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Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study.
Duarte, Dante; Schütze, Manuel; Elkhayat, Mazen; Neves, Maila de Castro; Romano-Silva, Marco A; Correa, Humberto.
Afiliação
  • Duarte D; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada. Seniors Mental Health Program, St. Joseph's Healthcare Hamilton, ON, Canada.
  • Schütze M; Instituto Nacional de Ciência e Tecnologia de Medicina Molecular (INCT-MM), Universidade Federal de Minas Gerais (UFMG), MG, Brazil.
  • Elkhayat M; Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada.
  • Neves MC; Programa de Pós-Graduação em Medicina Molecular, UFMG, MG, Brazil.
  • Romano-Silva MA; Instituto Nacional de Ciência e Tecnologia de Medicina Molecular (INCT-MM), Universidade Federal de Minas Gerais (UFMG), MG, Brazil. Programa de Pós-Graduação em Medicina Molecular, UFMG, MG, Brazil.
  • Correa H; Programa de Pós-Graduação em Medicina Molecular, UFMG, MG, Brazil. Departamento de Saúde Mental, UFMG, MG, Brazil.
Braz J Psychiatry ; 45(2): 127-131, 2023 May 11.
Article em En | MEDLINE | ID: mdl-37169366
OBJECTIVE: Childhood maltreatment (CM) is a significant risk factor for the development and severity of bipolar disorder (BD) with increased risk of suicide attempts (SA). This study evaluated whether a machine learning algorithm could be trained to predict if a patient with BD has a history of CM or previous SA based on brain metabolism measured by positron emission tomography. METHODS: Thirty-six euthymic patients diagnosed with BD type I, with and without a history of CM were assessed using the Childhood Trauma Questionnaire. Suicide attempts were assessed through the Mini International Neuropsychiatric Interview (MINI-Plus) and a semi-structured interview. Resting-state positron emission tomography with 18F-fluorodeoxyglucose was conducted, electing only grey matter voxels through the Statistical Parametric Mapping toolbox. Imaging analysis was performed using a supervised machine learning approach following Gaussian Process Classification. RESULTS: Patients were divided into 18 participants with a history of CM and 18 participants without it, along with 18 individuals with previous SA and 18 individuals without such history. The predictions for CM and SA were not significant (accuracy = 41.67%; p = 0.879). CONCLUSION: Further investigation is needed to improve the accuracy of machine learning, as its predictive qualities could potentially be highly useful in determining histories and possible outcomes of high-risk psychiatric patients.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transtorno Bipolar / Maus-Tratos Infantis Tipo de estudo: Prognostic_studies / Qualitative_research / Risk_factors_studies Limite: Child / Humans Idioma: En Revista: Braz J Psychiatry Assunto da revista: PSIQUIATRIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Canadá País de publicação: Brasil
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: Transtorno Bipolar / Maus-Tratos Infantis Tipo de estudo: Prognostic_studies / Qualitative_research / Risk_factors_studies Limite: Child / Humans Idioma: En Revista: Braz J Psychiatry Assunto da revista: PSIQUIATRIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Canadá País de publicação: Brasil