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

Abstract

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.

Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech

“…However, the model is not fully accurate and did not predict the remaining talker differences after matching f0 or the latency changes in click ABRs with increasing rate. These limitations of the computational model are consistent with other studies that also found appropriate predictions of amplitude but not latency changes with stimulus level changes in click ABRs (e.g., Dau, 2003;Temboury-Gutierrez et al, 2024). They suggested that the limitations could be due to an oversimplification of neural processing in the brainstem model, which assumes constant delays through the brainstem that may not be accurate for broadband or complex stimuli.…”

“…Computational modeling has been previously used to simulate ABRs and explore stimulus differences (e.g., Dau, 2003;Stoll and Maddox, 2023;Temboury-Gutierrez et al, 2024). Average ABRs (areas show ± SE) measured in previous work (left, n = 11) and the current study (right, n = 15) are larger and earlier for male-than female-narrated speech.…”