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Gap junctions and epileptic seizures--two sides of the same coin?

Volman V, Perc M, Bazhenov M - PLoS ONE (2011)

Bottom Line: Here we used a computational modeling approach to address the role of neuronal gap junctions in shaping the stability of a network to perturbations that are often associated with the onset of epileptic seizures.This implies that the experimentally observed post-seizure additions of gap junctions could serve to prevent further escalations, suggesting furthermore that they are a consequence of an adaptive response of the neuronal network to the pathological activity.Our results thus reveal a complex role of electrical coupling in relation to epileptiform events.

View Article: PubMed Central - PubMed

Affiliation: Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America. volman@salk.edu

ABSTRACT
Electrical synapses (gap junctions) play a pivotal role in the synchronization of neuronal ensembles which also makes them likely agonists of pathological brain activity. Although large body of experimental data and theoretical considerations indicate that coupling neurons by electrical synapses promotes synchronous activity (and thus is potentially epileptogenic), some recent evidence questions the hypothesis of gap junctions being among purely epileptogenic factors. In particular, an expression of inter-neuronal gap junctions is often found to be higher after the experimentally induced seizures than before. Here we used a computational modeling approach to address the role of neuronal gap junctions in shaping the stability of a network to perturbations that are often associated with the onset of epileptic seizures. We show that under some circumstances, the addition of gap junctions can increase the dynamical stability of a network and thus suppress the collective electrical activity associated with seizures. This implies that the experimentally observed post-seizure additions of gap junctions could serve to prevent further escalations, suggesting furthermore that they are a consequence of an adaptive response of the neuronal network to the pathological activity. However, if the seizures are strong and persistent, our model predicts the existence of a critical tipping point after which additional gap junctions no longer suppress but strongly facilitate the escalation of epileptic seizures. Our results thus reveal a complex role of electrical coupling in relation to epileptiform events. Which dynamic scenario (seizure suppression or seizure escalation) is ultimately adopted by the network depends critically on the strength and duration of seizures, in turn emphasizing the importance of temporal and causal aspects when linking gap junctions with epilepsy.

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Strong topological connectivity can suppress spiking activity in a                            network of neurons coupled by gap junctions.A Network-averaged firing rate                                    (meanS.E.M.) vs.                            the intensity of driving noise, for different scenarios of topological                            connectivity: Z = 4 connections per neuron (black                            closed squares); Z = 24 connections per neuron (red                            open circles). The firing rate was computed over the time window of 20                            seconds. The coupling strength was .                                B Spike number disorder vs. the number of topological                            connections, for different driving noise intensities:                                     (red);                                     (black).                                C Sample raster plots of network activity, for                            different scenarios of topological connectivity and driving noise                            intensities:  (left                            top);  (left middle);  (left                            bottom);  (right                            top);  (right middle);  (right                            bottom).
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pone-0020572-g003: Strong topological connectivity can suppress spiking activity in a network of neurons coupled by gap junctions.A Network-averaged firing rate (meanS.E.M.) vs. the intensity of driving noise, for different scenarios of topological connectivity: Z = 4 connections per neuron (black closed squares); Z = 24 connections per neuron (red open circles). The firing rate was computed over the time window of 20 seconds. The coupling strength was . B Spike number disorder vs. the number of topological connections, for different driving noise intensities: (red); (black). C Sample raster plots of network activity, for different scenarios of topological connectivity and driving noise intensities: (left top); (left middle); (left bottom); (right top); (right middle); (right bottom).

Mentions: Figure 3A shows the dependence of mean and standard deviation of averaged (over network) neuronal firing rate on the intensity of the driving current, , for different patterns of topological connectivity. While for sufficiently intense inputs, intense gap junction connectivity (more pair-wise connections between any 2 model neurons) led to more intense and more regular firing (Figure 3A), this effect was opposite for the low driving current (described by lower ). Effect of the connectivity pattern (Z) on the firing rate inverted at . Thus, for weak driving current the higher number of topological connections between the neurons appeared to suppress the stimulus-driven spiking (Figure 3A). As was explained in [25] this suppressing effect is attributed to the faster relaxation of sub-threshold perturbations in a presence of gap junction coupling between neurons that allows depolarizing currents to “escape” from perturbed neurons to its neighbors. Indeed, for relatively weak electrical coupling strength and large number of neighbor neurons, the effective leak conductance of a neuron is increased; the rheobase (a minimal constant current that is needed to evoke spiking) is increased as well. Under these conditions, a perturbation to a given neuron is effectively shared by all neighbor neurons and the effect of the perturbation is diluted, reducing the chances to generate action potential. This mechanism promotes the stability against relatively modest perturbations (below the rheobase which is determined by the local gap junction connectivity). In contrast, for supra-rheobase perturbations, the presence of gap junctions aids in activity propagation and network synchronization.


Gap junctions and epileptic seizures--two sides of the same coin?

