High-Resolution Multi-Scale Computational Model for Non-Invasive Cervical Vagus Nerve Stimulation.

Area of Science:

• Biophysics
• Computational Neuroscience
• Medical Engineering

Background:

• Non-invasive vagus nerve stimulation (nVNS) is a therapeutic modality requiring precise understanding of nerve activation.
• Current models often lack the multi-scale resolution needed for accurate prediction of vagus nerve fiber type recruitment.

Purpose of the Study:

• To create the first high-resolution, multi-scale computational model of cervical nVNS.
• To predict vagus nerve fiber type activation based on clinically relevant electrical thresholds.

Main Methods:

• Developed a Finite Element Method (FEM) model using MRI data to simulate cervical anatomy from macroscopic to mesoscopic scales.
• Incorporated microscopic biophysical models of axons (Aα-, Aβ-, Aδ-, B-, and C-fibers) to assess activation thresholds.
• Used rheobase threshold estimates with a step function waveform for simulations.

Main Results:

• Macro-scale model accuracy determined electric field (E-field) magnitudes, while mesoscopic precision refined E-field changes and activating function.
• Mesoscopic anatomical details, particularly the nerve's passage through varying tissue environments, significantly increased predicted axon sensitivity.
• Nerve sheath encapsulation was found to reduce axon sensitivity to nVNS.

Conclusions:

• Accurate quantitative predictions of vagus nerve activation necessitate realistic modeling at both macroscopic and mesoscopic scales.
• Predicted that standard cervical nVNS protocols activate A- and B-fibers, but not C-fibers.
• The developed multi-scale modeling approach is applicable to other neuromodulation techniques like spinal cord and deep brain stimulation.