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.
Highlights
Most of the communication between the neurons in the brain of an adult animal is achieved by means of chemical synapses [1]
Computational modeling studies supported by mathematical analysis suggested that a network of neurons coupled by gap junctions can support collective activity in the form of waves that are generated spontaneously and propagate through the network [13]
We considered 2D networks of 50650 model neurons coupled with gap junctions, with periodic boundary conditions (Figure 1)
Summary
Most of the communication between the neurons in the brain of an adult animal is achieved by means of chemical synapses [1]. A network of neurons coupled by gap junctions has often been likened to excitable reaction-diffusion (RD) media [10]. Computational modeling studies supported by mathematical analysis suggested that a network of neurons coupled by gap junctions can support collective activity in the form of waves that are generated spontaneously and propagate through the network [13]. The existence of such dynamical state requires the coupling by gap junctions to be sparse and strong [13]. Gap junctions play an important role in promoting synchronization in networks of inhibitory interneurons, which is believed to be necessary for the generation of collective oscillatory activity in the gamma band (30–80 Hz) [14,15]
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