Applicability of subcortical EEG metrics of synaptopathy to older listeners with impaired audiograms

Emerging evidence suggests that cochlear synaptopathy is a common feature of sensorineural hearing loss, but it is not known to what extent electrophysiological metrics targeting synaptopathy in animals can be applied to people, such as those with impaired audiograms. This study investigates the applicability of subcortical electrophysiological measures associated with synaptopathy, i.e., auditory brainstem responses (ABRs) and envelope following responses (EFRs), to older participants with high-frequency sloping audiograms. The outcomes of this study are important for the development of reliable and sensitive synaptopathy diagnostics in people with normal or impaired outer-hair-cell function. Click-ABRs at different sound pressure levels and EFRs to amplitude-modulated stimuli were recorded, as well as relative EFR and ABR metrics which reduce the influence of individual factors such as head size and noise floor level on the measures. Most tested metrics showed significant differences between the groups and did not always follow the trends expected from synaptopathy. Age was not a reliable predictor for the electrophysiological metrics in the older hearing-impaired group or young normal-hearing control group. This study contributes to a better understanding of how electrophysiological synaptopathy metrics differ in ears with healthy and impaired audiograms, which is an important first step towards unravelling the perceptual consequences of synaptopathy.(C) 2019 Elsevier B.V. All rights reserved

Relating the variability of tone-burst otoacoustic emission and auditory brainstem response latencies to the underlying cochlear mechanics

Forward and reverse cochlear latency and its relation to the frequency tuning of the auditory filters can be assessed using tone bursts (TBs). Otoacoustic emissions (TBOAEs) estimate the cochlear roundtrip time, while auditory brainstem responses (ABRs) to the same stimuli aim at measuring the auditory filter buildup time. Latency ratios are generally close to two and controversy exists about the relationship of this ratio to cochlear mechanics. We explored why the two methods provide different estimates of filter buildup time, and ratios with large inter-subject variability, using a time-domain model for OAEs and ABRs. We compared latencies for twenty models, in which all parameters but the cochlear irregularities responsible for reflection-source OAEs were identical, and found that TBOAE latencies were much more variable than ABR latencies. Multiple reflection-sources generated within the evoking stimulus bandwidth were found to shape the TBOAE envelope and complicate the interpretation of TBOAE latency and TBOAE/ABR ratios in terms of auditory filter tuning

Acquisition of subcortical auditory potentials with around-the-Ear cEEGrid technology in normal and hearing impaired listeners

Even though the principles of recording brain electrical activity remain unchanged since their discovery, their acquisition has seen major improvements. The cEEGrid, a recently developed flex-printed multi-channel sensory array, can be placed around the ear and successfully record well-known cortical electrophysiological potentials such as late auditory evoked potentials (AEPs) or the P300. Due to its fast and easy application as well as its long-lasting signal recording window, the cEEGrid technology offers great potential as a flexible and 'wearable' solution for the acquisition of neural correlates of hearing. Early potentials of auditory processing such as the auditory brainstem response (ABR) are already used in clinical assessment of sensorineural hearing disorders and envelope following responses (EFR) have shown promising results in the diagnosis of suprathreshold hearing deficits. This study evaluates the suitability of the cEEGrid electrode configuration to capture these AEPs. cEEGrid potentials were recorded and compared to cap-EEG potentials for young normal-hearing listeners and older listeners with high-frequency sloping audiograms to assess whether the recordings are adequately sensitive for hearing diagnostics. ABRs were elicited by presenting clicks (70 and 100-dB peSPL) and stimulation for the EFRs consisted of 120 Hz amplitudemodulated white noise carriers presented at 70-dB SPL. Data from nine bipolar cEEGrid channels and one classical cap-EEG montage (earlobes to vertex) were analysed and outcome measures were compared. Results show that the cEEGrid is able to record ABRs and EFRs with comparable shape to those recorded using a conventional capEEG recording montage and the same amplifier. Signal strength is lower but can still produce responses above the individual neural electrophysiological noise floor. This study shows that the application of the cEEGrid can be extended to the acquisition of early auditory evoked potentials

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 highfrequency cochlear gain loss steepens the slope of the ABRWave-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 interwaveI-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 convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications

Auditory models are commonly used as feature extractors for automatic speech-recognition systems or as front-ends for robotics, machine-hearing and hearing-aid applications. Although auditory models can capture the biophysical and nonlinear properties of human hearing in great detail, these biophysical models are computationally expensive and cannot be used in real-time applications. We present a hybrid approach where convolutional neural networks are combined with computational neuroscience to yield a real-time end-to-end model for human cochlear mechanics, including level-dependent filter tuning (CoNNear). The CoNNear model was trained on acoustic speech material and its performance and applicability were evaluated using (unseen) sound stimuli commonly employed in cochlear mechanics research. The CoNNear model accurately simulates human cochlear frequency selectivity and its dependence on sound intensity, an essential quality for robust speech intelligibility at negative speech-to-background-noise ratios. The CoNNear architecture is based on parallel and differentiable computations and has the power to achieve real-time human performance. These unique CoNNear features will enable the next generation of human-like machine-hearing applications