Volume 13 Supplement 1

Twenty First Annual Computational Neuroscience Meeting: CNS*2012

Open Access

Interpretation of seizure evolution pathways via a mean-field cortical model

  • Vera M Dadok1Email author,
  • Andrew J Szeri1, 2,
  • Heidi Kirsch3,
  • Jamie Sleigh4 and
  • Beth Lopour5
BMC Neuroscience201213(Suppl 1):P95

DOI: 10.1186/1471-2202-13-S1-P95

Published: 16 July 2012

Treatment of epilepsy is a challenging task. Difficulties arise in choosing the best pharmaceutical drugs for specific patients, and if limited benefits result, attempting to choose the best alternative treatment. It is not always clear why a treatment that works well for one patient does not work well for another. Better understanding of the epileptic brain, such as differentiating between possible biological mechanisms driving seizure evolution, may offer insight into these problems.

This work makes use of a dynamical model of the human cortex based on the underlying physiology and local anatomy of the brain [1]. This mean-field model can generate electrocorticogram-like (ECoG-like) data in both normal states and seizing states under different plausible parameter configurations [2, 3]. There are several different sets of parameters which, when changed, drive the model from a non-seizing state into a seizing state and back. An example of one such pathway is displayed in Figure 1. The presence of different parameter sets that can trigger seizures or exit seizures when varied indicates that there are different biological pathways to and from seizure, and these pathways can be described in terms of the fundamental physical quantities in the model [2]. This work will demonstrate a method that takes ECoG-like data from a model cortex evolving into seizure and identifies which parameters are changing—thus identifying which associated physiological mechanisms may be leading the brain into a seizure. In conjunction with other tools, this method will leverage feature selection and dimensionality reduction algorithms. The same technique may be applied to experimentally collected ECoG seizure data as well.
Figure 1

Illustrates a possible path via two model parameters varying and the resultant ECoG-like data. The exact definitions of all parameters are left to the poster. In this illustrative figure parameter 1 is related to the number of inhibitory synaptic connections and parameter 2 is related to the strength of the subcortical input on excitatory neurons in the cortex. A. Locations in parameter space. Shading relates to the permutation complexity of the signal. B. ECoG-like data signals generated by the model corresponding to locations in parameter space. At locations 1 and 2, the simulated signal through time shows normal cortex activity. At location 3, the ECoG-like data signal through time is in a seizure state with more regular sustained oscillations.



This work was partly supported by a research grant from the National Science Foundation and an NSF Graduate Research Fellowship to V. Dadok.

Authors’ Affiliations

Department of Mechanical Engineering, University of California
Center for Neural Engineering and Prostheses, UC Berkeley and UC San Francisco
School of Medicine, University of California
School of Medicine, University of Auckland, Grafton
Department of Neurobiology, University of California


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  3. Loupour BA, Tasoglu S, Kirsch HE, Sleigh JW, Szeri AJ: A continuous mapping of sleep states through association of EEG with a mesoscale cortical model. J. Comput Neurosci. 2011, 30: 471-487. 10.1007/s10827-010-0272-1.View ArticleGoogle Scholar


© Dadok et al; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.