Pang Yue, Yang Yixuan, Lin Yunqing, Zhu Jianyu, Liu Penghui, Tian Yu, Wang Feng, Mei Zhen, Kang Dezhi, Cao Miao, Lin Yuanxiang
Fujian Medical University, Fuzhou, China.
Beijing City Key Lab for Medical Physics and Engineering, Beijing, China.
PeerJ. 2025 Jul 1;13:e19548. doi: 10.7717/peerj.19548. eCollection 2025.
Epilepsy is a chronic neurological disorder affecting approximately 70 million individuals worldwide, with a significant subset of patients exhibiting drug-resistant epilepsy (DRE). Accurate identification of the seizure onset zone (SOZ) is crucial for successful surgical intervention. This study investigates interictal neural fragility as a potential biomarker for predicting SOZ and guiding treatment outcomes in DRE patients. By applying dynamic mode decomposition (DMD) techniques to interictal stereoelectroencephalography (SEEG) data from 30 patients, we generated patient-specific dynamic network models and constructed fragility heatmaps. Our findings demonstrate that patients with favorable surgical outcomes exhibit significantly higher fragility in the SOZ during interictal periods. The fragility-based SOZ prediction model showed high sensitivity and specificity, with a strong concordance between the predicted SOZ and clinically identified treatment targets. This study highlights the clinical utility of interictal neural fragility in enhancing SOZ localization and improving treatment strategies for patients with low seizure frequency. Future research should focus on integrating this model into clinical workflows and exploring its potential in personalized treatment approaches.
癫痫是一种慢性神经系统疾病，全球约有7000万人受其影响，其中相当一部分患者表现为药物难治性癫痫（DRE）。准确识别癫痫发作起始区（SOZ）对于成功的手术干预至关重要。本研究调查发作间期神经脆弱性作为预测DRE患者SOZ和指导治疗结果的潜在生物标志物。通过将动态模式分解（DMD）技术应用于30例患者的发作间期立体脑电图（SEEG）数据，我们生成了患者特异性动态网络模型并构建了脆弱性热图。我们的研究结果表明，手术结果良好的患者在发作间期SOZ表现出明显更高的脆弱性。基于脆弱性的SOZ预测模型显示出高敏感性和特异性，预测的SOZ与临床确定的治疗靶点之间具有很强的一致性。本研究强调了发作间期神经脆弱性在增强SOZ定位和改善癫痫发作频率低的患者治疗策略方面的临床效用。未来的研究应侧重于将该模型整合到临床工作流程中，并探索其在个性化治疗方法中的潜力。
