Age-related hearing loss (ARHL) is the predominant neurodegenerative disease of aging. Recent animal studies have demonstrated that the earliest cochlear pathology due to noise and aging involves a loss of the inner hair cell ribbon synapse. The early consequences of this synaptic loss are not believed to affect hearing sensitivity, but rather, impair speech understanding in background noise. While this condition, called cochlear synaptopathy or hidden hearing loss, can be detected in animal models by a reduction of suprathreshold sound evoked wave-1 amplitude of auditory brainstem response (ABR) and confirmed by post-mortem quantitative histology, its diagnosis in humans remains challenging. Several non-invasive electrophysiological methods such as ABR and electrocochleography (ECochG) have been tested to detect human cochlear synaptopathy. Two major obstacles to their proposed application include: (1) a high variability of ABR/ECochG wave amplitudes, and (2) a lack of direct histological validation for living humans. My strategy here is to develop a new electrical device able to generate a calibration pulse to reduce high variability in ABR/ECochG wave metrics. The validation issue will be addressed by applying machine learning to identify multiple ABR/ECochG markers associated with cochlear synaptopathy first in animal models, in which cochlear synaptopathy can be directly validated by histologic synaptic counting, and then apply these identified features for human diagnosis. My hypothesis is that clinical diagnosis of human cochlear synaptopathy can be achieved by improving both hardware and software related to ABR/ECochG data collection and analysis. Based on our preliminary studies, we will continue to improve our electrical circuit to test if our calibration pulse device can address the variability issue, and at the same time, we will identify ABR/ECochG features associated with age-related cochlear synaptopathy in mice and then validate these identified features in gerbils since its hearing range is similar to humans. Finally, I will perform a longitudinal study to test identified features in humans during aging. Early detection of cochlear synaptopathy may lead to effective preventive strategies that would delay or potentially prevent further development of ARHL. Completion of this project is therefore expected to lead to a major shift in current clinical diagnosis of ARHL, while providing a unique training opportunity for the principal investigator to develop skills in developing commercial medical devices.
Public Health Relevance Statement: Age-related hearing loss or presbycusis is the most prevalent neurodegenerative disease and number one communication disorder of our aged population; and affects hundreds of millions of people worldwide. Here, I will develop a clinical diagnosis tool to detect age-related cochlear synaptic loss. Through this funding, I will also receive training in medical device development and commercialization and become an industry leader in this field.
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