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A computational model for estimating recruitment of primary afferent fibers by intraneural stimulation in the dorsal root ganglia

Primary afferent microstimulation has been proposed as a method for activating cutaneous and muscle afferent fibers to restore tactile and proprioceptive feedback after limb loss or peripheral neuropathy. Large populations of primary afferent fibers can be accessed directly by implanting microelectrode arrays in the dorsal root ganglia (DRG), which provide a compact and stable target for stimulating a diverse group of sensory fibers. To gain insight into factors affecting the number and types of primary afferents activated, we developed a computational model that simulates the recruitment of fibers in the feline L7 DRG. The model comprises two parts. The first part is a single-fiber model used to describe the current–distance relation and was based on the McIntyre–Richardson–Grill model for excitability. The second part uses the results of the singe-fiber model and published data on fiber size distributions to predict the probability of recruiting a given number of fibers as a function of stimulus intensity. The range of intensities over which exactly one fiber was recruited was approximately 0.5–5 µA (0.1–1 nC per phase); the stimulus intensity at which the probability of recruiting exactly one fiber was maximized was 2.3 µA. However, at 2.3 µA, it was also possible to recruit up to three fibers, albeit with a lower probability. Stimulation amplitudes up to 6 µA were tested with the population model, which showed that as the amplitude increased, the number of fibers recruited increased exponentially. The distribution of threshold amplitudes predicted by the model was similar to that previously reported by in vivo experimentation. Finally, the model suggested that medium diameter fibers (7.3–11.5 µm) may be recruited with much greater probability than large diameter fibers (12.8–16 µm). This model may be used to efficiently test a range of stimulation parameters and nerve morphologies to complement results from electrophysiology experiments and to aid in the design of microelectrode arrays for neural interfaces.