Desynchronization Index: A new connectivity approach for exploring epileptogenic networks

In drug-resistant epilepsy, Stereo-Electroencephalography (SEEG) monitoring is one of the most effective techniques to identify the Epileptogenic Zone (EZ), the fundamental prerequisite for epilepsy surgery. Despite recent technological advances, SEEG recordings remain difficult to interpret, and SEEG-guided surgery still achieves success rates below 70%. In this work, we develop a novel computational framework for SEEG analysis, with the ultimate aim of improving the accuracy in EZ definition. Specifically, we investigate the hypothesis that epileptogenic regions exhibit a tendency to behave independently and thus desynchronize from neighboring brain structures before seizure onset. To this end, we design the Desynchronization Index (DI), an algorithm that identifies the Epileptogenic Zone (EZ) as the subset of channels that disconnect from the SEEG network during the ictal transition. We evaluate the DI algorithm against Epileptogenicity Index (EI), one of the most common tools for EZ definition, on a clinical dataset of 20 patients, considering the channels that were thermocoagulated at the end of SEEG monitoring as the detection target. Our results show that DI overcomes EI in terms of area under the ROC curve (AUC =0.86 vs. AUC =0.83), while combining the two algorithms into a single framework leads to the best performance (AUC =0.88). Overall, the DI algorithm underscores anomalous connectivity patterns that are difficult to detect through visual inspection, improving the accuracy in the EZ definition and providing new insights into the dynamics of seizure generation.