Related Experiment Video
Updated: Oct 4, 2025
10:31
In Vitro Wedge Slice Preparation for Mimicking In Vivo Neuronal Circuit Connectivity
Published on: August 18, 2020
5.7K
Predicting synapse counts in living humans by combining computational models with auditory physiology.
Brad N Buran1, Garnett P McMillan2, Sarineh Keshishzadeh3
1Oregon Hearing Research Center (OHRC), Department of Otolaryngology-Head & Neck Surgery, Oregon Health & Science University, Portland, Oregon, USA.
The Journal of the Acoustical Society of America
|February 2, 2022
Summary
A new computational model predicts cochlear synapse loss in humans using auditory brainstem response (ABR) and distortion product otoacoustic emissions (DPOAEs). This method links physiological measures to hearing deficits like tinnitus and speech-in-noise difficulties.
Area of Science:
- Auditory Neuroscience
- Computational Biology
- Otoacoustic Emissions
Background:
- Cochlear synapse loss, or synaptopathy, is linked to aging, noise, and ototoxic drugs in animal models.
- Synaptopathy is hypothesized to cause human hearing issues like tinnitus and impaired speech-in-noise perception.
- Current diagnostic methods for human synaptopathy are lacking, hindering research and treatment development.
Purpose of the Study:
- To develop and validate a human computational model for predicting cochlear synapse loss.
- To correlate predicted synaptic loss with physiological measures like auditory brainstem response (ABR) wave I amplitude and distortion product otoacoustic emissions (DPOAEs).
- To investigate the relationship between predicted synapse counts and audiological/perceptual outcomes in humans.
Main Methods:
- Development of a human computational model of the auditory periphery.
- Prediction of auditory brainstem response (ABR) waveforms and distortion product otoacoustic emissions (DPOAEs) using the model.
- Correlation of model-predicted synaptic loss with measured DPOAE levels and ABR wave I amplitudes in human participants.
- Statistical analysis to link predicted synapse counts with age, noise exposure, tinnitus, and speech-in-noise perception.
Main Results:
- The computational model successfully predicted cochlear synapse counts in individual human participants.
- Lower predicted synapse numbers correlated with increased age and greater noise exposure history.
- Reduced synapse counts were associated with a higher likelihood of tinnitus and poorer speech-in-noise understanding.
Conclusions:
- This computational modeling approach provides a viable method for estimating synaptic loss from physiological data in humans.
- The findings support the hypothesis that synaptopathy contributes to age-related hearing decline and noise-induced auditory deficits.
- This model can facilitate future research into the causes, consequences, and treatments of human cochlear synaptopathy.
