Sarah Verhulst

Auditory Brainstem Response Latency in Noise as a Marker of Cochlear Synaptopathy

Evidence from animal and human studies suggests that moderate acoustic exposure, causing only transient threshold elevation, can nonetheless cause “hidden hearing loss” that interferes with coding of suprathreshold sound. Such noise exposure destroys synaptic connections between cochlear hair cells and auditory nerve fibers; however, there is no clinical test of this synaptopathy in humans. In animals, synaptopathy reduces the amplitude of auditory brainstem response (ABR) wave-I. Unfortunately, ABR wave-I is difficult to measure in humans, limiting its clinical use. Here, using analogous measurements in humans and mice, we show that the effect of masking noise on the latency of the more robust ABR wave-V mirrors changes in ABR wave-I amplitude. Furthermore, in our human cohort, the effect of noise on wave-V latency predicts perceptual temporal sensitivity. Our results suggest that measures of the effects of noise on ABR wave-V latency can be used to diagnose cochlear synaptopathy in humans. SIGNIFICANCE STATEMENT Although there are suspicions that cochlear synaptopathy affects humans with normal hearing thresholds, no one has yet reported a clinical measure that is a reliable marker of such loss. By combining human and animal data, we demonstrate that the latency of auditory brainstem response wave-V in noise reflects auditory nerve loss. This is the first study of human listeners with normal hearing thresholds that links individual differences observed in behavior and auditory brainstem response timing to cochlear synaptopathy. These results can guide development of a clinical test to reveal this previously unknown form of noise-induced hearing loss in humans.

Individual Differences in Auditory Brainstem Response Wave Characteristics: Relations to Different Aspects of Peripheral Hearing Loss.

Little is known about how outer hair cell loss interacts with noise-induced and age-related auditory nerve degradation (i.e., cochlear synaptopathy) to affect auditory brainstem response (ABR) wave characteristics. Given that listeners with impaired audiograms likely suffer from mixtures of these hearing deficits and that ABR amplitudes have successfully been used to isolate synaptopathy in listeners with normal audiograms, an improved understanding of how different hearing pathologies affect the ABR source generators will improve their sensitivity in hearing diagnostics. We employed a functional model for human ABRs in which different combinations of hearing deficits were simulated and show that high-frequency cochlear gain loss steepens the slope of the ABR Wave-V latency versus intensity and amplitude versus intensity curves. We propose that grouping listeners according to a ratio of these slope metrics (i.e., the ABR growth ratio) might offer a way to factor out the outer hair cell loss deficit and maximally relate individual differences for constant ratios to other peripheral hearing deficits such as cochlear synaptopathy. We compared the model predictions to recorded click-ABRs from 30 participants with normal or high-frequency sloping audiograms and confirm the predicted relationship between the ABR latency growth curve and audiogram slope. Experimental ABR amplitude growth showed large individual differences and was compared with the Wave-I amplitude, Wave-V/I ratio, or the interwave I – W latency in the same listeners. The model simulations along with the ABR recordings suggest that a hearing loss profile depicting the ABR growth ratio versus the Wave-I amplitude or Wave-V/I ratio might be able to differentiate outer hair cell deficits from cochlear synaptopathy in listeners with mixed pathologies.

A comparative study of seven human cochlear filter models

Auditory models have been developed for decades to simulate characteristics of the human auditory system, but it is often unknown how well auditory models compare to each other or perform in tasks they were not primarily designed for. This study systematically analyzes predictions of seven publicly-available cochlear filter models in response to a fixed set of stimuli to assess their capabilities of reproducing key aspects of human cochlear mechanics. The following features were assessed at frequencies of 0.5, 1, 2, 4, and 8u2009kHz: cochlear excitation patterns, nonlinear response growth, frequency selectivity, group delays, signal-in-noise processing, and amplitude modulation representation. For each task, the simulations were compared to available physiological data recorded in guinea pigs and gerbils as well as to human psychoacoustics data. The presented results provide application-oriented users with comprehensive information on the advantages, limitations and computation costs of these seven mainstream cochlear filter models.

