Modeling Studies Of Abnormal Discharges And Spreading Dynamics In Partial Epilepsy

According to the World Health Organization,epilepsy is the second high-risk nervous disease,which is characterized by its recurrence,temporality and suddenness in clinic.Partial epilepsy is one of the most frequent epilepsy,and its abnormal discharges have complicated temporal and spatial scales.Because seizures can occur under different temporal-spatial scales,it is difficult to understand the generation,propagation and termination of the seizure dynamics.In this thesis,we investigated the mechanisms of abnormal discharges of partial epilepsy at both macro and micro scales and then investigated how the network topology influences partial seizure dynamics at macro scales.The main findings are summarized as following:Firstly,using the paradigmatic Epileptor model,we investigated how stochastic fluctuations of permittivity coupling modulate seizure dynamics in partial epilepsy,and found that a certain level of permittivity noise can make the stochastic Epileptor model produce more comparable seizure-like events that capture temporal variability in realistic partial seizures.Furthermore,stochastic fluctuations of permittivity coupling had a negative contribution to partial epileptic patients,and the fluctuations of permittivity can still trigger the seizure dynamics even when the epileptogenic factor operates in the theoretical nonepileptogenic regime.These results suggest that recurrent spontaneous seizures can arise from abnormal fluctuation of the permittivity.Secondly,by establishing coupled neuronal networks,we investigated the seizure mechanisms of partial epilepsy at the micro scale.To some extent,our results show that coupled neuronal networks has similar discharge phenomena and dynamical mechanism as the macroscopic Epileptor model.However,compared to the Epileptor model,we found the mean survival time of seizure was more sensitive to the intensity of the permittivity noise in the coupled neuronal networks.In particular,strong noise can induce the transition of seizures of the coupled neuronal network from ictal-state to interictal-state.These findings provide theoretical supports for future researches on the mechanism of partial epilepsy termination.Thirdly,we investigated the spreading dynamics of partial epilepsy by establishing a randomly coupled Epileptor network.By enumerating the connectivity probability,we found that the degree of seizures of the whole network reach the maximum under acertain connectivity density.Besides,the network scale also had an important effect on the propagation of the epilepsy.When the connectivity density was low,the degree of seizures of the whole network became stronger as the network size increased;but when the connectivity density is high,the degree of seizures of the whole network became weaker as the network size increased.These results provided a reference for further investigation on the spreading of the partial epilepsy.In summary,we found that permittivity,permittivity coupling noise and the epileptogenic factor play important roles in the generation of abnormal discharges in partial epilepsy by computational model.Besides,we found that the network topology had an important effect on the propagation of partial epilepsy.These findings had important theoretical values for further exploring the neural mechanisms of partial epilepsy.