Controlling epileptic seizures in a neural mass model

Niranjan Chakravarthy, Shivkumar Sabesan, Konstantinos Tsakalis, Leon Iasemidis

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

In an effort to understand basic functional mechanisms that can produce epileptic seizures, we introduce some key features in a model of coupled neural populations that enable the generation of seizure-like events and similar dynamics with the ones observed during the route of the epileptic brain towards real seizures. In this model, modified from David and Friston's neural mass model, an internal feedback mechanism is incorporated to maintain synchronous behavior within normal levels despite elevated coupling. Normal internal feedback quickly regulates an abnormally high coupling between the neural populations, whereas pathological internal feedback can lead to hypersynchronization and the appearance of seizure-like high amplitude oscillations. Feedback decoupling is introduced as a robust seizure control strategy. An external feedback decoupling controller is introduced to maintain normal synchronous behavior. The results from the analysis in this model have an interesting physical interpretation and specific implications for the treatment of epileptic seizures. The proposed model and control scheme 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.

Original languageEnglish (US)
Pages (from-to)98-116
Number of pages19
JournalJournal of Combinatorial Optimization
Volume17
Issue number1
DOIs
StatePublished - Jan 2009

Keywords

  • Coupled neural populations
  • Epileptic seizures modeling
  • Feedback decoupling control
  • Internal feedback

ASJC Scopus subject areas

  • Computer Science Applications
  • Discrete Mathematics and Combinatorics
  • Control and Optimization
  • Computational Theory and Mathematics
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Controlling epileptic seizures in a neural mass model'. Together they form a unique fingerprint.

Cite this