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A mixed-modeling framework for analyzing multitask whole-brain network data
The emerging area of brain network analysis considers the brain as a system, providing profound insight into links between system-level properties and health outcomes. Network science has facilitated these analyses and our understanding of how the brain is organized. While network science has cataly...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MIT Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370463/ https://www.ncbi.nlm.nih.gov/pubmed/30793084 http://dx.doi.org/10.1162/netn_a_00065 |
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author | Simpson, Sean L. Bahrami, Mohsen Laurienti, Paul J. |
author_facet | Simpson, Sean L. Bahrami, Mohsen Laurienti, Paul J. |
author_sort | Simpson, Sean L. |
collection | PubMed |
description | The emerging area of brain network analysis considers the brain as a system, providing profound insight into links between system-level properties and health outcomes. Network science has facilitated these analyses and our understanding of how the brain is organized. While network science has catalyzed a paradigmatic shift in neuroscience, methods for statistically analyzing networks have lagged behind. To address this for cross-sectional network data, we developed a mixed-modeling framework that enables quantifying the relationship between phenotype and connectivity patterns, predicting connectivity structure based on phenotype, simulating networks to gain a better understanding of topological variability, and thresholding individual networks leveraging group information. Here we extend this comprehensive approach to enable studying system-level brain properties across multiple tasks. We focus on rest-to-task network changes, but this extension is equally applicable to the assessment of network changes for any repeated task paradigm. Our approach allows (a) assessing population network differences in changes between tasks, and how these changes relate to health outcomes; (b) assessing individual variability in network differences in changes between tasks, and how this variability relates to health outcomes; and (c) deriving more accurate and precise estimates of the relationships between phenotype and health outcomes within a given task. |
format | Online Article Text |
id | pubmed-6370463 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MIT Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-63704632019-02-21 A mixed-modeling framework for analyzing multitask whole-brain network data Simpson, Sean L. Bahrami, Mohsen Laurienti, Paul J. Netw Neurosci Research Articles The emerging area of brain network analysis considers the brain as a system, providing profound insight into links between system-level properties and health outcomes. Network science has facilitated these analyses and our understanding of how the brain is organized. While network science has catalyzed a paradigmatic shift in neuroscience, methods for statistically analyzing networks have lagged behind. To address this for cross-sectional network data, we developed a mixed-modeling framework that enables quantifying the relationship between phenotype and connectivity patterns, predicting connectivity structure based on phenotype, simulating networks to gain a better understanding of topological variability, and thresholding individual networks leveraging group information. Here we extend this comprehensive approach to enable studying system-level brain properties across multiple tasks. We focus on rest-to-task network changes, but this extension is equally applicable to the assessment of network changes for any repeated task paradigm. Our approach allows (a) assessing population network differences in changes between tasks, and how these changes relate to health outcomes; (b) assessing individual variability in network differences in changes between tasks, and how this variability relates to health outcomes; and (c) deriving more accurate and precise estimates of the relationships between phenotype and health outcomes within a given task. MIT Press 2019-02-01 /pmc/articles/PMC6370463/ /pubmed/30793084 http://dx.doi.org/10.1162/netn_a_00065 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 Simpson, Sean L. Bahrami, Mohsen Laurienti, Paul J. A mixed-modeling framework for analyzing multitask whole-brain network data |
title | A mixed-modeling framework for analyzing multitask whole-brain network data |
title_full | A mixed-modeling framework for analyzing multitask whole-brain network data |
title_fullStr | A mixed-modeling framework for analyzing multitask whole-brain network data |
title_full_unstemmed | A mixed-modeling framework for analyzing multitask whole-brain network data |
title_short | A mixed-modeling framework for analyzing multitask whole-brain network data |
title_sort | mixed-modeling framework for analyzing multitask whole-brain network data |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370463/ https://www.ncbi.nlm.nih.gov/pubmed/30793084 http://dx.doi.org/10.1162/netn_a_00065 |
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