Selective stimulation of nociceptive small fibers during intraepidermal electrical stimulation: Experiment and computational analysis

Electrical stimulation of skin nociceptors is gaining attention in pain research and peripheral neuropathy diagnosis. However, the optimal parameters for selective stimulation are still difficult to determine because they require simultaneous characterization of the electrical response of small fibers (Aδ- and C-fibers). In this study, we measured the in vivo electrical threshold responses of small fibers to train-pulse stimulation in humans for the first time. We also examined selective stimulation via a computational model, which combines electrical analysis, and terminal fiber and synaptic models, including the first cutaneous pain C-fiber model. Selective stimulation of small fibers is performed by injecting train-pulse stimulation via coaxial electrodes with an intraepidermal needle tip at varying pulse counts and frequencies. The activation Aδ- or C-fibers was discriminated from the differences in reaction time. Aδ-fiber elicited a pinpricking sensation with a mean reaction time of 0.522 s, and C-fiber elicited a tingling sensation or slight burning itch with a mean reaction time of 1.243 s. The implemented multiscale electrical model investigates synaptic effects while considering stimulation waveform characteristics. Experimental results showed that perception thresholds decreased with the number of consecutive pulses and frequency up to convergence (five pulses or 70 Hz) during the selective stimulation of Aδ- and C-fibers. Considering the synaptic properties, the optimal stimulus conditions for selective stimulation of Aδ- vs. C-fibers were train of at least four pulses and a frequency of 40–70 Hz at a pulse width of 1 ms. The experimental results were modeled with high fidelity by incorporating temporal synaptic effects into the computational model. Numerical analysis revealed terminal axon thickness to be the most important biophysical factor affecting threshold variability. The computational model can be used to estimate perception thresholds while understanding the mechanisms underlying the selective stimulation of small fibers. The parameters derived here are important in exploring selective stimulation between Aδ- and C-fibers for diagnosing neuropathies.