Identyfikacja ogniska padaczkowego u pacjentów opornych na leki przy użyciu mapy przyczynowych połączeń mózgowych

OBJECTIVE: While neurosurgical treatment is the primary intervention for Drug-resistant epilepsy (DRE) patients, its success rate is limited to approximately 60%. Precise localization of epileptogenic zone (EZ) is a critical factor for surgical success. This study aims to achieve cross-subject localization of the EZ in DRE patients using causal brain network features extracted from stereotactic electroencephalography (SEEG). METHODS: We collected SEEG recordings from 12 DRE patients who achieved complete seizure freedom (Engel Class I). The causal coupling algorithm, full convergent cross mapping (FCCM), was applied to both ictal and interictal states to extract causal brain network features from SEEG recordings. We fed these features into multiple machine learning models to achieve automated identification of EZ within a leave-one-patient-out cross-validation framework. Under a unified evaluation protocol, FCCM was compared with directed information, frequency-domain convergent cross mapping (FD CCM),high-frequencyoscillation-based methods, source-sink con nectivity, and SEEGformer. RESULTS: At the individual level, the Mann-Whitney U test demonstrated that the raw FCCM values sig nificantly differentiated the EZ from the non-epileptogenic zones (NEZ) in 83.3% (10/12) of patients during the ictal state and in all patients (12/12) during the interictal state (p < 0.001). Per mutational multivariate analysis of variance on the 8 dimensional statistical features revealed significant global network variations in 83.3% (10/12) of ictal and 91.7% (11/12) of interictal states (p < 0.001). The combined model, which integrated both ictal and interictal features using the logistic regression classifier, achieved the best performance among all evaluated methods This approach achieved the highest accuracy (0.83 ± 0.10), recall (0.87 ± 0.09), and F1-score (0.80 ± 0.12), while the second-best baseline (FD CCM) yielded an accuracy of 0.79 ± 0.15, a recall of 0.81 ± 0.20, and an F1-score of 0.75±0.18. These results demonstrate that our method achieves state-of-the-art performance. CONCLUSION: Causal brain network features extracted by FCCM are robust biomarkers for localizing the EZ. SIGNIFICANCE: This study could support the development of more personalized treatment strategies for DRE patients. It is expected to promote the development of precision medicine for DRE.