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Quasistatic approximation in neuromodulation.

Publication ,  Journal Article
Wang, B; Peterchev, AV; Gaugain, G; Ilmoniemi, RJ; Grill, WM; Bikson, M; Nikolayev, D
Published in: J Neural Eng
July 24, 2024

We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuromodulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g. Laplace's equation) are solved for the spatial distribution of the field, which is separated from the field's temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.

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Published In

J Neural Eng

DOI

EISSN

1741-2552

Publication Date

July 24, 2024

Volume

21

Issue

4

Location

England

Related Subject Headings

  • Transcranial Magnetic Stimulation
  • Models, Neurological
  • Humans
  • Electric Stimulation
  • Deep Brain Stimulation
  • Computer Simulation
  • Biomedical Engineering
  • Animals
  • 4003 Biomedical engineering
  • 3209 Neurosciences
 

Citation

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Wang, B., Peterchev, A. V., Gaugain, G., Ilmoniemi, R. J., Grill, W. M., Bikson, M., & Nikolayev, D. (2024). Quasistatic approximation in neuromodulation. J Neural Eng, 21(4). https://doi.org/10.1088/1741-2552/ad625e
Wang, Boshuo, Angel V. Peterchev, Gabriel Gaugain, Risto J. Ilmoniemi, Warren M. Grill, Marom Bikson, and Denys Nikolayev. “Quasistatic approximation in neuromodulation.J Neural Eng 21, no. 4 (July 24, 2024). https://doi.org/10.1088/1741-2552/ad625e.
Wang B, Peterchev AV, Gaugain G, Ilmoniemi RJ, Grill WM, Bikson M, et al. Quasistatic approximation in neuromodulation. J Neural Eng. 2024 Jul 24;21(4).
Wang, Boshuo, et al. “Quasistatic approximation in neuromodulation.J Neural Eng, vol. 21, no. 4, July 2024. Pubmed, doi:10.1088/1741-2552/ad625e.
Wang B, Peterchev AV, Gaugain G, Ilmoniemi RJ, Grill WM, Bikson M, Nikolayev D. Quasistatic approximation in neuromodulation. J Neural Eng. 2024 Jul 24;21(4).
Journal cover image

Published In

J Neural Eng

DOI

EISSN

1741-2552

Publication Date

July 24, 2024

Volume

21

Issue

4

Location

England

Related Subject Headings

  • Transcranial Magnetic Stimulation
  • Models, Neurological
  • Humans
  • Electric Stimulation
  • Deep Brain Stimulation
  • Computer Simulation
  • Biomedical Engineering
  • Animals
  • 4003 Biomedical engineering
  • 3209 Neurosciences