Evaluating electrophysiological and behavioral measures of neural health in cochlear implant users: a computational simulation study.

Objective: Neural health refers to the condition and functionality of the auditory nerve fibers (ANFs), essential for transmitting sound signals from the cochlea to the brain. However, neural health cannot be directly measured due to current technological limitations. We utilize a computational model to evaluate different indirect methods for estimating neural health.

Method: Two distinct measures for estimating neural health, (i) threshold levels for focused partial tripolar stimulation 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. The model combined a 3D finite element method model, a realistic ANF geometry, and a neuron model, including an existing phenomenological single-ANF model and an eCAP model. Our experiments simulated different neural health conditions (healthy, shrunk, and degenerated) to model nueral dead region in the cochlea.

Results: Experiment results demonstrated that the threshold levels with partial tripolar stimulation were more sensitive to neural health deficits than monopolar stimulation. The threshold difference between partial tripolar and monopolar stimulation seems to be a promising measure of neural health status. However, results from the eCAP IPG slope and offset effects were not consistently associated with neural health conditions.

Conclusion: Our results suggest that the difference in threshold levels with partial tripolar and monopolar stimulation is a possible method for estimating neural health.

Significance: This study enhances the understanding of neural health through a computational model, contributing to new approaches for neural health estimation.