Model-based hearing-enhancement strategies for cochlear synaptopathy pathologies

Predicting early auditory evoked potentials using a computational model of auditory-nerve processing

Non-invasive electrophysiological measures, such as auditory evoked potentials (AEPs), play a crucial role in diagnosing auditory pathology. However, the relationship between AEP morphology and cochlear degeneration remains complex and not well understood. Dau [J. Acoust. Soc. Am. 113, 936-950 (2003)] proposed a computational framework for modeling AEPs that utilized a nonlinear auditory-nerve (AN) model followed by a linear unitary response function. While the model captured some important features of the measured AEPs, it also exhibited several discrepancies in response patterns compared to the actual measurements. In this study, an enhanced AEP modeling framework is presented, incorporating an improved AN model, and the conclusions from the original study were reevaluated. Simulation results with transient and sustained stimuli demonstrated accurate auditory brainstem responses (ABRs) and frequency-following responses (FFRs) as a function of stimulation level, although wave-V latencies remained too short, similar to the original study. When compared to physiological responses in animals, the revised model framework showed a more accurate balance between the contributions of auditory-nerve fibers (ANFs) at on- and off-frequency regions to the predicted FFRs. These findings emphasize the importance of cochlear processing in brainstem potentials. This framework may provide a valuable tool for assessing human AN models and simulating AEPs for various subtypes of peripheral pathologies, offering opportunities for research and clinical applications.

Computational modeling of the human compound action potential

The auditory nerve (AN) compound action potential (CAP) is an important tool for assessing auditory disorders and monitoring the health of the auditory periphery during surgical procedures. The CAP has been mathematically conceptualized as the convolution of a unit response (UR) waveform with the firing rate of a population of AN fibers. Here, an approach for predicting experimentally recorded CAPs in humans is proposed, which involves the use of human-based computational models to simulate AN activity. CAPs elicited by clicks, chirps, and amplitude-modulated carriers were simulated and compared with empirically recorded CAPs from human subjects. In addition, narrowband CAPs derived from noise-masked clicks and tone bursts were simulated. Many morphological, temporal, and spectral aspects of human CAPs were captured by the simulations for all stimuli tested. These findings support the use of model simulations of the human CAP to refine existing human-based models of the auditory periphery, aid in the design and analysis of auditory experiments, and predict the effects of hearing loss, synaptopathy, and other auditory disorders on the human CAP.