A methodological framework for the efficient characterization of peripheral nerve stimulation parameters.

Objective: Restoring movement and somatosensation with peripheral nerve stimulation (PNS) requires precise neural activation. Because pulse amplitude (PA) and pulse width (PW) recruit axons differently, intentionally modulating both could enable more complex PNS. However, mapping the PA-PW space is currently prohibitively time-intensive. This paper proposes and clinically validates an efficient method to characterize multiple intensities in the PA-PW space for motor and perceptual sensory applications using minimal data collection.

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

Main results: SD curves accurately fit all levels of motor activation and perceptual intensity (median R^2 = 0.996 and 0.984, respectively). Reliable estimates of SD curves at any intensity require only two sufficiently-spaced points (motor R2 = 0.991, sensory R2 = 0.977). Using this data, we present and validate a novel method for efficiently characterizing the PA-PW space using SD curves, including a metric that quantifies mapping accuracy based on two sampled points. In silico, intensity-matched high-PW and high-PA stimulation recruited overlapping, but not equivalent, axon sets, with high-PA stimuli preferentially recruiting large-diameter fibers and axons farther from the contact.

Significance: This method enables rapid, accurate mapping of the stimulation parameter space for clinical motor and sensory PNS. The efficiency of the proposed characterization approach enhances the clinical feasibility of multiparameter modulation, establishing a framework for further exploration of two-parameter modulation for increased selectivity and resolution, reduced fatigue, and unique percept generation. (ClinicalTrials.gov ID NCT03898804).