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Homeostasis of Brain Dynamics in Epilepsy: A Feedback Control Systems Perspective of Seizures

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Abstract

In an effort to understand basic functional mechanisms that can produce epileptic seizures, some key features are introduced in coupled lumped-parameter neural population models that produce “seizure”-like events and dynamics similar to the ones during the route of the epileptic brain towards seizures. In these models, modified from existing ones in the literature, internal feedback mechanisms are incorporated to maintain the normal low level of synchronous behavior in the presence of coupling variations. While the internal feedback is developed using basic feedback systems principles, it is also functionally equivalent to actual neurophysiological mechanisms such as homeostasis that act to maintain normal activity in neural systems that are subject to extrinsic and intrinsic perturbations. Here it is hypothesized that a plausible cause of seizures is a pathology in the internal feedback action; normal internal feedback quickly regulates an abnormally high coupling between the neural populations, whereas pathological internal feedback can lead to “seizure”-like high amplitude oscillations. Several external seizure-control paradigms, that act to achieve the operational objective of maintaining normal levels of synchronous behavior, are also developed and tested in this paper. In particular, closed-loop “modulating” control with predefined stimuli, and closed-loop feedback decoupling control are considered. Among these, feedback decoupling control is the consistently successful and robust seizure-control strategy. The proposed model and remedies are consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology. The results from the analysis of these models have two key implications, namely, developing a basic theory for epilepsy and other brain disorders, and the development of a robust seizure-control device through electrical stimulation and/or drug intervention modalities.

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Notes

  1. Based on the terminology that two coupled systems of the form \(\dot{x}_1=f_1(x_1)+\epsilon (x_2-x_1)\) and \(\dot{x}_2=f_2(x_2)+\epsilon (x_1-x_2)\) are diffusively coupled, we coin the term “quasi-diffusive” since the control signal in this context is essentially of the form −\(\epsilon\) x 2.

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Acknowledgments

This work was supported in part by the American Epilepsy Research Foundation and Ali Paris Fund for LKS Research and Education, and NSF Grant ECS-0601740. We would also like to acknowledge Prof. Piotr Suffczynski for providing the Simulink code for his thalamocortical model.

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  1. Department of Electrical Engineering, Arizona State University, Tempe, AZ, 85287, USA

    Niranjan Chakravarthy, Kostas Tsakalis & Shivkumar Sabesan

  2. Harrington Department of Bioengineering, Arizona State University, Tempe, AZ, 85287, USA

    Leon Iasemidis

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  1. Niranjan Chakravarthy
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Correspondence to Kostas Tsakalis.

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Chakravarthy, N., Tsakalis, K., Sabesan, S. et al. Homeostasis of Brain Dynamics in Epilepsy: A Feedback Control Systems Perspective of Seizures. Ann Biomed Eng 37, 565–585 (2009). https://doi.org/10.1007/s10439-008-9625-6

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