Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: a pet and machine learning study

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dc.creatorDante G.duarte
dc.creatorHumberto Corrêa da Silva Filho
dc.date.accessioned2025-07-28T20:53:57Z
dc.date.accessioned2025-09-09T01:24:50Z
dc.date.available2025-07-28T20:53:57Z
dc.date.issued2023
dc.format.mimetypepdf
dc.identifier.doihttps://doi.org/10.47626/1516-4446-2022-2811
dc.identifier.issn15164446
dc.identifier.urihttps://hdl.handle.net/1843/83910
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofBrazilian Journal of Psychiatry
dc.rightsAcesso Aberto
dc.subjectBipolar disorder
dc.subjectChild Abuse
dc.subjectSuicide, Attempted
dc.subjectFluorodeoxyglucose F18
dc.subjectPositron-Emission Tomography
dc.subjectMachine Learning
dc.subject.otherBipolar disorder
dc.subject.otherChildhood maltreatment
dc.subject.otherSuicide, Attempted
dc.subject.other18F-FDG
dc.subject.otherPositron-Emission Tomography
dc.subject.otherMachine Learning
dc.titleExamining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: a pet and machine learning study
dc.typeArtigo de periódico
local.citation.epage131
local.citation.issue2
local.citation.spage127
local.citation.volume45
local.description.resumoObjective: 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.
local.publisher.countryBrasil
local.publisher.departmentICB - DEPARTAMENTO DE MORFOLOGIA
local.publisher.departmentMED - DEPARTAMENTO DE PSIQUIATRIA E NEUROLOGIA
local.publisher.departmentMED - DEPARTAMENTO DE SAÚDE MENTAL
local.publisher.initialsUFMG
local.url.externahttps://www.bjp.org.br/details/2335/en-US/examining-differences-in-brain-metabolism-associated-with-childhood-maltreatment-and-suicidal-attempt-in-euthymic-patients-with-bipolar-disorder--a-pe

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