Brain network topology predicts participant adherence to mental training programs

Adherence determines the success and benefits of mental training (e.g., meditation) programs. It is unclear why some participants engage more actively in programs for mental training than others. Understanding neurobiological factors that predict adherence is necessary for unde...

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Main Authors: Saghayi, Marzie, Greenberg, Jonathan, O’Grady, Christopher, Varno, Farshid, Hashmi, Muhammad Ali, Bracken, Bethany, Matwin, Stan, Lazar, Sara W., Hashmi, Javeria Ali
Format: Article
Language:English
Published: The MIT Press 2020-01-01
Series:Network Neuroscience
Online Access:https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00136
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spelling doaj-2b0d4c0b81394cd5a872ef77d08ae25e2020-11-25T03:21:26ZengThe MIT PressNetwork Neuroscience2472-17512020-01-014352855510.1162/netn_a_00136Brain network topology predicts participant adherence to mental training programsSaghayi, MarzieGreenberg, JonathanO’Grady, ChristopherVarno, FarshidHashmi, Muhammad AliBracken, BethanyMatwin, StanLazar, Sara W.Hashmi, Javeria Ali Adherence determines the success and benefits of mental training (e.g., meditation) programs. It is unclear why some participants engage more actively in programs for mental training than others. Understanding neurobiological factors that predict adherence is necessary for understanding elements of learning and to inform better designs for new learning regimens. Clustering patterns in brain networks have been suggested to predict learning performance, but it is unclear whether these patterns contribute to motivational aspects of learning such as adherence. This study tests whether configurations of brain connections in resting-state fMRI scans can be used to predict adherence to two programs: meditation and creative writing. Results indicate that greater system segregation and clustering predict the number of practice sessions and class participation in both programs at a wide range of network thresholds (corrected p value < 0.05). At a local level, regions in subcortical circuitry such as striatum and accumbens predicted adherence in all subjects. Furthermore, there were also some important distinctions between groups: Adherence to meditation was predicted by connectivity within local network of the anterior insula and default mode network; and in the writing program, adherence was predicted by network neighborhood of frontal and temporal regions. Four machine learning methods were applied to test the robustness of the brain metric for classifying individual capacity for adherence and yielded reasonable accuracy. Overall, these findings underscore the fact that adherence and the ability to perform prescribed exercises is associated with organizational patterns of brain connectivity. https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00136
collection DOAJ
language English
format Article
sources DOAJ
author Saghayi, Marzie
Greenberg, Jonathan
O’Grady, Christopher
Varno, Farshid
Hashmi, Muhammad Ali
Bracken, Bethany
Matwin, Stan
Lazar, Sara W.
Hashmi, Javeria Ali
spellingShingle Saghayi, Marzie
Greenberg, Jonathan
O’Grady, Christopher
Varno, Farshid
Hashmi, Muhammad Ali
Bracken, Bethany
Matwin, Stan
Lazar, Sara W.
Hashmi, Javeria Ali
Brain network topology predicts participant adherence to mental training programs
Network Neuroscience
author_facet Saghayi, Marzie
Greenberg, Jonathan
O’Grady, Christopher
Varno, Farshid
Hashmi, Muhammad Ali
Bracken, Bethany
Matwin, Stan
Lazar, Sara W.
Hashmi, Javeria Ali
author_sort Saghayi, Marzie
title Brain network topology predicts participant adherence to mental training programs
title_short Brain network topology predicts participant adherence to mental training programs
title_full Brain network topology predicts participant adherence to mental training programs
title_fullStr Brain network topology predicts participant adherence to mental training programs
title_full_unstemmed Brain network topology predicts participant adherence to mental training programs
title_sort brain network topology predicts participant adherence to mental training programs
publisher The MIT Press
series Network Neuroscience
issn 2472-1751
publishDate 2020-01-01
description Adherence determines the success and benefits of mental training (e.g., meditation) programs. It is unclear why some participants engage more actively in programs for mental training than others. Understanding neurobiological factors that predict adherence is necessary for understanding elements of learning and to inform better designs for new learning regimens. Clustering patterns in brain networks have been suggested to predict learning performance, but it is unclear whether these patterns contribute to motivational aspects of learning such as adherence. This study tests whether configurations of brain connections in resting-state fMRI scans can be used to predict adherence to two programs: meditation and creative writing. Results indicate that greater system segregation and clustering predict the number of practice sessions and class participation in both programs at a wide range of network thresholds (corrected p value < 0.05). At a local level, regions in subcortical circuitry such as striatum and accumbens predicted adherence in all subjects. Furthermore, there were also some important distinctions between groups: Adherence to meditation was predicted by connectivity within local network of the anterior insula and default mode network; and in the writing program, adherence was predicted by network neighborhood of frontal and temporal regions. Four machine learning methods were applied to test the robustness of the brain metric for classifying individual capacity for adherence and yielded reasonable accuracy. Overall, these findings underscore the fact that adherence and the ability to perform prescribed exercises is associated with organizational patterns of brain connectivity.
url https://www.mitpressjournals.org/doi/abs/10.1162/netn_a_00136
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