dc.contributor.author |
Sanders, Philip J |
|
dc.contributor.author |
Doborjeh, Zohreh G |
|
dc.contributor.author |
Doborjeh, Maryam G |
|
dc.contributor.author |
Kasabov, Nikola K |
|
dc.contributor.author |
Searchfield, Grant D |
|
dc.coverage.spatial |
Switzerland |
|
dc.date.accessioned |
2023-04-17T02:18:52Z |
|
dc.date.available |
2023-04-17T02:18:52Z |
|
dc.date.issued |
2021-01 |
|
dc.identifier.citation |
(2021). Brain Sciences, 11(1), 52-. |
|
dc.identifier.issn |
2076-3425 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/63627 |
|
dc.description.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. |
|
dc.format.medium |
Electronic |
|
dc.language |
eng |
|
dc.publisher |
MDPI |
|
dc.relation.ispartofseries |
Brain sciences |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
amplitude modulated |
|
dc.subject |
individualised treatment |
|
dc.subject |
prediction |
|
dc.subject |
residual inhibition |
|
dc.subject |
spiking neural network |
|
dc.subject |
tinnitus |
|
dc.subject |
Brain Disorders |
|
dc.subject |
Neurosciences |
|
dc.subject |
Neurological |
|
dc.subject |
Ear |
|
dc.subject |
Science & Technology |
|
dc.subject |
Life Sciences & Biomedicine |
|
dc.subject |
Neurosciences & Neurology |
|
dc.subject |
1109 Neurosciences |
|
dc.subject |
1701 Psychology |
|
dc.subject |
1702 Cognitive Sciences |
|
dc.title |
Prediction of Acoustic Residual Inhibition of Tinnitus Using a Brain-Inspired Spiking Neural Network Model. |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.3390/brainsci11010052 |
|
pubs.issue |
1 |
|
pubs.begin-page |
52 |
|
pubs.volume |
11 |
|
dc.date.updated |
2023-03-05T22:41:18Z |
|
dc.rights.holder |
Copyright: The authors |
en |
dc.identifier.pmid |
33466500 (pubmed) |
|
pubs.author-url |
https://www.ncbi.nlm.nih.gov/pubmed/33466500 |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
research-article |
|
pubs.subtype |
Journal Article |
|
pubs.elements-id |
833895 |
|
pubs.org-id |
Bioengineering Institute |
|
pubs.org-id |
Medical and Health Sciences |
|
pubs.org-id |
Population Health |
|
pubs.org-id |
Audiology |
|
dc.identifier.eissn |
2076-3425 |
|
dc.identifier.pii |
brainsci11010052 |
|
pubs.number |
ARTN 52 |
|
pubs.record-created-at-source-date |
2023-03-06 |
|
pubs.online-publication-date |
2021-01-05 |
|