Mesial-to-lateral gradients of epileptiform activity to localize mesial temporal lobe epilepsy.

OBJECTIVE

Mesial temporal lobe epilepsy is a common localization of drug-resistant epilepsy in adults. Patients often undergo intracranial electroencephalographic monitoring to confirm localization and determine candidacy for focal ablation or resection. Clinicians primarily base surgical decision-making on seizure onset patterns, with imaging abnormalities and information from interictal epileptiform discharge (spikes) used as ancillary data. How the morphology and timing of spikes within multielectrode sequences may inform surgical planning is unknown, in part due to the lack of measurement methods for large datasets. We hypothesized that patients with mesial temporal lobe epilepsy have a distinct mesial-to-lateral spike gradient that differentiates them from other epilepsy localizations.

METHODS

In a multicenter study at the University of Pennsylvania and the Medical University of South Carolina, we analyzed the timing and morphology of spikes and seizure high-frequency energy ratio in 75 patients with drug-resistant epilepsy. We compared these features across patients with mesial temporal lobe epilepsy, temporal neocortical epilepsy, and other localizations.

RESULTS

A logistic regression model combining all features predicted a clinical localization of mesial temporal lobe epilepsy in unseen patients with an area under the receiver operating characteristic curve of .82 (compared to an area under the receiver operating characteristic curve of .70 for seizure-only features, DeLong test p = .08) and an average precision of .84. Spike rate was the most important feature in the combined model.

SIGNIFICANCE

These findings advance surgical planning by demonstrating that quantitative spike analysis can effectively supplement seizure data in localizing mesial temporal lobe epilepsy. This approach could reduce reliance on prolonged seizure monitoring, potentially decreasing patient risk and hospitalization costs while improving surgical targeting. Our results support incorporating automated spike analysis into standard clinical workflows for epilepsy surgery evaluation.