Identification of psychopathic individuals using pattern classification of MRI images

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dc.contributor.authorSato J.R.
dc.contributor.authorde Oliveira-Souza R.
dc.contributor.authorThomaz C.E.
dc.contributor.authorBasilio R.
dc.contributor.authorBramati I.E.
dc.contributor.authorAmaro Jr. E.
dc.contributor.authorTovar-Moll F.
dc.contributor.authorHare R.D.
dc.contributor.authorMoll J.
dc.date.accessioned2019-08-19T23:45:23Z
dc.date.available2019-08-19T23:45:23Z
dc.date.issued2011
dc.description.abstractBackground: Psychopathy is a disorder of personality characterized by severe impairments of social conduct, emotional experience, and interpersonal behavior. Psychopaths consistently violate social norms and bring considerable financial, emotional, or physical harm to others and to society as a whole. Recent developments in analysis methods of magnetic resonance imaging (MRI), such as voxel-based-morphometry (VBM), have become major tools to understand the anatomical correlates of this disorder. Nevertheless, the identification of psychopathy by neuroimaging or other neurobiological tools (e.g., genetic testing) remains elusive. Methods/Principal findings: The main aim of this study was to develop an approach to distinguish psychopaths from healthy controls, based on the integration between pattern recognition methods and gray matter quantification. We employed support vector machines (SVM) and maximum uncertainty linear discrimination analysis (MLDA), with a feature-selection algorithm. Imaging data from 15 healthy controls and 15 psychopathic individuals (7 women in each group) were analyzed with SPM2 and the optimized VBM preprocessing routines. Participants were scanned with a 1.5 Tesla MRI system. Both SVM and MLDA achieved an overall leave-one-out accuracy of 80%, but SVM mapping was sparser than using MLDA. The superior temporal sulcus/gyrus (bilaterally) was identified as a region containing the most relevant information to separate the two groups. Conclusion/significance: These results indicate that gray matter quantitative measures contain robust information to predict high psychopathy scores in individual subjects. The methods employed herein might prove useful as an adjunct to the established clinical and neuropsychological measures in patient screening and diagnostic accuracy. © 2011 Copyright 2011 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business.
dc.description.firstpage627
dc.description.issuenumber5-6
dc.description.lastpage639
dc.description.volume6
dc.identifier.citationSATO, J. R.; OLIVEIRA-SOUZA, R.; THOMAZ, C. E.; BASILIO, R.; BRAMATI, I. E.; Amaro Jr., Edson; TOVAR-MOLL, F.; HARE, R. D.; MOLL, J.. Identification of psychopathic individuals using pattern classification of MR images. Social Neuroscience, v. May 14, p. 1-13, 2011.
dc.identifier.doi10.1080/17470919.2011.562687
dc.identifier.issn1747-0919
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/1256
dc.relation.ispartofSocial Neuroscience
dc.rightsAcesso Restrito
dc.subject.otherlanguageAntisocial
dc.subject.otherlanguageMachine learning
dc.subject.otherlanguageMoral
dc.subject.otherlanguagePsychopathy
dc.subject.otherlanguageVoxel-based morphometry
dc.titleIdentification of psychopathic individuals using pattern classification of MRI images
dc.typeArtigo
fei.scopus.citations36
fei.scopus.eid2-s2.0-84857433267
fei.scopus.updated2024-03-04
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84857433267&origin=inward
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