title: Computational model prospective on the observation of proictal states in epileptic neuronal systems.
abstract: Epilepsy is a pathological condition of the human central nervous system in which normal brain functions are impaired by unexpected transitions to states called seizures. We developed a lumped neuronal model that has the property of switching between two states as a result of intrinsic or extrinsic perturbations, such as noisy fluctuations. In one version of the model, seizure risk is controlled by a single connectivity parameter representing excitatory couplings between two model lumps. We show that this risk can be reconstructed from calculation of the cross-covariance between the activities of the two neural populations during the nonictal phase. In a second simulation sequence, we use a system of 10 interconnected lumps with randomly generated connectivity matrices. We show again that the tendency to develop seizures can be inferred from the cross-covariances calculated during the nonictal states. Our conclusion is that the risk of epileptic transitions in biological systems can be objectively quantified. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
authors.affiliation: Foundation Epilepsy Institute of The Netherlands (SEIN), Heemstede, The Netherlands. skalitzin@sein.nl
authors.forename: Stiliyan
authors.initials: S
authors.lastname: Kalitzin
authors.forename: Marcus
authors.initials: M
authors.lastname: Koppert
authors.forename: George
authors.initials: G
authors.lastname: Petkov
authors.forename: Demetrios
authors.initials: D
authors.lastname: Velis
authors.forename: F Lopes
authors.initials: FL
authors.lastname: da Silva
mesh_terms: D003198:Computer Simulation; D004827:Epilepsy / Q000473:pathology* / Q000503:physiopathology; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*