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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

Activity of populations of excitatory and inhibitory ensembles, slow variable and mean time-series during a spontaneous seizure.Raster plots 1 & 2 display spikes of population 1 and 2 neurons, with activation threshold at 0 mV; third trace is the mean of the two populations with contribution of 80% from excitatory and 20% from inhibitory neurons; slow permittivity variable is the z variable (in arbitrary unit) from Eq 3, which trigger onset and offset of seizure; bottom plot is experimental data from rat scalp recording in vivo. Parameters: CE = 1.0; x0 = 3.0; Wmax = 0.4; Gsi,j = 0.2; Gsi,i = 0.1; r = 0.000004.
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pcbi.1004209.g006: Activity of populations of excitatory and inhibitory ensembles, slow variable and mean time-series during a spontaneous seizure.Raster plots 1 & 2 display spikes of population 1 and 2 neurons, with activation threshold at 0 mV; third trace is the mean of the two populations with contribution of 80% from excitatory and 20% from inhibitory neurons; slow permittivity variable is the z variable (in arbitrary unit) from Eq 3, which trigger onset and offset of seizure; bottom plot is experimental data from rat scalp recording in vivo. Parameters: CE = 1.0; x0 = 3.0; Wmax = 0.4; Gsi,j = 0.2; Gsi,i = 0.1; r = 0.000004.

Mentions: After SE, animals experience a latent period during which complex network reorganizations take place. During such period, although neuronal networks exhibit interictal-like activity [38], there are no spontaneous seizures. The latter occur during the chronic phase, a few days or weeks after SE. They are difficult to predict; the brain appears to operate “normally” before an abrupt change happens, characterized by 2 to 10-fold larger amplitude oscillations, which is the seizure. Our model reproduces the most important features of such transitions i.e. an abrupt fast firing discharge pattern at seizure onset, and a decrease of spike-wave frequency towards the end of seizure. We predict interictal spikes and spike-wave discharges are generated from synchronized activity of inhibitory neurons, and are affected by synaptic coupling strengths within and between the two populations of neurons. Fig 6 displays a simulation of about a minute of activity in which a seizure takes place, together with its experimental counterpart. The model produces the different states of seizure evolution without any change of parameters; the states include pre-ictal population spikes, abrupt transitions to tonic firing, and seizure offset. Hysteresis effects have been predicted in the Epileptor [11] and are preserved in the coupled neuronal population dynamics relayed by the slow permittivity variable. As permittivity traces out its trajectory, seizure onset and offset occur at different values of permittivity and the two different neuronal spiking patterns of the populations may co-exist for the same permittivity value. These behaviors are characteristic for hysteresis.


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

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

Activity of populations of excitatory and inhibitory ensembles, slow variable and mean time-series during a spontaneous seizure.Raster plots 1 & 2 display spikes of population 1 and 2 neurons, with activation threshold at 0 mV; third trace is the mean of the two populations with contribution of 80% from excitatory and 20% from inhibitory neurons; slow permittivity variable is the z variable (in arbitrary unit) from Eq 3, which trigger onset and offset of seizure; bottom plot is experimental data from rat scalp recording in vivo. Parameters: CE = 1.0; x0 = 3.0; Wmax = 0.4; Gsi,j = 0.2; Gsi,i = 0.1; r = 0.000004.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4430284&req=5

pcbi.1004209.g006: Activity of populations of excitatory and inhibitory ensembles, slow variable and mean time-series during a spontaneous seizure.Raster plots 1 & 2 display spikes of population 1 and 2 neurons, with activation threshold at 0 mV; third trace is the mean of the two populations with contribution of 80% from excitatory and 20% from inhibitory neurons; slow permittivity variable is the z variable (in arbitrary unit) from Eq 3, which trigger onset and offset of seizure; bottom plot is experimental data from rat scalp recording in vivo. Parameters: CE = 1.0; x0 = 3.0; Wmax = 0.4; Gsi,j = 0.2; Gsi,i = 0.1; r = 0.000004.
Mentions: After SE, animals experience a latent period during which complex network reorganizations take place. During such period, although neuronal networks exhibit interictal-like activity [38], there are no spontaneous seizures. The latter occur during the chronic phase, a few days or weeks after SE. They are difficult to predict; the brain appears to operate “normally” before an abrupt change happens, characterized by 2 to 10-fold larger amplitude oscillations, which is the seizure. Our model reproduces the most important features of such transitions i.e. an abrupt fast firing discharge pattern at seizure onset, and a decrease of spike-wave frequency towards the end of seizure. We predict interictal spikes and spike-wave discharges are generated from synchronized activity of inhibitory neurons, and are affected by synaptic coupling strengths within and between the two populations of neurons. Fig 6 displays a simulation of about a minute of activity in which a seizure takes place, together with its experimental counterpart. The model produces the different states of seizure evolution without any change of parameters; the states include pre-ictal population spikes, abrupt transitions to tonic firing, and seizure offset. Hysteresis effects have been predicted in the Epileptor [11] and are preserved in the coupled neuronal population dynamics relayed by the slow permittivity variable. As permittivity traces out its trajectory, seizure onset and offset occur at different values of permittivity and the two different neuronal spiking patterns of the populations may co-exist for the same permittivity value. These behaviors are characteristic for hysteresis.

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