Interictal intracranial EEG for predicting surgical success: the importance of space and time

TL;DR

This study improves prediction of surgical success in epilepsy by normalizing for electrode proximity, considering coverage extent, and accounting for temporal variations in intracranial EEG networks.

Contribution

It introduces methods to address spatial bias, coverage differences, and temporal dynamics in interictal EEG analysis for better outcome prediction.

Findings

Normalizing for spatial proximity enhances prediction accuracy.

Greater electrode coverage correlates with higher prediction success.

Predictions remain robust across different time segments.

Abstract

Predicting post-operative seizure freedom using functional correlation networks derived from interictal intracranial EEG has shown some success. However, there are important challenges to consider. 1: electrodes physically closer to each other naturally tend to be more correlated causing a spatial bias. 2: implantation location and number of electrodes differ between patients, making cross-subject comparisons difficult. 3: functional correlation networks can vary over time but are currently assumed as static. In this study we address these three substantial challenges using intracranial EEG data from 55 patients with intractable focal epilepsy. Patients additionally underwent preoperative MR imaging, intra-operative CT, and post-operative MRI allowing accurate localisation of electrodes and delineation of removed tissue. We show that normalising for spatial proximity between nearby electrodes improves prediction of post-surgery seizure outcomes. Moreover, patients with more extensive electrode coverage were more likely to have their outcome predicted correctly (ROC-AUC >0.9, p<<0.05), but not necessarily more likely to have a better outcome. Finally, our predictions are robust regardless of the time segment. Future studies should account for the spatial proximity of electrodes in functional network construction to improve prediction of post-surgical seizure outcomes. Greater coverage of both removed and spared tissue allows for predictions with higher accuracy.