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

Journal: Biological psychiatry. Cognitive neuroscience and neuroimaging
Published Date: Feb 18, 2025

Abstract

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.

Authors

  • Zongya Zhao
    College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China.
  • Xiangying Ran
    School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China; Henan Engineering Research Center of Medical Virtual Reality Intelligent Sensing Feedback, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China.
  • Yanxiang Niu
    Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
  • Mengyue Qiu
    School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China; Henan Engineering Research Center of Medical Virtual Reality Intelligent Sensing Feedback, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China.
  • Shiyang Lv
    School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China; Henan Engineering Research Center of Medical Virtual Reality Intelligent Sensing Feedback, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China.
  • Mingjie Zhu
    School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China; Henan Engineering Research Center of Medical Virtual Reality Intelligent Sensing Feedback, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China.
  • Junming Wang
    Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR 999077, China.
  • Mingcai Li
    School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China; Henan Engineering Research Center of Medical Virtual Reality Intelligent Sensing Feedback, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China.
  • Zhixian Gao
    Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, PR China. Electronic address: gaozhx@163.com.
  • Chang Wang
    Key Laboratory of the plateau of environmental damage control, Lanzhou General Hospital of Lanzhou Military Command, Lanzhou, China.
  • Yongtao Xu
    School of Chemistry and Chemical Engineering, Queen's University Belfast, David Keir Building, Stranmillis Road, Belfast, Northern Ireland, United Kingdom.
  • Wu Ren
    School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China; Henan Engineering Research Center of Medical Virtual Reality Intelligent Sensing Feedback, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China.
  • Xuezhi Zhou
    College of Medical Engineering, Xinxiang Medical University, Xinxiang, 453000, People's Republic of China.
  • Xiaofeng Fan
    Clinical Medicine Department of Hangzhou Normal University, Hangzhou, Zhejiang, People's Republic of China.
  • Jinggui Song
    Henan Engineering Research Center of Physical Diagnostics and Treatment Technology for Mental and Neurological Diseases, Henan, China.
  • Mingchao Qi
    School of Medical Engineering, School of Mathematical Medicine, Xinxiang Medical University, Xinxiang, China.
  • Yi Yu
    Center of Reproductive Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang 315100， China.

Keywords

External Resources

Popular Topics
Recent Journals