Computational Modeling of Seizure Dynamics Using Coupled Neuronal Networks: Factors Shaping Epileptiform Activity

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.

Deep learning applied to whole‐brain connectome to determine seizure control after epilepsy surgery

“…32 The widespread pervasive disorganization of neural networks in patients with epilepsy reflects microstructural and dynamic circuit rearrangements in this disorder. 48 Our findings confirm that atypical structural connectivity patterns spanning throughout the brain are predictive of seizure refractoriness. This may reflect the concept that brain microcircuits are susceptible to being modulated beyond the area of ictogenesis to recruit seizures, 49 likely due but not limited to long excitatory projections 50,51 linking interhemisphere and intrahemisphere distal regions that support seizure propagation.…”

“…Accordingly, volumetric imaging studies in patients with TLE confirm abnormalities extending throughout the rest of the cortex, both ipsilateral and contralateral to the side of seizure onset and independently from the hemisphere (left or right) of seizure onset, with changes to network properties including even frontal, parietal, occipital, and insular cortices . The widespread pervasive disorganization of neural networks in patients with epilepsy reflects microstructural and dynamic circuit rearrangements in this disorder . Our findings confirm that atypical structural connectivity patterns spanning throughout the brain are predictive of seizure refractoriness.…”

Virtual Cortical Resection Reveals Push-Pull Network Control Preceding Seizure Evolution

“…Mechanistically, synchronizing controllers theoretically pull the network towards a particular synchronous state, and, conversely, desynchronizing controllers push the network away from these states. Such a potential neurobiological mechanism also aligns with the recently proposed Epileptor model of seizure dynamics, where any brain network might be capable of seizure generation depending on its vulnerability to crossing a critical separatrix barrier (Jirsa et al, 2014; Naze et al, 2015). In the framework of the Epileptor, our results suggest that synchronizing and desynchronizing nodes might regulate a critical level of network synchronizability and prevent the extent to which the network crosses a separatrix.…”

Connectomics and graph theory analyses: Novel insights into network abnormalities in epilepsy

“…Taken together, the data from clinical, neuropathology, and imaging studies suggest that there is a pervasive disorganization of neural networks in patients with epilepsy. Although it is still not possible to map connectivity on a microscopic scale across the entire brain in humans, the in vivo human data are mirrored and corroborated by model studies, which suggest microstructural and dynamic circuit rearrangements in epilepsy . In particular, microcircuits are susceptible to being modulated beyond the area of ictogenesis to recruit seizures .…”