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Neuronal network model of interictal and recurrent ictal activity
M A Lopes1,2,3,4, K-E Lee4,5, A V Goltsev4,6
1Living Systems Institute, University of Exeter, Exeter EX4 4QD, United Kingdom.
Physical Review. E
|January 20, 2018
Summary
This study introduces a neuronal network model demonstrating a saddle-node bifurcation mechanism for seizure transitions. The model captures key dynamics of interictal and ictal states, predicting early seizure warnings.
Area of Science:
- Computational Neuroscience
- Epilepsy Research
- Dynamical Systems Theory
Background:
- Epilepsy is characterized by recurrent seizures, with transitions between interictal (non-seizure) and ictal (seizure) states.
- Understanding the underlying mechanisms of these state transitions is crucial for developing effective treatments.
Purpose of the Study:
- To propose a neuronal network model that explains the transition from interictal to ictal states using a specific bifurcation mechanism.
- To investigate the dynamical features of interictal and ictal states within this model.
- To explore the emergence of recurrent seizures from network interactions.
Main Methods:
- Development of a neuronal network model incorporating a saddle-node on an invariant circle bifurcation.
- Analysis of model dynamics to capture interictal and ictal state features.
- Investigation of interictal spikes and early warning signals.
- Simulation of interacting networks to study recurrent seizure generation.
Main Results:
- The proposed model successfully replicates the transition from interictal to ictal states via a saddle-node on an invariant circle bifurcation.
- The model exhibits important dynamical features characteristic of both interictal and ictal brain activity.
- Interictal spikes and potential early warning signals preceding seizures were identified.
- Recurrent seizure activity was observed to emerge from the interaction of two coupled networks.
Conclusions:
- A saddle-node on an invariant circle bifurcation provides a viable mechanism for seizure generation in neuronal networks.
- The model offers insights into the dynamics of interictal states and the prediction of seizures.
- Network interactions play a significant role in the generation of recurrent epileptic seizures.
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