Assessment of cochlear synaptopathy by electrocochleography to low frequencies in a preclinical model and human subjects

Raymond A. Haggerty, Kendall A. Hutson, William J. Riggs, Kevin D. Brown, Harold C. Pillsbury, Oliver F. Adunka, Craig A. Buchman, Douglas C. Fitzpatrick

Research output: Contribution to journalArticlepeer-review

Abstract

Cochlear synaptopathy is the loss of synapses between the inner hair cells and the auditory nerve despite survival of sensory hair cells. The findings of extensive cochlear synaptopathy in animals after moderate noise exposures challenged the long-held view that hair cells are the cochlear elements most sensitive to insults that lead to hearing loss. However, cochlear synaptopathy has been difficult to identify in humans. We applied novel algorithms to determine hair cell and neural contributions to electrocochleographic (ECochG) recordings from the round window of animal and human subjects. Gerbils with normal hearing provided training and test sets for a deep learning algorithm to detect the presence of neural responses to low frequency sounds, and an analytic model was used to quantify the proportion of neural and hair cell contributions to the ECochG response. The capacity to detect cochlear synaptopathy was validated in normal hearing and noise-exposed animals by using neurotoxins to reduce or eliminate the neural contributions. When the analytical methods were applied to human surgical subjects with access to the round window, the neural contribution resembled the partial cochlear synaptopathy present after neurotoxin application in animals. This result demonstrates the presence of viable hair cells not connected to auditory nerve fibers in human subjects with substantial hearing loss and indicates that efforts to regenerate nerve fibers may find a ready cochlear substrate for innervation and resumption of function.

Original languageEnglish
Article number1104574
JournalFrontiers in Neurology
Volume14
DOIs
StatePublished - 2023

Keywords

  • auditory nerve
  • cochlear microphonic
  • deep learning
  • electrocochleography
  • hair cells

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