Bayesian brain in tinnitus: Computational modeling of three perceptual phenomena using a modified Hierarchical Gaussian Filter.

Recently, Bayesian brain-based models emerged as a possible composite of existing theories, providing an universal explanation of tinnitus phenomena. Yet, the involvement of multiple synergistic mechanisms complicates the identification of behavioral and physiological evidence. To overcome this, an empirically tested computational model could support the evaluation of theoretical hypotheses by intrinsically encompassing different mechanisms. The aim of this work was to develop a generative computational tinnitus perception model based on the Bayesian brain concept. The behavioral responses of 46 tinnitus subjects who underwent ten consecutive residual inhibition assessments were used for model fitting. Our model was able to replicate the behavioral responses during residual inhibition in our cohort (median linear correlation coefficient of 0.79). Using the same model, we simulated two additional tinnitus phenomena: residual excitation and occurrence of tinnitus in non-tinnitus subjects after sensory deprivation. In the simulations, the trajectories of the model were consistent with previously obtained behavioral and physiological observations. Our work introduces generative computational modeling to the research field of tinnitus. It has the potential to quantitatively link experimental observations to theoretical hypotheses and to support the search for neural signatures of tinnitus by finding correlates between the latent variables of the model and measured physiological data.

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