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Categorization of tinnitus listeners with a focus on cochlear synaptopathy

Author

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  • Chiara Casolani
  • James Michael Harte
  • Bastian Epp

Abstract

Tinnitus is a complex and not yet fully understood phenomenon. Often the treatments provided are effective only for subgroups of sufferers. We are presently not able to predict benefit with the currently available diagnostic tools and analysis methods. Being able to identify and specifically treat sub-categories of tinnitus would help develop and implement more targeted treatments with higher success rate. In this study we use a clustering analysis based on 17 predictors to cluster an audiologically homogeneous group of normal hearing participants, both with and without tinnitus. The predictors have been chosen to be either tinnitus-specific measures or measures that are thought to be connected to cochlear synaptopathy. Our aim was to identify a subgroup of participants with characteristics consistent with the current hypothesized impact of cochlear synaptopathy. Our results show that this approach can separate the listeners into different clusters. But not in all cases could the tinnitus sufferers be separated from the control group. Another challenge is the use of categorical measures which seem to dominate the importance analysis of the factors. The study showed that data-driven clustering of a homogeneous listener group based on a mixed set of experimental outcome measures is a promising tool for tinnitus sub-typing, with the caveat that sample sizes might need to be sufficiently high, and higher than in the present study, to keep a meaningful sample size after clustering.

Suggested Citation

  • Chiara Casolani & James Michael Harte & Bastian Epp, 2022. "Categorization of tinnitus listeners with a focus on cochlear synaptopathy," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-24, December.
    • Handle: RePEc:plo:pone00:0277023
    DOI: 10.1371/journal.pone.0277023
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    References listed on IDEAS

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    1. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
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