Electrical Stimulation in the Human Cochlea: A Computational Study Based on High-Resolution Micro-CT Scans

Siwei Bai; Jörg Encke; Miguel Obando-Leitón; Robin Weiß; Friederike Schäfer; Jakob Eberharter; Frank Böhnke; Werner Hemmert

doi:10.3389/fnins.2019.01312

Electrical Stimulation in the Human Cochlea: A Computational Study Based on High-Resolution Micro-CT Scans

Front Neurosci. 2019 Dec 5:13:1312. doi: 10.3389/fnins.2019.01312. eCollection 2019.

Authors

Siwei Bai^{1

2

3}, Jörg Encke^{1

2

4}, Miguel Obando-Leitón^{1

2

5}, Robin Weiß^{1

2}, Friederike Schäfer², Jakob Eberharter², Frank Böhnke⁶, Werner Hemmert^{1

2

5}

Affiliations

¹ Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany.
² Munich School of Bioengineering, Technical University of Munich, Garching, Germany.
³ Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, Australia.
⁴ Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, Oldenburg, Germany.
⁵ Graduate School of Systemic Neurosciences, Ludwig Maximilian University of Munich, Planegg, Germany.
⁶ Department of Otorhinolaryngology, Klinikum rechts der Isar, Munich, Germany.

Abstract

Background: Many detailed features of the cochlear anatomy have not been included in existing 3D cochlear models, including the microstructures inside the modiolar bone, which in turn determines the path of auditory nerve fibers (ANFs). Method: We captured the intricate modiolar microstructures in a 3D human cochlea model reconstructed from μCT scans. A new algorithm was developed to reconstruct ANFs running through the microstructures within the model. Using the finite element method, we calculated the electrical potential as well as its first and second spatial derivatives along each ANF elicited by the cochlear implant electrodes. Simulation results of electrical potential was validated against intracochlear potential measurements. Comparison was then made with a simplified model without the microstructures within the cochlea. Results: When the stimulus was delivered from an electrode located deeper in the apex, the extent of the auditory nerve influenced by a higher electric potential grew larger; at the same time, the maximal potential value at the auditory nerve also became larger. The electric potential decayed at a faster rate toward the base of the cochlea than toward the apex. Compared to the cochlear model incorporating the modiolar microstructures, the simplified version resulted in relatively small differences in electric potential. However, in terms of the first and second derivatives of electric potential along the fibers, which are relevant for the initiation of action potentials, the two models exhibited large differences: maxima in both derivatives with the detailed model were larger by a factor of 1.5 (first derivative) and 2 (second derivative) in the exemplary fibers. More importantly, these maxima occurred at different locations, and opposite signs were found for the values of second derivatives between the two models at parts along the fibers. Hence, while one model predicts depolarization and spike initiation at a given location, the other may instead predict a hyperpolarization. Conclusions: Although a cochlear model with fewer details seems sufficient for analysing the current spread in the cochlear ducts, a detailed-segmented cochlear model is required for the reconstruction of ANF trajectories through the modiolus, as well as the prediction of firing thresholds and spike initiation sites.

Keywords: auditory nerve fibers; cochlear implant; computational model; electrical stimulation; finite element analysis; model reconstruction.