|M.Sc Student||Shelli Natali|
|Subject||Synchronization in the CA3 Hippocamapal Cortical|
Network during Development and Propagation of
Pilocarpine-Induced Epileptic Seizures
|Department||Department of Medicine||Supervisor||Professor Yitzhak Schiller|
|Full Thesis text|
Epilepsy is a common disease affecting approximately 1% of the population. In epilepsy the cortical network fluctuates between two fundamental states, the asymptomatic inter-ictal state and the symptomatic ictal state of epileptic seizures. From the clinical standpoint epilepsy manifests as recurrent unprovoked seizures with varying frequencies and clinical manifestation.
Previous work of our lab in the CA1 subfield of the hippocampus found a biphasic network dynamics signature during development of chemoconvulsant seizures. This biphasic dynamics consisted of an early pre-ictal reduction in network synchronization followed by a later resynchronization of the cortical network.
The purpose of our work was to investigate the network dynamics of the CA3 subfield during development of seizures. We concentrated on the CA3 subfield for 3 reasons: 1) The CA1 subarea is extensively feed by the CA3 subfield. 2) CA3 forms a recurrent network, and as such is a good candidate for the development of synchronization. 3) Previous studies in brain slices in-vitro showed that epileptiform discharges are generated from the CA3 subarea and partially from the Enthorinal cortex and not the CA1 subfield.
In this study we used multi-electrodes single unit recordings to investigate the firing frequencies of individual neurons and network synchronization during development of Pilocarpine induced seizures in rats in vivo.
Similar to the CA1 region we found that during development of Pilocarpine induced seizure in-vivo the CA3 hippocampal subfield demonstrates a gradual increase in the average firing rate of individual neurons and a biphasic network dynamics with early desynchronization, followed by a later resynchronization of the cortical network.
These results advance our understanding of the network dynamics leading to seizure initiation and maintenance and may bring us closer to the ability to predict impending seizure in drug-resistant epilepsy patients.