A Methodological Framework for the Efficient Characterization of Peripheral Nerve Stimulation Parameters

Restoring movement and somatosensation with peripheral nerve stimulation (PNS) requires precise neural activation. Pulse amplitude (PA) and pulse width (PW) modulate neural response differently, offering potential for improved selectivity. However, simultaneously modulating both parameters is rare due to the time required to map the two-dimensional space. This paper proposes and clinically validates an efficient method to characterize multiple intensities in the PA-PW space for motor and perceptual sensory applications. We also explore distinct activation patterns and potential applications of stimulation within the two-dimensional space.

We used cuff electrodes implanted in one participant with a spinal cord injury to generate equal muscle activation contours and two participants with upper limb loss to generate perceptual intensity contours across multiple intensities in the PA-PW space. Strength-duration (SD) curves were mapped to the contours using varying sample point subsets and assessed for fit. Finite element modeling of the human nerve and activation simulations evaluated differences in recruited axon populations across the PA-PW space.

SD curves accurately fit all levels of motor activation and perceptual sensory intensity (median R ² = 0.996 and 0.984, respectively). Reliable estimates of SD curves at any intensity require only two sufficiently spaced points. We propose a novel method for efficiently characterizing the PA-PW space utilizing the SD curve, including a metric to assess estimation accuracy based on the sampled points. In silico, intensity-matched high PW and high PA stimulation activated unique axon subsets, with high PA stimuli preferentially recruiting large-diameter motor and sensory axons farther from the contact.

This work supports the use of SD curves to efficiently define a two-dimensional stimulation region for clinical motor and sensory PNS. Applying the proposed characterization method could enhance selectivity and resolution, reducing fatigue, improving fine motor control, and enabling unique percept generation. (ClinicalTrials.gov ID NCT03898804).