Using Neurogram Similarity Index Measure (NSIM) to Model Hearing Loss and Cochlear Neural Degeneration

TL;DR

This paper introduces NSIM, a computational method to quantify hearing loss and cochlear neural degeneration by comparing auditory nerve responses, potentially aiding diagnosis and understanding of hearing impairments.

Contribution

The study presents a novel objective measure, NSIM, for modeling hearing loss and cochlear neural degeneration using auditory nerve fiber simulations.

Findings

NSIM accurately maps phoneme recognition performance in hearing-impaired individuals.

NSIM detects deficits caused by cochlear neural degeneration.

NSIM shows potential as a noninvasive biomarker for auditory synaptopathy.

Abstract

Trouble hearing in noisy situations remains a common complaint for both individuals with hearing loss and individuals with normal hearing. This is hypothesized to arise due to condition called: cochlear neural degeneration (CND) which can also result in significant variabilities in hearing aids outcomes. This paper uses computational models of auditory periphery to simulate various hearing tasks. We present an objective method to quantify hearing loss and CND by comparing auditory nerve fiber responses using a Neurogram Similarity Index Measure (NSIM). Specifically study 1, shows that NSIM can be used to map performance of individuals with hearing loss on phoneme recognition task with reasonable accuracy. In the study 2, we show that NSIM is a sensitive measure that can also be used to capture the deficits resulting from CND and can be a candidate for noninvasive biomarker of auditory synaptopathy.