Predicting obsessive-compulsive disorder severity combining neuroimaging and machine learning methods

Autor(es)

Hoexter, Marcelo Q. ^[1] ; Miguel, Euripedes C. ^[1] ; Diniz, Juliana B. ^[1] ; Shavitt, Roseli G. ^[1] ; Busatto, Geraldo F. ^[1] ; Sato, Joao R. ^[2]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sch Med, Dept & Inst Psychiat, BR-05403010 Sao Paulo - Brazil

^[2] Univ Fed ABC, Ctr Math Computat & Cognit, Santo Andre - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Journal of Affective Disorders; v. 150, n. 3, p. 1213-1216, SEP 25 2013.
• Citações Web of Science: 19

Resumo

Background: Recently, machine learning methods have been used to discriminate, on an individual basis, patients from healthy controls through brain structural magnetic resonance imaging (MRI). However, the application of these methods to predict the severity of psychiatric symptoms is less common. Methods: Herein, support vector regression (SVR) was employed to evaluate whether gray matter volumes encompassing cortical-subcortical loops contain discriminative information to predict obsessive compulsive disorder (OCD) symptom severity in 37 treatment naive adult OCD patients. Results: The Pearson correlation coefficient between predicted and observed symptom severity scores was 0.49 (p=0.002) for total Dimensional Yale Brown Obsessive-Compulsive Scale (DY-BOCS) and 044 (p=0.006) for total Yale Brown Obsessive-Compulsive Scale (Y-BOCS). The regions that contained the most discriminative information were the left medial orbitofrontal cortex and the left putamen for both scales. Limitations: Our sample is relatively small and our results must be replicated with independent and larger samples. Conclusions: These results indicate that machine learning methods such as SVR analysis may identify neurobiological markers to predict OCD symptom severity based on individual structural MRI datasets. (C) 2013 Elsevier B.V. All rights reserved (AU)

• Processo FAPESP: 05/55628-8 - Caracterização fenotípica, genética, imunológica e neurobiológica do transtorno obsessivo-compulsivo e suas implicações para o tratamento
• Beneficiário: Eurípedes Constantino Miguel Filho
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 06/50273-0 - Estratégia de potencialização medicamentosa no transtorno obsessivo-compulsivo resistente: um estudo duplo-cego controlado
• Beneficiário: Juliana Belo Diniz
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto
• Processo FAPESP: 06/61459-7 - Fatores preditivos de resposta ao tratamento do transtorno obsessivo compulsivo no longo prazo.
• Beneficiário: Roseli Gedanke Shavitt
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 05/04206-6 - Avaliação do transportador dopaminérgico no transtorno obsessivo compulsivo através de imagens de SPECT com 99mTc - TRODAT-1
• Beneficiário: Marcelo Queiroz Hoexter
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto