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dc.contributor.authorDoborjeh, Zen_NZ
dc.contributor.authorDoborjeh, Men_NZ
dc.contributor.authorCrook-Rumsey, Men_NZ
dc.contributor.authorTaylor, Ten_NZ
dc.contributor.authorWang, GYen_NZ
dc.contributor.authorMoreau, Den_NZ
dc.contributor.authorKrägeloh, Cen_NZ
dc.contributor.authorWrapson, Wen_NZ
dc.contributor.authorSiegert, RJen_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.contributor.authorSearchfield, Gen_NZ
dc.contributor.authorSumich, Aen_NZ
dc.date.accessioned2021-01-11T00:18:38Z
dc.date.available2021-01-11T00:18:38Z
dc.identifier.citationSensors, 20(24), 7354.
dc.identifier.issn1424-8220en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/13898
dc.description.abstractMindfulness 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.en_NZ
dc.languageenen_NZ
dc.publisherMDPI AGen_NZ
dc.relation.urihttps://www.mdpi.com/1424-8220/20/24/7354
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.subjectMindfulness; Oddball-paradigm event-related potential (ERP) data; Target and distractor stimuli; Dynamic spatiotemporal brain data; Computational modelling; Spiking neural network
dc.titleInterpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architectureen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.3390/s20247354en_NZ
aut.relation.endpage7354
aut.relation.issue24en_NZ
aut.relation.startpage7354
aut.relation.volume20en_NZ
pubs.elements-id396458
aut.relation.journalSensorsen_NZ


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