Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

dc.creatorNunes, Abraham
dc.creatorDíaz Zuluaga, Ana María
dc.creatorLópez Jaramillo, Carlos Alberto
dc.creatorPineda Zapata, Julián Alberto
dc.date2021-10-11T22:24:47Z
dc.date2021-10-11T22:24:47Z
dc.date2020
dc.date.accessioned2023-08-28T20:12:10Z
dc.date.available2023-08-28T20:12:10Z
dc.identifierNunes, A., Schnack, H.G., Ching, C.R.K. et al. Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group. Mol Psychiatry 25, 2130–2143 (2020). https://doi.org/10.1038/s41380-018-0228-9
dc.identifier1359-4184
dc.identifierhttp://hdl.handle.net/10495/23143
dc.identifier10.1038/s41380-018-0228-9
dc.identifier1476-5578
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8475433
dc.descriptionABSTRACT: Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/ voxelwise neuroimaging data.
dc.descriptionCOL0029147
dc.format14
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherSpringer Nature
dc.publisherGrupo de Investigación en Psiquiatría GIPSI
dc.publisherLondres, Inglaterra
dc.relationMol. Psychiatry.
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/2.5/co/
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectEspectroscopía de Resonancia Magnética
dc.subjectMagnetic Resonance Spectroscopy
dc.subjectTrastorno Bipolar
dc.subjectBipolar Disorder
dc.titleUsing structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/resource_type/c_2df8fbb1
dc.typehttps://purl.org/redcol/resource_type/ART
dc.typeArtículo de investigación


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