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Computational modelling of the long-term effects of brain stimulation on the local and global structural connectivity of epileptic patients

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  • Emmanouil Giannakakis
  • Frances Hutchings
  • Christoforos A Papasavvas
  • Cheol E Han
  • Bernd Weber
  • Chencheng Zhang
  • Marcus Kaiser

Abstract

Computational studies of the influence of different network parameters on the dynamic and topological network effects of brain stimulation can enhance our understanding of different outcomes between individuals. In this study, a brain stimulation session along with the subsequent post-stimulation brain activity is simulated for a period of one day using a network of modified Wilson-Cowan oscillators coupled according to diffusion imaging based structural connectivity. We use this computational model to examine how differences in the inter-region connectivity and the excitability of stimulated regions at the time of stimulation can affect post-stimulation behaviours. Our findings indicate that the initial inter-region connectivity can heavily affect the changes that stimulation induces in the connectivity of the network. Moreover, differences in the excitability of the stimulated regions seem to lead to different post-stimulation connectivity changes across the model network, including on the internal connectivity of non-stimulated regions.

Suggested Citation

  • Emmanouil Giannakakis & Frances Hutchings & Christoforos A Papasavvas & Cheol E Han & Bernd Weber & Chencheng Zhang & Marcus Kaiser, 2020. "Computational modelling of the long-term effects of brain stimulation on the local and global structural connectivity of epileptic patients," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-21, February.
    • Handle: RePEc:plo:pone00:0221380
    DOI: 10.1371/journal.pone.0221380
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