Electroencephalographically detected interictal spikes have played a central role in the diagnosis of epilepsy for over 60 years. Despite the importance of interictal spikes in the diagnosis of epilepsy, we don't know why interictal spikes are present in epileptic patients, nor do we understand the mechanisms that determine spike timing. Three findings from our experiments in the hippocampal CAS in vitro model of interictal spikes form the rationale for the proposed research: 1) interictal spikes induce long-term potentiation of the strength of excitatory synapses in the epileptic network that generates the spikes. 2) the temporal pattern of interictal spikes is in turn dependent on the strength of the excitatory synapses in the epileptic network. 3) partly blocking NMDA receptor-mediated calcium influx during interictal activity results in long-term depression rather than potentiation of the strength of the excitatory synapses in epileptic networks. We hypothesize that in vivo, 1) the temporal pattern of interictal activity reflects the strength of the excitatory synapses between neurons in the epileptic network. 2) interictal spikes are associated with epilepsy because they drive the strengthening of excitatory synapses in the epileptic network 3) long-term decreases in seizure risk can be produced by transient, partial NMDA receptor blockade during interictal spike activity to induce long-term depression of the strength of the excitatory synapses in the epileptic network. To test these hypotheses in vivo, we will correlate interictal spike patterns with seizure risk using newly developed techniques for continuous EEG radiotelemetric monitoring and computer analysis of the EEG data in a rat model of chronic epilepsy. We will use acute in vivo and chronic organotypic slice preparations to determine the maximum amount of LTD that can be induced using the NMDA antagonist technique, and then use this technique to induce long-term reductions in seizure risk in vivo. These experiments will 1) provide the basis for using EEG data to quantitatively determine long-term seizure risk, and 2) develop a powerful and noninvasive technique for inducing long-term reductions in seizure risk without the need for daily medication.
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