EVALUATION OF SOURCE LOCALIZATION ALGORITHMS FROM EEG INTRACRANIAL RECORDINGS SIMULATED WITH PHYSIOLOGICALLY PLAUSIBLE NEURONAL POPULATIONS MODEL

BLENKMANN ALEJANDRO; BELTRACHINI LEANDRO; PRINCICH JUAN PABLO; MURAVCHIK CARLOS; KOCHEN SILVIA

Quito

Congreso; VII Congreso Latinoamericano de Epilepsia; 2012

International League Against Epilepsy

Objective Evaluate two algorithms of source localization from iEEG recordings. Methods Focal interictal activity was simulated with PPNPM [1]. Populations were delimited to a region of the cortex and coupled to each other according to their relative distance, following the model proposed in [2]. Varying model parameters defined different foci radii: 10, 15 and 20 mm. Population activities were associated to current dipoles. Then, iEEG signals were calculated with a realistic head model. Finally, sources were localized with the MNE (minimum norm estimator) and sLORETA algorithms [3]. Results For the 3 different radii, 72 epileptogenic spike sources were localized with both algorithms. Localization error of the foci center was 2.4mm in all cases for MNE and 4.6, 52.1 and 39.2mm for radius 10, 15 and 20mm when using sLORETA. With both algorithms source localization reconstructions presented a higher spatial dispersion when increasing foci size, being greater with sLORETA. Conclusion The evaluation of the algorithms with PPNPM allows very realistic conditions. MNE presented better performance than SLORETA for estimating the parameters of these iEEG signals and plausible models. More extensive studies are needed to improve the reliability of these conclusions. Observación (para nos): en rigor debemos saber si la mejora es significativa. Para eso conviene comparar con la cota de Cramer-Rao para el problema. Si MNE te deja cerca de la cota ya se sabría que no vale la pena buscar un mejor algoritmo. Me resulta no del todo claro que MNE de el mismo error para las 3 extensiones del foco. References [1]Wendling, Bellanger, Bartolomei, Chauvel, Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals. Biol.Cybernetics, 83(4):367?78,2000. [2]Blenkmann, Beltrachini, vonEllenrieder, Kochen, Muravchik. Evaluación de sLORETA para la resolución del problema inverso en ECoG utilizando modelos de poblaciones neuronales fisiológicamente plausibles. RPIC2011,2011. [3]Michel, Murray, Lantz, Gonzalez, Spinelli, Grave. EEG source imaging. Clinical neurophysiology, 115(10):2195?222,2004.