Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture
Zohreh Gholami Doborjeh, Maryam Gholami Doborjeh, Mark Crook‐Rumsey, Tamasin Taylor, Grace Y. Wang, David Moreau, Christian U. Krägeloh, Wendy Wrapson, Richard John Siegert, Nikola Kirilov Kasabov, Grant Donald Searchfield, Alexander Luke Sumich
Sensors December 21, 2020 DOI: 10.3390/s20247354 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Sample size | 20 |
| Population | Adults participating in mindfulness training, divided into Group A (n=10) and Group B (n=10) |
| Intervention | Mindfulness training |
| Duration | 6-week mindfulness training with 3-week follow-up |
| Topics | Meditation |
| Citations | 27 |
| Key findings | Findings suggest anteriorisation of connection weights in response to distractors relative to targets, with right frontal distractor weights positively associated with trait mindfulness and inversely with depression. Mindfulness training was associated with increased connection weights to targets in bilateral frontal, left frontocentral, and temporal regions, and to distractors. SNN models outperformed other machine learning methods in classifying brain states as a function of mindfulness training. |
Abstract
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.
Comparable studies
Other observational and cohort studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students | 2006 | Observational cohort | |
| Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia | 1992 | Observational cohort | n = 22 |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls | 2004 | Observational cohort |
Citations in the library
Cited by 2
- BILMB: Design of a hybrid Bio inspired Incremental Learning-based model for analysing effects of Meditation on different Body-parts 2023 6th International Conference on Information Systems and Computer Networks March 3, 2023
- Cognitive Effect Following a Blended (Face to Face and Videoconference-Delivered) Format Mindfulness Training. Frontiers in Psychology July 30, 2021
Cites 35
- Using Self-Report Assessment Methods to Explore Facets of Mindfulness Assessment January 27, 2006
- Meditation Programs for Psychological Stress and Well-being JAMA Internal Medicine January 6, 2014
- Meditation experience is associated with increased cortical thickness Neuroreport November 7, 2005
- Mindfulness Interventions Annual Review of Psychology September 30, 2016
- Meditation experience is associated with differences in default mode network activity and connectivity Proceedings of the National Academy of Sciences November 23, 2011
and 30 more in the library