Abstract
Auditory Residual Inhibition (ARI) is a temporary suppression of tinnitus that occurs in some people following the presentation of masking sounds. Differences in neural response to ARI stimuli may enable classification of tinnitus and a tailored approach to intervention in the future. In an exploratory study, we investigated the use of a brain-inspired artificial neural network to examine the effects of ARI on electroencephalographic function, as well as the predictive ability of the model. Ten tinnitus patients underwent two auditory stimulation conditions (constant and amplitude modulated broadband noise) at two time points and were then characterised as responders or non-responders, based on whether they experienced ARI or not. Using a spiking neural network model, we evaluated concurrent neural patterns generated across space and time from features of electroencephalographic data, capturing the neural dynamic changes before and after stimulation. Results indicated that the model may be used to predict the effect of auditory stimulation on tinnitus on an individual basis. This approach may aid in the development of predictive models for treatment selection.
Highlights
Tinnitus is the perception of a sound when no physical external sound source is present, and is often described as a ringing, buzzing, or hissing in the ears [1]
To create computational models from EEG data based on brain inspired Spiking Neural Networks (SNN) architecture to explore modelling of neural networks underlying the tinnitus percept and examine how these altered when tinnitus was suppressed in an Auditory Residual Inhibition (ARI) paradigm
It may be that differences in the effectiveness of the stimuli on a group level would be clearer in a larger sample, but our results highlight the heterogeneous nature of tinnitus even within a small sample
Summary
Tinnitus is the perception of a sound when no physical external sound source is present, and is often described as a ringing, buzzing, or hissing in the ears [1]. One prominent model proposes that decreased sensory input from the periphery leads to a compensatory increase in central gain that amplifies spontaneous activity beyond a threshold or lowers the threshold required to reach conscious perception, and propagates it through the auditory system via increased temporal synchrony within and between regions [4,7] This model suggests that the tinnitus percept is similar to conscious perception of auditory stimuli in that it relies on a distributed neural network. To create computational models from EEG data based on brain inspired SNN architecture to explore modelling of neural networks underlying the tinnitus percept and examine how these altered when tinnitus was suppressed in an ARI paradigm. To assess whether the SNN model could predict which participants experienced ARI and which did not, using baseline data
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