Validation of a Cochlear Implant Patient-Specific Model of the Voltage Distribution in a Clinical Setting

Abstract

Cochlear Implants (CIs) are medical implantable devices that can restore the sense of hearing in people with profound hearing loss. Clinical trials assessing speech intelligibility in CI users have found large intersubject variability. One possibility to explain the variability is the individual differences in the interface created between electrodes of the CI and the auditory nerve. In order to understand the variability, models of the voltage distribution of the electrically stimulated cochlea may be useful. With this purpose in mind, we developed a parametric model that can be adapted to each CI user based on landmarks from individual cone beam computed tomography (CBCT) scans of the cochlea before and after implantation. The conductivity values of each cochlea compartment as well as the weighting factors of different grounding modes have also been parameterized. Simulations were performed modeling the cochlea and electrode positions of 12 CI users. Three models were compared with different levels of detail: a homogeneous model (HM), a non-patient-specific model (NPSM), and a patient-specific model (PSM). The model simulations were compared with voltage distribution measurements obtained from the backward telemetry of the 12 CI users. Results show that the PSM produces the lowest error when predicting individual voltage distributions. Given a patient-specific geometry and electrode positions, we show an example on how to optimize the parameters of the model and how to couple it to an auditory nerve model. The model here presented may help to understand speech performance variability and support the development of new sound coding strategies for CIs.

Effect of Channel Interaction on Vocal Cue Perception in Cochlear Implant Users

“…For each nerve fiber, the activation function in the most peripheral nerve was computed as in Eq. 4 from Nogueira et al (2016). The currents delivered to each electrode across time, also known as electrodograms, for…”

“…Finally, the neural excitation patterns, i.e. the neural activity across time, were computed using the nerve fiber model described in Nogueira et al (2016) which is very similar to the one presented by Litvak et al (2007). German tokens were processed with the Sequential, Paired and Triplet sound coding strategies using the same levels of stimulation (threshold and most comfortable levels).…”

Towards a Complete In Silico Assessment of the Outcome of Cochlear Implantation Surgery

“…Although in-silico studies have not been commonly applied into the clinical practice of CI, they have shown their potential to become valuable tools to compute predictive performance of the implantable device [6][7][8][9]. In the past, some authors assessed the resulting electric field accounting for different cochlear implant set-ups, such as stimulation protocols [10][11][12], or neural fiber conditions, contrasting both intact and degenerated fibers [8,13].…”

“…However, these studies did not lead to a complete and automatic computational approach encompassing all stages, from the generation of a highly detailed patient-specific model of the cochlear anatomy to the neural response evaluation. Some previous studies considered volume conduction models from simplified [7,16] and parametric [9] representations of the cochlear anatomy, built from guinea pig cochleae [19], or considered a more detailed geometrical model [20]. However, simplified or generic models limit the insight on CI performance, due to their high dependence on patient-specific factors, pointing out to the need for personalized detailed models [20].…”

Loudness and pitch perception using Dynamically Compensated Virtual Channels

“…A computational model has been designed to make quantitative predictions of loudness and pitch perception using the DC-VC mode (Nogueira et al, 2016). Computational models have been successfully used to investigate multipolar stimulation in the implanted cochlea (e.g.…”