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dc.creatorVieira, Sandraes
dc.creatorGong, Qi Yonges
dc.creatorPinaya, Walter H.L.es
dc.creatorScarpazza, Cristinaes
dc.creatorTognin, Stefaniaes
dc.creatorCrespo Facorro, Benedictoes
dc.creatorMechelli, Andreaes
dc.date.accessioned2023-04-14T11:24:05Z
dc.date.available2023-04-14T11:24:05Z
dc.date.issued2020
dc.identifier.citationVieira, S., Gong, Q.Y., Pinaya, W.H.L., Scarpazza, C., Tognin, S., Crespo Facorro, B. y Mechelli, A. (2020). Using machine learning and structural neuroimaging to detect first episode psychosis: Reconsidering the evidence. Schizophrenia Bulletin: The Journal of Psychoses and Related Disorders, 46 (1), 17-26. https://doi.org/10.1093/schbul/sby189.
dc.identifier.issn0586-7614es
dc.identifier.issn1745-1701(electrónico)es
dc.identifier.urihttps://hdl.handle.net/11441/144393
dc.description.abstractDespite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation.es
dc.formatapplication/pdfes
dc.format.extent10 p.es
dc.language.isoenges
dc.publisherOxford University Presses
dc.relation.ispartofSchizophrenia Bulletin: The Journal of Psychoses and Related Disorders, 46 (1), 17-26.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectPsychosises
dc.subjectMultivariate pattern recognition/classificationes
dc.subjectNeuroimaging/multi-sitees
dc.titleUsing machine learning and structural neuroimaging to detect first episode psychosis: Reconsidering the evidencees
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Psiquiatríaes
dc.relation.projectIDMC_PC_11003es
dc.relation.projectIDMRF_C0439es
dc.relation.projectID208519/Z/17/Zes
dc.relation.publisherversionhttps://academic.oup.com/schizophreniabulletin/article/46/1/17/5365736es
dc.identifier.doi10.1093/schbul/sby189es
dc.journaltitleSchizophrenia Bulletin: The Journal of Psychoses and Related Disorderses
dc.publication.volumen46es
dc.publication.issue1es
dc.publication.initialPage17es
dc.publication.endPage26es
dc.contributor.funderMedical Research Counciles
dc.contributor.funderMRFes
dc.contributor.funderWellcome Trustes

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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