Abstracts

RATIONALE: In the field of epilepsy, the analysis of stereoelectroencephalographic (SEEG, intra-cerebral recording) signals with signal processing methods can help to better identify the epileptogenic zone (responsible of the triggering of seizures) and to better understand its organization. In order to evaluate existing methods, to design new ones and to physiologically interpret results they provide, we developed a model able to produce EEG signals from networks of neural populations. METHODS: A neurophysiologically relevant model initially proposed by Lopes Da Silva et al. is extended to generate spontaneous EEG from multiple coupled neural populations. In the model, parameters (related to excitation, inhibition, degree and directions of couplings) can be altered to produce realistic epileptiform EEG signals. These signals, simulated from "organized" networks, are used to study signal processing methods, especially those dedicated to the analysis of signal interdependencies. In this context, a new estimator of the degree and the direction of couplings between populations (based on nonlinear regression computed on pairs of signals) is designed and evaluated with the model. RESULTS: This estimator is used on real SEEG signals in patients suffering from drug-resistant temporal lobe epilepsy (TLE). Three regions of temporal lobe (anterior T2, neocortex (NCx), amygdala (A) and the anterior hippocampus (H)) were systematically analyzed. Results show that functional couplings - in and between - medial and lateral structures can be interpreted. Results extend our previous results (that were based on coherence analysis) on the classification of TLE seizures (Bartolomei et al., 1999). CONCLUSIONS: From a methodological viewpoint, we designed a method able to reliably estimate couplings (degree/direction) in an EEG model based on coupled neural populations. From a clinical viewpoint, the analysis of SEEG signals using this method provides insight into functional couplings establishing during TLE seizures. A better understanding of the dynamical organization of the epileptogenic zone is expected.