Studying the interactions in a mammalian nerve fiber: a functional modeling approach

Vijay Sadashivaiah, Pierre Sacre, Yun Guan, William S Anderson, Sridevi V. Sarma

Research output: Contribution to journalArticle

Abstract

Modern therapeutic interventions are increasingly favoring electrical stimulation to treat neurophysiological dis-orders. These therapies are associated with suboptimal efficacy since most neurostimulation devices operate in an open-loop manner $(i.e.$, stimulation settings like frequency, amplitude are preprogrammed). A closed-loop system can dynamically adjust stimulation parameters and may provide efficient therapies. Computational models used to design these systems vary in complexity which can adversely affect their real-time performance. In this study, we compare two models of varying degrees of complexity. We constructed two computational models of a myelinated nerve fiber (functional versus mechanistic) each receiving two inputs: the underlying physiological activity at one end of the fiber, and the external stimulus applied to the middle of the fiber. We then defined relay reliability as the percentage of physiological action potentials that make it to the other end of the nerve fiber. We applied the two inputs to the fiber at various frequencies and analyze reliability. We found that the functional model and the mechanistic model have similar reliability properties, but the functional model significantly decreases the computational complexity and simulation run time. This modeling effort is the first step towards understanding and designing closed loop, real-time neurostimulation devices.

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Nerve Fibers
Fibers
Myelinated Nerve Fibers
Equipment and Supplies
Electric Stimulation
Action Potentials
Therapeutics
Closed loop systems
Computational complexity

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

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title = "Studying the interactions in a mammalian nerve fiber: a functional modeling approach",
abstract = "Modern therapeutic interventions are increasingly favoring electrical stimulation to treat neurophysiological dis-orders. These therapies are associated with suboptimal efficacy since most neurostimulation devices operate in an open-loop manner $(i.e.$, stimulation settings like frequency, amplitude are preprogrammed). A closed-loop system can dynamically adjust stimulation parameters and may provide efficient therapies. Computational models used to design these systems vary in complexity which can adversely affect their real-time performance. In this study, we compare two models of varying degrees of complexity. We constructed two computational models of a myelinated nerve fiber (functional versus mechanistic) each receiving two inputs: the underlying physiological activity at one end of the fiber, and the external stimulus applied to the middle of the fiber. We then defined relay reliability as the percentage of physiological action potentials that make it to the other end of the nerve fiber. We applied the two inputs to the fiber at various frequencies and analyze reliability. We found that the functional model and the mechanistic model have similar reliability properties, but the functional model significantly decreases the computational complexity and simulation run time. This modeling effort is the first step towards understanding and designing closed loop, real-time neurostimulation devices.",
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AU - Sarma, Sridevi V.

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N2 - Modern therapeutic interventions are increasingly favoring electrical stimulation to treat neurophysiological dis-orders. These therapies are associated with suboptimal efficacy since most neurostimulation devices operate in an open-loop manner $(i.e.$, stimulation settings like frequency, amplitude are preprogrammed). A closed-loop system can dynamically adjust stimulation parameters and may provide efficient therapies. Computational models used to design these systems vary in complexity which can adversely affect their real-time performance. In this study, we compare two models of varying degrees of complexity. We constructed two computational models of a myelinated nerve fiber (functional versus mechanistic) each receiving two inputs: the underlying physiological activity at one end of the fiber, and the external stimulus applied to the middle of the fiber. We then defined relay reliability as the percentage of physiological action potentials that make it to the other end of the nerve fiber. We applied the two inputs to the fiber at various frequencies and analyze reliability. We found that the functional model and the mechanistic model have similar reliability properties, but the functional model significantly decreases the computational complexity and simulation run time. This modeling effort is the first step towards understanding and designing closed loop, real-time neurostimulation devices.

AB - Modern therapeutic interventions are increasingly favoring electrical stimulation to treat neurophysiological dis-orders. These therapies are associated with suboptimal efficacy since most neurostimulation devices operate in an open-loop manner $(i.e.$, stimulation settings like frequency, amplitude are preprogrammed). A closed-loop system can dynamically adjust stimulation parameters and may provide efficient therapies. Computational models used to design these systems vary in complexity which can adversely affect their real-time performance. In this study, we compare two models of varying degrees of complexity. We constructed two computational models of a myelinated nerve fiber (functional versus mechanistic) each receiving two inputs: the underlying physiological activity at one end of the fiber, and the external stimulus applied to the middle of the fiber. We then defined relay reliability as the percentage of physiological action potentials that make it to the other end of the nerve fiber. We applied the two inputs to the fiber at various frequencies and analyze reliability. We found that the functional model and the mechanistic model have similar reliability properties, but the functional model significantly decreases the computational complexity and simulation run time. This modeling effort is the first step towards understanding and designing closed loop, real-time neurostimulation devices.

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