Can neuroimaging be used as a support to diagnosis of borderline personality disorder? An approach based on computational neuroanatomy and machine learning

Can neuroimaging be used as a support to diagnosis of borderline personality disorder? An approach based on computational neuroanatomy and machine learning

Autor Sato, Joao Ricardo Autor UNIFESP Google Scholar
Araujo Filho, Gerardo Maria de Autor UNIFESP Google Scholar
Araujo, Thabata Bueno de Autor UNIFESP Google Scholar
Bressan, Rodrigo Affonseca Autor UNIFESP Google Scholar
Oliveira, Pedro Paulo de Google Scholar
Jackowski, Andrea Parolin Autor UNIFESP Google Scholar
Instituição Universidade Federal do ABC (UFABC)
Universidade Federal de São Paulo (UNIFESP)
Universidade de São Paulo (USP)
Resumo Several recent studies in literature have identified brain morphological alterations associated to Borderline Personality Disorder (BPD) patients. These findings are reported by studies based on voxel-based-morphometry analysis of structural MRI data, comparing mean gray-matter concentration between groups of BPD patients and healthy controls. On the other hand, mean differences between groups are not informative about the discriminative value of neuroimaging data to predict the group of individual subjects. in this paper, we go beyond mean differences analyses, and explore to what extent individual BPD patients can be differentiated from controls (25 subjects in each group), using a combination of automated-morphometric tools for regional cortical thickness/volumetric estimation and Support Vector Machine classifier. the approach included a feature selection step in order to identify the regions containing most discriminative information. the accuracy of this classifier was evaluated using the leave-one-subject-out procedure. the brain regions indicated as containing relevant information to discriminate groups were the orbitofrontal, rostral anterior cingulate, posterior cingulate, middle temporal cortices, among others. These areas, which are distinctively involved in emotional and affect regulation of BPD patients, were the most informative regions to achieve both sensitivity and specificity values of 80% in SVM classification. the findings suggest that this new methodology can add clinical and potential diagnostic value to neuroimaging of psychiatric disorders. (C) 2012 Elsevier B.V. All rights reserved.
Assunto Borderline
Neuroimaging
Morphometry
Classification
Support Vector Machines
Biomarker
Idioma Inglês
Financiador Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Data 2012-09-01
Publicado em Journal of Psychiatric Research. Oxford: Pergamon-Elsevier B.V., v. 46, n. 9, p. 1126-1132, 2012.
ISSN 0022-3956 (Sherpa/Romeo, fator de impacto)
Editor Elsevier B.V.
Extensão 1126-1132
Fonte http://dx.doi.org/10.1016/j.jpsychires.2012.05.008
Direito de acesso Acesso restrito
Tipo Artigo
Web of Science WOS:000307863900003
URI http://repositorio.unifesp.br/handle/11600/35230

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