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Modeling of Age-Dependent Epileptogenesis by Differential Homeostatic Synaptic Scaling - PubMed

  • ️Thu Jan 01 2015

Modeling of Age-Dependent Epileptogenesis by Differential Homeostatic Synaptic Scaling

Oscar C González et al. J Neurosci. 2015.

Abstract

Homeostatic synaptic plasticity (HSP) has been implicated in the development of hyperexcitability and epileptic seizures following traumatic brain injury (TBI). Our in vivo experimental studies in cats revealed that the severity of TBI-mediated epileptogenesis depends on the age of the animal. To characterize mechanisms of these differences, we studied the properties of the TBI-induced epileptogenesis in a biophysically realistic cortical network model with dynamic ion concentrations. After deafferentation, which was induced by dissection of the afferent inputs, there was a reduction of the network activity and upregulation of excitatory connections leading to spontaneous spike-and-wave type seizures. When axonal sprouting was implemented, the seizure threshold increased in the model of young but not the older animals, which had slower or unidirectional homeostatic processes. Our study suggests that age-related changes in the HSP mechanisms are sufficient to explain the difference in the likelihood of seizure onset in young versus older animals. Significance statement: Traumatic brain injury (TBI) is one of the leading causes of intractable epilepsy. Likelihood of developing epilepsy and seizures following severe brain trauma has been shown to increase with age. Specific mechanisms of TBI-related epileptogenesis and how these mechanisms are affected by age remain to be understood. We test a hypothesis that the failure of homeostatic synaptic regulation, a slow negative feedback mechanism that maintains neural activity within a physiological range through activity-dependent modulation of synaptic strength, in older animals may augment TBI-induced epileptogenesis. Our results provide new insight into understanding this debilitating disorder and may lead to novel avenues for the development of effective treatments of TBI-induced epilepsy.

Keywords: epileptic seizures; homeostatic plasticity; ion concentration dynamics; network models; synaptic plasticity.

Copyright © 2015 the authors 0270-6474/15/3513448-15$15.00/0.

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Figures

Figure 1.
Figure 1.

Cortical undercut leads to electrographic seizures in vivo. A, Transition between slow-wave sleep (SWS), rapid eye movement sleep (REM), waking state, and seizure. Brain drawing represents location of intracortical LFP electrodes. The other traces include the following: electro-oculogram, EMG, and accelerometer (Acc.). Segments of recordings indicated by the gray area are expanded in time in B1 (SWS), C1 (seizure), and D1 (postictal recovery). Further expansion in time and amplitude is shown in B2, C2, and D2.

Figure 2.
Figure 2.

Network deafferentation leads to reduction of seizure threshold. A, Raster plot of activity in control network without deafferentation. Brief 1 s stimulus was applied at 300 s (red bar). B, Single-cell activity from control network in A (location indicated by arrow). C, Zoom-in of activity from B showing spontaneous firing pattern. D, Raster plot of activity of a network with 50% deafferentation applied at 100 s (green triangle). Stimulation was applied at 300 s (red bar) of equal duration and strength as that applied in A. E, Single-cell activity from the deafferented network in D. F, Left, synchronized bursting events with spike inactivation during spike and wave seizure-like activity in E. Right, Background bursting firing pattern generated between seizures. G, Left, LFP of the network corresponding to the spike and wave epileptiform activity shown in F (left). Right, the LFP corresponding to background bursting in F (right).

Figure 3.
Figure 3.

Synaptic weights and ion concentration dynamics. A, Average firing rate of a network without deafferentation (red) and network with 50% deafferentation (black). B, Average synaptic weight dynamics. HSP scaling was blocked during seizure state to avoid nonphysiologically fast changes of synaptic weights. C, Phase space projection shows dynamics of the averaged network firing rate and synaptic weight for intact (red) and deafferented (black) networks. D, Left (Right), Evolution of the extracellular potassium (intracellular sodium) concentrations near (from) a single cortical pyramidal neuron. E, F, Raster plots of extracellular potassium and intracellular sodium concentrations, respectively, for the network with deafferentation. Green triangle represents time of deafferentation. Red bar represents stimulus application.

Figure 4.
Figure 4.

Severity of deafferentation affects seizure threshold. A, Threshold values for different degrees of deafferentation. Red line indicates the mean threshold (±SD) for a given degree of deafferentation. Black bars represent a range of thresholds from individual simulations. B, Raster plot of activity from a network with 10% deafferentation (green triangle). Bottom, Activity of a single neuron from the network (top panel). Arrow indicates location. C, Threshold values for different sizes of deafferented area. D, Raster plot of activity from a network with a block of 70 neurons undergoing 50% deafferentation (green triangle). Bottom, Activity of a single neuron from the network.

Figure 5.
Figure 5.

Seizures initiate at the boundaries and propagate toward intact and deafferented regions. A, Raster plot of activity from a network with 90% deafferentation applied at 100 s (green triangle) followed by stimulus at 300 s (red bar). B, Zoom-in of the raster plot of activity during one episode of seizure in A. C, D, Single-cell activity from the network in B. Arrow indicate locations. Note bursting with spike inactivation followed by tonic spiking (fast run) just before termination of seizure.

Figure 6.
Figure 6.

Spontaneous epileptiform events. A, Raster plot of activity with spontaneous, recurrent seizure-like events. Deafferentation time is indicated by the green triangle (αHSP = 0.06 and check time = 20 s). B, Top, Single-cell activity from the network in A (black arrow). Bottom, Zoom-in shows individual bursts with spike inactivation during seizure. C, Average firing rate of the network in A. D, Mean synaptic weights dynamics as a function of time for the network in A. HSP scaling was blocked during seizure state to avoid nonphysiologically fast changes of synaptic weights.

Figure 7.
Figure 7.

Effect of axonal sprouting rate on seizure threshold. A, Seizure threshold (color map) as a function of HSP αHSP and sprouting rate, γSyn. All thresholds were tested in late HSP condition representing the last data points in B. The tiles numbered 1 and 2 correspond to the thresholds of the fast and slow networks, respectively, in B. B, Threshold dynamics (±SD) for two sample networks. A total of 50% deafferentation was applied at 100 s (arrow). The “fast” network (red) has a fast HSP and fast sprouting rate (αHSP = 0.005, γSyn = 0.001), whereas the “slow” network (blue) has slow HSP and slow sprouting rate (αHSP = 0.001, γSyn = 0.0002). A total of 100% represents seizure threshold of an intact network.

Figure 8.
Figure 8.

Seizure susceptibility in the “young” versus “old” animal models. A total of 50% deafferentation was applied at 100 s (black arrow or green triangle). A, Number of spontaneous seizures versus HSP downregulation rate, αHSP−. Inset, Seizure thresholds for networks with different αHSP−. B, Top, Raster plot of activity of a network with αHSP− = 0.009. Bottom, Phase space projection shows dynamics of synaptic weights and averaged firing rate. Inset, Amplitude of the steady-state oscillation in the phase space projections for values of αHSP− = 0.003 − 0.01; these values did not lead to spontaneous seizures. C, Top, Raster plot of the network activity for αHSP− = 0.002. Bottom, Phase space projection shows dynamics of synaptic weights and averaged firing rate leading to seizure. D, Time evolution of seizure threshold for networks with varying αHSP−. All networks implemented synaptic sprouting. Vertical arrow indicates the time of deafferentation. E, Raster plots of the network activity with bidirectional HSP (left) and unidirectional HSP (right). F, Single-cell activity from the networks with bidirectional HSP (left) and unidirectional HSP (right).

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