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Open AccessArticle

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

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Faculty of Medical and Health Sciences, School of Population Health, Section of Audiology, The University of Auckland, Auckland 1142, New Zealand
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Eisdell Moore Centre, The University of Auckland, Auckland 1142, New Zealand
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Centre for Brain Research, The University of Auckland, Auckland 1142, New Zealand
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Information Technology and Software Engineering Department, Auckland University of Technology, Auckland 1010, New Zealand
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School of Psychology, Nottingham Trent University, Nottingham NG25 0QF, UK
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Faculty of Medical and Health Sciences, The University of Auckland, Auckland 1142, New Zealand
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Department of Psychology and Neuroscience, Auckland University of Technology, Auckland 0627, New Zealand
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School of Psychology, The University of Auckland, Auckland 1142, New Zealand
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School of Public Health and Interdisciplinary Studies, Auckland University of Technology, Auckland 0627, New Zealand
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Intelligent Systems Research Centre, Ulster University, Londonderry BT48 7JL, UK
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School of Engineering, Computing and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(24), 7354; https://doi.org/10.3390/s20247354
Received: 24 November 2020 / Revised: 16 December 2020 / Accepted: 17 December 2020 / Published: 21 December 2020
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. View Full-Text
Keywords: mindfulness; oddball-paradigm event-related potential (ERP) data; target and distractor stimuli; dynamic spatiotemporal brain data; computational modelling; spiking neural network mindfulness; oddball-paradigm event-related potential (ERP) data; target and distractor stimuli; dynamic spatiotemporal brain data; computational modelling; spiking neural network
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MDPI and ACS Style

Doborjeh, Z.; Doborjeh, M.; Crook-Rumsey, M.; Taylor, T.; Wang, G.Y.; Moreau, D.; Krägeloh, C.; Wrapson, W.; Siegert, R.J.; Kasabov, N.; Searchfield, G.; Sumich, A. Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture. Sensors 2020, 20, 7354. https://doi.org/10.3390/s20247354

AMA Style

Doborjeh Z, Doborjeh M, Crook-Rumsey M, Taylor T, Wang GY, Moreau D, Krägeloh C, Wrapson W, Siegert RJ, Kasabov N, Searchfield G, Sumich A. Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture. Sensors. 2020; 20(24):7354. https://doi.org/10.3390/s20247354

Chicago/Turabian Style

Doborjeh, Zohreh; Doborjeh, Maryam; Crook-Rumsey, Mark; Taylor, Tamasin; Wang, Grace Y.; Moreau, David; Krägeloh, Christian; Wrapson, Wendy; Siegert, Richard J.; Kasabov, Nikola; Searchfield, Grant; Sumich, Alexander. 2020. "Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture" Sensors 20, no. 24: 7354. https://doi.org/10.3390/s20247354

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