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This Article Full Text Full Text (PDF) All Versions of this Article: 96/1/363    most recent 01342.2005v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Web of Science (3) Citing Articles via Google Scholar Google Scholar Articles by Labyt, E. Articles by Wendling, F. Search for Related Content PubMed PubMed Citation Articles by Labyt, E. Articles by Wendling, F.

Realistic Modeling of Entorhinal Cortex Field Potentials and Interpretation of Epileptic Activity in the Guinea Pig Isolated Brain Preparation

¹Laboratoire Traitement du Signal et de L'Image, Institut National de la Santé et de la Recherche Médicale U642, Rennes University 1, Rennes, France; and ²Department of Experimental Neurophysiology, Istituto Nazionale Neurologico, Milan, Italy

Submitted 20 December 2005; accepted in final form 30 March 2006

Mechanisms underlying epileptic activities recorded from entorhinal cortex (EC) were studied through a computational model based on review of cytoarchitectonic and neurobiological data about this structure. The purpose of this study is to describe and use this model to interpret epileptiform discharge patterns recorded in an experimental model of ictogenesis (guinea pig isolated brain perfused with bicuculline). A macroscopic modeling approach representing synaptic interactions between cells subpopulations in the EC was chosen for its adequacy to mimic field potentials reflecting overall dynamics rising from interconnected cells populations. Therefore intrinsic properties of neurons were not included in the modeling design. Model parameters were adjusted from an identification procedure based on quantitative comparison between real and simulated signals. For both EC deep and superficial layers, results show that the model generates very realistic signals regarding temporal dynamics, spectral features, and cross-correlation values. These simulations allowed us to infer information about the evolution of synaptic transmission between principal cell and interneuronal populations and about connectivity between deep and superficial layers during the transition from background to ictal activity. In the model, this transition was obtained for increased excitation in deep versus superficial layers. Transitions between epileptiform activities [interictal spikes, fast onset activity (25 Hz), ictal bursting activity] were explained by changes of parameters mainly related to GABAergic interactions. Notably, the model predicted an important role of GABA_a,fast- and GABA_b-receptor–mediated inhibition in the generation of ictal fast onset and burst activities, respectively. These findings are discussed with respect to experimental data.