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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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Changes in network topology and membrane conductance underlie the                            overall effect of gap junction connectivity on the network                            stability.A Color panel is the surface plot of firing rate (averaged                            over non-overlapping bins of 100 ms and over all model neurons) vs. the                            probability  to                            increase the leak conductance by  in each                            one of the model neurons from the affected area                                    (,) that did                            not previously share gap junction connection. Color code is blue for low                            firing rate and red for high firing rate. Horizontal axis is simulation                            time ([0–10] seconds). Background noise intensity                                     for the                            set of  model                            neurons was perturbed at  (dashed                            red line through Panels B,C), and progressively increased to achieve                            5-fold higher values at time 10 seconds (scale bar in lowest panel).                            Membrane leak conductance for  model                            neurons was increased (as specified by ) at time                                     (dashed                            blue line through Panels B,C). B Raster plot of                            network's activity for . Other                            parameters: .                                C Third panel: Raster plot of network's activity                            for . Other parameters:                                    .
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pone-0020572-g006: Changes in network topology and membrane conductance underlie the overall effect of gap junction connectivity on the network stability.A Color panel is the surface plot of firing rate (averaged over non-overlapping bins of 100 ms and over all model neurons) vs. the probability to increase the leak conductance by in each one of the model neurons from the affected area (,) that did not previously share gap junction connection. Color code is blue for low firing rate and red for high firing rate. Horizontal axis is simulation time ([0–10] seconds). Background noise intensity for the set of model neurons was perturbed at (dashed red line through Panels B,C), and progressively increased to achieve 5-fold higher values at time 10 seconds (scale bar in lowest panel). Membrane leak conductance for model neurons was increased (as specified by ) at time (dashed blue line through Panels B,C). B Raster plot of network's activity for . Other parameters: . C Third panel: Raster plot of network's activity for . Other parameters: .

Mentions: Because the basic mechanism by which gap junctions affect network stability is a change in the value of rheobase current (and thus a change in the excitability of individual neurons), the results presented in Figure 5 could be a consequence of reduced excitability of model neurons after adding new gap junctions. Alternatively, both altered neuronal excitability and change of the efficiency with which a perturbation of activity can propagate through the network with different density of connections might be involved. To test this question we performed the following test. Same network (as the one studied in Figure 5) was considered, and the same perturbation (time of perturbation marked with red dashed line in Figure 6), was applied to the network as in Figure 5. At time (marked with blue dashed line in Figure 6, ), pairs of model neurons in perturbed sub-network were picked at random with probability , and membrane conductance of each one of these model neurons was increased by (which corresponds to the conductance of individual gap junction in our model). In this way, we obtained a network in which pattern of gap junction connectivity was the same one as in the baseline “healthy” network, but membrane conductances of neurons in perturbed sub-network were increased by the same amount as would have occurred if actual new gap junctions were added. This allowed us to separate the effects of increased membrane conductance from the effects of increased connectivity.


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

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

Changes in network topology and membrane conductance underlie the                            overall effect of gap junction connectivity on the network                            stability.A Color panel is the surface plot of firing rate (averaged                            over non-overlapping bins of 100 ms and over all model neurons) vs. the                            probability  to                            increase the leak conductance by  in each                            one of the model neurons from the affected area                                    (,) that did                            not previously share gap junction connection. Color code is blue for low                            firing rate and red for high firing rate. Horizontal axis is simulation                            time ([0–10] seconds). Background noise intensity                                     for the                            set of  model                            neurons was perturbed at  (dashed                            red line through Panels B,C), and progressively increased to achieve                            5-fold higher values at time 10 seconds (scale bar in lowest panel).                            Membrane leak conductance for  model                            neurons was increased (as specified by ) at time                                     (dashed                            blue line through Panels B,C). B Raster plot of                            network's activity for . Other                            parameters: .                                C Third panel: Raster plot of network's activity                            for . Other parameters:                                    .
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3105095&req=5

pone-0020572-g006: Changes in network topology and membrane conductance underlie the overall effect of gap junction connectivity on the network stability.A Color panel is the surface plot of firing rate (averaged over non-overlapping bins of 100 ms and over all model neurons) vs. the probability to increase the leak conductance by in each one of the model neurons from the affected area (,) that did not previously share gap junction connection. Color code is blue for low firing rate and red for high firing rate. Horizontal axis is simulation time ([0–10] seconds). Background noise intensity for the set of model neurons was perturbed at (dashed red line through Panels B,C), and progressively increased to achieve 5-fold higher values at time 10 seconds (scale bar in lowest panel). Membrane leak conductance for model neurons was increased (as specified by ) at time (dashed blue line through Panels B,C). B Raster plot of network's activity for . Other parameters: . C Third panel: Raster plot of network's activity for . Other parameters: .
Mentions: Because the basic mechanism by which gap junctions affect network stability is a change in the value of rheobase current (and thus a change in the excitability of individual neurons), the results presented in Figure 5 could be a consequence of reduced excitability of model neurons after adding new gap junctions. Alternatively, both altered neuronal excitability and change of the efficiency with which a perturbation of activity can propagate through the network with different density of connections might be involved. To test this question we performed the following test. Same network (as the one studied in Figure 5) was considered, and the same perturbation (time of perturbation marked with red dashed line in Figure 6), was applied to the network as in Figure 5. At time (marked with blue dashed line in Figure 6, ), pairs of model neurons in perturbed sub-network were picked at random with probability , and membrane conductance of each one of these model neurons was increased by (which corresponds to the conductance of individual gap junction in our model). In this way, we obtained a network in which pattern of gap junction connectivity was the same one as in the baseline “healthy” network, but membrane conductances of neurons in perturbed sub-network were increased by the same amount as would have occurred if actual new gap junctions were added. This allowed us to separate the effects of increased membrane conductance from the effects of increased connectivity.

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