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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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Characteristics of gap-junction coupled neuronal network                            model.A Schematic presentation of network connectivity, for                            Z = 4 (four gap junction connections per model                            neuron). B Intensity of stimulation current determines the                            rate of neuronal spiking. Top panel: . Middle                            panel: . Bottom                            panel: . These                            traces of membrane potential are for an isolated model neuron (not                            connected to other model neurons). C Strong topological                            coupling by gap junctions can impede signal transmission. Model neuron 1                            was stimulated by a brief step-like constant current which was                            sufficient to generate a spike. Depending on the extent of its gap                            junction connectivity, model neuron 2 could either generate a spike or                            responded with a sub-threshold voltage to the spike event in model                            neuron 1. The intensity of stimulation current was set to zero in this                            example. The strength of functional coupling was                                     (10 fold                            higher as compared to the baseline network model, to compensate for the                            lack of noisy stimulation current).
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pone-0020572-g001: Characteristics of gap-junction coupled neuronal network model.A Schematic presentation of network connectivity, for Z = 4 (four gap junction connections per model neuron). B Intensity of stimulation current determines the rate of neuronal spiking. Top panel: . Middle panel: . Bottom panel: . These traces of membrane potential are for an isolated model neuron (not connected to other model neurons). C Strong topological coupling by gap junctions can impede signal transmission. Model neuron 1 was stimulated by a brief step-like constant current which was sufficient to generate a spike. Depending on the extent of its gap junction connectivity, model neuron 2 could either generate a spike or responded with a sub-threshold voltage to the spike event in model neuron 1. The intensity of stimulation current was set to zero in this example. The strength of functional coupling was (10 fold higher as compared to the baseline network model, to compensate for the lack of noisy stimulation current).

Mentions: We considered 2D networks of 50×50 model neurons coupled with gap junctions, with periodic boundary conditions (Figure 1). Neuronal dynamics were described using the well-studied Morris-Lecar model [23] that was slightly modified by Prescott et al. [24] to account for correct biological mechanisms of action potential generation. This simplified model provides an optimal balance between realistic electrical properties of neuron and computational performance that allows simulations of large-scale 2D networks. In the baseline model, each model neuron was coupled by gap junctions to its Z nearest neighbors (Z ranged from 4 to 24, as described in Methods); pattern of gap junction connectivity determined properties of spike propagation through the network (Figure 1C). Random (see Methods for details) external input with amplitude determined by parameter Dn was applied to all the neurons to drive them beyond the spiking threshold. This external stimulation can be interpreted as a random synaptic input from the rest of the neuronal population that was not included in this network model.


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

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

Characteristics of gap-junction coupled neuronal network                            model.A Schematic presentation of network connectivity, for                            Z = 4 (four gap junction connections per model                            neuron). B Intensity of stimulation current determines the                            rate of neuronal spiking. Top panel: . Middle                            panel: . Bottom                            panel: . These                            traces of membrane potential are for an isolated model neuron (not                            connected to other model neurons). C Strong topological                            coupling by gap junctions can impede signal transmission. Model neuron 1                            was stimulated by a brief step-like constant current which was                            sufficient to generate a spike. Depending on the extent of its gap                            junction connectivity, model neuron 2 could either generate a spike or                            responded with a sub-threshold voltage to the spike event in model                            neuron 1. The intensity of stimulation current was set to zero in this                            example. The strength of functional coupling was                                     (10 fold                            higher as compared to the baseline network model, to compensate for the                            lack of noisy stimulation current).
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getmorefigures.php?uid=PMC3105095&req=5

pone-0020572-g001: Characteristics of gap-junction coupled neuronal network model.A Schematic presentation of network connectivity, for Z = 4 (four gap junction connections per model neuron). B Intensity of stimulation current determines the rate of neuronal spiking. Top panel: . Middle panel: . Bottom panel: . These traces of membrane potential are for an isolated model neuron (not connected to other model neurons). C Strong topological coupling by gap junctions can impede signal transmission. Model neuron 1 was stimulated by a brief step-like constant current which was sufficient to generate a spike. Depending on the extent of its gap junction connectivity, model neuron 2 could either generate a spike or responded with a sub-threshold voltage to the spike event in model neuron 1. The intensity of stimulation current was set to zero in this example. The strength of functional coupling was (10 fold higher as compared to the baseline network model, to compensate for the lack of noisy stimulation current).
Mentions: We considered 2D networks of 50×50 model neurons coupled with gap junctions, with periodic boundary conditions (Figure 1). Neuronal dynamics were described using the well-studied Morris-Lecar model [23] that was slightly modified by Prescott et al. [24] to account for correct biological mechanisms of action potential generation. This simplified model provides an optimal balance between realistic electrical properties of neuron and computational performance that allows simulations of large-scale 2D networks. In the baseline model, each model neuron was coupled by gap junctions to its Z nearest neighbors (Z ranged from 4 to 24, as described in Methods); pattern of gap junction connectivity determined properties of spike propagation through the network (Figure 1C). Random (see Methods for details) external input with amplitude determined by parameter Dn was applied to all the neurons to drive them beyond the spiking threshold. This external stimulation can be interpreted as a random synaptic input from the rest of the neuronal population that was not included in this network model.

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