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Nonlinear Neurodynamical Features in an Animal Model of Generalized Epilepsy

  • Chapter
Quantitative Neuroscience

Part of the book series: Biocomputing ((BCOM,volume 2))

Abstract

Epileptic seizures result from the intermittent spatial and temporal summation of bnormally discharging neurons [3, 7, 20, 24]. Complex dynamical interactions between brain regions act to recruit and entrain neurons by loss of inhibition and synchronization. Long-term continuous 4-channel intracranial electroencephalographic (EEG) recordings were obtained from a genetically engineered model of generalized epilepsy (n = 3) and littermate controls (n = 3) in order to perform nonlinear dynamical analyses of intracranial brain electrical activity. Signal processing techniques included reconstruction of the EEG signal as trajectories in a phase space, applying nonlinear indicators on the trajectories (i.e., short-term maximum Lyapunov exponent), and statistical index (T-index) for quantifying interactions among distant brain sites. Analysis of interictal (seizure-free) and seizure prone periods in 2 to 3 week old Н218 epileptic knockout mice revealed that (1) the brain electrical activity is of higher order during the seizure-prone period, and (2) the interaction among brain sites is more active during the seizure-prone period. In addition, dynamical analyses do not show significant difference between interictal periods in epileptic mice and littermate controls. These results suggest that the development of seizures in an animal model of generalized epilepsy is determined in part by non-stochastic neural processes. Results further suggest that it may be possible to identify the occurrence of seizures in advance through dynamical analytic distinction of interictal and seizure-prone periods.

This work was supported by the Department of Veterans Affairs and grants from NIH/NINDS (RO1-NS31451), NIH/NIBIB (8R01ЕВ002089-03), University of Florida Division of Sponsored Research, and the Children’s Miracle Network.

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Author information

Authors and Affiliations

  1. Department of Pediatrics, Neurology, and Neuroscience, University of Florida, USA

    P. R. Carney

  2. VA Medical Center, Department of Neuroscience, University of Florida, Gainesville, FL, USA

    D.-S. Shiau

  3. Departments of Industrial and Systems Engineering, Computer Science, and Biomedical Engineering, University of Florida, USA

    P. M. Pardalos

  4. Department of Biomedical Engineering, Arizona State University, USA

    L. D. Iasemidis

  5. VA Medical Center, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    W. Chaovalitwongse

  6. VA Medical Center, Departments of Neuroscience, Neurology, Pediatrics, and Biomedical Engineering, University of Florida, Gainesville, FL, USA

    J. C. Sackellares

Authors
  1. P. R. Carney
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  2. D.-S. Shiau
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  3. P. M. Pardalos
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  4. L. D. Iasemidis
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  5. W. Chaovalitwongse
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  6. J. C. Sackellares
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Editor information

Editors and Affiliations

  1. Departments of Industrial and Systems Engineering, Biomedical Engineering, and McKnight Brain Institute, University of Florida, USA

    P. M. Pardalos

  2. Departments of Neuroscience, Neurology, Pediatrics, and Biomedical Engineering, University of Florida; VA Medical Center, USA

    J. C. Sackellares

  3. Departments of Pediatrics, Neurology, and Neuroscience, University of Florida, USA

    P. R. Carney

  4. Department of Biomedical Engineering, Arizona State University, USA

    L. D. Iasemidis

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© 2004 Kluwer Academic Publishers

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Carney, P.R., Shiau, DS., Pardalos, P.M., Iasemidis, L.D., Chaovalitwongse, W., Sackellares, J.C. (2004). Nonlinear Neurodynamical Features in an Animal Model of Generalized Epilepsy. In: Pardalos, P.M., Sackellares, J.C., Carney, P.R., Iasemidis, L.D. (eds) Quantitative Neuroscience. Biocomputing, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0225-4_2

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  • DOI: https://doi.org/10.1007/978-1-4613-0225-4_2

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7951-5

  • Online ISBN: 978-1-4613-0225-4

  • eBook Packages: Springer Book Archive

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