Refining connectivity analysis in stereoelectroencephalography applied to epilepsy surgery

Purpose This study evaluates the influence of connectivity analysis parameters on the ability of weighted Phase Lag Index (wPLI)-based networks, derived from stereoelectroencephalography (SEEG) recordings, to represent the epileptogenic network. It also identifies the parameter combinations that most accurately delineate the epileptogenic zone. By comparing network metrics obtained under different connectivity configurations with surgical resections and postoperative outcomes, we aimed to establish a framework for selecting connectivity analysis parameters in epilepsy surgery planning. This optimized framework could improve the precision of stimulation zone identification, thereby refining resection or ablation strategies and potentially leading to better surgical outcomes and improved quality of life for patients with refractory epilepsy, while complementing clinical judgment. Methods Ictal SEEG recordings from 28 patients with refractory epilepsy were analyzed. Functional networks were generated using wPLI, varying frequency bands, start/end analysis times, and epoch durations. Connectivity degree and betweenness centrality were then compared with surgical outcomes using the R coefficient and Mann-Whitney test. Results Optimal configurations showed significant overlap between resected nodes and key network nodes in patients with successful surgical outcomes. Both the theta (4–8 Hz) and high-frequency (160–220 Hz) bands were represented among the most effective configurations, most commonly combined with analysis windows beginning at or shortly before ictal onset; an epoch duration of 0.5 s was the most frequent choice among these configurations. Conclusions Rather than assessing a single connectivity configuration, this study establishes a framework for selecting connectivity analysis parameters in ictal SEEG. The identified configurations generated network representations that were more consistent with surgically validated epileptogenic regions, providing methodological guidance for future network-based surgical planning.