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dc.contributor.authorSato, Joao Ricardo [UNIFESP]
dc.contributor.authorBiazoli, Claudinei Eduardo, Jr.
dc.contributor.authorSalum, Giovanni Abrahao
dc.contributor.authorGadelha, Ary [UNIFESP]
dc.contributor.authorCrossley, Nicolas
dc.contributor.authorVieira, Gilson
dc.contributor.authorZugman, Andre [UNIFESP]
dc.contributor.authorPicon, Felipe Almeida
dc.contributor.authorPan, Pedro Mario [UNIFESP]
dc.contributor.authorHoexter, Marcelo Queiroz [UNIFESP]
dc.contributor.authorAmaro, Edson, Jr.
dc.contributor.authorAnes, Mauricio
dc.contributor.authorMoura, Luciana Monteiro [UNIFESP]
dc.contributor.authorGomes Del'Aquilla, Marco Antonio [UNIFESP]
dc.contributor.authorMcguire, Philip
dc.contributor.authorRohde, Luis Augusto
dc.contributor.authorMiguel, Euripedes Constantino
dc.contributor.authorJackowski, Andrea Parolin [UNIFESP]
dc.contributor.authorBressan, Rodrigo Affonseca [UNIFESP]
dc.identifier.citationWorld Journal Of Biological Psychiatry. Abingdon, v. 19, n. 2, p. 119-129, 2018.-
dc.description.abstractObjectives: One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity.In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM).Methods: We applied this approach to resting-state fMRI data from 622 children and adolescents. Eigenvector centrality (EVC) of nodes from positive- and negative-task networks were extracted from each subject and used as input to an OC-SVM to label individual brain networks as typical or atypical. We hypothesised that classification of these subjects regarding the pattern of brain connectivity would predict the level of psychopathology.Results: Subjects with atypical brain network organisation had higher levels of psychopathology (p<0.001). There was a greater EVC in the typical group at the bilateral posterior cingulate and bilateral posterior temporal corticesen
dc.description.abstractand significant decreases in EVC at left temporal pole.Conclusions: The combination of graph theory methods and an OC-SVM is a promising method to characterise neurodevelopment, and may be useful to understand the deviations leading to mental disorders.en
dc.description.sponsorshipSao Paulo Research Foundation - FAPESP [2013/10498-6, 2013/00506-1, 2013/08531-5]
dc.description.sponsorshipCNPq, Brazil
dc.description.sponsorshipCNPq [573974/2008-0]
dc.description.sponsorshipFAPESP [2008/57896-8, 2013/16864-4]
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofWorld Journal Of Biological Psychiatry
dc.rightsAcesso restrito
dc.subjectmachine learningen
dc.titleAssociation between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learningen
dc.description.affiliationUniv Fed ABC, Ctr Math Computat & Cognit, Santo Andre, Brazil
dc.description.affiliationUniv Fed Sao Paulo UNIFESP, Interdisciplinary Lab Clin Neurosci LiNC, Dept Psychiat, Sao Paulo, Brazil
dc.description.affiliationUniv Sao Paulo, Sch Med, Dept Radiol, Sao Paulo, Brazil
dc.description.affiliationCNPq, Natl Inst Dev Psychiat Children & Adolescents, Brasilia, DF, Brazil
dc.description.affiliationUniv Fed Rio Grande do Sul, Hosp Clin Porto Alegre, Porto Alegre, RS, Brazil
dc.description.affiliationUniv Fed Rio Grande do Sul, Dept Psychiat, Porto Alegre, RS, Brazil
dc.description.affiliationKings Coll London, Inst Psychiat, Dept Psychosis Studies, London, England
dc.description.affiliationUniv Sao Paulo, Inst Math & Stat, Bioinformat Program, Sao Paulo, Brazil
dc.description.affiliationUniv Sao Paulo, Sch Med, Dept Psychiat, Sao Paulo, Brazil
dc.description.affiliationUniv Sao Paulo, Fac Med, Inst Radiol InRad, Sao Paulo, Brazil
dc.description.affiliationUnifespUniv Fed Sao Paulo UNIFESP, Interdisciplinary Lab Clin Neurosci LiNC, Dept Psychiat, Sao Paulo, Brazil
dc.description.sponsorshipIDFAPESP [2013/10498-6, 2013/00506-1, 2013/08531-5]
dc.description.sponsorshipIDCNPq, Brazil
dc.description.sponsorshipIDCNPq [573974/2008-0]
dc.description.sponsorshipIDFAPESP [2008/57896-8, 2013/16864-4]
dc.description.sourceWeb of Science
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