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From static to temporal network theory: Applications to functional brain connectivity
Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuation...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MIT Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5988396/ https://www.ncbi.nlm.nih.gov/pubmed/29911669 http://dx.doi.org/10.1162/NETN_a_00011 |
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author | Thompson, William Hedley Brantefors, Per Fransson, Peter |
author_facet | Thompson, William Hedley Brantefors, Per Fransson, Peter |
author_sort | Thompson, William Hedley |
collection | PubMed |
description | Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto. |
format | Online Article Text |
id | pubmed-5988396 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MIT Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-59883962018-06-15 From static to temporal network theory: Applications to functional brain connectivity Thompson, William Hedley Brantefors, Per Fransson, Peter Netw Neurosci Methods Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto. MIT Press 2017-06-01 /pmc/articles/PMC5988396/ /pubmed/29911669 http://dx.doi.org/10.1162/NETN_a_00011 Text en © 2017 Massachusetts Institute of Technology http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methods Thompson, William Hedley Brantefors, Per Fransson, Peter From static to temporal network theory: Applications to functional brain connectivity |
title | From static to temporal network theory: Applications to functional brain connectivity |
title_full | From static to temporal network theory: Applications to functional brain connectivity |
title_fullStr | From static to temporal network theory: Applications to functional brain connectivity |
title_full_unstemmed | From static to temporal network theory: Applications to functional brain connectivity |
title_short | From static to temporal network theory: Applications to functional brain connectivity |
title_sort | from static to temporal network theory: applications to functional brain connectivity |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5988396/ https://www.ncbi.nlm.nih.gov/pubmed/29911669 http://dx.doi.org/10.1162/NETN_a_00011 |
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