|Ph.D Student||Cymerblit-Sabba Adi|
|Subject||Network Dynamics Leading to Seizure Initiation and|
Maintenance and Improving the Antiepileptic
Efficacy of Cortical Stimulation in
Pharmacologically Induced Epil
|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 manifestations. The majority of epilepsy patients are fully controlled with antiepileptic drugs. However, approximately 30 % of patients suffer from drug-resistant epilepsy, and continue to experience seizures despite appropriate drug therapy. Unfortunately, modern medicine has no good solution to offer the majority of drug-resistant epileptic patients, and new treatment modalities are needed.
One of the most promising potential new treatments for drug-resistant epilepsy involves the use of closed loop neurostimulators. These neurostimulators are planned to detect the pre-ictal state preceding clinical seizures, and in turn automatically generate the appropriate stimulation paradigms to abort impending seizures and prevent initiation of a full-blown clinical seizure. To construct an effective closed-loop neurostimulator two conditions must be fulfilled. First, we must have the ability to detect the pre-ictal state, and second we must have the appropriate stimulation paradigms to effectively abort impending seizures.
In this study we used multi-electrode single unit recordings in rats in vivo to simultaneously record the activity of multiple single neurons and monitor inter-neuronal synchronization. We first, characterized biphasic network dynamics preceding initiation of chemoconvulsant-induced epileptic seizures. This novel "network dynamics signature" may be used to detect the pre-ictal state preceding epileptic seizures.
Next, we investigated the network activity during the ictal state of epileptic seizures. We found that chemoconvulsant-induced seizures were composed of three different ictal phases, with a gradual progression from the first to the third phase. Surprisingly, we found the early phase of seizures was accompanied by a marked reduction in the averaged network synchronization. As seizures progress hyper-synchrony gradually spread and intensified until the entire neuronal population belongs to a single unified hyper-synchronized network.
Finally, we introduced novel asynchronous stimulation paradigms which markedly enhanced the antiepileptic efficacy of the neurostimulation. We believe this multi-electrode stimulation paradigms will present greater antiepileptic effects in human patients.
To summarize, the results presented in this thesis advance our understanding of the network dynamics leading to seizure initiation and maintenance and may enhance the development of effective closed-loop neurostimulators for treating drug-resistant epilepsy.