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Time-evolving dynamics in brain networks forecast responses to health messaging

Neuroimaging measures have been used to forecast complex behaviors, including how individuals change decisions about their health in response to persuasive communications, but have rarely incorporated metrics of brain network dynamics. How do functional dynamics within and between brain networks rel...

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Detalles Bibliográficos
Autores principales: Cooper, Nicole, Garcia, Javier O., Tompson, Steven H., O’Donnell, Matthew B., Falk, Emily B., Vettel, Jean M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MIT Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6372021/
https://www.ncbi.nlm.nih.gov/pubmed/30793078
http://dx.doi.org/10.1162/netn_a_00058
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author Cooper, Nicole
Garcia, Javier O.
Tompson, Steven H.
O’Donnell, Matthew B.
Falk, Emily B.
Vettel, Jean M.
author_facet Cooper, Nicole
Garcia, Javier O.
Tompson, Steven H.
O’Donnell, Matthew B.
Falk, Emily B.
Vettel, Jean M.
author_sort Cooper, Nicole
collection PubMed
description Neuroimaging measures have been used to forecast complex behaviors, including how individuals change decisions about their health in response to persuasive communications, but have rarely incorporated metrics of brain network dynamics. How do functional dynamics within and between brain networks relate to the processes of persuasion and behavior change? To address this question, we scanned 45 adult smokers by using functional magnetic resonance imaging while they viewed anti-smoking images. Participants reported their smoking behavior and intentions to quit smoking before the scan and 1 month later. We focused on regions within four atlas-defined networks and examined whether they formed consistent network communities during this task (measured as allegiance). Smokers who showed reduced allegiance among regions within the default mode and fronto-parietal networks also demonstrated larger increases in their intentions to quit smoking 1 month later. We further examined dynamics of the ventromedial prefrontal cortex (vmPFC), as activation in this region has been frequently related to behavior change. The degree to which vmPFC changed its community assignment over time (measured as flexibility) was positively associated with smoking reduction. These data highlight the value in considering brain network dynamics for understanding message effectiveness and social processes more broadly.
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spelling pubmed-63720212019-02-21 Time-evolving dynamics in brain networks forecast responses to health messaging Cooper, Nicole Garcia, Javier O. Tompson, Steven H. O’Donnell, Matthew B. Falk, Emily B. Vettel, Jean M. Netw Neurosci Research Articles Neuroimaging measures have been used to forecast complex behaviors, including how individuals change decisions about their health in response to persuasive communications, but have rarely incorporated metrics of brain network dynamics. How do functional dynamics within and between brain networks relate to the processes of persuasion and behavior change? To address this question, we scanned 45 adult smokers by using functional magnetic resonance imaging while they viewed anti-smoking images. Participants reported their smoking behavior and intentions to quit smoking before the scan and 1 month later. We focused on regions within four atlas-defined networks and examined whether they formed consistent network communities during this task (measured as allegiance). Smokers who showed reduced allegiance among regions within the default mode and fronto-parietal networks also demonstrated larger increases in their intentions to quit smoking 1 month later. We further examined dynamics of the ventromedial prefrontal cortex (vmPFC), as activation in this region has been frequently related to behavior change. The degree to which vmPFC changed its community assignment over time (measured as flexibility) was positively associated with smoking reduction. These data highlight the value in considering brain network dynamics for understanding message effectiveness and social processes more broadly. MIT Press 2018-11-01 /pmc/articles/PMC6372021/ /pubmed/30793078 http://dx.doi.org/10.1162/netn_a_00058 Text en © 2018 Massachusetts Institute of Technology This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.
spellingShingle Research Articles
Cooper, Nicole
Garcia, Javier O.
Tompson, Steven H.
O’Donnell, Matthew B.
Falk, Emily B.
Vettel, Jean M.
Time-evolving dynamics in brain networks forecast responses to health messaging
title Time-evolving dynamics in brain networks forecast responses to health messaging
title_full Time-evolving dynamics in brain networks forecast responses to health messaging
title_fullStr Time-evolving dynamics in brain networks forecast responses to health messaging
title_full_unstemmed Time-evolving dynamics in brain networks forecast responses to health messaging
title_short Time-evolving dynamics in brain networks forecast responses to health messaging
title_sort time-evolving dynamics in brain networks forecast responses to health messaging
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6372021/
https://www.ncbi.nlm.nih.gov/pubmed/30793078
http://dx.doi.org/10.1162/netn_a_00058
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