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Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project

An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural comp...

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Autores principales: McDonough, Ian M., Nashiro, Kaoru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4051265/
https://www.ncbi.nlm.nih.gov/pubmed/24959130
http://dx.doi.org/10.3389/fnhum.2014.00409
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author McDonough, Ian M.
Nashiro, Kaoru
author_facet McDonough, Ian M.
Nashiro, Kaoru
author_sort McDonough, Ian M.
collection PubMed
description An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity—a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity.
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spelling pubmed-40512652014-06-23 Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project McDonough, Ian M. Nashiro, Kaoru Front Hum Neurosci Neuroscience An emerging field of research focused on fluctuations in brain signals has provided evidence that the complexity of those signals, as measured by entropy, conveys important information about network dynamics (e.g., local and distributed processing). While much research has focused on how neural complexity differs in populations with different age groups or clinical disorders, substantially less research has focused on the basic understanding of neural complexity in populations with young and healthy brain states. The present study used resting-state fMRI data from the Human Connectome Project (Van Essen et al., 2013) to test the extent that neural complexity in the BOLD signal, as measured by multiscale entropy (1) would differ from random noise, (2) would differ between four major resting-state networks previously associated with higher-order cognition, and (3) would be associated with the strength and extent of functional connectivity—a complementary method of estimating information processing. We found that complexity in the BOLD signal exhibited different patterns of complexity from white, pink, and red noise and that neural complexity was differentially expressed between resting-state networks, including the default mode, cingulo-opercular, left and right frontoparietal networks. Lastly, neural complexity across all networks was negatively associated with functional connectivity at fine scales, but was positively associated with functional connectivity at coarse scales. The present study is the first to characterize neural complexity in BOLD signals at a high temporal resolution and across different networks and might help clarify the inconsistencies between neural complexity and functional connectivity, thus informing the mechanisms underlying neural complexity. Frontiers Media S.A. 2014-06-10 /pmc/articles/PMC4051265/ /pubmed/24959130 http://dx.doi.org/10.3389/fnhum.2014.00409 Text en Copyright © 2014 McDonough and Nashiro. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
McDonough, Ian M.
Nashiro, Kaoru
Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project
title Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project
title_full Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project
title_fullStr Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project
title_full_unstemmed Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project
title_short Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project
title_sort network complexity as a measure of information processing across resting-state networks: evidence from the human connectome project
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4051265/
https://www.ncbi.nlm.nih.gov/pubmed/24959130
http://dx.doi.org/10.3389/fnhum.2014.00409
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