Model-based estimation of the frequency tuning of the inner-hair-cell stereocilia from neural tuning curves

This study proposes that the frequency tuning of the inner-hair-cell (IHC) stereocilia in the intact organ of Corti can be derived from the responses of the auditory fibers (AFs) using computational tools. The frequency-dependent relationship between the AF threshold and the amplitude of the stereocilia vibration is estimated using a model of the IHC-mediated mechanical to neural transduction. Depending on the response properties of the considered AF, the amplitude of stereocilia deflection required to drive the simulated AF above threshold is 1.4 to 9.2u2009dB smaller at low frequencies (≤500u2009Hz) than at high frequencies (≥4 kHz). The estimated frequency-dependent relationship between ciliary deflection and neural threshold is employed to derive constant-stereocilia-deflection contours from previously published AF recordings from the chinchilla cochlea. This analysis shows that the transduction process partially accounts for the observed differences between the tuning of the basilar membrane and that of the AFs.

Computational modeling of the human auditory periphery: auditory-nerve responses, evoked potentials and hearing loss

&NA; Models of the human auditory periphery range from very basic functional descriptions of auditory filtering to detailed computational models of cochlear mechanics, inner‐hair cell (IHC), auditory‐nerve (AN) and brainstem signal processing. It is challenging to include detailed physiological descriptions of cellular components into human auditory models because single‐cell data stems from invasive animal recordings while human reference data only exists in the form of population responses (e.g., otoacoustic emissions, auditory evoked potentials). To embed physiological models within a comprehensive human auditory periphery framework, it is important to capitalize on the success of basic functional models of hearing and render their descriptions more biophysical where possible. At the same time, comprehensive models should capture a variety of key auditory features, rather than fitting their parameters to a single reference dataset. In this study, we review and improve existing models of the IHC‐AN complex by updating their equations and expressing their fitting parameters into biophysical quantities. The quality of the model framework for human auditory processing is evaluated using recorded auditory brainstem response (ABR) and envelope‐following response (EFR) reference data from normal and hearing‐impaired listeners. We present a model with 12 fitting parameters from the cochlea to the brainstem that can be rendered hearing impaired to simulate how cochlear gain loss and synaptopathy affect human population responses. The model description forms a compromise between capturing well‐described single‐unit IHC and AN properties and human population response features. HighlightsAn overview of computational models from cochlea to auditory‐nerve (AN).IHC‐AN model descriptions are made biophysical to reduce model fitting parameters.The presented auditory model captures key aspects of human OAE, ABR and EFRs.Simulated impact of sensorineural hearing loss on human population responses.

Remote Sensing the Cochlea: Otoacoustics

The ear is a remarkable detector. It is both highly sensitive and selective and operates over a large dynamic range spanning more than 12 orders of magnitude of energy. Perhaps surprisingly, not only does it respond to sound but emits it as well. These sounds, known as otoacoustic emissions (OAEs), provide a means to probe the fundamental biophysics underlying transduction and amplification in the ear. This chapter outlines the theoretical considerations describing the underlying biomechanics of OAE generation, highlights the various uses of OAEs (both scientific and clinical), including comparative approaches, and motivates open questions.

Individual differences in hearing-impaired data: Stats, troubles, and approaches

Individual differences in hearing ability might be dominated by subcomponents of hearing loss, e.g., cochlear gain loss, cochlear neuropathy, temporal coding deficits in low/high frequency regions, or combinations of these components. Unfortunately, we can only rely on indirect and hypothesis-driven objective (e.g., OAE/ABR/EFR) and psychoacoustic threshold metrics that aim to quantify these subcomponents of hearing loss, complicating a straightforward explanation of study results. Because correlations statistics often rely on small listener groups in which each data point could have resulted from different SNRs, metric-specific variability, it is not always clear which correlations are significant and meaningful. Additionally, multiple measures provide a multitude of correlations that should all support the common underlying hypothesis before conclusions can be drawn. In this tutorial, I provide some examples and approaches to more (and less) meaningful correlations based on recently collected objective ...

