Intracochlear electric potential measurements for estimating electrode array position in cochlear implantation: The...

Stimulation of cochlear implant electrodes generates intracochlear electric potentials. The local electric potentials can be assessed using e.g. transimpedance matrix (TIM) and four-point impedance (Z_fp). Both of these measurements are dependent on the cochlear dimensions and the distance between the electrode and the medial wall of the scala tympani (d_EM). In a recent temporal bone study, a model based on electric potential measurements gave good predictions of scalar cross-sectional area (A_scala) and d_EM. The purpose of this study was to further improve this model and evaluate its clinical usefulness. To this end, the intraoperative TIM and Z_fp measurements from cochlear implant patients were used as independent variables in the model to predict their A_scala and d_EM at each electrode contact, which were then compared to those measured from postoperative cone-beam computed tomography (CBCT) images.

In an earlier study, six cadaveric temporal bones were sequentially implanted with three different electrode arrays: a lateral-wall electrode array, Slim Straight, and two precurved perimodiolar electrode arrays, Contour Advance and Slim Modiolar (Cochlear Ltd, Sydney, Australia). The TIM and Z_fp measurements were performed alongside CBCT imaging, from which the A_scala and d_EM at each electrode contact were measured. From the TIM measurements, the peak amplitudes and decay rate of the electric potentials (EP_slope) were computed. In this follow-up study, the statistical modeling of the ex vivo measurements was refined to better account for individual characteristics by employing mixed-effect models to predict the A_scalas and d_EMs. Then, in vivo recordings from thirteen patients, of which six were implanted with the Slim Straight and seven with the Contour Advance electrode arrays, were retrospectively analyzed. The A_scalas and d_EMs were measured from their postoperative CBCT images in a similar manner to the temporal bones. To validate the mixed-effects models developed with the temporal bone data, the patients' intraoperative TIM and Z_fp measurements were used as independent parameters in the models to predict their A_scalas and d_EMs. Finally, the TIM and Z_fp parameters measured in vivo and ex vivo and the measured and predicted A_scalas and d_EMs were compared using t-tests. Also, Pearson's correlation coefficients were computed between the measured and predicted in vivo A_scalas and d_EMs.

Both the amplitudes, indicating electric potential peaks, and Z_fps, reflecting local potential differences, were lower in vivo than ex vivo (790 vs. 1090 Ω and 253 vs. 270 Ω, respectively, p < 0.001 for both), but no differences were detected in the decay of the electric potentials. In addition, the in vivo Z_fps were lower, and the electric potential decay was slower with the lateral wall (Slim Straight) compared to perimodiolar (Contour Advance) array (234 vs. 284 Ω and -94 vs. -160 Ω/mm, p < 0.001 for both). The mixed effects models with and without random effects explained 73 % and 51 % of the variance, respectively, for A_scala. The mean absolute error between measured and predicted A_scalas was 12 %. For d_EMs, the corresponding percentages were 65 %, 50 %, and 51 %. The correlations between the patients' measured and predicted A_scalas and d_EMs were r = 0.60 and r = 0.48 (p < 0.001, for both). When compared in the basal, middle, and apical sections, the predicted A_scalas differed significantly from the measured values only in the middle section of the array (4.0 ± 0.48 mm vs 3.50 ± 0.36 mm, p < 0.001). For d_EMs, the model gave too large estimates in the apical section of the array (1.04 ± 0.49 mm vs. 1.52 ± 0.48, p < 0.001).

The A_scala at each electrode contact can be estimated using the TIM and Z_fp measurements, which may help verify the correct alignment of the electrode array during the surgery. While the measured and predicted d_EMs correlated with each other, there were significant differences between their absolute values. Given the large variation in d_EMs for different array types, electrode-specific d_EMs models could improve the accuracy of the predictions.