Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.
Journal
Molecular psychiatry
ISSN: 1476-5578
Titre abrégé: Mol Psychiatry
Pays: England
ID NLM: 9607835
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
15
02
2018
accepted:
24
07
2018
revised:
11
06
2018
pubmed:
2
9
2018
medline:
15
5
2021
entrez:
2
9
2018
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 30171211
doi: 10.1038/s41380-018-0228-9
pii: 10.1038/s41380-018-0228-9
pmc: PMC7473838
doi:
Types de publication
Journal Article
Meta-Analysis
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2130-2143Subventions
Organisme : NIMH NIH HHS
ID : R56 MH097940
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH083968
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH101111
Pays : United States
Organisme : NIBIB NIH HHS
ID : U54 EB020403
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH107248
Pays : United States
Organisme : NIMH NIH HHS
ID : K23 MH085096
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH078143
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH105630
Pays : United States
Organisme : Medical Research Council
ID : MR/L010305/1
Pays : United Kingdom
Organisme : NIMH NIH HHS
ID : R01 MH107703
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH080912
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG058464
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH083824
Pays : United States
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