Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study

Detalhes bibliográficos
Autor(a) principal: Dante Duarte
Data de Publicação: 2023
Outros Autores: Manuel Schütze, Mazen Elkhayat, Maila de Castro Lourenço Das Neves, Marco Aurélio Romano Silva, Humberto Correa
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: http://doi.org/10.47626/1516-4446-2022-2811
http://hdl.handle.net/1843/60519
https://orcid.org/0000-0001-7516-5473
https://orcid.org/0000-0003-1947-9675
https://orcid.org/0000-0003-1876-2022
https://orcid.org/0000-0003-4125-3736
Resumo: CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
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spelling 2023-11-06T20:40:26Z2023-11-06T20:40:26Z2023-04-19452127131http://doi.org/10.47626/1516-4446-2022-28111809-452Xhttp://hdl.handle.net/1843/60519https://orcid.org/0000-0001-7516-5473https://orcid.org/0000-0003-1947-9675https://orcid.org/0000-0003-1876-2022https://orcid.org/0000-0003-4125-3736CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoOBJECTIVE: 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.engUniversidade Federal de Minas GeraisUFMGBrasilMED - DEPARTAMENTO DE SAÚDE MENTALMEDICINA - FACULDADE DE MEDICINABrazilian Journal of PsychiatryInfânciaAprendizado do computadorTomografia por emissão de pósitronsTentativa de SuicídioTranstorno BipolarBipolar disorderChildhood maltreatmentSuicide attempt18F-FDGPositron emission tomographyMachine learningExamining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Studyinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://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-peDante DuarteManuel SchützeMazen ElkhayatMaila de Castro Lourenço Das NevesMarco Aurélio Romano SilvaHumberto Correaapplication/pdfinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/60519/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALExamining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder_ A PET and Machine Learning Study.pdfExamining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder_ A PET and Machine Learning Study.pdfapplication/pdf155077https://repositorio.ufmg.br/bitstream/1843/60519/2/Examining%20differences%20in%20brain%20metabolism%20associated%20with%20childhood%20maltreatment%20and%20suicidal%20attempt%20in%20euthymic%20patients%20with%20bipolar%20disorder_%20A%20PET%20and%20Machine%20Learning%20Study.pdf383ebeb9b54e17ac1bd5f5642005d0a1MD521843/605192023-11-06 17:40:26.664oai:repositorio.ufmg.br:1843/60519Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2023-11-06T20:40:26Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.pt_BR.fl_str_mv Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study
title Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study
spellingShingle Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study
Dante Duarte
Bipolar disorder
Childhood maltreatment
Suicide attempt
18F-FDG
Positron emission tomography
Machine learning
Infância
Aprendizado do computador
Tomografia por emissão de pósitrons
Tentativa de Suicídio
Transtorno Bipolar
title_short Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study
title_full Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study
title_fullStr Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study
title_full_unstemmed Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study
title_sort Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study
author Dante Duarte
author_facet Dante Duarte
Manuel Schütze
Mazen Elkhayat
Maila de Castro Lourenço Das Neves
Marco Aurélio Romano Silva
Humberto Correa
author_role author
author2 Manuel Schütze
Mazen Elkhayat
Maila de Castro Lourenço Das Neves
Marco Aurélio Romano Silva
Humberto Correa
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Dante Duarte
Manuel Schütze
Mazen Elkhayat
Maila de Castro Lourenço Das Neves
Marco Aurélio Romano Silva
Humberto Correa
dc.subject.por.fl_str_mv Bipolar disorder
Childhood maltreatment
Suicide attempt
18F-FDG
Positron emission tomography
Machine learning
topic Bipolar disorder
Childhood maltreatment
Suicide attempt
18F-FDG
Positron emission tomography
Machine learning
Infância
Aprendizado do computador
Tomografia por emissão de pósitrons
Tentativa de Suicídio
Transtorno Bipolar
dc.subject.other.pt_BR.fl_str_mv Infância
Aprendizado do computador
Tomografia por emissão de pósitrons
Tentativa de Suicídio
Transtorno Bipolar
description CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
publishDate 2023
dc.date.accessioned.fl_str_mv 2023-11-06T20:40:26Z
dc.date.available.fl_str_mv 2023-11-06T20:40:26Z
dc.date.issued.fl_str_mv 2023-04-19
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1843/60519
dc.identifier.doi.pt_BR.fl_str_mv http://doi.org/10.47626/1516-4446-2022-2811
dc.identifier.issn.pt_BR.fl_str_mv 1809-452X
dc.identifier.orcid.pt_BR.fl_str_mv https://orcid.org/0000-0001-7516-5473
https://orcid.org/0000-0003-1947-9675
https://orcid.org/0000-0003-1876-2022
https://orcid.org/0000-0003-4125-3736
url http://doi.org/10.47626/1516-4446-2022-2811
http://hdl.handle.net/1843/60519
https://orcid.org/0000-0001-7516-5473
https://orcid.org/0000-0003-1947-9675
https://orcid.org/0000-0003-1876-2022
https://orcid.org/0000-0003-4125-3736
identifier_str_mv 1809-452X
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv Brazilian Journal of Psychiatry
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.publisher.initials.fl_str_mv UFMG
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv MED - DEPARTAMENTO DE SAÚDE MENTAL
MEDICINA - FACULDADE DE MEDICINA
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Repositório Institucional da UFMG
collection Repositório Institucional da UFMG
bitstream.url.fl_str_mv https://repositorio.ufmg.br/bitstream/1843/60519/1/License.txt
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