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Dynamic Mechanisms of Neocortical Focal Seizure Onset

Overview of attention for article published in PLoS Computational Biology, August 2014
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Title
Dynamic Mechanisms of Neocortical Focal Seizure Onset
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003787
Pubmed ID
Authors

Yujiang Wang, Marc Goodfellow, Peter Neal Taylor, Gerold Baier

Abstract

Recent experimental and clinical studies have provided diverse insight into the mechanisms of human focal seizure initiation and propagation. Often these findings exist at different scales of observation, and are not reconciled into a common understanding. Here we develop a new, multiscale mathematical model of cortical electric activity with realistic mesoscopic connectivity. Relating the model dynamics to experimental and clinical findings leads us to propose three classes of dynamical mechanisms for the onset of focal seizures in a unified framework. These three classes are: (i) globally induced focal seizures; (ii) globally supported focal seizures; (iii) locally induced focal seizures. Using model simulations we illustrate these onset mechanisms and show how the three classes can be distinguished. Specifically, we find that although all focal seizures typically appear to arise from localised tissue, the mechanisms of onset could be due to either localised processes or processes on a larger spatial scale. We conclude that although focal seizures might have different patient-specific aetiologies and electrographic signatures, our model suggests that dynamically they can still be classified in a clinically useful way. Additionally, this novel classification according to the dynamical mechanisms is able to resolve some of the previously conflicting experimental and clinical findings.

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Geographical breakdown

Country Count As %
United Kingdom 3 3%
Hong Kong 1 1%
India 1 1%
Unknown 88 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 29%
Student > Ph. D. Student 26 28%
Professor > Associate Professor 8 9%
Student > Master 7 8%
Student > Bachelor 5 5%
Other 12 13%
Unknown 8 9%
Readers by discipline Count As %
Neuroscience 26 28%
Engineering 15 16%
Agricultural and Biological Sciences 13 14%
Medicine and Dentistry 8 9%
Mathematics 7 8%
Other 12 13%
Unknown 12 13%