Intracranial electroencephalographic connectivity analysis to localize epileptogenic networks: Systematic review and meta‐analysis from ILAE Epilepsy Surgery Networks Task Force

Intracranial electroencephalographic (iEEG) connectivity analysis is a promising method to localize epileptic networks and guide surgical planning in focal drug-resistant epilepsy. Despite numerous studies exploring its utility, the added value of iEEG connectivity over standard clinical presurgical evaluation remains unclear. We assess the current evidence on the efficacy of iEEG connectivity analyses to improve seizure outcomes following epilepsy surgery through a systematic review and meta-analysis. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines, we searched PubMed and Embase for studies (2006-2024) of adult focal drug-resistant epilepsy patients who underwent surgical resection or ablation, reported outcomes at least 1 year postsurgery, and used iEEG connectivity analysis to localize networks. Reviews, nonhuman studies, and studies lacking iEEG connectivity analysis or network localization were excluded. We derived classification metrics (true/false positives/negatives) based on concordance between iEEG findings, clinical localization, and outcome. Subgroup meta-analyses and meta-regressions determined differences by seizure type, lesion status, and analysis approach. Of 2881 studies screened, 25 met criteria (n = 909). The pooled odds ratio comparing seizure outcome prediction using iEEG connectivity versus standard clinical evaluation was 1.36 (95% confidence interval = 1.10-1.69, p = .004), indicating a significant overall benefit. Subgroup analyses found no significant differences by directionality, modeling method (linear/nonlinear), or iEEG epoch (interictal/peri-ictal). Meta-regression revealed greater added value of iEEG connectivity in studies with higher proportions of non-seizure-free patients following surgery for temporal lobe or lesional epilepsy. However, no individual study achieved statistical significance on its own, reflecting limited power and lack of individual patient-level data. Power analysis confirmed that detecting a clinically meaningful effect requires substantially larger, potentially multicenter datasets. iEEG connectivity analysis offers modest but consistent increased value over standard clinical methods to predict seizure freedom in adult patients with focal drug-resistant epilepsy. For clinical translation, we propose recommendations for future studies to address sample size limitations, standardize reporting, and prioritize individual patient-level data sharing.