Abstract
Pharmacoresistant epilepsy is a common neurological disorder in which increased neuronal intrinsic excitability and synaptic excitation lead to pathologically synchronous behavior in the brain. In the majority of experimental and theoretical epilepsy models, epilepsy is associated with reduced inhibition in the pathological neural circuits, yet effects of intrinsic excitability are usually not explicitly analyzed. Here we present a novel neural mass model that includes intrinsic excitability in the form of spike-frequency adaptation in the excitatory population. We validated our model using local field potential data recorded from human hippocampal/subicular slices. We found that synaptic conductances and slow adaptation in the excitatory population both play essential roles for generating seizures and pre-ictal oscillations. Using bifurcation analysis, we found that transitions towards seizure and back to the resting state take place via Andronov-Hopf bifurcations. These simulations therefore suggest that single neuron adaptation as well as synaptic inhibition are responsible for orchestrating seizure dynamics and transition towards the epileptic state.
Significance statement Epileptic seizures are commonly thought to arise from a pathology of inhibition in the brain circuits. Theoretical models aiming to explain epileptic oscillations usually describe the neural activity solely in terms of inhibition and excitation. Single neuron adaptation properties are usually assumed to have only a limited contribution to seizure dynamics. To explore this issue, we developed a novel neural mass model with adaption in the excitatory population. By including adaptation and intrinsic excitability together with inhibition in this model, we were able to account for several experimentally observed properties of seizures, resting state dynamics, and pre-ictal oscillations, leading to improved understanding of epileptic seizures.
Author contributions
Anatoly Buchin: designed research, performed research, analyzed data, wrote the paper
Cliff C. Kerr: designed research, analyzed data, wrote the paper
Gilles Huberfeld: designed research, performed research, wrote the paper
Richard Miles: designed research, wrote the paper
Boris Gutkin: designed research, wrote the paper
Acknowledgments
We would like to thank Anton Chizhov for useful comments and criticism of our work. Initial ideas were developed during the Advanced Course in Computational Neuroscience in Będlevo, Poland (http://www.neuroinf.pl/accn).
Footnotes
Conflict of Interest: Authors report no conflict of interest
Funding sources: This work was supported by the Swartz Foundation, FRM FDT20140930942, ANR-10-LABX-0087 IEC and ANR-10-IDEX-0001-02 PSL grants. Boris Gutkin acknowledges funding from the RF Program 5-100 to the National Research University Higher School of Economics. Cliff C. Kerr was supported by the Australian Research Council (ARC) Discovery Early Career Researcher Award DE140101375. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
