Authors: Yixuan Zhang¹, Daniel Kipping¹, Waldo Nogueira¹
¹Hannover Medical School
Background: Cochlear implants (CIs) restore hearing by electrically stimulating auditory nerve fibers (ANFs), but assessing neural health—critical for optimal outcomes—remains a challenge. In this study, we present a computational framework to evaluate indirect neural health estimation. Meanwhile, this computational model could be utilized to compare neural activation between conventional CI stimulation and a novel auditory nerve implant (ANI) designed for direct ANF stimulation.
Methods: Two distinct measures for estimating neural health, (i) threshold levels for focused partial tripolar stimulation (pTP) and (ii) changes in the electrically evoked compound action potential (eCAP) amplitude growth function for different inter-phase gaps (IPGs), were evaluated in a computational model of an electrically stimulated implanted cochlea.
Additionally, a phenomenological stochastic neuron model was applied to simulate excitation of the ANFs, resulting in excitation profiles that show the activation of the ANFs over their tonotopic frequency. Excitation profiles derived at different stimulation levels were concatenated to spatial tuning curves (STCs). Based on the STCs, we estimated the thresholds, dynamic ranges, and specificity of stimulation with the ANI and the CI for different conductivity values of the auditory nerve.
Results and Conclusions: Experimental results show that pTP thresholds were more sensitive to neural health deficits than monopolar threshods, suggesting that the pTP–monopolar threshold difference could serve as a reliable indicator. In contrast, eCAP IPG effects were less consistently linked to neural health. Comparing devices, the ANI produced STCs ranging from sharply peaked to broad or multimodal, depending on electrode placement and neural organization. Notably, the ANI required significantly lower current levels than the CI to reach threshold, consistent with prior findings. This model offers insights into auditory nerve activation mechanisms and supports future development of patient-specific fitting strategies for ANI-based clinical applications.
