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Background <p>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.</p> Methods <p>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.</p> Results <p>The model achieved an area under the receiver operating characteristic curve (AUROC) of 0.859 in identifying treatment responders. SHAP analysis indicated that the most influential features were periodic α-band power localized to the cuneus, pericalcarine cortex, medial orbitofrontal cortex, frontal pole, and precuneus. Principal component analysis (PCA) of the functional brain networks comprising these regions showed that the principal components of the visual network (VN) and default mode network (DMN) were significantly associated with headache improvement, and their joint inclusion in a LOOCV regression model explained 26.1% of the variance in treatment efficacy.</p> Conclusion <p>Periodic α activity represents a strong predictive biomarker of topiramate efficacy, with higher α power indicating poorer clinical response. These findings provide an interpretable neurobiological basis for individualized prediction in migraine preventive therapy.</p> Clinical trial number <p>Not applicable.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月17日

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  • Siyuan Xie,
  • Chenghui Pi,
  • Kang Jin,
  • Longteng Ma,
  • Yunyun Huo,
  • Miaomiao Hu,
  • Xiaoyan Zhang,
  • Suyuan Tai,
  • Jiayin Lin,
  • Shengyuan Yu,
  • Ye Ran,
  • Zhao Dong

摘要

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 operating characteristic curve (AUROC) of 0.859 in identifying treatment responders. SHAP analysis indicated that the most influential features were periodic α-band power localized to the cuneus, pericalcarine cortex, medial orbitofrontal cortex, frontal pole, and precuneus. Principal component analysis (PCA) of the functional brain networks comprising these regions showed that the principal components of the visual network (VN) and default mode network (DMN) were significantly associated with headache improvement, and their joint inclusion in a LOOCV regression model explained 26.1% of the variance in treatment efficacy.

Conclusion

Periodic α activity represents a strong predictive biomarker of topiramate efficacy, with higher α power indicating poorer clinical response. These findings provide an interpretable neurobiological basis for individualized prediction in migraine preventive therapy.

Clinical trial number

Not applicable.