Abstracts

Rationale: Physiologically-relevant computational models of epileptic activity have proven useful to deepen understanding of the pathophysiological processes underlying seizure generation in neural circuits (Soltesz & Staley, 2008). In this context, the model by Wendling et al. (2002) explains, among other phenomena, how the imbalance between excitation and inhibition in the hippocampal network can reproduce the different activity patterns observed during the transition from interictal to ictal activity in mesial temporal lobe epilepsy. However, model parameters related to synaptic efficacy in excitatory and inhibitory feedback loops have to be adjusted manually in order to reproduce the different stages of seizure spread. Therefore, the mechanisms by which neural circuits switch from interictal to seizure activity are still to be formalized and integrated into models for mechanistic analysis. In this study, we addressed this issue. Methods: We developed a biomathematical model of EEG activity in the hippocampus that accounts for the contribution of calcium currents at the level of NMDA receptors to the regulation of synaptic efficacy, as reported in Modolo et al. (2013). Results: Results based on the quantitative comparison between simulated and real local field potentials (LFPs) showed that the model can accurately reproduce interictal to ictal patterns. In particular, sporadic epileptic spikes gradually resulted in an increase of extracellular calcium at the synaptic level, leading to an increasing number of epileptic spikes and ultimately to seizure onset. Modeling results support the hypothesis that short-term synaptic plasticity processes are involved in the transition between pre-ictal and ictal activity.Conclusions: We have extended a well-established computational model of the hippocampus (Wendling et al. 2002) that reproduces the interictal/ictal transition without manual tuning of model parameters. Results illustrate the importance to take into account the contribution of synaptic plasticity processes to gain further insights in the mechanisms of seizure generation. References Soltesz, I. and K.J. Staley, Computational Neuroscience in Epilepsy. Elsevier, 2008 Wendling F, Bartolomei F, Bellanger JJ, Chauvel P. 2002. Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur J Neurosci 15(9):1499-508. Modolo J, Thomas AW, Legros A. 2013. Neural mass modeling of power-line magnetic fields effects on brain activity. Front Comput Neurosci 7:34.