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dc.contributor.authorNieuwenhuis, Mireille
dc.contributor.authorSchnack, Hugo G.
dc.contributor.authorHaren, Neeltje E. van
dc.contributor.authorLappin, Julia
dc.contributor.authorMorgan, Craig
dc.contributor.authorReinders, Antje A.
dc.contributor.authorTordesillas Gutiérrez, Diana 
dc.contributor.authorRoiz Santiáñez, Roberto Miguel
dc.contributor.authorSchaufelberger, Maristela S.
dc.contributor.authorRosa, Pedro G.
dc.contributor.authorZanetti, Marcus V.
dc.contributor.authorBusatto, Geraldo F.
dc.contributor.authorCrespo Facorro, Benedicto 
dc.contributor.authorMcGorry, Patrick D.
dc.contributor.authorVelakoulis, Dennis
dc.contributor.authorPantelis, Christos
dc.contributor.authorWood, Stephen J.
dc.contributor.authorKahn, René S.
dc.contributor.authorMourao Miranda, Janaina
dc.contributor.authorDazzan, Paola
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2018-05-18T16:43:36Z
dc.date.available2018-05-18T16:43:36Z
dc.date.issued2017-01
dc.identifier.issn1053-8119
dc.identifier.issn1095-9572
dc.identifier.urihttp://hdl.handle.net/10902/13712
dc.description.abstractStructural Magnetic Resonance Imaging (MRI) studies have attempted to use brain measures obtained at the first-episode of psychosis to predict subsequent outcome, with inconsistent results. Thus, there is a real need to validate the utility of brain measures in the prediction of outcome using large datasets, from independent samples, obtained with different protocols and from different MRI scanners. This study had three main aims: 1) to investigate whether structural MRI data from multiple centers can be combined to create a machine-learning model able to predict a strong biological variable like sex; 2) to replicate our previous finding that an MRI scan obtained at first episode significantly predicts subsequent illness course in other independent datasets; and finally, 3) to test whether these datasets can be combined to generate multicenter models with better accuracy in the prediction of illness course. The multi-center sample included brain structural MRI scans from 256 males and 133 females patients with first episode psychosis, acquired in five centers: University Medical Center Utrecht (The Netherlands) (n=67); Institute of Psychiatry, Psychology and Neuroscience, London (United Kingdom) (n=97); University of São Paulo (Brazil) (n=64); University of Cantabria, Santander (Spain) (n=107); and University of Melbourne (Australia) (n=54). All images were acquired on 1.5-Tesla scanners and all centers provided information on illness course during a follow-up period ranging 3 to 7years. We only included in the analyses of outcome prediction patients for whom illness course was categorized as either "continuous" (n=94) or "remitting" (n=118). Using structural brain scans from all centers, sex was predicted with significant accuracy (89%; p<0.001). In the single- or multi-center models, illness course could not be predicted with significant accuracy. However, when reducing heterogeneity by restricting the analyses to male patients only, classification accuracy improved in some samples. This study provides proof of concept that combining multi-center MRI data to create a well performing classification model is possible. However, to create complex multi-center models that perform accurately, each center should contribute a sample either large or homogeneous enough to first allow accurate classification within the single-center.es_ES
dc.description.sponsorshipThe Melbourne research was supported by grants from the National Health andMedical Research Council of Australia (Program Grants, IDs: 350241; 566529). Christos Pantelis and Patrick McGorry were supported by NHMRC Senior Principal Research Fellowships (ID: 628386; 1060996). The London researchwas supported by UKMedical Research Council (Ref: G0500817) and the Department of Health via the National Institute for Health Research (NIHR) Specialist Biomedical Research Center for Mental Health award to South London and Maudsley NHS Foundation Trust (SLaM) and the Institute of Psychiatry at King's College London, London. Diana Tordesillas-Gutiérrez is funded by a contract from the Carlos III Health Institute (CA12/00312). A.A.T.S. Reinders was supported by the Netherlands Organization for Scientific Research (www.nwo.nl), NWO-VENI grant no. 451-07-009. Janaina Mourao-Miranda was supported by the Wellcome Trust under grants no. WT086565/Z/08/Z and no. WT102845/Z/13/Z.es_Es
dc.format.extent8 p.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rights© 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC Attribution 4.0 Internationales_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceNeuroimage. 2017 Jan 15;145(Pt B):246-253es_ES
dc.titleMulti-center MRI prediction models: Predicting sex and illness course in first episode psychosis patientses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://dx.doi.org/10.1016/j.neuroimage.2016.07.027es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1016/j.neuroimage.2016.07.027
dc.type.versionpublishedVersiones_ES


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© 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC Attribution 4.0 InternationalExcept where otherwise noted, this item's license is described as © 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC Attribution 4.0 International