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dc.contributor.authorDoan, Nhat Trung
dc.contributor.authorEngvig, Andreas
dc.contributor.authorZaske, Krystal
dc.contributor.authorPersson, Karin
dc.contributor.authorLund, Martina Jonette
dc.contributor.authorKaufmann, Tobias
dc.contributor.authorCòrdova Palomera, Aldo
dc.contributor.authorAlnæs, Dag
dc.contributor.authorMoberget, Torgeir
dc.contributor.authorBrækhus, Anne
dc.contributor.authorBarca, Maria Lage
dc.contributor.authorNordvik, Jan Egil
dc.contributor.authorEngedal, Knut
dc.contributor.authorAgartz, Ingrid
dc.contributor.authorSelbæk, Geir
dc.contributor.authorAndreassen, Ole Andreas
dc.contributor.authorWestlye, Lars Tjelta
dc.date.accessioned2018-10-10T11:32:36Z
dc.date.available2018-10-10T11:32:36Z
dc.date.created2017-07-12T11:51:19Z
dc.date.issued2017
dc.identifier.citationNeuroImage. 2017, 158 282-295.nb_NO
dc.identifier.issn1053-8119
dc.identifier.urihttp://hdl.handle.net/11250/2567408
dc.description.abstractAbstract Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18-78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis. Using the lasso classifier, we performed group classification and prediction of cognition and age at different age ranges to assess the sensitivity and diagnostic accuracy of the LICA patterns in relation to AD, as well as early and late healthy aging. Three components showed high sensitivity to the diagnosis and cognitive status of AD, with different relationships with age: one reflected an anterior-posterior gradient in thickness and gray matter density and was uniquely related to diagnosis, whereas the other two, reflecting widespread cortical thickness and medial temporal lobe volume, respectively, also correlated significantly with age. Repeating the LICA decomposition and between-subject analysis on ADNI data, including 186 AD, 395 MCI and 220 age-matched healthy controls, revealed largely consistent brain patterns and clinical associations across samples. Classification results showed that multivariate LICA-derived brain characteristics could be used to predict AD and age with high accuracy (area under ROC curve up to 0.93 for classification of AD from controls). Comparison between classifiers based on feature ranking and feature selection suggests both common and unique feature sets implicated in AD and aging, and provides evidence of distinct age-related differences in early compared to late aging.nb_NO
dc.description.sponsorshipThe work was supported by the European Commission’s 7th Framework Programme (#602450, IMAGEMEND), Research Council of Norway (213837, 223273, 204966/F20), the South-Eastern Norway Regional Health Authority (2013123, 2014097, 2015073, 2016083), The Norwegian Health Association's Dementia Research Program, and KG Jebsen Foundation. We acknowledge the contribution of patient data from the Norwegian registry for persons being evaluated for cognitive symptoms in specialized care (NorCog) by the Norwegian National Advisory Unit on Ageing and Health. 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). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectAlzheimer's disease;nb_NO
dc.subjectAlzheimer's disease spectrum;nb_NO
dc.subjectEarly and late aging;nb_NO
dc.subjectLinked independent component analysis;nb_NO
dc.subjectMachine learning;nb_NO
dc.titleDistinguishing early and late brain aging from the Alzheimer's disease spectrum: Consistent morphological patterns across independent samplesnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holder© 2017 Elsevier Inc. All rights reserved. The Authors.Creative Commons Attribution Non-Commercial No Derivatives License.nb_NO
dc.source.pagenumber282-295nb_NO
dc.source.volume158nb_NO
dc.source.journalNeuroImagenb_NO
dc.identifier.doi10.1016/j.neuroimage.2017.06.070
dc.identifier.cristin1482015
dc.relation.projectNorges forskningsråd: 249795nb_NO
dc.relation.projectHelse Sør-Øst RHF: 2015073nb_NO
dc.relation.projectHelse Sør-Øst RHF: 2014097nb_NO
cristin.unitcode1991,9,1,0
cristin.unitnameAvd Alderspsykiatri
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.fulltextpostprint
cristin.qualitycode2


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