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Machine learning applied to fMRI patterns of brain activation in response to mutilation pictures predicts PTSD symptoms.
Portugal, Liana Catarina Lima; Ramos, Taiane Coelho; Fernandes, Orlando; Bastos, Aline Furtado; Campos, Bruna; Mendlowicz, Mauro Vitor; da Luz, Mariana; Portella, Carla; Berger, William; Volchan, Eliane; David, Isabel Antunes; Erthal, Fátima; Pereira, Mirtes Garcia; de Oliveira, Leticia.
Afiliação
  • Portugal LCL; Neurophysiology Laboratory, Department of Physiological Sciences, Roberto Alcantara Gomes Biology Institute, Biomedical Center, Universidade do Estado do Rio de Janeiro, Boulevard 28 de Setembro, 87 - Vila Isabel, Rio de Janeiro, RJ, 20551-030, Brazil.
  • Ramos TC; Laboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Universidade Federal Fluminense, R. Prof. Hernani Pires de Mello, 101, São Domingos, Niterói, RJ, 24210-130, Brazil.
  • Fernandes O; Laboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Universidade Federal Fluminense, R. Prof. Hernani Pires de Mello, 101, São Domingos, Niterói, RJ, 24210-130, Brazil.
  • Bastos AF; Mídiacom Lab, Institute of Computing, Universidade Federal Fluminense, Av. Gal. Milton Tavares de Souza, s/n, São Domingos, Niterói, RJ, 24210-310, Brazil.
  • Campos B; Laboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Universidade Federal Fluminense, R. Prof. Hernani Pires de Mello, 101, São Domingos, Niterói, RJ, 24210-130, Brazil.
  • Mendlowicz MV; Laboratório de Neurobiologia, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, 373 - Cidade Universitária da Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, 21941-902, Brazil.
  • da Luz M; Laboratório de Neurobiologia, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, 373 - Cidade Universitária da Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, 21941-902, Brazil.
  • Portella C; Linpes, Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Av. Venceslau Brás, 71 - Botafogo, Rio de Janeiro, RJ, 22290-140, Brazil.
  • Berger W; Linpes, Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Av. Venceslau Brás, 71 - Botafogo, Rio de Janeiro, RJ, 22290-140, Brazil.
  • Volchan E; Linpes, Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Av. Venceslau Brás, 71 - Botafogo, Rio de Janeiro, RJ, 22290-140, Brazil.
  • David IA; Linpes, Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Av. Venceslau Brás, 71 - Botafogo, Rio de Janeiro, RJ, 22290-140, Brazil.
  • Erthal F; Laboratório de Neurobiologia, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, 373 - Cidade Universitária da Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, 21941-902, Brazil.
  • Pereira MG; Linpes, Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Av. Venceslau Brás, 71 - Botafogo, Rio de Janeiro, RJ, 22290-140, Brazil.
  • de Oliveira L; Laboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Universidade Federal Fluminense, R. Prof. Hernani Pires de Mello, 101, São Domingos, Niterói, RJ, 24210-130, Brazil.
BMC Psychiatry ; 23(1): 719, 2023 10 05.
Article em En | MEDLINE | ID: mdl-37798693
BACKGROUND: The present study aimed to apply multivariate pattern recognition methods to predict posttraumatic stress symptoms from whole-brain activation patterns during two contexts where the aversiveness of unpleasant pictures was manipulated by the presence or absence of safety cues. METHODS: Trauma-exposed participants were presented with neutral and mutilation pictures during functional magnetic resonance imaging (fMRI) collection. Before the presentation of pictures, a text informed the subjects that the pictures were fictitious ("safe context") or real-life scenes ("real context"). We trained machine learning regression models (Gaussian process regression (GPR)) to predict PTSD symptoms in real and safe contexts. RESULTS: The GPR model could predict PTSD symptoms from brain responses to mutilation pictures in the real context but not in the safe context. The brain regions with the highest contribution to the model were the occipito-parietal regions, including the superior parietal gyrus, inferior parietal gyrus, and supramarginal gyrus. Additional analysis showed that GPR regression models accurately predicted clusters of PTSD symptoms, nominal intrusion, avoidance, and alterations in cognition. As expected, we obtained very similar results as those obtained in a model predicting PTSD total symptoms. CONCLUSION: This study is the first to show that machine learning applied to fMRI data collected in an aversive context can predict not only PTSD total symptoms but also clusters of PTSD symptoms in a more aversive context. Furthermore, this approach was able to identify potential biomarkers for PTSD, especially in occipitoparietal regions.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transtornos de Estresse Pós-Traumáticos Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: BMC Psychiatry Assunto da revista: PSIQUIATRIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transtornos de Estresse Pós-Traumáticos Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: BMC Psychiatry Assunto da revista: PSIQUIATRIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido