TY - JOUR
T1 - Predicting synapse counts in living humans by combining computational models with auditory physiology
AU - Buran, Brad N.
AU - McMillan, Garnett P.
AU - Keshishzadeh, Sarineh
AU - Verhulst, Sarah
AU - Bramhall, Naomi F.
N1 - Publisher Copyright:
© 2021 U.S. Government
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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.
AB - 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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U2 - 10.1121/10.0009238
DO - 10.1121/10.0009238
M3 - Article
C2 - 35105019
AN - SCOPUS:85123759932
SN - 0001-4966
VL - 151
SP - 561
EP - 576
JO - Journal of the Acoustical Society of America
JF - Journal of the Acoustical Society of America
IS - 1
ER -