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.
|Number of pages||29|
|Early online date||21 Dec 2020|
|Publication status||Published (in print/issue) - 23 Dec 2020|
Bibliographical noteFunding Information:
Acknowledgments: We acknowledge the support of the Auckland University of Technology (AUT) Strategic Research Investment Fund (SRIF) for funding this study.
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright 2020 Elsevier B.V., All rights reserved.
- ERP data
- Dynamic spatio-temporal brain data
- Computational modelling
- spiking neural networks
- target and distractor stimuli
- computational modelling
- oddball-paradigm event-related potential (ERP) data
- dynamic spatiotemporal brain data
- spiking neural network