Abstract
Epileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes. We discuss potential mechanisms underlying such machinery and the relevance of our approach, supporting previous detailed modeling studies and reflecting on the limitations of our methodology.
Highlights
Epilepsy is characterized by seizures, a paroxysmal behavior that results from abnormal, excessive or hypersynchronous neuronal activity in the brain [1], with a various set of symptomatic outcomes depending on brain regions involved in its generation and propagation processes
We explored a set of physiologically pertinent parameters
After comparing simulated data with experimental recordings from animal models of epilepsy, we propose sequential stages that may underlie status epilepticus and validate our model
Summary
Epilepsy is characterized by seizures, a paroxysmal behavior that results from abnormal, excessive or hypersynchronous neuronal activity in the brain [1], with a various set of symptomatic outcomes depending on brain regions involved in its generation and propagation processes. By incorporating slow extracellular potassium or oxygen levels as key parameters, state-of-art studies displayed transitions between pathological brain states observed during paroxysmal activity [12,13,14] Such approaches combine dynamical systems theory and largescale neural network computations to propose key insights into seizure mechanisms. Many different biophysical factors can lead to seizure genesis [15,16], in keeping with the concept that different parameter sets can produce the same type of activity at the network level [17] Introducing all those parameters in a detailed model poses a computational and theoretical challenge, but may be only useful if the arising network behavior can be characterized dynamically
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