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

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Nerve fibers on the XY (A) and XZ (B) planes for the NPSM model. In total, 9000 nerve fibers were modeled. For better representation, only 900 fibers are shown in the figure. Each fiber is composed by 10 nodes. The most peripheral node (node 10) is represented in green color, nodes 9 and 8 are represented in yellow color, and the rest of nodes are represented in red color.
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Figure 8: Nerve fibers on the XY (A) and XZ (B) planes for the NPSM model. In total, 9000 nerve fibers were modeled. For better representation, only 900 fibers are shown in the figure. Each fiber is composed by 10 nodes. The most peripheral node (node 10) is represented in green color, nodes 9 and 8 are represented in yellow color, and the rest of nodes are represented in red color.

Mentions: A model of the auditory nerve activity was coupled to the voltage distribution model. The model is based on the model of Litvak et al. (2007), and it assumes the following: (1) there exist a finite number of discrete neuronal elements spread out over the cochlear space, (2) these elements have a range of thresholds drawn from a log-normal distribution, (3) the electric field at a given spatial location is obtained from a FEM simulation. The physiology of the auditory nerve fiber was modeled based on a simplified version of Smit et al. (2008). Each nerve fiber is composed by k = 10 sections, nodes 10–6 corresponded to the dendrites, node 5 to the soma, and nodes 4–1 to the axon (Figure 6B). In total, 7000 nerve fibers all along the cochlea were simulated. Figure 8 shows the distribution of the nerve nodes along the cochlea for the NPSM model. The same scaling method used to create the PSM models was used to scale the nerve fibers. The internode distance, however, was kept the same for all models. The voltage distribution is sampled in each nerve section and is denoted as Vi(k), where k denotes the section, and i denotes the nerve fiber. For each nerve fiber, the activation function is computed discretizing the second derivative of the voltage distribution along the nerve axon (Eq. 4). The assumption is that for long homogeneous fiber (i.e., an unmyelinated axon), the neural elements are cylinders of constant diameter and length (Rattay, 1999; Rattay et al., 2001).


Validation of a Cochlear Implant Patient-Specific Model of the Voltage Distribution in a Clinical Setting
Nerve fibers on the XY (A) and XZ (B) planes for the NPSM model. In total, 9000 nerve fibers were modeled. For better representation, only 900 fibers are shown in the figure. Each fiber is composed by 10 nodes. The most peripheral node (node 10) is represented in green color, nodes 9 and 8 are represented in yellow color, and the rest of nodes are represented in red color.
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Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120131&req=5

Figure 8: Nerve fibers on the XY (A) and XZ (B) planes for the NPSM model. In total, 9000 nerve fibers were modeled. For better representation, only 900 fibers are shown in the figure. Each fiber is composed by 10 nodes. The most peripheral node (node 10) is represented in green color, nodes 9 and 8 are represented in yellow color, and the rest of nodes are represented in red color.
Mentions: A model of the auditory nerve activity was coupled to the voltage distribution model. The model is based on the model of Litvak et al. (2007), and it assumes the following: (1) there exist a finite number of discrete neuronal elements spread out over the cochlear space, (2) these elements have a range of thresholds drawn from a log-normal distribution, (3) the electric field at a given spatial location is obtained from a FEM simulation. The physiology of the auditory nerve fiber was modeled based on a simplified version of Smit et al. (2008). Each nerve fiber is composed by k = 10 sections, nodes 10–6 corresponded to the dendrites, node 5 to the soma, and nodes 4–1 to the axon (Figure 6B). In total, 7000 nerve fibers all along the cochlea were simulated. Figure 8 shows the distribution of the nerve nodes along the cochlea for the NPSM model. The same scaling method used to create the PSM models was used to scale the nerve fibers. The internode distance, however, was kept the same for all models. The voltage distribution is sampled in each nerve section and is denoted as Vi(k), where k denotes the section, and i denotes the nerve fiber. For each nerve fiber, the activation function is computed discretizing the second derivative of the voltage distribution along the nerve axon (Eq. 4). The assumption is that for long homogeneous fiber (i.e., an unmyelinated axon), the neural elements are cylinders of constant diameter and length (Rattay, 1999; Rattay et al., 2001).

View Article: PubMed Central - PubMed

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.

No MeSH data available.