In vivo assessment of the optical cochlear implant performance: coding strategy optimization

ended

Hearing loss affects millions of people worldwide. In cases of pronounced cochlear dysfunction, an electrical cochlear implant (eCI) can partially restore hearing sensation by electrically stimulating the auditory nerve. Until now, the eCI is the most successful and broadly used neuroprosthesis, with more than 1 million users worldwide (WHO, 2021). However, eCI hearing is far from normal: eCI users can typically not comprehend speech in noisy environments, because the electrical signal spreads widely and excites a large number of neurons of the auditory nerve, which limits the number of separate perceptual channels. Using optogenetics, it is possible to stimulate the auditory nerve using an optical cochlear implant (oCI). As light spread can be better confined in space, oCIs offer lower spread of excitation and, hence, greater frequency selectivity. This way, future clinical oCIs promise more perceptual channels, allowing for more pitch appreciation and better understanding of speech in noise. However, there are many challenges in the development of the oCI en route to clinical application. Importantly, we are currently missing a holistic assessment tool for preclinical efficacy which could serve oCI optimization. I will develop a set of methodological and computational tools to assess the oCI performance in vivo, in the Mongolian gerbil. First, I will use the brainstem responses to map the frequency activation of separate optical channels. Second, I will develop predictive models that derive the optogenetically and acoustically evoked responses of the midbrain, applying machine learning techniques. Third, I will use the above-mentioned tools to identify the optimal coding strategy for the oCI. This project will accelerate the development of the oCI, and provide benchmarking standards for the clinical trials of optical neural implants.