دورية أكاديمية
Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.
| العنوان: | Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures. |
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| المؤلفون: | Belov, V, Erwin-Grabner, T, Aghajani, M, Aleman, A, Amod, AR, Basgoze, Z, Benedetti, F, Besteher, B, Bülow, R, Ching, CRK, Connolly, CG, Cullen, K, Davey, CG, Dima, D, Dols, A, Evans, JW, Fu, CHY, Gonul, AS, Gotlib, IH, Grabe, HJ, Groenewold, N, Hamilton, JP, Harrison, BJ, Ho, TC, Mwangi, B, Jaworska, N, Jahanshad, N, Klimes-Dougan, B, Koopowitz, S-M, Lancaster, T, Li, M, Linden, DEJ, MacMaster, FP, Mehler, DMA, Melloni, E, Mueller, BA, Ojha, A, Oudega, ML, Penninx, BWJH, Poletti, S, Pomarol-Clotet, E, Portella, MJ, Pozzi, E, Reneman, L, Sacchet, MD, Sämann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Thomopoulos, SI, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJA, Völzke, H, Whittle, S, Wittfeld, K, Wright, MJ, Wu, M-J, Yang, TT, Zarate, C, Veltman, DJ, Schmaal, L, Thompson, PM, Goya-Maldonado, R, ENIGMA Major Depressive Disorder working group |
| بيانات النشر: | Springer Science and Business Media LLC |
| سنة النشر: | 2024 |
| المجموعة: | The University of Melbourne: Digital Repository |
| الوصف: | Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2045-2322 |
| العلاقة: | NHMRC/1125504; pii: 10.1038/s41598-023-47934-8; Belov, V., Erwin-Grabner, T., Aghajani, M., Aleman, A., Amod, A. R., Basgoze, Z., Benedetti, F., Besteher, B., Bülow, R., Ching, C. R. K., Connolly, C. G., Cullen, K., Davey, C. G., Dima, D., Dols, A., Evans, J. W., Fu, C. H. Y., Gonul, A. S., Gotlib, I. H. ,. ENIGMA Major Depressive Disorder working group, (2024). Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures. Sci Rep, 14 (1), pp.1084-. https://doi.org/10.1038/s41598-023-47934-8Test.; http://hdl.handle.net/11343/344427Test |
| الإتاحة: | https://doi.org/10.1038/s41598-023-47934-8Test http://hdl.handle.net/11343/344427Test |
| حقوق: | CC BY ; https://creativecommons.org/licenses/by/4.0Test |
| رقم الانضمام: | edsbas.7485BA86 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 20452322 |
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