Title |
A Critical Role for Network Structure in Seizure Onset: A Computational Modeling Approach
|
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Published in |
Frontiers in Neurology, December 2014
|
DOI | 10.3389/fneur.2014.00261 |
Pubmed ID | |
Authors |
George Petkov, Marc Goodfellow, Mark P. Richardson, John R. Terry |
Abstract |
Recent clinical work has implicated network structure as critically important in the initiation of seizures in people with idiopathic generalized epilepsies. In line with this idea, functional networks derived from the electroencephalogram (EEG) at rest have been shown to be significantly different in people with generalized epilepsy compared to controls. In particular, the mean node degree of networks from the epilepsy cohort was found to be statistically significantly higher than those of controls. However, the mechanisms by which these network differences can support recurrent transitions into seizures remain unclear. In this study, we use a computational model of the transition into seizure dynamics to explore the dynamic consequences of these differences in functional networks. We demonstrate that networks with higher mean node degree are more prone to generating seizure dynamics in the model and therefore suggest a mechanism by which increased mean node degree of brain networks can cause heightened ictogenicity. |
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Geographical breakdown
Country | Count | As % |
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Unknown | 3 | 43% |
Demographic breakdown
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Scientists | 2 | 29% |
Mendeley readers
Geographical breakdown
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Switzerland | 1 | <1% |
Unknown | 101 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 25 | 24% |
Student > Ph. D. Student | 16 | 15% |
Student > Master | 10 | 10% |
Student > Doctoral Student | 9 | 9% |
Student > Postgraduate | 5 | 5% |
Other | 18 | 17% |
Unknown | 21 | 20% |
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Mathematics | 6 | 6% |
Other | 13 | 13% |
Unknown | 32 | 31% |