On the Interplay Between Cochlear Gain Loss and Temporal Envelope Coding Deficits

Hearing impairment is characterized by two potentially coexisting sensorineural components: (i) cochlear gain loss that yields wider auditory filters, elevated hearing thresholds and compression loss, and (ii) cochlear neuropathy, a noise-induced component of hearing loss that may impact temporal coding fidelity of supra-threshold sound. This study uses a psychoacoustic amplitude modulation (AM) detection task in quiet and multiple noise backgrounds to test whether these aspects of hearing loss can be isolated in listeners with normal to mildly impaired hearing ability. Psychoacoustic results were compared to distortion-product otoacoustic emission (DPOAE) thresholds and envelope-following response (EFR) measures. AM thresholds to pure-tone carriers (4 kHz) in normal-hearing listeners depended on temporal coding fidelity. AM thresholds in hearing-impaired listeners were normal, indicating that reduced cochlear gain may counteract how reduced temporal coding fidelity degrades AM thresholds. The amount with which a 1-octave wide masking noise worsened AM detection was inversely correlated to DPOAE thresholds. The narrowband noise masker was shown to impact the hearing-impaired listeners more so than the normal hearing listeners, suggesting that this masker may be targeting a temporal coding deficit. This study offers a window into how psychoacoustic difference measures can be adopted in the differential diagnostics of hearing deficits in listeners with mixed forms of sensorineural hearing loss.

Contributions of Low- and High-Frequency Sensorineural Hearing Deficits to Speech Intelligibility in Noise

The degree to which supra-threshold hearing deficits affect speech recognition in noise is poorly understood. To clarify the role of hearing sensitivity in different stimulus frequency ranges, and to test the contribution of low- and high-pass speech information to broadband speech recognition, we collected speech reception threshold (SRTs) for low-pass (LP < 1.5 kHz), high-pass (HP > 1.6 kHz) and broadband (BB) speech-in-noise stimuli in 34 listeners. Two noise types with similar long-term spectra were considered: stationary (SSN) and temporally modulated noise (ICRA5-250). Irrespective of the tested listener group (i.e., young normal-hearing, older normal- or impaired-hearing), the BB SRT performance was strongly related to the LP SRT. The encoding of LP speech information was different for SSN and ICRA5-250 noise but similar for HP speech, suggesting a single noise-type invariant coding mechanism for HP speech. Masking release was observed for all filtered conditions and related to the ICRA5-250 SRT. Lastly, the role of hearing sensitivity to the SRT was studied using the speech intelligibility index (SII), which failed to predict the SRTs for the filtered speech conditions and for the older normal-hearing listeners. This suggests that supra-threshold hearing deficits are important contributors to the SRT of older normal-hearing listeners.

The effect of the inner-hair-cell mediated transduction on the shape of neural tuning curves

The inner hair cells of the mammalian cochlea transform the vibrations of their stereocilia into releases of neurotransmitter at the ribbon synapses, thereby controlling the activity of the afferent auditory fibers. The mechanical-to-neural transduction is a highly nonlinear process and it introduces differences between the frequency-tuning of the stereocilia and that of the afferent fibers. Using a computational model of the inner hair cell that is based on in vitro data, we estimated that smaller vibrations of the stereocilia are necessary to drive the afferent fibers above threshold at low (≤0.5u2005kHz) than at high (≥4u2005kHz) driving frequencies. In the base of the cochlea, the transduction process affects the low-frequency tails of neural tuning curves. In particular, it introduces differences between the frequency-tuning of the stereocilia and that of the auditory fibers resembling those between basilar membrane velocity and auditory fibers tuning curves in the chinchilla base. For units with a characteristic frequency between 1 and 4u2005kHz, the transduction process yields shallower neural than stereocilia tuning curves as the characteristic frequency decreases. This study proposes that transduction contributes to the progressive broadening of neural tuning curves from the base to the apex.