Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture

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dc.contributor.author Doborjeh, Zohreh
dc.contributor.author Doborjeh, Maryam
dc.contributor.author Crook-Rumsey, Mark
dc.contributor.author Taylor, Tamasin
dc.contributor.author Wang, Grace Y
dc.contributor.author Moreau, David
dc.contributor.author Krägeloh, Christian
dc.contributor.author Wrapson, Wendy
dc.contributor.author Siegert, Richard J
dc.contributor.author Kasabov, Nikola
dc.contributor.author Searchfield, Grant
dc.contributor.author Sumich, Alexander
dc.date.accessioned 2021-01-12T01:38:57Z
dc.date.available 2021-01-12T01:38:57Z
dc.date.issued 2020-12-21
dc.identifier.citation Sensors 20(24) 21 Dec 2020
dc.identifier.issn 1424-8220
dc.identifier.uri https://hdl.handle.net/2292/54168
dc.description.abstract <jats:p>Mindfulness training is associated with improvements in psychological wellbeing and cognition, yet the specific underlying neurophysiological mechanisms underpinning these changes are uncertain. This study uses a novel brain-inspired artificial neural network to investigate the effect of mindfulness training on electroencephalographic function. Participants completed a 4-tone auditory oddball task (that included targets and physically similar distractors) at three assessment time points. In Group A (n = 10), these tasks were given immediately prior to 6-week mindfulness training, immediately after training and at a 3-week follow-up; in Group B (n = 10), these were during an intervention waitlist period (3 weeks prior to training), pre-mindfulness training and post-mindfulness training. Using a spiking neural network (SNN) model, we evaluated concurrent neural patterns generated across space and time from features of electroencephalographic data capturing the neural dynamics associated with the event-related potential (ERP). This technique capitalises on the temporal dynamics of the shifts in polarity throughout the ERP and spatially across electrodes. Findings support anteriorisation of connection weights in response to distractors relative to target stimuli. Right frontal connection weights to distractors were associated with trait mindfulness (positively) and depression (inversely). Moreover, mindfulness training was associated with an increase in connection weights to targets (bilateral frontal, left frontocentral, and temporal regions only) and distractors. SNN models were superior to other machine learning methods in the classification of brain states as a function of mindfulness training. Findings suggest SNN models can provide useful information that differentiates brain states based on distinct task demands and stimuli, as well as changes in brain states as a function of psychological intervention.</jats:p>
dc.language en
dc.publisher MDPI AG
dc.relation.ispartofseries Sensors
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 0301 Analytical Chemistry
dc.subject 0805 Distributed Computing
dc.subject 0906 Electrical and Electronic Engineering
dc.subject 0502 Environmental Science and Management
dc.subject 0602 Ecology
dc.title Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture
dc.type Journal Article
dc.identifier.doi 10.3390/s20247354
pubs.issue 24
pubs.begin-page 7354
pubs.volume 20
dc.date.updated 2020-12-27T10:25:24Z
dc.rights.holder Copyright: The authors en
pubs.publication-status Published online
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.elements-id 833217
dc.identifier.eissn 1424-8220
pubs.online-publication-date 2020-12-21


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