Cargando…

Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain

There has been substantial interest in Mindfulness Training (MT) to understand how it can benefit healthy individuals as well as people with a broad range of health conditions. Research has begun to delineate associated changes in brain function. However, whether measures of brain function can be us...

Descripción completa

Detalles Bibliográficos
Autores principales: Doborjeh, Zohreh, Doborjeh, Maryam, Taylor, Tamasin, Kasabov, Nikola, Wang, Grace Y., Siegert, Richard, Sumich, Alex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6478904/
https://www.ncbi.nlm.nih.gov/pubmed/31015534
http://dx.doi.org/10.1038/s41598-019-42863-x
_version_ 1783413240024268800
author Doborjeh, Zohreh
Doborjeh, Maryam
Taylor, Tamasin
Kasabov, Nikola
Wang, Grace Y.
Siegert, Richard
Sumich, Alex
author_facet Doborjeh, Zohreh
Doborjeh, Maryam
Taylor, Tamasin
Kasabov, Nikola
Wang, Grace Y.
Siegert, Richard
Sumich, Alex
author_sort Doborjeh, Zohreh
collection PubMed
description There has been substantial interest in Mindfulness Training (MT) to understand how it can benefit healthy individuals as well as people with a broad range of health conditions. Research has begun to delineate associated changes in brain function. However, whether measures of brain function can be used to identify individuals who are more likely to respond to MT remains unclear. The present study applies a recently developed brain-inspired Spiking Neural Network (SNN) model to electroencephalography (EEG) data to provide novel insight into: i) brain function in depression; ii) the effect of MT on depressed and non-depressed individuals; and iii) neurobiological characteristics of depressed individuals who respond to mindfulness. Resting state EEG was recorded from before and after a 6 week MT programme in 18 participants. Based on self-report, 3 groups were formed: non-depressed (ND), depressed before but not after MT (responsive, D(+)) and depressed both before and after MT (unresponsive, D(−)). The proposed SNN, which utilises a standard brain-template, was used to model EEG data and assess connectivity, as indicated by activation levels across scalp regions (frontal, frontocentral, temporal, centroparietal and occipitoparietal), at baseline and follow-up. Results suggest an increase in activation following MT that was site-specific as a function of the group. Greater initial activation levels were seen in ND compared to depressed groups, and this difference was maintained at frontal and occipitoparietal regions following MT. At baseline, D(+) had great activation than D(−). Following MT, frontocentral and temporal activation reached ND levels in D(+) but remained low in D(−). Findings support the SNN approach in distinguishing brain states associated with depression and responsiveness to MT. The results also demonstrated that the SNN approach can be used to predict the effect of mindfulness on an individual basis before it is even applied.
format Online
Article
Text
id pubmed-6478904
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-64789042019-05-03 Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain Doborjeh, Zohreh Doborjeh, Maryam Taylor, Tamasin Kasabov, Nikola Wang, Grace Y. Siegert, Richard Sumich, Alex Sci Rep Article There has been substantial interest in Mindfulness Training (MT) to understand how it can benefit healthy individuals as well as people with a broad range of health conditions. Research has begun to delineate associated changes in brain function. However, whether measures of brain function can be used to identify individuals who are more likely to respond to MT remains unclear. The present study applies a recently developed brain-inspired Spiking Neural Network (SNN) model to electroencephalography (EEG) data to provide novel insight into: i) brain function in depression; ii) the effect of MT on depressed and non-depressed individuals; and iii) neurobiological characteristics of depressed individuals who respond to mindfulness. Resting state EEG was recorded from before and after a 6 week MT programme in 18 participants. Based on self-report, 3 groups were formed: non-depressed (ND), depressed before but not after MT (responsive, D(+)) and depressed both before and after MT (unresponsive, D(−)). The proposed SNN, which utilises a standard brain-template, was used to model EEG data and assess connectivity, as indicated by activation levels across scalp regions (frontal, frontocentral, temporal, centroparietal and occipitoparietal), at baseline and follow-up. Results suggest an increase in activation following MT that was site-specific as a function of the group. Greater initial activation levels were seen in ND compared to depressed groups, and this difference was maintained at frontal and occipitoparietal regions following MT. At baseline, D(+) had great activation than D(−). Following MT, frontocentral and temporal activation reached ND levels in D(+) but remained low in D(−). Findings support the SNN approach in distinguishing brain states associated with depression and responsiveness to MT. The results also demonstrated that the SNN approach can be used to predict the effect of mindfulness on an individual basis before it is even applied. Nature Publishing Group UK 2019-04-23 /pmc/articles/PMC6478904/ /pubmed/31015534 http://dx.doi.org/10.1038/s41598-019-42863-x Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Doborjeh, Zohreh
Doborjeh, Maryam
Taylor, Tamasin
Kasabov, Nikola
Wang, Grace Y.
Siegert, Richard
Sumich, Alex
Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain
title Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain
title_full Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain
title_fullStr Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain
title_full_unstemmed Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain
title_short Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain
title_sort spiking neural network modelling approach reveals how mindfulness training rewires the brain
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6478904/
https://www.ncbi.nlm.nih.gov/pubmed/31015534
http://dx.doi.org/10.1038/s41598-019-42863-x
work_keys_str_mv AT doborjehzohreh spikingneuralnetworkmodellingapproachrevealshowmindfulnesstrainingrewiresthebrain
AT doborjehmaryam spikingneuralnetworkmodellingapproachrevealshowmindfulnesstrainingrewiresthebrain
AT taylortamasin spikingneuralnetworkmodellingapproachrevealshowmindfulnesstrainingrewiresthebrain
AT kasabovnikola spikingneuralnetworkmodellingapproachrevealshowmindfulnesstrainingrewiresthebrain
AT wanggracey spikingneuralnetworkmodellingapproachrevealshowmindfulnesstrainingrewiresthebrain
AT siegertrichard spikingneuralnetworkmodellingapproachrevealshowmindfulnesstrainingrewiresthebrain
AT sumichalex spikingneuralnetworkmodellingapproachrevealshowmindfulnesstrainingrewiresthebrain