Literature DB >> 33371459

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

Zohreh Doborjeh1,2,3, Maryam Doborjeh4, Mark Crook-Rumsey5, Tamasin Taylor6, Grace Y Wang7, David Moreau3,8, Christian Krägeloh7, Wendy Wrapson9, Richard J Siegert7, Nikola Kasabov10,11, Grant Searchfield1,2,3, Alexander Sumich5.   

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.

Entities:  

Keywords:  computational modelling; dynamic spatiotemporal brain data; mindfulness; oddball-paradigm event-related potential (ERP) data; spiking neural network; target and distractor stimuli

Mesh:

Year:  2020        PMID: 33371459      PMCID: PMC7767448          DOI: 10.3390/s20247354

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  82 in total

Review 1.  A systematic review of the neurophysiology of mindfulness on EEG oscillations.

Authors:  Tim Lomas; Itai Ivtzan; Cynthia H Y Fu
Journal:  Neurosci Biobehav Rev       Date:  2015-10-09       Impact factor: 8.989

2.  Impact of mindfulness training on attentional control and anger regulation processes for psychotherapists in training.

Authors:  Beatriz Rodriguez Vega; Javier Melero-Llorente; Carmen Bayon Perez; Susana Cebolla; Jorge Mira; Carla Valverde; Alberto Fernández-Liria
Journal:  Psychother Res       Date:  2013-10-18

3.  Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior.

Authors:  Sofie Therese Hansen; Lars Kai Hansen
Journal:  Neuroimage       Date:  2016-12-13       Impact factor: 6.556

4.  Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks.

Authors:  Nikola K Kasabov; Maryam Gholami Doborjeh; Zohreh Gholami Doborjeh
Journal:  IEEE Trans Neural Netw Learn Syst       Date:  2016-10-06       Impact factor: 10.451

5.  Mindfulness-Based Interventions for University Students: A Systematic Review and Meta-Analysis of Randomised Controlled Trials.

Authors:  Anna F Dawson; William W Brown; Joanna Anderson; Bella Datta; James N Donald; Karen Hong; Sophie Allan; Tom B Mole; Peter B Jones; Julieta Galante
Journal:  Appl Psychol Health Well Being       Date:  2019-11-19

6.  Stimulus novelty, task relevance and the visual evoked potential in man.

Authors:  E Courchesne; S A Hillyard; R Galambos
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1975-08

7.  Meditation increases the depth of information processing and improves the allocation of attention in space.

Authors:  Sara van Leeuwen; Wolf Singer; Lucia Melloni
Journal:  Front Hum Neurosci       Date:  2012-05-15       Impact factor: 3.169

Review 8.  Mindfulness training for adolescents: A neurodevelopmental perspective on investigating modifications in attention and emotion regulation using event-related brain potentials.

Authors:  Kevanne Louise Sanger; Dusana Dorjee
Journal:  Cogn Affect Behav Neurosci       Date:  2015-09       Impact factor: 3.282

9.  Validation of the depression anxiety stress scales (DASS) 21 as a screening instrument for depression and anxiety in a rural community-based cohort of northern Vietnamese women.

Authors:  Thach Duc Tran; Tuan Tran; Jane Fisher
Journal:  BMC Psychiatry       Date:  2013-01-12       Impact factor: 3.630

Review 10.  Web-Based Mindfulness Interventions for Mental Health Treatment: Systematic Review and Meta-Analysis.

Authors:  Julia Sevilla-Llewellyn-Jones; Olga Santesteban-Echarri; Ingrid Pryor; Patrick McGorry; Mario Alvarez-Jimenez
Journal:  JMIR Ment Health       Date:  2018-09-25
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  3 in total

1.  Behavioral Outcomes and Neural Network Modeling of a Novel, Putative, Recategorization Sound Therapy.

Authors:  Mithila Durai; Zohreh Doborjeh; Philip J Sanders; Dunja Vajsakovic; Anne Wendt; Grant D Searchfield
Journal:  Brain Sci       Date:  2021-04-27

2.  Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing.

Authors:  Moshe Bensimon; Shlomo Greenberg; Moshe Haiut
Journal:  Sensors (Basel)       Date:  2021-02-04       Impact factor: 3.576

3.  An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals.

Authors:  Olivia Vargas-Lopez; Carlos A Perez-Ramirez; Martin Valtierra-Rodriguez; Jesus J Yanez-Borjas; Juan P Amezquita-Sanchez
Journal:  Sensors (Basel)       Date:  2021-05-01       Impact factor: 3.576

  3 in total

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