A Large-Scale Neuro-glial Network Model of Seizure Termination
Yuhan Helena Liu (1), Berj Bardakian (1,2)
1. Department of Electrical & Computer Engineering, University of Toronto
2. Institute of Biomaterials and Biomedical Engineering, University of Toronto
Background: How seizures terminate remains elusive. This is clinically relevant as seizure termination is sometimes followed by the postictal generalized EEG suppression state (PGES), a period with suppressed activities and has been found to be associated with sudden unexpected death in epilepsy.
Method: Large-scale neuroglial network modeling was combined with EEG data analysis to elucidate the processes involved in seizure termination. Results: Dominant frequency decay, increase in cross-frequency coupling strength, as well as shift in frequency of the phase signal were observed in EEG recordings with PGES as a seizure progresses. Those experimental observations were reproduced in the simulated local field potential (LFP) by changing synaptic strengths in the model network. Different effects on dominant frequency and cross-frequency coupling were observed by varying the strengths of four different types of synapses: connections between excitatory to excitatory, excitatory to inhibitory, inhibitory to excitatory and inhibitory to inhibitory neurons. Moreover, simulations showed that microglia could modulate synaptic strengths in response to neuronal activity to produce the aforementioned experimental observations.
Conclusion: Changes in the functional connectivity of the neural network could underlie the dynamics in seizure termination and microglia could play a role in shaping the connectivity. Significance: Combining computer modeling and electrophysiological observations can formulate testable hypotheses for guiding future studies to elucidate mechanisms in seizure termination.