A computational approach to optimal deactivation of cochlear implant electrodes

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PROJECT SUMMARY Cochlear implantation has successfully restored hearing to hundreds of thousands of individuals worldwide. However, speech understanding performance with CIs remains highly variable. Despite this variability in outcomes, CIs are typically programmed with default settings with only a few subject-specific programming parameters adjusted. This likely occurs because too many possible CI device setting combinations are available and data-driven guidance to achieve maximal benefit for an individual CI user is lacking. For example, most CIs are programmed with all viable electrodes active even though some electrodes may be poorly suited to convey speech information. Previous studies evaluated whether selective deactivation of some of these electrodes improved speech understanding, with limited success. However, these studies did not consider how speech information is represented across remaining electrodes, or the optimal number of electrodes to deactivate. The goal of the present study is to use computationally driven models of speech understanding in CI users to guide the search for which combination of active electrodes can yield the best speech understanding for a specific patient. Aim 1 is to quantify speech understanding and sound quality with model-recommended combinations of active electrodes compared to clinical standard-of-care settings, and compared to two control active electrode combinations. These alternative conditions will use the same number of electrodes as the model- recommended condition, but with electrodes selected in a similar way as previous studies. Subjects will have 1.5 months of regular use with each experimental active electrode conditions. Performance with experimental and clinical active electrode conditions will be compared using a repeated-measures design. It is hypothesized that the model-recommended condition will result in significantly better speech understanding than the other conditions. Aim 2 is to translate the model-driven recommendations from Aim 1 into practical guidance about how many (and possibly which) CI electrodes to deactivate. Subjects’ performance with the experimental and clinical active electrode conditions of Aim 1 will be used to build a hierarchical linear model. This model will relate performance with experimental active electrode settings to the following independent variables: number of active electrodes selected for subjects by the model, physical span of active electrodes, subjects’ speech- cue resolution with each experimental condition, and three demographic variables. It is hypothesized that subjects’ performance will be more strongly correlated with the number of active electrodes and subjects’ speech-cue resolution, and moderately correlated to physical span of active electrodes. Data obtained under the auspices of the current proposal will be foundational for larger studies to provide data- driven guidance for optimal fitting of CI devices.