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Predicting synapse counts in living humans by combining computational models with auditory physiology
Aging, noise exposure, and ototoxic medications lead to cochlear synapse loss in animal models. As cochlear function is highly conserved across mammalian species, synaptopathy likely occurs in humans as well. Synaptopathy is predicted to result in perceptual deficits including tinnitus, hyperacusis,...
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Published in: | The Journal of the Acoustical Society of America 2022-01, Vol.151 (1), p.561-576 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Aging, noise exposure, and ototoxic medications lead to cochlear synapse loss in animal
models. As cochlear function is highly conserved across mammalian species, synaptopathy
likely occurs in humans as well. Synaptopathy is predicted to result in perceptual
deficits including tinnitus, hyperacusis, and difficulty understanding speech-in-noise.
The lack of a method for diagnosing synaptopathy in living humans hinders studies designed
to determine if noise-induced synaptopathy occurs in humans, identify the perceptual
consequences of synaptopathy, or test potential drug treatments. Several physiological
measures are sensitive to synaptopathy in animal models including auditory brainstem
response (ABR) wave I amplitude. However, it is unclear how to translate these measures to
synaptopathy diagnosis in humans. This work demonstrates how a human computational model
of the auditory periphery, which can predict ABR waveforms and distortion product
otoacoustic emissions (DPOAEs), can be used to predict synaptic loss in individual human
participants based on their measured DPOAE levels and ABR wave I amplitudes. Lower
predicted synapse numbers were associated with advancing age, higher noise exposure
history, increased likelihood of tinnitus, and poorer speech-in-noise perception. These
findings demonstrate the utility of this modeling approach in predicting synapse counts
from physiological data in individual human subjects. |
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ISSN: | 0001-4966 1520-8524 |
DOI: | 10.1121/10.0009238 |