Back

Persistent impairment of spatial hearing and neural binaural interaction after temporary noise-induced hearing loss

Mackey, C.; Mondul, J. A.; Conner, A. N.; Peacock, J.; Benson, M. A.; Hackett, T. A.; Tollin, D. J.; Ramachandran, R.

2026-01-20 neuroscience
10.64898/2026.01.16.699944 bioRxiv
Show abstract

Many people have trouble understanding speech in noisy environments despite normal audiometric thresholds, a condition referred to as "hidden hearing loss." A leading hypothesis attributes this deficit to inner hair cell synaptic pathology (cochlear synaptopathy), which can persist after recovery from noise-induced temporary elevation of audiometric thresholds. This pathology impairs the temporally precise sound encoding necessary for spatial hearing, but direct evidence in primates, human or nonhuman, is lacking. Here, we show that a single noise exposure, producing only a temporary threshold elevation, induced long-lasting alterations in synaptic morphology, without synapse loss, resulting in persistent spatial hearing deficits for up to 11 months post-exposure in nonhuman primates of both sexes. These perceptual deficits were accompanied by a reduction in a neurophysiological measure of binaural processing - the binaural interaction component (BIC) of the auditory brainstem response (ABR). Both behavioral and neural deficits persisted despite full recovery of audiometric thresholds. Together, these findings provide the first evidence in primates that noise exposure disrupts spatial hearing and binaural neural circuit function without loss of hair cells or synapses. Because spatial hearing tests and the ABR/BIC are clinically accessible, this work also establishes translational biomarkers for early neural dysfunction underlying hidden hearing loss.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.