Multimodal, noninvasive seizure network mapping software: A novel tool for preoperative epilepsy evaluation.

BACKGROUND

Despite rigorous preoperative evaluation, epilepsy surgery achieves seizure freedom in only two-thirds of cases. Current preoperative evaluation does not include a detailed network analysis despite the association of network-level changes with epilepsy.

OBJECTIVE

We sought to create a software algorithm to map individualized epilepsy networks by combining noninvasive electroencephalography (EEG) source localization and nonconcurrent resting state functional magnetic resonance imaging (rsfMRI).

METHODS

Scalp EEG and rsfMRI data were acquired for three sample cases: one healthy control case, one case of right temporal lobe epilepsy, and one case of bitemporal seizure onset. Data from rsfMRI were preprocessed, and a time-series function was extracted. Connection coefficients were used to threshold out spurious connections and model global functional networks in a 3D map. Epileptic discharges were localized using a forward model of cortical mesh dipoles followed by an empirical Bayesian approach of inverse source reconstruction and co-registered with rsfMRI. Co-activating brain regions were mapped.

RESULTS

Three illustrative sample cases are presented. In the healthy control case, the software showed symmetrical global connectivity. In the right temporal lobe epilepsy case, asymmetry was found in the global connectivity metrics with a paucity of connectivity ipsilateral to the epileptogenic cortex. The superior longitudinal fasciculus, uncinate fasciculus, and commissural fibers connecting disparate and discontinuous cortical regions involved in the epilepsy network were visualized. In the case with bitemporal lobe epilepsy, global connectivity was symmetric. It showed a network of correlating cortical activity local to epileptogenic tissue in both temporal lobes. The network involved white matter tracks in a similar pattern to those seen in the right temporal case.

CONCLUSIONS

This modeling algorithm allows better definition of the global brain network and potentially demonstrates differences in connectivity between an epileptic and a non-epileptic brain. This finding may be useful for mapping cortico-cortical connections representing the putative epilepsy networks. With this methodology, we localized the epileptogenic brain and showed network asymmetry and long-distance cortical co-activation. This software tool is the first to use a multimodal, nonconcurrent, and noninvasive approach to model and visualize the epilepsy network.