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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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Topological connectivity determines the effect of driving noise and                            functional coupling on neuronal firing rate.Left: map of network-averaged firing rate vs. the strength of functional                            coupling (unitary conductance , vertical                            axis) and the intensity of driving noise                                    (,                            horizontal axis), for Z = 4. Right: map of                            network-averaged firing rate vs. the strength of functional coupling                            (unitary conductance , vertical                            axis) and the intensity of driving noise                                    (,                            horizontal axis), for Z = 24. Color code is blue                            for low firing rates and red for high firing rates, and color scale is                            the same for both panels.
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pone-0020572-g002: Topological connectivity determines the effect of driving noise and functional coupling on neuronal firing rate.Left: map of network-averaged firing rate vs. the strength of functional coupling (unitary conductance , vertical axis) and the intensity of driving noise (, horizontal axis), for Z = 4. Right: map of network-averaged firing rate vs. the strength of functional coupling (unitary conductance , vertical axis) and the intensity of driving noise (, horizontal axis), for Z = 24. Color code is blue for low firing rates and red for high firing rates, and color scale is the same for both panels.

Mentions: Just as synaptic connectivity is determined both by the number of connecting neurons (topological aspect) and the strength of synaptic weight between a particular pair of neurons (functional aspect), the electrical connectivity is determined by the number of neurons that establish gap junctions with a particular neuron (topological aspect, parameterized by the number of neighbors Z in our model), and the unitary gap junction conductance and the number of gap junctions between a given pair of neurons (functional aspect, parameterized by the strength of coupling in our model). How do the topological and the functional aspects of gap junction connectivity determine the patterns of neuronal activity? Figure 2 shows the dependence of network-averaged firing rate on the strength of coupling () and on the intensity of stimulation current (Dn). For low topological connectivity (Z = 4, left plot) the strength of coupling only weakly affected the dependence of the firing rate on the stimulation intensity. On the other hand, in highly interconnected network (Z = 24, right plot) the “firing rate-driving noise” relation was critically shaped by the value of parameter . The critical intensity of stimulation current for which the transition from low to high rate firing occurred generally moved to the left for higher (note, however, a “gap” for an intermediate coupling strength , indicating a transition from the regime of stimulus-driven activity to the regime in which spiking actively spreads through the network via strong gap junctions). Thus, for a given strength of functional coupling (gap junction conductance) the topological coupling (number of neighbors) could set the context for the intensity of noisy synaptic-like stimulation that was needed in order to drive the network to relatively high firing rates (>20 Hz). In particular, in the regime of weak coupling (small ), stronger topological connectivity (high Z) tended to suppress stimulus-driven activity over a wide range of stimulation intensities (compare, e.g., left and right plots in Figure 2 for ). The implication of this regime (very weak expression of gap junctions, as appears in “healthy” mature cortex) to the regulation of collective activity is discussed below.


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

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

Topological connectivity determines the effect of driving noise and                            functional coupling on neuronal firing rate.Left: map of network-averaged firing rate vs. the strength of functional                            coupling (unitary conductance , vertical                            axis) and the intensity of driving noise                                    (,                            horizontal axis), for Z = 4. Right: map of                            network-averaged firing rate vs. the strength of functional coupling                            (unitary conductance , vertical                            axis) and the intensity of driving noise                                    (,                            horizontal axis), for Z = 24. Color code is blue                            for low firing rates and red for high firing rates, and color scale is                            the same for both panels.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3105095&req=5

pone-0020572-g002: Topological connectivity determines the effect of driving noise and functional coupling on neuronal firing rate.Left: map of network-averaged firing rate vs. the strength of functional coupling (unitary conductance , vertical axis) and the intensity of driving noise (, horizontal axis), for Z = 4. Right: map of network-averaged firing rate vs. the strength of functional coupling (unitary conductance , vertical axis) and the intensity of driving noise (, horizontal axis), for Z = 24. Color code is blue for low firing rates and red for high firing rates, and color scale is the same for both panels.
Mentions: Just as synaptic connectivity is determined both by the number of connecting neurons (topological aspect) and the strength of synaptic weight between a particular pair of neurons (functional aspect), the electrical connectivity is determined by the number of neurons that establish gap junctions with a particular neuron (topological aspect, parameterized by the number of neighbors Z in our model), and the unitary gap junction conductance and the number of gap junctions between a given pair of neurons (functional aspect, parameterized by the strength of coupling in our model). How do the topological and the functional aspects of gap junction connectivity determine the patterns of neuronal activity? Figure 2 shows the dependence of network-averaged firing rate on the strength of coupling () and on the intensity of stimulation current (Dn). For low topological connectivity (Z = 4, left plot) the strength of coupling only weakly affected the dependence of the firing rate on the stimulation intensity. On the other hand, in highly interconnected network (Z = 24, right plot) the “firing rate-driving noise” relation was critically shaped by the value of parameter . The critical intensity of stimulation current for which the transition from low to high rate firing occurred generally moved to the left for higher (note, however, a “gap” for an intermediate coupling strength , indicating a transition from the regime of stimulus-driven activity to the regime in which spiking actively spreads through the network via strong gap junctions). Thus, for a given strength of functional coupling (gap junction conductance) the topological coupling (number of neighbors) could set the context for the intensity of noisy synaptic-like stimulation that was needed in order to drive the network to relatively high firing rates (>20 Hz). In particular, in the regime of weak coupling (small ), stronger topological connectivity (high Z) tended to suppress stimulus-driven activity over a wide range of stimulation intensities (compare, e.g., left and right plots in Figure 2 for ). The implication of this regime (very weak expression of gap junctions, as appears in “healthy” mature cortex) to the regulation of collective activity is discussed below.

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