Simulation insights on the compound action potential in multifascicular nerves.

Area of Science:

• Computational neuroscience
• Bioelectromagnetics
• Neural engineering

Background:

• Accurate modeling of neural signals is crucial for advancing bioelectronic medicine and understanding nerve function.
• Existing models often struggle with heterogeneous nerve environments and complex electrode geometries.

Purpose of the Study:

• To develop and validate a computational model for simulating evoked compound action potential (eCAP) signals in multi-fascicular nerves.
• To investigate the influence of nerve structure and stimulation parameters on eCAP signals.
• To demonstrate the model's utility in optimizing nerve stimulation and recording configurations.

Main Methods:

• Developed an extended reciprocity theorem approach for neuron signal modeling.
• Established a semi-analytic model integrating hybrid electromagnetic-electrophysiological simulations.
• Validated the model against in vivo porcine vagus nerve stimulation experiments using cuff electrodes.

Main Results:

• The semi-analytic model accurately predicted the shape and amplitude of in vivo eCAP recordings.
• The model accounts for variations in eCAP due to electrode placement and shape.
• Partially activated fascicles significantly contribute to the eCAP, and signal magnitude is not monotonically related to stimulation current.

Conclusions:

• The developed model provides a powerful tool for assessing nerve stimulation and recording setups.
• It enables optimization for signal information content and closed-loop control in bioelectronic medicine.
• The method shows potential for non-destructive reconstruction of nerve topology via inverse problem solving.