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A mixed-modeling framework for whole-brain dynamic network analysis
The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and dra...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MIT Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208000/ https://www.ncbi.nlm.nih.gov/pubmed/35733427 http://dx.doi.org/10.1162/netn_a_00238 |
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author | Bahrami, Mohsen Laurienti, Paul J. Shappell, Heather M. Dagenbach, Dale Simpson, Sean L. |
author_facet | Bahrami, Mohsen Laurienti, Paul J. Shappell, Heather M. Dagenbach, Dale Simpson, Sean L. |
author_sort | Bahrami, Mohsen |
collection | PubMed |
description | The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools, which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks by using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks. |
format | Online Article Text |
id | pubmed-9208000 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MIT Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92080002022-06-21 A mixed-modeling framework for whole-brain dynamic network analysis Bahrami, Mohsen Laurienti, Paul J. Shappell, Heather M. Dagenbach, Dale Simpson, Sean L. Netw Neurosci Research Article The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools, which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks by using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks. MIT Press 2022-06-01 /pmc/articles/PMC9208000/ /pubmed/35733427 http://dx.doi.org/10.1162/netn_a_00238 Text en © 2022 Massachusetts Institute of Technology https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://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/. |
spellingShingle | Research Article Bahrami, Mohsen Laurienti, Paul J. Shappell, Heather M. Dagenbach, Dale Simpson, Sean L. A mixed-modeling framework for whole-brain dynamic network analysis |
title | A mixed-modeling framework for whole-brain dynamic network analysis |
title_full | A mixed-modeling framework for whole-brain dynamic network analysis |
title_fullStr | A mixed-modeling framework for whole-brain dynamic network analysis |
title_full_unstemmed | A mixed-modeling framework for whole-brain dynamic network analysis |
title_short | A mixed-modeling framework for whole-brain dynamic network analysis |
title_sort | mixed-modeling framework for whole-brain dynamic network analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208000/ https://www.ncbi.nlm.nih.gov/pubmed/35733427 http://dx.doi.org/10.1162/netn_a_00238 |
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