Abstract
Introduction: Burst stimulation patterns consist of packets of relatively high frequency stimulation pulses repeated over time at lower frequencies and have produced promising outcomes when used with spinal cord stimulation (SCS) to treat neuropathic pain. A recent hypothesis proposes that the passive or active nature of the burst waveform recharge phase may influence outcomes due to the “postsynaptic non-linear buildup of the super-action potential” by passive recharge burst (De Ridder, commentary in Neuromodulation, 2017), but this hypothesis has not been proven. The aim of this study is to use computational modeling to discern differences between physiological effects in the activation of dorsal columns (DC), such as axon activation threshold and action potential (AP) shape, produced by active vs. passive recharge burst stimulation to gain mechanistic insights that may drive waveform optimization.
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