Abstract

This study examines the relationship between focused-stimulation thresholds, electrode positions, and speech understanding in deaf subjects treated with a cochlear implant (CI). Focused stimulation is more selective than monopolar stimulation, which excites broad regions of the cochlea, so may be more sensitive as a probe of neural survival patterns. Focused thresholds are on average higher and more variable across electrodes than monopolar thresholds. We presume that relatively high focused thresholds are the result of larger distances between the electrodes and the neurons. Two factors are likely to contribute to this distance: (1) the physical position of electrodes relative to the modiolus, where the excitable auditory neurons are normally located, and (2) the pattern of neural survival along the length of the cochlea, since local holes in the neural population will increase the distance between an electrode and the nearest neurons. Electrode-to-modiolus distance was measured from high-resolution CT scans of the cochleae of CI users whose focused-stimulation thresholds were also measured. A hierarchical set of linear models of electrode-to-modiolus distance versus threshold showed a significant increase in threshold with electrode-to-modiolus distance (average slope = 11 dB/mm). The residual of these models was hypothesized to reflect neural survival in each subject. Consonant-Nucleus-Consonant (CNC) word scores were significantly correlated with the within-subject variance of threshold (r(2) = 0.82), but not with within-subject variance of electrode distance (r(2) = 0.03). Speech understanding also significantly correlated with how well distance explained each subject's threshold data (r(2) = 0.63). That is, subjects with focused thresholds that were well described by electrode position had better speech scores. Our results suggest that speech understanding is highly impacted by individual patterns of neural survival and that these patterns manifest themselves in how well (or poorly) electrode position predicts focused thresholds.

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