作者
Abraham Nunes, Hugo G Schnack, Christopher RK Ching, Ingrid Agartz, Theophilus N Akudjedu, Martin Alda, Dag Alnæs, Silvia Alonso-Lana, Jochen Bauer, Bernhard T Baune, Erlend Bøen, Caterina del Mar Bonnin, Geraldo F Busatto, Erick J Canales-Rodríguez, Dara M Cannon, Xavier Caseras, Tiffany M Chaim-Avancini, Udo Dannlowski, Ana M Díaz-Zuluaga, Bruno Dietsche, Nhat Trung Doan, Edouard Duchesnay, Torbjørn Elvsåshagen, Daniel Emden, Lisa T Eyler, Mar Fatjó-Vilas, Pauline Favre, Sonya F Foley, Janice M Fullerton, David C Glahn, Jose M Goikolea, Dominik Grotegerd, Tim Hahn, Chantal Henry, Derrek P Hibar, Josselin Houenou, Fleur M Howells, Neda Jahanshad, Tobias Kaufmann, Joanne Kenney, Tilo TJ Kircher, Axel Krug, Trine V Lagerberg, Rhoshel K Lenroot, Carlos López-Jaramillo, Rodrigo Machado-Vieira, Ulrik F Malt, Colm McDonald, Philip B Mitchell, Benson Mwangi, Leila Nabulsi, Nils Opel, Bronwyn J Overs, Julian A Pineda-Zapata, Edith Pomarol-Clotet, Ronny Redlich, Gloria Roberts, Pedro G Rosa, Raymond Salvador, Theodore D Satterthwaite, Jair C Soares, Dan J Stein, Henk S Temmingh, Thomas Trappenberg, Anne Uhlmann, Neeltje EM van Haren, Eduard Vieta, Lars T Westlye, Daniel H Wolf, Dilara Yüksel, Marcus V Zanetti, Ole A Andreassen, Paul M Thompson, Tomas Hajek, ENIGMA Bipolar Disorders Working Group
发表日期
2020/9
期刊
Molecular psychiatry
卷号
25
期号
9
页码范围
2130-2143
出版商
Nature Publishing Group UK
简介
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 …
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A Nunes, HG Schnack, CRK Ching, I Agartz… - Molecular psychiatry, 2020