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Finite Element Modelling of a Cellular Electric Microenvironment
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Finite Element Modelling for Biophysical Models of Nervous System Stimulation: Best Practices for Multiscale Adaptive

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    Finite element modeling (FEM) of nervous systems requires careful meshing. Finer meshes improve accuracy in predicting action potentials and reduce errors in computational biophysical models.

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    Area of Science:

    • Computational Neuroscience
    • Biophysics
    • Finite Element Method

    Background:

    • Finite element modeling (FEM) is crucial for simulating nervous system responses to stimuli.
    • Reproducible and transparent biophysical models are essential for advancing neuroscience research.
    • Best practices for FEM in biophysical modeling are not well-established.

    Purpose of the Study:

    • To present methods for FEM of the peripheral and central nervous systems.
    • To assess the impact of mesh size and material transitions on model accuracy.
    • To provide guidance for transparent and reproducible FEM experiments.

    Main Methods:

    • Evaluated differentiation errors from varying mesh sizes in FEM.
    • Analyzed the effect of material transitions on model accuracy.
    • Utilized a Hodgkin-Huxley (H-H) axon model to predict action potentials.

    Main Results:

    • Coarser meshes increased activation thresholds (57.5 mA) compared to finer meshes (55 mA).
    • Poor spatial discretization generated double-derivative noise but not false action potential predictions.
    • Multiscale meshes minimized discontinuities at material interfaces.

    Conclusions:

    • Finest spatial discretizations are recommended within computational limits, potentially using adaptive meshing.
    • Coupling extracellular fields with H-H axons can further reduce error sources.
    • This work supports the development of accurate and reproducible FEM for neurostimulation.