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Computational modeling of seizure dynamics using coupled neuronal networks: factors shaping epileptiform activity.

Naze S, Bernard C, Jirsa V - PLoS Comput. Biol. (2015)

Bottom Line: Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied.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.

View Article: PubMed Central - PubMed

Affiliation: UMR1106 Inserm, Institut de Neurosciences des Systèmes, Marseille, France; Aix-Marseille University, Marseille, France.

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.

No MeSH data available.


Related in: MedlinePlus

Seizure lengths according to inter-population coupling (left) and background noise (right).Simulations were performed to reproduce 15 minutes of activity for each parameter setting, resulting in 10 to 30 seizures per simulations. Mean, minimum and maximum seizure lengths were calculated from these generated time series by isolating the fast firing neurons’ activity and counting the time spent in up-state (threshold at -40 mV). Up-states of less than 5s were not considered as seizure and down-states of less than 3s between consecutive seizures were considered as being part of the same seizure. Noise is inserted in both excitatory and inhibitory neurons. Parameters: CE = 0.8, x0 = 3.0; Wmax = 0.2 (left plot); Gsi,j = 0.1 (right plot); Gsi,i = 0.1; r = 0.000004.
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pcbi.1004209.g008: Seizure lengths according to inter-population coupling (left) and background noise (right).Simulations were performed to reproduce 15 minutes of activity for each parameter setting, resulting in 10 to 30 seizures per simulations. Mean, minimum and maximum seizure lengths were calculated from these generated time series by isolating the fast firing neurons’ activity and counting the time spent in up-state (threshold at -40 mV). Up-states of less than 5s were not considered as seizure and down-states of less than 3s between consecutive seizures were considered as being part of the same seizure. Noise is inserted in both excitatory and inhibitory neurons. Parameters: CE = 0.8, x0 = 3.0; Wmax = 0.2 (left plot); Gsi,j = 0.1 (right plot); Gsi,i = 0.1; r = 0.000004.

Mentions: Running systematic simulations over coupling strength parameters, we also observe that seizure duration decreases as we increase inter-population coupling (Fig 8, left). This can be understood as follows: as inter-population coupling increases, the synaptic gain of GABA synapses coming from synchronized spiking of inhibitory cells increases, which reduces the activity in the excitatory cells, eventually leading to the destruction of the self-sustained epileptiform discharges. It is notable that the modification of inter-population coupling results in multi-scale effects on fast and slow time scales and, in particular, significantly affects slower time-scales such as seizure duration.


Computational modeling of seizure dynamics using coupled neuronal networks: factors shaping epileptiform activity.

Naze S, Bernard C, Jirsa V - PLoS Comput. Biol. (2015)

Seizure lengths according to inter-population coupling (left) and background noise (right).Simulations were performed to reproduce 15 minutes of activity for each parameter setting, resulting in 10 to 30 seizures per simulations. Mean, minimum and maximum seizure lengths were calculated from these generated time series by isolating the fast firing neurons’ activity and counting the time spent in up-state (threshold at -40 mV). Up-states of less than 5s were not considered as seizure and down-states of less than 3s between consecutive seizures were considered as being part of the same seizure. Noise is inserted in both excitatory and inhibitory neurons. Parameters: CE = 0.8, x0 = 3.0; Wmax = 0.2 (left plot); Gsi,j = 0.1 (right plot); Gsi,i = 0.1; r = 0.000004.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4430284&req=5

pcbi.1004209.g008: Seizure lengths according to inter-population coupling (left) and background noise (right).Simulations were performed to reproduce 15 minutes of activity for each parameter setting, resulting in 10 to 30 seizures per simulations. Mean, minimum and maximum seizure lengths were calculated from these generated time series by isolating the fast firing neurons’ activity and counting the time spent in up-state (threshold at -40 mV). Up-states of less than 5s were not considered as seizure and down-states of less than 3s between consecutive seizures were considered as being part of the same seizure. Noise is inserted in both excitatory and inhibitory neurons. Parameters: CE = 0.8, x0 = 3.0; Wmax = 0.2 (left plot); Gsi,j = 0.1 (right plot); Gsi,i = 0.1; r = 0.000004.
Mentions: Running systematic simulations over coupling strength parameters, we also observe that seizure duration decreases as we increase inter-population coupling (Fig 8, left). This can be understood as follows: as inter-population coupling increases, the synaptic gain of GABA synapses coming from synchronized spiking of inhibitory cells increases, which reduces the activity in the excitatory cells, eventually leading to the destruction of the self-sustained epileptiform discharges. It is notable that the modification of inter-population coupling results in multi-scale effects on fast and slow time scales and, in particular, significantly affects slower time-scales such as seizure duration.

Bottom Line: Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied.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.

View Article: PubMed Central - PubMed

Affiliation: UMR1106 Inserm, Institut de Neurosciences des Systèmes, Marseille, France; Aix-Marseille University, Marseille, France.

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.

No MeSH data available.


Related in: MedlinePlus