[Identification of epileptogenic networks from modeling and nonlinear analysis of SEEG signals]

This work is focused on the study of the organization of the epileptogenic zone (E.Z.) in humans based on the analysis of stereo-electroencephalographic (SEEG) signals with signal processing methods, and more especially those dedicated to the estimation of signal interdependencies. In order to evaluate quantities provided by these methods and in order to relate them to the notion of functional coupling between cerebral structures, we developed a neurophysiologically relevant model able to generate EEG signals from organized networks of neural populations. We showed that the model can produce realistic multichannel epileptiform signals (when compared to real SEEG signals) under certain conditions (excitation/inhibition ratio within populations, uni/bi-directional coupling between populations). In this paper, the model framework is used to evaluate the performances of nonlinear regression analysis as a method to characterize couplings between cerebral structures from the SEEG signals they produce. Two quantities, a nonlinear correlation coefficient and a direction index, respectively related to coupling parameters in the model (degree/direction) are presented. These two quantities are measured on real SEEG signals recorded in patients suffering from temporal lobe epilepsy and candidate to surgical treatment. Results show that the characterization of functional couplings leads to the identification of networks referred to as 'epileptogenic networks', which might be responsible for the triggering of seizures. These results also corroborate our previous results on the classification of temporal lobe epilepsies, showing that there exist recurrent seizure patterns that can be classified on the basis of interactions between medial and lateral neocortical structures.