Excitation spreads through a neural network via positive feedback connections between the neurons. The amount of positive feedback in the network is determined by the number and strength of these excitatory synaptic connections, as well as the degree to which these connections are masked by pre and postsynaptic inhibition. The proposed research will test the hypothesis that the amount of positive feedback in a neural network is correlated with the probability that the network will initiate a seizure. To test this hypothesis, we have developed two noninvasive methods. The first method quantifies the amount of positive feedback based on the temporal pattern of interictal spikes on the electroencephalogram (EEG). The second method modifies the amount of positive feedback by selective long-term depression (LTD) of the strength of recurrent excitatory synapses. Using a well-characterized rat kainate model of chronic epilepsy, the amount of positive feedback measured from the EEG will be correlated with seizure probability during epileptogenesis. As an additional test of the hypothesis, the amount of positive feedback in the epileptic networks will be decreased by LTD of the recurrent synapses, and the seizure probability will be compared to EEG measures of positive feedback before and after LTD. These experiments may provide two important tools for treating epilepsy. The first is the ability to estimate seizure probability from the pattern of interictal spike activity on the EEG, which would make possible the prospective evaluation of the risk of seizures and the efficacy of anticonvulsant therapy. The second is the induction of long-term decreases in seizure probability by synapse-specific LTD of recurrent excitatory synapses in the epileptic network, which may prove to be a very useful anticonvulsant strategy.
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