Abstract
Epilepsy is one of the most common neurological disorders and is characterized by recurrent seizures. We use theoretical neuroscience tools to study brain dynamics during seizures. We derive and simulate a computational model of a network of hippocampal neuronal populations. Each population within the network is based on a model that has been shown to replicate the electrophysiological dynamics observed during seizures. The results provide insights into possible mechanisms for seizure spread. We observe that epileptiform activity remains localized to a pathological region when a global connectivity parameter is less than a critical value. After establishing the critical value for seizure spread, we explored how to correct the effect by altering particular synaptic gains. The spreading of seizures is quantified using numerical methods for seizure detection. The results from this study provide a new avenue of exploration for seizure control.
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Acknowledgments
This research was supported by the Australian Research Council through the Discovery Early Career Researcher Award (DECRA) DE120102210. Dr Freestone acknowledges the support of the Australian-American Fulbright Association. Dr Freestone would also like to thank Prof Liam Paninski at Columbia University for support and guidance. The authors would also like to thank and acknowledge important input from Prof Mark Cook, Dr Andre Peterson, Prof David Grayden, Prof Anthony Burkitt and all the members of the NeuroEngineering lab at The University of Melbourne.
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Kameneva, T., Ying, T., Guo, B. et al. Neural mass models as a tool to investigate neural dynamics during seizures. J Comput Neurosci 42, 203–215 (2017). https://doi.org/10.1007/s10827-017-0636-x
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DOI: https://doi.org/10.1007/s10827-017-0636-x