# Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validation

## TL;DR

This study combines brain activity data from two techniques to better predict successful epilepsy surgery outcomes.

## Contribution

A novel dynamic network model combining rs-iEEG and rs-fMRI for improved surgical outcome prediction in epilepsy.

## Key findings

- The combined dynamic index outperformed individual biomarkers in predicting surgical outcomes.
- The model showed superior predictive performance compared to standalone rs-fMRI or rs-iEEG indices.
- The approach could improve surgical candidacy and reduce invasive monitoring duration.

## Abstract

Accurate localization of the seizure onset zone (SOZ) is critical for successful epilepsy surgery but remains challenging with current techniques. We developed a novel seizure onset network characterization tool that combines dynamic biomarkers of resting-state intracranial stereoelectroencephalography (rs-iEEG) and resting-state functional magnetic resonance imaging (rs-fMRI), vetted against surgical outcomes. This approach aims to reduce reliance on capturing seizures during invasive monitoring to pinpoint the SOZ.

We computed the source-sink index (SSI) from rs-iEEG for all implanted regions and from rs-fMRI for regions identified as potential SOZs by noninvasive modalities. The SSI scores were evaluated in 17 pediatric drug-resistant epilepsy (DRE) patients (ages 3–15 years) by comparing outcomes classified as successful (Engel I or II) versus unsuccessful (Engel III or IV) at 1 year post-surgery.

Of 30 reviewed patients, 17 met the inclusion criteria. The combined dynamic index (im-DNM) integrating rs-iEEG and rs-fMRI significantly differentiated good (Engel I–II) from poor (Engel III–IV) surgical outcomes, outperforming the predictive accuracy of individual biomarkers from either modality alone.

The combined dynamic network model demonstrated superior predictive performance than standalone rs-fMRI or rs-iEEG indices.

By leveraging interictal data from two complementary modalities, this combined approach has the potential to improve epilepsy surgical outcomes, increase surgical candidacy, and reduce the duration of invasive monitoring.