TY - JOUR
T1 - Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture
AU - Doborjeh, Zohreh
AU - Doborjeh, Maryam
AU - Crook-Rumsay, Mark
AU - Taylor, Tamasin
AU - Wang, Grace
AU - Moreau, David
AU - Krageloh, Chris
AU - Wrapson, Wendy
AU - Siegert, Richard
AU - Kasabov, Nikola
AU - Searchfield, Grant
AU - Sumich, Alexander
N1 - Funding Information:
Acknowledgments: We acknowledge the support of the Auckland University of Technology (AUT) Strategic Research Investment Fund (SRIF) for funding this study.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12/23
Y1 - 2020/12/23
N2 - 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 ( = 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 ( = 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.
AB - 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 ( = 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 ( = 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.
KW - Mindfulness
KW - ERP data
KW - Dynamic spatio-temporal brain data
KW - Computational modelling
KW - spiking neural networks
KW - Oddball-paradigm
KW - mindfulness
KW - target and distractor stimuli
KW - computational modelling
KW - oddball-paradigm event-related potential (ERP) data
KW - dynamic spatiotemporal brain data
KW - spiking neural network
UR - http://www.scopus.com/inward/record.url?scp=85098332019&partnerID=8YFLogxK
U2 - 10.3390/s20247354
DO - 10.3390/s20247354
M3 - Article
C2 - 33371459
VL - 20
SP - 1
EP - 29
JO - Sensors
JF - Sensors
SN - 1424-8220
IS - 24
M1 - 7354
ER -