Predicting Treatment Response of Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder Using an Explainable Machine Learning Model Based on Electroencephalography and Clinical Features

作者：Zongya Zhao, Xiangying Ran, Yanxiang Niu, Mengyue Qiu, Shiyang Lv, Mingjie Zhu, Junming Wang, Mingcai Li, Zhixian Gao, Chang Wang, Yongtao Xu, Wu Ren, Xuezhi Zhou, Xiaofeng Fan, Jinggui Song, Meng Qi, Yi Yu · 发表于：Biological Psychiatry Cognitive Neuroscience and Neuroimaging · 年份：2025 · DOI：10.1016/j.bpsc.2025.02.002 · 被引用次数：6 · 研究领域：Functional Brain Connectivity Studies、EEG and Brain-Computer Interfaces、Transcranial Magnetic Stimulation Studies

Major depressive disorder (MDD) is highly heterogeneous in response to repetitive transcranial magnetic stimulation (rTMS), and identifying predictive biomarkers is essential for personalized treatment. However, most prior research studies have used either electroencephalography (EEG) or clinical features, lack interpretability, or have small sample sizes. This study included 74 patients with MDD who responded (responders) and 43 patients with MDD who did not respond (nonresponders) to rTMS. Eight baseline EEG metrics and clinical features were sent to 7 machine learning models to classify responders and nonresponders. Shapley additive explanations (SHAP) was used to interpret feature contributions. Combining phase locking value and clinical features with support vector machine achieved optimal classification performance (accuracy = 97.33%). SHAP revealed that delta and beta band functional connectivity (F3-P7, F3-P4, P3-P8, T7-Cz) significantly influenced predictions and differed between groups. This study developed an explainable predictive framework to predict rTMS response in MDD, enhancing the accuracy of rTMS response prediction and supporting personalized treatment in MDD.