P307 A Computational Model to Study Effects of Hidden Hearing Loss in Noisy Environments
Siddhant Tripathy*1, Maral Budak2, Ross Maddox3, Gabriel Corfas3, Michael T. Roberts3, Anahita H. Mehta3, Victoria Booth4, Michal Zochowski1,5
1Department of Physics, University of Michigan, Ann Arbor, USA
2Department of Microbiology and Immunology, University of Michigan, Ann Arbor, USA
3Kresge Hearing Research Institute, University of Michigan, Ann Arbor, USA
4Department of Mathematics, University of Michigan, Ann Arbor, USA
5Biophysics Program, University of Michigan, Ann Arbor, USA
*Email: tripps@umich.edu
Introduction
Hidden Hearing Loss (HHL) is an auditory neuropathy leading to reduced speech intelligibility in noisy environments despite normal audiometric thresholds. One of the leading hypotheses for such degraded performance is myelinopathy, a permanent disruption in the myelination patterns of type 1 Spiral Ganglion Neuron (SGN) fibers [1,2]. Previous studies on location discriminability in the Medial Superior Olive (MSO) cells in the left and right hemispheres as a function of the interaural time difference (ITD), have shown that myelinopathy leads to signatures of HHL [3]. However, the effects of noise on location discriminability is unknown.
Methods
To investigate these effects, we developed a physiologically based model that incorporates SGN fiber activity to sound stimuli processed through a peripheral auditory system model [4]. To simulate myelinopathy, we introduced random variations in the position of myelination heminodes, which generates phase shifts in the spike timing of affected fibers. To test the subsequent effects on sound localization, we constructed a network model that simulates the propagation of SGN responses to cochlear nuclei and the MSO populations. We varied the location of the sound impulse by introducing a phase shift in the input in one ear relative to the other, with background noise signals kept stationary.
Results
Upon adding noise to the sound stimuli, we find that spikes in a given SGN fiber's spike train are shifted inhomogeneously, leading to a reduction in phase locking of single fibers to sound. The effects of myelinopathy on population behavior are thus more pronounced in the presence of noise. Subsequently in the localization network, we find that the sensitivity to ITD is reduced in myelinopathy conditions, and that this effect is significantly exacerbated when we introduce noisy background stimuli, a signature of HHL.
Discussion
We find that noisy environments exacerbate HHL symptoms. This model may be useful in understanding the downstream impacts of SGN neuropathies.
Acknowledgements
This research was supported in part by National Institute of Health grant: NIH MH135565 (MZ and ST) and R01DC000188 (GC).R01DC000188
References
● https://doi.org/10.1038/ncomms14487
● Budak, M., Grosh, K., Sasmal, A., Corfas, G., Zochowski, M., and Booth, V. (2021). Contrasting mechanisms for hidden hearing loss: Synaptopathy vs myelin defects. PLoS Comput. Biol. 17:e1008499. doi: 10.1371/journal.pcbi.1008499
● Budak, M., Roberts, M. T., Grosh, K., Corfas, G., Booth, V. and Zochowski, M. (2022). Binaural Processing Deficits Due to Synaptopathy and Myelin Defects. Front. Neural Circuits 16:856926. doi: 10.3389/fncir.2022.856926
● https://doi.org/10.1121/1.1453451PMID: 12051437.
