Localization of Epileptogenic Zone Based on Cortico-Cortical Evoked Potential (CCEP): A Feature Extraction and Graph Theory Approach.

Objective: Epilepsy is a chronic brain disease, which is prone to relapse and affects individuals of all ages worldwide, particularly the very young and elderly. Up to one-third of these patients are medically intractable and require resection surgery. However, the outcomes of epilepsy surgery rely upon the clear identification of epileptogenic zone (EZ). The combination of cortico-cortical evoked potential (CCEP) and electrocorticography (ECoG) provides an opportunity to observe the connectivity of human brain network and more comprehensive information that may help the clinicians localize the epileptogenic focus more precisely. However, there is no standard analysis method in the clinical application of CCEPs, especially for the quantitative analysis of abnormal connectivity of epileptic networks. The aim of this paper was to present an approach on the batch processing of CCEPs and provide information relating to the localization of EZ for clinical study. Methods: Eight medically intractable epilepsy patients were included in this study. Each patient was implanted with subdural grid electrodes and electrical stimulations were applied directly to their cortex to induce CCEPs. After signal preprocessing, we constructed three effective brain networks at different spatial scales for each patient, regarding the amplitudes of CCEPs as the connection weights. Graph theory was then applied to analyze the brain network topology of epileptic patients, and the topological metrics of EZ and non-EZ (NEZ) were compared. Results: The effective connectivity network reconstructed from CCEPs was asymmetric, both the number and the amplitudes of effective CCEPs decreased with increasing distance between stimulating and recording sites. Besides, the distribution of CCEP responses was associated with the locations of EZ which tended to have higher degree centrality (DC) and nodal shortest path length (NLP) than NEZ. Conclusion: Our results indicated that the brain networks of epileptics were asymmetric and mainly composed of short-distance connections. The DC and NLP were highly consistent to the distribution of the EZ, and these topological parameters have great potential to be readily applied to the clinical localization of the EZ.