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Recording and Modulation of Epileptiform Activity in Rodent Brain Slices Coupled to Microelectrode Arrays
Published on: May 15, 2018
Controlling Epileptic Seizures through Hippocampal Regulation: A Complex Network Analysis in the Mouse Brain
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For focal epilepsy, modeling the virtual brain through large-scale network dynamics to customize treatments is currently a highly promising approach. However, after obtaining the epileptic brain connectome of subjects, most research has focused on exploring ways to help clinicians better perform brain resections. From the perspective of complex networks, we explore the possibility of utilizing the strength of network coupling to treat seizures non-destructively. We use the Epileptor model to construct heterogeneous dynamic networks with epileptogenic zones and design global indices appropriate for this model to describe systemic seizures. Based on these, we explored the effects of epileptogenic proportion and global coupling strength on different artificial networks, and finally verified on a real Allen mouse connectome that the enhancement of coupling strength can effectively control epilepsy. Our simulations found that as the epileptogenic proportion increased, seizure propagation steadily increased for the small-world and the scale-free networks, while both the random network jumped from a sustaining state of global suppression to a state of global bursting. As for the increase in global coupling strength, the small-world network maintained a steady spread, while both the random and scale-free networks had their seizures significantly controlled. Subsequently, we validated the suppression of focal seizures in the Allen mouse brain by boosting the coupling strength a little in its hippocampal formation. Our study shows that the structural nature of networks significantly affects seizure propagation and synchronization. The topology of the random network is significantly anti-epileptic, while others are more prone to maintaining seizures. Coupling strength is an effective way to control epilepsy in both random and scale-free networks. Thus, we propose the idea of using the structural nature of networks to control seizures non-destructively, which may also serve as the theoretical basis for other cognitive training therapies, such as emotional or exercise based interventions aimed at controlling epilepsy by training projection strengths from different brain regions.

