Exploring the propagation pathway in individual patients with epilepsy: A stepwise effective connection approach

Abstract

ObjectiveThe unclear propagation pathway of seizures is one of the main reasons for failure of surgical treatment, and the propagation process involves the directional brain networks. However, few network analysis techniques have successfully traced specific seizure propagation pathways. This study proposed a stepwise transfer entropy (STE) approach to describe the propagation of effective connections in brain networks. MethodsThe proposed STE technique is applied to stereoelectroencephalography (SEEG) data collected from patients with epilepsy, which can identify characteristic regions connected to specific seed brain regions at different link-step levels. Importantly, the underlying TE-based network construction method can accurately obtain electrode-to-electrode connections, and the stepwise approach can capture the interactions between electrodes at the connection level. Finally, simulation and clinical data were used to evaluate the STE approach according to the similarity, confusion matrix, accuracy and recall. ResultsWe used three datasets: a simulation dataset, a clinical dataset, and a public dataset. We compared the results of different network construction methods with multiple datasets, and the STE approach successfully captured node changes in epilepsy patients, effectively identified early and late propagation nodes, and determined the propagation pathway. Moreover, the STE approach is superior to other methods in all evaluation indices, achieving 97.9% accuracy and 0.93 similarity. SignificanceCompared with the existing methods, the STE approach has significant advantages in accurately tracking propagation pathways. Moreover, the STE approach is simple and quickly performs calculations, making it an easy-to-use promising method for determining propagation pathways in clinical settings.