Source-level α periodic power in visual and default mode networks predicts topiramate treatment response in migraine

作者：Siyuan Xie, Chenghui Pi, kang Jin, Longteng Ma, Yunyun Huo, Miaomiao Hu, Xi Zhang, Suyuan Tai, Jiayin Lin, Shengyuan Yu, Ye Ran, Zhao Dong · 发表于：The Journal of Headache and Pain · 年份：2026 · DOI：10.1186/s10194-026-02461-5 · 研究领域：Migraine and Headache Studies、Functional Brain Connectivity Studies、EEG and Brain-Computer Interfaces

BACKGROUND: Migraine is highly heterogeneous, and patients exhibit substantial variability in their responses to preventive treatment. The dose-escalation strategy of topiramate further complicates early evaluation of therapeutic efficacy. Identifying neurobiological markers that can predict treatment response is therefore essential for individualized therapy. In this study, we constructed predictive models based on individualized periodic and aperiodic power features derived from source-reconstructed electroencephalography (EEG) to identify electrophysiological indicators associated with topiramate efficacy, thereby providing a foundation for personalized prediction in migraine prevention. METHODS: In total, 112 patients with episodic migraine without aura received baseline EEG assessment and subsequently completed 3 months of topiramate treatment. EEG signals were source-reconstructed, and periodic and aperiodic components were separated using FOOOF with adjustment by each participant's individual α frequency (IAF). Predictive models were developed using XGBoost, with stratified cross-validation and 0.632 + bootstrap used to estimate generalization performance. Shapley Additive exPlanations (SHAP) analysis quantified feature contributions. Correlation analysis and Leave-One-Out Cross-Validation (LOOCV) regression were subsequently performed to examine the relationships between key features and treatment outcomes. RESULTS: The model achieved an area under the receiver operat...