Dynamical changes in neurons during seizures determine tonic to clonic shift

Abstract

A tonic-clonic seizure transitions from high frequency asynchronous activity to low frequency coherent oscillations, yet the mechanism of transition remains unknown. We propose a shift in network synchrony due to changes in cellular response. Here we use phase-response curves (PRC) from Morris-Lecar (M-L) model neurons with synaptic depression and gradually decrease input current to cells within a network simulation. This method effectively decreases firing rates resulting in a shift to greater network synchrony illustrating a possible mechanism of the transition phenomenon. PRCs are measured from the M-L conductance based model cell with a range of input currents within the limit cycle. A large network of 3000 excitatory neurons is simulated with a network topology generated from second-order statistics which allows a range of population synchrony. The population synchrony of the oscillating cells is measured with the Kuramoto order parameter, which reveals a transition from tonic to clonic phase exhibited by our model network. The cellular response shift mechanism for the tonic-clonic seizure transition reproduces the population behavior closely when compared to EEG data.

Dynamic control of modeled tonic-clonic seizure states with closed-loop stimulation

“…The influence of DBS was tested in a network model that reproduces a tonic to clonic shift in network synchrony as a function of the firing rate of the neurons (Beverlin et al, 2011). In the simulations, at the seizure onset the firing rate of the neurons are very high, as might be expected with runaway excitation, and then over the duration of the seizure, the firing rate of the neurons slowly decreases, eventually bringing about a transition to the clonic phase of the seizure, seen in Figure 2.…”

“…In a recent paper, we proposed that this may be involved in treating Parkinsonian symptoms (Wilson et al, 2011). In this paper we use similar periodic stimulation theory to affect the tonic and clonic phases of a seizure in a computational model we have recently developed (Beverlin et al, 2011). Recently we proposed that the shift from the desynchronized tonic phase to the synchronous clonic phase occurs as the neuronal firing rate adapts over the duration of the seizure.…”

“…The model is explained in more detail in our recent seizure model paper (Beverlin et al, 2011). The parameters of the M–L model were chosen so that the phase response curve (PRC) is similar to PRCs we have measured in hippocampal pyramidal neurons (Netoff et al, 2005); they are as follows: C = 1.0 μF, g L = 8 nS, E L = −53.24 mV, g Na = 18.22 nS, E Na = 60 mV, g K = 4 nS, E K = −95.52 mV, V 1/2 m = −7.37 mV, k m = 11.97 mV, V 1/2n = −16.35 mV, k n = 4.21 mV, τ = 1 ms, spikeWidth = 0.03, E syn = 0, τ f = 0.25 ms, τ s =0.5 ms. Matlab code for this model is available at http://neuralnetoff.umn.edu/public/TonicClonicControl and from Model DB website (http://senselab.med.yale.edu/modeldb).…”

“…We have recently proposed a model explaining (1) a mechanism for the transition from tonic to clonic phases by slowing of firing of neurons over the seizure and (2) the differing ability of neurons to synchronize at high firing rates and low firing rates (Beverlin et al, 2011). The change in the firing rate in the model is due to due to synaptic depression of the neurons and spike rate adaptation of the neurons, both of which occur at high firing rates (Abbott et al, 1997; Manor and Nadim, 2001).…”

“…Based on this hypothesis, it has been shown that synchronizing populations with DBS pulses may promote seizure termination and truncate the seizure (Schindler et al, 2007a). We have recently developed a computational model that illustrates a mechanism by which synchrony changes during a seizure (Beverlin et al, 2011). …”