Prediction of Acoustic Residual Inhibition of Tinnitus Using a Brain-Inspired Spiking Neural Network Model

Sanders, Philip J.; Doborjeh, Zohreh G.; Doborjeh, Maryam G.; Kasabov, Nikola K.; Searchfield, Grant D.

doi:10.3390/brainsci11010052

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Prediction of Acoustic Residual Inhibition of Tinnitus Using a Brain-Inspired Spiking Neural Network Model

^1,2,3

^1,2,3

⁴,

^5,6,7

and

Grant D. Searchfield

^1,2,3,*,†

Section of Audiology, The University of Auckland, Auckland 1023, New Zealand

Eisdell Moore Centre, Auckland 1023, New Zealand

Centre for Brain Research, The University of Auckland, Auckland 1023, New Zealand

⁴

Information Technology and Software Engineering Department, Auckland University of Technology, Auckland 1010, New Zealand

⁵

School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand

⁶

Intelligent Systems Research Centre, Ulster University, Derry/Londonderry BT48 7JL, UK

⁷

Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand

Author to whom correspondence should be addressed.

^†

Brain Research New Zealand—Rangahau Roro Aotearoa, New Zealand.

Brain Sci. 2021, 11(1), 52; https://doi.org/10.3390/brainsci11010052

Received: 3 December 2020 / Revised: 23 December 2020 / Accepted: 2 January 2021 / Published: 5 January 2021

(This article belongs to the Special Issue Neurorehabilitation of Sensory Disorders)

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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. View Full-Text

Keywords: residual inhibition; amplitude modulated; tinnitus; spiking neural network; prediction; individualised treatment

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MDPI and ACS Style

Sanders, P.J.; Doborjeh, Z.G.; Doborjeh, M.G.; Kasabov, N.K.; Searchfield, G.D. Prediction of Acoustic Residual Inhibition of Tinnitus Using a Brain-Inspired Spiking Neural Network Model. Brain Sci. 2021, 11, 52. https://doi.org/10.3390/brainsci11010052

AMA Style

Sanders PJ, Doborjeh ZG, Doborjeh MG, Kasabov NK, Searchfield GD. Prediction of Acoustic Residual Inhibition of Tinnitus Using a Brain-Inspired Spiking Neural Network Model. Brain Sciences. 2021; 11(1):52. https://doi.org/10.3390/brainsci11010052

Chicago/Turabian Style

Sanders, Philip J., Zohreh G. Doborjeh, Maryam G. Doborjeh, Nikola K. Kasabov, and Grant D. Searchfield. 2021. "Prediction of Acoustic Residual Inhibition of Tinnitus Using a Brain-Inspired Spiking Neural Network Model" Brain Sciences 11, no. 1: 52. https://doi.org/10.3390/brainsci11010052

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Prediction of Acoustic Residual Inhibition of Tinnitus Using a Brain-Inspired Spiking Neural Network Model

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