Abstract

ObjectiveOur goal was to determine the conditions for which a more precise calculation of the electric potential than the quasi-static approximation may be needed in models of electrical neurostimulation, particularly for signals with kilohertz-frequency components. ApproachWe conducted a comprehensive quantitative study of the differences in nerve fiber activation and conduction block when using the quasi-static and Helmholtz approximations for the electric potential in a model of electrical neurostimulation. Main resultsWe first show that the potentials generated by sources of unbalanced pulses exhibit different transients as compared to those of energy-balanced pulses, and this is disregarded by the quasi-static assumption. Secondly, the relative errors for current-distance curves were below 3%, while for strength-duration curves the variations ranged between 1-17%, but could be improved to less than 3% across the range of pulse duration by providing a corrected quasi-static conductivity. Third, we extended our analysis to trains of pulses and reported a "congruence area" below 700 Hz, where the fidelity of fiber responses is maximal for suprathreshold stimulation. Further examination of waveforms and polarities revealed similar fidelities in the congruence area, but significant differences were observed beyond this area. However, the spike-train distance revealed differences in activation patterns when comparing the response generated by each model. Finally, in simulations of conduction-block, we found that block thresholds exhibited errors above 20% for repetition rates above 10 kHz. Yet, employing a corrected value of the conductivity improved the agreement between models, with errors no greater than 8%. SignificanceOur results emphasize that the quasi-static approximation cannot be naively extended to electrical stimulation with high-frequency components, and notable differences can be observed in activation patterns. As well, we introduce a methodology to obtain more precise model responses using the quasi-static approach, which can be a valuable resource in computational neuroengineering.