基于皮层与皮下测量的机器学习多站点重性抑郁障碍基准分类
定量方法
2022-10-26 v3
摘要
机器学习（ML）技术因其在分类神经精神疾病方面的潜力而在神经影像领域日益流行。然而，现有算法的诊断预测能力受限于小样本量、缺乏代表性、数据泄漏和/或过拟合。在此，我们克服这些局限，使用迄今最大的多站点样本量（n=5,356）提供重性抑郁障碍（MDD）的可泛化 ML 分类基准。利用 FreeSurfer 中标准化 ENIGMA 分析流程的脑测量，我们能将 MDD 与健康对照（HC）以约 62% 平衡准确率分类，但使用 ComBat 协调数据后平衡准确率降至约 52%。在按发病年龄、抗抑郁药使用、发作次数和性别分层的组中观察到类似结果。未来研究若纳入更高维脑成像/表型特征，和/或使用更先进的机器与深度学习方法，可能取得更令人鼓舞的前景。
引用
@article{arxiv.2206.08122,
title = {Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures},
author = {Vladimir Belov and Tracy Erwin-Grabner and Ali Saffet Gonul and Alyssa R. Amod and Amar Ojha and Andre Aleman and Annemiek Dols and Anouk Scharntee and Aslihan Uyar-Demir and Ben J Harrison and Benson M. Irungu and Bianca Besteher and Bonnie Klimes-Dougan and Brenda W. J. H. Penninx and Bryon A. Mueller and Carlos Zarate and Christopher G. Davey and Christopher R. K. Ching and Colm G. Connolly and Cynthia H. Y. Fu and Dan J. Stein and Danai Dima and David E. J. Linden and David M. A. Mehler and Edith Pomarol-Clotet and Elena Pozzi and Elisa Melloni and Francesco Benedetti and Frank P. MacMaster and Hans J. Grabe and Henry Völzke and Ian H. Gotlib and Jair C. Soares and Jennifer W. Evans and Kang Sim and Katharina Wittfeld and Kathryn Cullen and Liesbeth Reneman and Mardien L. Oudega and Margaret J. Wright and Maria J. Portella and Matthew D. Sacchet and Meng Li and Moji Aghajani and Mon-Ju Wu and Natalia Jaworska and Neda Jahanshad and Nic J. A. van der Wee and Nynke Groenewold and Paul J. Hamilton and Philipp Saemann and Robin Bülow and Sara Poletti and Sarah Whittle and Sophia I. Thomopoulos and Steven J. A. van and der Werff and Sheri-Michelle Koopowitz and Thomas Lancaster and Tiffany C. Ho and Tony T. Yang and Zeynep Basgoze and Dick J. Veltman and Lianne Schmaal and Paul M. Thompson and Roberto Goya-Maldonado},
journal= {arXiv preprint arXiv:2206.08122},
year = {2022}
}
备注
main document 37 pages; supplementary material 24 pages