  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.
