Mouse, Man, and Machine: Combining Model Systems to Develop a Biomarker for Cochlear Deafferentation in Humans (Administrative Supplement)

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Project Summary Clinical testing for peripheral auditory dysfunction focuses on the audiogram. However, many auditory perceptual deficits, such as tinnitus, hyperacusis, and difficulty with speech perception, cannot be fully explained by the audiogram. Cochlear deafferentation (i.e., loss of inner hair cells, spiral ganglion cells, or cochlear synapses), may contribute to these perceptual problems. However, there is currently no method for diagnosing deafferentation in living humans. This prevents us from determining the prevalence of deafferentation in humans, identifying deafferentation risk factors and perceptual consequences, or testing potential drug treatments. Several non-invasive physiological measures are sensitive to loss of cochlear synapses (a form of deafferentation) in animal models, including the auditory brainstem response (ABR), the envelope following response (EFR), and the middle ear muscle reflex (MEMR). However, it is unclear how cochlear gain loss (e.g., due to outer hair cell damage) impacts the relationship between deafferentation and these physiological measures, hindering translation to a diagnostic test for deafferentation. The overall objective of this proposal is to develop a computational model that can estimate deafferentation from non-invasive physiological measurements in humans with varying degrees of cochlear gain loss. The central hypothesis is that cochlear gain loss can be predicted from distortion product otoacoustic emissions (DPOAEs) and deafferentation can be predicted from a combination of ABR, EFR, and MEMR measurements. This hypothesis will be tested by pursuing four specific aims: 1) Expand a computational model of the auditory periphery (CMAP) to predict ABR, EFR, MEMR, and DPOAE responses in mice and humans based on both cochlear gain and afferent function, 2) Validate and refine the CMAP by collecting physiological and histological data from mouse, 3) Predict deafferentation in individual human subjects from physiological measurements by fitting the CMAP using Bayesian regression, and 4) Evaluate deafferentation predictions for their relationship with risk factors and predicted perceptual consequences of deafferentation. This approach is innovative because it extends prior work to animal and human models with both cochlear gain loss and deafferentation, uses computational modeling to bridge the gap between model systems, and combines multiple physiological measurements to predict deafferentation in individual human subjects. The proposed research is significant because we currently have no means of diagnosing deafferentation. Thus, the prevalence, associated risk factors, and perceptual impacts of this condition are unclear. This project is expected to result in a biomarker of deafferentation for individual patients that is based on their physiological measurements. This will enable us to identify peripheral auditory damage that is independent of cochlear gain loss. If the biomarker is correlated with risk factors such as noise exposure and auditory perceptual deficits such as speech perception difficulty, it will allow for the development of targeted treatments for auditory perceptual deficits and strategies for damage prevention.