Stereo-Electroencephalography Guided Radiofrequency Thermocoagulation Prognosis Prediction Based on Brain Network Features of Patients with Refractory Epilepsy
Yang Shuyao1, Xie Yuhai1, Gong Yuchen1, Liu Qiangqiang2, Zhang Puming1*
1(School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China) 2(Clinical Neuroscience Center, Department of FunctionalNeurosurgery, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China)
Abstract：The outcome of radiofrequency thermocoagulation (RFTC) in different patients with refractory epilepsy is usually largely different. This study aimed to investigate graph theory indexes of brain networks and establish a RFTC prognosis prediction model. Based on the stereo-electroencephalography (SEEG) signals of 45 patients with refractory epilepsy before RFTC, a time-variant multi-variate autoregressive model was constructed. Spectrum-weighted time-variant partial directed coherence was computed to build an effective connectivity network of the brain and graph theory indexes of the effective connectivity network were analyzed. According to the Engel classification at least three months after RFTC, the patients were divided into RFTC responder group (Engel I & II) and RFTC non-responder group (Engel III). The graph theory indexes were used for statistical analysis between the two groups and for establishing prognosis prediction by support vector machine (SVM). The normalized average clustering coefficient (P=0.000) and small-worldness (P=0.022) of the patients in RFTC responder group were significantly higher than those in RFTC non-responder group, and the normalized characteristic path length was significantly lower than those in the RFTC non-responder group (P=0.032) (The normalized average clustering coefficient, the small-worldness and the normalized characteristic path length of the patients in the RFTC responder group were 0.995 3±0.000 2, 0.853 0±0.006 2 and 1.168 8±0.008 5, respectively. The normalized average clustering coefficient, small-worldness and normalized characteristic path length of the patients in RFTC non-responder group were 0.994 0±0.000 2, 0.833 5±0.005 6 and 1.194 4±0.008 0, respectively. Based on the three indexes, the accuracy of the prognosis prediction reached 81.97% by SVM. The RFTC prognosis prediction model based on the graph theory indexes (normalized average clustering coefficient, normalized characteristic path length, and small-worldness) of the effective connectivity networks before RFTC could effectively predict the postoperative outcome.
杨淑窈, 谢宇海, 宫语晨, 刘强强, 张溥明. 基于难治性癫痫患者脑网络特征的立体脑电图引导射频热凝毁损术预后预测[J]. 中国生物医学工程学报, 2023, 42(6): 651-658.
Yang Shuyao, Xie Yuhai, Gong Yuchen, Liu Qiangqiang, Zhang Puming. Stereo-Electroencephalography Guided Radiofrequency Thermocoagulation Prognosis Prediction Based on Brain Network Features of Patients with Refractory Epilepsy. Chinese Journal of Biomedical Engineering, 2023, 42(6): 651-658.
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