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

Molecular psychiatry, 2020-09, Vol.25 (9), p.2130-2143 [Peer Reviewed Journal]

COPYRIGHT 2020 Nature Publishing Group ;The Author(s) 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;info:eu-repo/semantics/openAccess ;The Author(s) 2018 ;ISSN: 1359-4184 ;ISSN: 1476-5578 ;EISSN: 1476-5578 ;DOI: 10.1038/s41380-018-0228-9 ;PMID: 30171211

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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 ; Schnack, Hugo G ; Ching, Christopher R K ; Agartz, Ingrid ; Akudjedu, Theophilus N ; Alda, Martin ; Alnæs, Dag ; Alonso-Lana, Silvia ; Bauer, Jochen ; Baune, Bernhard T ; Bøen, Erlend ; Bonnin, Caterina Del Mar ; Busatto, Geraldo F ; Canales-Rodríguez, Erick J ; Cannon, Dara M ; Caseras, Xavier ; Chaim-Avancini, Tiffany M ; Dannlowski, Udo ; Díaz-Zuluaga, Ana M ; Dietsche, Bruno ; Doan, Nhat Trung ; Duchesnay, Edouard ; Elvsåshagen, Torbjørn ; Emden, Daniel ; Eyler, Lisa T ; Fatjó-Vilas, Mar ; Favre, Pauline ; Foley, Sonya F ; Fullerton, Janice M ; Glahn, David C ; Goikolea, Jose M ; Grotegerd, Dominik ; Hahn, Tim ; Henry, Chantal ; Hibar, Derrek P ; Houenou, Josselin ; Howells, Fleur M ; Jahanshad, Neda ; Kaufmann, Tobias ; Kenney, Joanne ; Kircher, Tilo T J ; Krug, Axel ; Lagerberg, Trine V ; Lenroot, Rhoshel K ; López-Jaramillo, Carlos ; Machado-Vieira, Rodrigo ; Malt, Ulrik F ; McDonald, Colm ; Mitchell, Philip B ; Mwangi, Benson ; Nabulsi, Leila ; Opel, Nils ; Overs, Bronwyn J ; Pineda-Zapata, Julian A ; Pomarol-Clotet, Edith ; Redlich, Ronny ; Roberts, Gloria ; Rosa, Pedro G ; Salvador, Raymond ; Satterthwaite, Theodore D ; Soares, Jair C ; Stein, Dan J ; Temmingh, Henk S ; Trappenberg, Thomas ; Uhlmann, Anne ; van Haren, Neeltje E M ; Vieta, Eduard ; Westlye, Lars T ; Wolf, Daniel H ; Yüksel, Dilara ; Zanetti, Marcus V ; Andreassen, Ole A ; Thompson, Paul M ; Hajek, Tomas
Subjects: Accuracy ; Anatomy ; Anticonvulsants ; Biomarkers ; Bipolar disorder ; Bipolar Disorder - diagnostic imaging ; Brain ; Brain - diagnostic imaging ; Brain architecture ; Classification ; Consortia ; Humans ; Learning algorithms ; Machine Learning ; Magnetic Resonance Imaging ; Medical imaging ; Medical research ; Medicin och hälsovetenskap ; Medicine, Experimental ; Morbidity ; Neuroimaging ; Systematic review
Is Part Of: Molecular psychiatry, 2020-09, Vol.25 (9), p.2130-2143
Description: 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.
Publisher: England: Nature Publishing Group
Language: English;Norwegian
Identifier: ISSN: 1359-4184
ISSN: 1476-5578
EISSN: 1476-5578
DOI: 10.1038/s41380-018-0228-9
PMID: 30171211
Source: ProQuest One Psychology
AUTh Library subscriptions: ProQuest Central
MEDLINE
NORA Norwegian Open Research Archives
SWEPUB Freely available online

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

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