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. 2018 Aug 31.
doi: 10.1038/s41380-018-0228-9. Online ahead of print.

Using Structural MRI to Identify Bipolar Disorders - 13 Site Machine Learning Study in 3020 Individuals From the ENIGMA Bipolar Disorders Working Group

Abraham Nunes  1   2 Hugo G Schnack  3 Christopher R K Ching  4   5 Ingrid Agartz  6   7   8   9 Theophilus N Akudjedu  10 Martin Alda  1 Dag Alnæs  6   7 Silvia Alonso-Lana  11   12 Jochen Bauer  13 Bernhard T Baune  14 Erlend Bøen  8 Caterina Del Mar Bonnin  15 Geraldo F Busatto  16   17 Erick J Canales-Rodríguez  11   12 Dara M Cannon  10 Xavier Caseras  18 Tiffany M Chaim-Avancini  16   17 Udo Dannlowski  19 Ana M Díaz-Zuluaga  20 Bruno Dietsche  21 Nhat Trung Doan  6   7 Edouard Duchesnay  22 Torbjørn Elvsåshagen  6   23 Daniel Emden  19 Lisa T Eyler  24   25 Mar Fatjó-Vilas  11   12   26 Pauline Favre  22 Sonya F Foley  27 Janice M Fullerton  28   29 David C Glahn  30   31 Jose M Goikolea  15 Dominik Grotegerd  19 Tim Hahn  19 Chantal Henry  32 Derrek P Hibar  5 Josselin Houenou  22   33 Fleur M Howells  34   35 Neda Jahanshad  5 Tobias Kaufmann  6   7 Joanne Kenney  10 Tilo T J Kircher  21 Axel Krug  21 Trine V Lagerberg  6 Rhoshel K Lenroot  36   37 Carlos López-Jaramillo  20   38 Rodrigo Machado-Vieira  16   39 Ulrik F Malt  40   41 Colm McDonald  10 Philip B Mitchell  36   42 Benson Mwangi  39 Leila Nabulsi  10 Nils Opel  19 Bronwyn J Overs  28 Julian A Pineda-Zapata  43 Edith Pomarol-Clotet  11   12 Ronny Redlich  19 Gloria Roberts  36   42 Pedro G Rosa  16   17 Raymond Salvador  11   12 Theodore D Satterthwaite  44 Jair C Soares  39 Dan J Stein  45 Henk S Temmingh  45   46 Thomas Trappenberg  2 Anne Uhlmann  45   47 Neeltje E M van Haren  3   48 Eduard Vieta  15 Lars T Westlye  6   7   49 Daniel H Wolf  44 Dilara Yüksel  21 Marcus V Zanetti  16   17   50 Ole A Andreassen  6   7 Paul M Thompson  5 Tomas Hajek  51 ENIGMA Bipolar Disorders Working Group
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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

Abraham Nunes et al. Mol Psychiatry. .

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.

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