Computational motility models of neurogastroenterology and neuromodulation.

Published

Journal Article (Review)

The success of neuromodulation therapies, particularly in the brain, spinal cord, and peripheral nerves, has been greatly aided by computational, biophysical models. However, treating gastrointestinal disorders with electrical stimulation has been much less explored, partly because the mode of action of such treatments is unclear, and selection of stimulation parameters is often empirical. Progress in gut neuromodulation is limited by the comparative lack of biophysical models capable of simulating neuromodulation of gastrointestinal function. Here, we review the recently developed biophysical models of electrically-active cells in the gastrointestinal system that contribute to motility. Biophysical models are replacing phenomenologically-defined models due to advancements in electrophysiological characterization of key players in the gut: enteric neurons, smooth muscle fibers, and interstitial cells of Cajal. In this review, we explore existing biophysically-defined cellular and network models that contribute to gastrointestinal motility. We focus on recent models that are laying the groundwork for modeling electrical stimulation of the gastrointestinal system. Developing models of gut neuromodulation will improve our mechanistic understanding of these treatments, leading to better parameterization, selectivity, and efficacy of neuromodulation to treat gastrointestinal disorders. Such models may have direct clinical translation to current neuromodulation therapies, such as sacral nerve stimulation.

Full Text

Duke Authors

Cited Authors

  • Barth, BB; Shen, X

Published Date

  • August 2018

Published In

Volume / Issue

  • 1693 / Pt B

Start / End Page

  • 174 - 179

PubMed ID

  • 29903620

Pubmed Central ID

  • 29903620

Electronic International Standard Serial Number (EISSN)

  • 1872-6240

International Standard Serial Number (ISSN)

  • 0006-8993

Digital Object Identifier (DOI)

  • 10.1016/j.brainres.2018.02.038

Language

  • eng