title: Dynamical changes in neurons during seizures determine tonic to clonic shift.
abstract: A tonic-clonic seizure transitions from high frequency asynchronous activity to low frequency coherent oscillations, yet the mechanism of transition remains unknown. We propose a shift in network synchrony due to changes in cellular response. Here we use phase-response curves (PRC) from Morris-Lecar (M-L) model neurons with synaptic depression and gradually decrease input current to cells within a network simulation. This method effectively decreases firing rates resulting in a shift to greater network synchrony illustrating a possible mechanism of the transition phenomenon. PRCs are measured from the M-L conductance based model cell with a range of input currents within the limit cycle. A large network of 3000 excitatory neurons is simulated with a network topology generated from second-order statistics which allows a range of population synchrony. The population synchrony of the oscillating cells is measured with the Kuramoto order parameter, which reveals a transition from tonic to clonic phase exhibited by our model network. The cellular response shift mechanism for the tonic-clonic seizure transition reproduces the population behavior closely when compared to EEG data.
authors: Department of Physics, University of Minnesota, Minneapolis, MN, USA.
authors: Bryce
authors: B
authors: Beverlin
authors: James
authors: J
authors: Kakalios
authors: Duane
authors: D
authors: Nykamp
authors: Theoden Ivan
authors: TI
authors: Netoff
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D058256:Brain Waves / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D012640:Seizures / Q000473:pathology* / Q000503:physiopathology*