Abstracts

A Means for Deducing Properties of Epileptic Neural Networks from Interictal Spike Timing

Abstract number : 1.070
Submission category :
Year : 2000
Submission ID : 1442
Source : www.aesnet.org
Presentation date : 12/2/2000 12:00:00 AM
Published date : Dec 1, 2000, 06:00 AM

Authors :
Kevin J Staley, Jaideep S Bains, Mark J Longacher, Univ of Colorado, Denver, CO.

RATIONALE: What determines the timing of interictal spikes on an EEG? We have used a simple in vitro system, the episodically discharging hippocampal CA3 network, to address this question. We have previously demonstrated that CA3 bursts terminate when the recurrent synapses that link the CA3 pyramidal cells become depressed, and bursts become possible when these synapses recover from depression. Here we demonstrate that the mean and variance of the interval between episodic CA3 population bursts correspond to recovery of a critical pool of depressed synapses in this network. METHODS: The intervals between periodic bursts of the CA3 network in vitro were fit to a 3-parameter model. The first parameter (N) was the number of synapses that were capable of triggering a burst discharge, and the second (K) was the number of such synapses that were actually necessary to initiate the next burst. We measured the rate of spontaneous excitatory post synaptic currents (EPSCs) to determine the probability of recovery from synaptic depression as a function of time since the last burst (p1). Then the probability of a burst could be calculated using a binomial distribution with parameters N, K, and p1. RESULTS: : The probability of synaptic recovery measured from EPSC rates increased monoexponentially after a burst with a time constant of 8 seconds (p1). The probability of a burst was accurately predicted by N, K, and p1 under a wide variety of experimental conditions (glutamate antagonists, changes in extracellular potassium, long-term changes in strength of recurrent synapses) and corresponding burst probabilities. CONCLUSIONS: The timing of CA3 bursts corresponded to the recovery of a subpopulation of recurrent synapses. This analysis is simple, quantifies the degree of excitatory feedback in a network, and can be readily applied to interictal spike intervals from EEG recordings.