Identification of interictal epileptic networks from dense-EEG

TL;DR

This study evaluates how different inverse solutions and connectivity measures affect the identification of epileptic networks from dense-EEG data, demonstrating that proper configuration can reveal clinically relevant brain networks.

Contribution

It systematically compares inverse algorithms and connectivity measures, introducing a new similarity index to improve noninvasive epileptic network detection from dense-EEG.

Findings

Nonlinear connectivity measures outperform linear ones.

wMNE inverse solution shows higher accuracy.

The combination of wMNE and PLV closely matches intracranial EEG networks.

Abstract

Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical level from noninvasive recordings. In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: i) the algorithm used in the solution of the EEG inverse problem and ii) the method used in the estimation of the functional connectivity. We evaluate four inverse solutions algorithms and four connectivity measures on data simulated from a combined biophysical/physiological model to generate realistic interictal epileptic spikes reflected in scalp EEG. We use a new network-based similarity index (SI) to compare between the network identified by each of the inverse/connectivity combination and the original network generated in the model. The method will be also applied on real data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focal epilepsy. In simulated data, results revealed that the selection of the inverse/connectivity combination has a significant impact on the identified networks. Results suggested that nonlinear methods for measuring the connectivity are more efficient than the linear one. The wMNE inverse solution showed higher performance than dSPM, cMEM and sLORETA. In real data, the combination (wMNE/PLV) led to a very good matching between the interictal epileptic network identified from noninvasive EEG recordings and the network obtained from connectivity analysis of intracerebral EEG recordings. These results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data.