Nunes, ASchnack, H.GChing, C.R.KAgartz, IAkudjedu, T.NAlda, MAlnæs, DAlonso-Lana, SBauer, JBaune, B.TBøen, EBonnin, C.D.MBusatto, G.FCanales-Rodríguez, E.JCannon, D.MCaseras, XChaim-Avancini, T.MDannlowski, UDíaz-Zuluaga, A.MDietsche, BDoan, N.TDuchesnay, EElvsåshagen, TEmden, DEyler, L.TFatjó-Vilas, MFavre, PFoley, S.FFullerton, J.MGlahn, D.CGoikolea, J.MGrotegerd, DHahn, THenry, CHibar, D.PHouenou, JHowells, F.MJahanshad, NKaufmann, TKenney, JoKircher, T T JKrug, ALagerberg, T.VLenroot, R.KLópez-Jaramillo, CMachado-Vieira, RMalt, U.FMcDonald, CMitchell, P.BMwangi, BNabulsi, LOpel, NOvers, B.JPineda-Zapata, J.APomarol-Clotet, ERedlich, RRoberts, GRosa, P.GSalvador, RSatterthwaite, DSoares, J.CStein, D. JTemmingh, H.STrappenberg, TUhlmann, Avan Haren, N.E .MVieta, EWestlye, LWolf, D.HYüksel, DZanetti, M.VAndreassen, O.AThompson, P.MHajek, Tthe ENIGMA Bipolar Disorders Working Group2024-07-112024-07-112020-08-30Nunes A, Schnack HG, Ching CRK, Agartz I, Akudjedu TN, Alda M, Alnæs D, Alonso-Lana S, Bauer J, Baune BT, Bøen E, Bonnin CdM, Busatto GF, Canales-Rodríguez EJ, Cannon DM, Caseras X, Chaim-Avancini TM, Dannlowski U, Díaz-Zuluaga AM, Dietsche B, Doan NT, Duchesnay E, Elvsåshagen T, Emden D, Eyler LT, Fatjó-Vilas M, Favre P, Foley SF, Fullerton JM, Glahn DC, Goikolea JM, Grotegerd D, Hahn T, Henry C, Hibar DP, Houenou J, Howells FM, Jahanshad N, Kaufmann T, Kenney J, Kircher TTJ, Krug A, Lagerberg TV, Lenroot RK, López-Jaramillo C, Machado-Vieira R, Malt UF, McDonald C, Mitchell PB, Mwangi B, Nabulsi L, Opel N, Overs BJ, Pineda-Zapata JA, Pomarol-Clotet E, Redlich R, Roberts G, Rosa PG, Salvador R, Satterthwaite TD, Soares JC, Stein DJ, Temmingh HS, Trappenberg T, Uhlmann A, van Haren NEM, Vieta E, Westlye LT, Wolf DH, Yüksel D, Zanetti MV, Andreassen OA, Thompson PM, Hajek T, for the EBDWG. 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;25(9):2130-43. DOI: 10.1038/s41380-018-0228-9.https://doi.org/10.1038/s41380-018-0228-9https://www.nature.com/articles/s41380-018-0228-9https://hdl.handle.net/11288/596860Bipolar 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.enArticle