Causal Brain Network in Clinically-Annotated Epileptogenic Zone Predicts Surgical Outcomes of Drug-Resistant Epilepsy

Objective: Patients with drug-resistant epilepsy (DRE) are commonly treated using neurosurgery, while its success rate is limited with approximately 50%. Predicting surgical outcomes is currently a prominent topic. The DRE is recognized as a network disorder involving a seizure triggering mechanism within epileptogenic zone (EZ); however, a systematic exploration of the EZ causal network remains lacking. Methods: This paper will advance DRE study by: 1) developing a novel causal coupling algorithm, "full convergent cross mapping (FCCM)"to improve the quantization performance; 2) characterizing the DRE's multi-frequency epileptogenic network by FCCM calculation of ictal iEEG; 3) predicting surgical outcomes using network features and machine learning. Numerical validations demonstrate the FCCM's superior quantization in terms of nonlinearity, accuracy, and stability. A multicenter cohort containing 22 DRE patients with 81 seizures is included. Result: Based on the Mann-Whitney-U-test, coupling strength of the epileptogenic network in successful surgeries is significantly higher than that of the failed group, with the most significant difference observed in α -iEEG network (P=1.52e- 07). Other clinical covariates are also considered and all the bmα -iEEG networks demonstrate consistent differences comparing successful and failed groups, with bmP= bm and 9.23 e 06 for lesional and non-lesional DRE, bm P= bm 2.32 e- 05 0.0074, and 0.0030$ for three clinical centers CHFU, JHU and NIH. Using FCCM features and 10-fold cross validation, the SVM achieves the highest accuracy of 87.65% in predicting surgical outcomes. Conclusion: The epileptogenic causal network is a reliable biomarker for estimating DRE's surgical outcomes. Significance: The proposed approach is promising to facilitate DRE precision medicine.