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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...

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Detalles Bibliográficos
Autores principales: Thompson, William Hedley, Brantefors, Per, Fransson, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MIT Press 2017
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.
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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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