Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/174824
Title: Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
Author: Nunes, Abraham
Fatjó-Vilas Mestre, Mar
Vieta i Pascual, Eduard, 1963-
ENIGMA Bipolar Disorders Working Group
Keywords: Trastorn bipolar
Morbiditat
Anticonvulsius
Manic-depressive illness
Morbidity
Anticonvulsants
Issue Date: 31-Aug-2018
Publisher: Nature Publishing Group
Abstract: 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.
Note: Reproducció del document publicat a: https://doi.org/10.1038/s41380-018-0228-9
It is part of: Molecular Psychiatry, 2018, vol. 25, num. 9, p. 2130-2143
URI: http://hdl.handle.net/2445/174824
Related resource: https://doi.org/10.1038/s41380-018-0228-9
ISSN: 1359-4184
Appears in Collections:Articles publicats en revistes (Medicina)
Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)
Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)

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