Volman V, Perc M, Bazhenov M - PLoS ONE (2011)

Strong topological connectivity can suppress spiking activity in a                            network of neurons coupled by gap junctions.A Network-averaged firing rate                                    (meanS.E.M.) vs.                            the intensity of driving noise, for different scenarios of topological                            connectivity: Z = 4 connections per neuron (black                            closed squares); Z = 24 connections per neuron (red                            open circles). The firing rate was computed over the time window of 20                            seconds. The coupling strength was .                                B Spike number disorder vs. the number of topological                            connections, for different driving noise intensities:                                     (red);                                     (black).                                C Sample raster plots of network activity, for                            different scenarios of topological connectivity and driving noise                            intensities:  (left                            top);  (left middle);  (left                            bottom);  (right                            top);  (right middle);  (right                            bottom).
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getmorefigures.php?uid=PMC3105095&req=5

pone-0020572-g003: Strong topological connectivity can suppress spiking activity in a network of neurons coupled by gap junctions.A Network-averaged firing rate (meanS.E.M.) vs. the intensity of driving noise, for different scenarios of topological connectivity: Z = 4 connections per neuron (black closed squares); Z = 24 connections per neuron (red open circles). The firing rate was computed over the time window of 20 seconds. The coupling strength was . B Spike number disorder vs. the number of topological connections, for different driving noise intensities: (red); (black). C Sample raster plots of network activity, for different scenarios of topological connectivity and driving noise intensities: (left top); (left middle); (left bottom); (right top); (right middle); (right bottom).
Mentions: Figure 3A shows the dependence of mean and standard deviation of averaged (over network) neuronal firing rate on the intensity of the driving current, , for different patterns of topological connectivity. While for sufficiently intense inputs, intense gap junction connectivity (more pair-wise connections between any 2 model neurons) led to more intense and more regular firing (Figure 3A), this effect was opposite for the low driving current (described by lower ). Effect of the connectivity pattern (Z) on the firing rate inverted at . Thus, for weak driving current the higher number of topological connections between the neurons appeared to suppress the stimulus-driven spiking (Figure 3A). As was explained in [25] this suppressing effect is attributed to the faster relaxation of sub-threshold perturbations in a presence of gap junction coupling between neurons that allows depolarizing currents to “escape” from perturbed neurons to its neighbors. Indeed, for relatively weak electrical coupling strength and large number of neighbor neurons, the effective leak conductance of a neuron is increased; the rheobase (a minimal constant current that is needed to evoke spiking) is increased as well. Under these conditions, a perturbation to a given neuron is effectively shared by all neighbor neurons and the effect of the perturbation is diluted, reducing the chances to generate action potential. This mechanism promotes the stability against relatively modest perturbations (below the rheobase which is determined by the local gap junction connectivity). In contrast, for supra-rheobase perturbations, the presence of gap junctions aids in activity propagation and network synchronization.

Bottom Line: Here we used a computational modeling approach to address the role of neuronal gap junctions in shaping the stability of a network to perturbations that are often associated with the onset of epileptic seizures.This implies that the experimentally observed post-seizure additions of gap junctions could serve to prevent further escalations, suggesting furthermore that they are a consequence of an adaptive response of the neuronal network to the pathological activity.Our results thus reveal a complex role of electrical coupling in relation to epileptiform events.

View Article: PubMed Central - PubMed

Affiliation: Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America. volman@salk.edu

ABSTRACT
Electrical synapses (gap junctions) play a pivotal role in the synchronization of neuronal ensembles which also makes them likely agonists of pathological brain activity. Although large body of experimental data and theoretical considerations indicate that coupling neurons by electrical synapses promotes synchronous activity (and thus is potentially epileptogenic), some recent evidence questions the hypothesis of gap junctions being among purely epileptogenic factors. In particular, an expression of inter-neuronal gap junctions is often found to be higher after the experimentally induced seizures than before. Here we used a computational modeling approach to address the role of neuronal gap junctions in shaping the stability of a network to perturbations that are often associated with the onset of epileptic seizures. We show that under some circumstances, the addition of gap junctions can increase the dynamical stability of a network and thus suppress the collective electrical activity associated with seizures. This implies that the experimentally observed post-seizure additions of gap junctions could serve to prevent further escalations, suggesting furthermore that they are a consequence of an adaptive response of the neuronal network to the pathological activity. However, if the seizures are strong and persistent, our model predicts the existence of a critical tipping point after which additional gap junctions no longer suppress but strongly facilitate the escalation of epileptic seizures. Our results thus reveal a complex role of electrical coupling in relation to epileptiform events. Which dynamic scenario (seizure suppression or seizure escalation) is ultimately adopted by the network depends critically on the strength and duration of seizures, in turn emphasizing the importance of temporal and causal aspects when linking gap junctions with epilepsy.

Show MeSH
Related in: MedlinePlus