Towards a Mechanistic-Driven Precision Medicine Approach for Tinnitus
In this position review, we propose to establish a path for replacing the empirical classification of tinnitus with a taxonomy from precision medicine. The goal of a classification system is to understand the inherent heterogeneity of individuals experiencing and suffering from tinnitus and to identify what differentiates potential subgroups. Identification of different patient subgroups with distinct audiological, psychophysical, and neurophysiological characteristics will facilitate the management of patients with tinnitus as well as the design and execution of drug development and clinical trials, which, for the most part, have not yielded conclusive results. An alternative outcome of a precision medicine approach in tinnitus would be that additional mechanistic phenotyping might not lead to the identification of distinct drivers in each individual, but instead, it might reveal that each individual may display a quantitative blend of causal factors. Therefore, a precision medicine approach towards identifying these causal factors might not lead to subtyping these patients but may instead highlight causal pathways that can be manipulated for therapeutic gain. These two outcomes are not mutually exclusive, and no matter what the final outcome is, a mechanistic-driven precision medicine approach is a win-win approach for advancing tinnitus research and treatment. Although there are several controversies and inconsistencies in the tinnitus field, which will not be discussed here, we will give a few examples, as to how the field can move forward by exploring the major neurophysiological tinnitus models, mostly by taking advantage of the common features supported by all of the models. Our position stems from the central concept that, as a field, we can and must do more to bring studies of mechanisms into the realm of neuroscience.
Keywordstinnitus precision medicine auditory physiology plasticity hearing loss slow-wave cortical oscillations thalamocortical dysrhythmia audiology electroencephalography magnetoencephalography big data machine learning
We thank Drs. Dean Salisbury, Avniel Ghuman, Chris Brown, Bharath Chandrasekaran, Maria Rubio, Barbara Shinn-Cunningham and Karl Kandler for their helpful discussions.
This work was supported by US Department of Defense grant W81XWH-14-1-0117 and NIH grant R01-DC007905 (both grants to T.T.).
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