.1002/hbm.25095
dc.identifier.issn1065-9471
dc.identifier.otherORCID: /0000-0003-4915-4847/work/80106716
dc.identifier.urihttps://hdl.handle.net/2164/15094
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85087179913&partnerID=8YFLogxKen
dc.language.isoeng
dc.relation.ispartofHuman Brain Mappingen
dc.subjectbrain structureen
dc.subjectclassical twinen
dc.subjectdepressionen
dc.subjectdiffusion MRIen
dc.subjectmachine learningen
dc.subjectmajor depressive disorderen
dc.subjectstructural MRIen
dc.subjectclassificationen
dc.subjectR Medicineen
dc.subjectClinical Neurologyen
dc.subjectNeurologyen
dc.subjectRadiological and Ultrasound Technologyen
dc.subjectRadiology Nuclear Medicine and imagingen
dc.subjectAnatomyen
dc.subjectMedical Research Council (MRC)en
dc.subjectMRC-MC_PC_17209en
dc.subjectWellcome Trusten
dc.subject104036/Z/14/Zen
dc.subjectSupplementary Dataen
dc.subject.lccRen
dc.titleAutomated classification of depression from structural brain measures across two independent community‐based cohortsen
dc.typeJournal articleen

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