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Resting-state networks representation of the global phenomena

Resting-state functional magnetic resonance imaging (rsfMRI) has been widely applied to investigate spontaneous neural activity, often based on its macroscopic organization that is termed resting-state networks (RSNs). Although the neurophysiological mechanisms underlying the RSN organization remain...

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Autores principales: Amemiya, Shiori, Takao, Hidemasa, Hanaoka, Shouhei, Abe, Osamu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469869/
https://www.ncbi.nlm.nih.gov/pubmed/37662100
http://dx.doi.org/10.3389/fnins.2023.1220848
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author Amemiya, Shiori
Takao, Hidemasa
Hanaoka, Shouhei
Abe, Osamu
author_facet Amemiya, Shiori
Takao, Hidemasa
Hanaoka, Shouhei
Abe, Osamu
author_sort Amemiya, Shiori
collection PubMed
description Resting-state functional magnetic resonance imaging (rsfMRI) has been widely applied to investigate spontaneous neural activity, often based on its macroscopic organization that is termed resting-state networks (RSNs). Although the neurophysiological mechanisms underlying the RSN organization remain largely unknown, accumulating evidence points to a substantial contribution from the global signals to their structured synchronization. This study further explored the phenomenon by taking advantage of the inter- and intra-subject variations of the time delay and correlation coefficient of the signal timeseries in each region using the global mean signal as the reference signal. Consistent with the hypothesis based on the empirical and theoretical findings, the time lag and correlation, which have consistently been proven to represent local hemodynamic status, were shown to organize networks equivalent to RSNs. The results not only provide further evidence that the local hemodynamic status could be the direct source of the RSNs’ spatial patterns but also explain how the regional variations in the hemodynamics, combined with the changes in the global events’ power spectrum, lead to the observations. While the findings pose challenges to interpretations of rsfMRI studies, they further support the view that rsfMRI can offer detailed information related to global neurophysiological phenomena as well as local hemodynamics that would have great potential as biomarkers.
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spelling pubmed-104698692023-09-01 Resting-state networks representation of the global phenomena Amemiya, Shiori Takao, Hidemasa Hanaoka, Shouhei Abe, Osamu Front Neurosci Neuroscience Resting-state functional magnetic resonance imaging (rsfMRI) has been widely applied to investigate spontaneous neural activity, often based on its macroscopic organization that is termed resting-state networks (RSNs). Although the neurophysiological mechanisms underlying the RSN organization remain largely unknown, accumulating evidence points to a substantial contribution from the global signals to their structured synchronization. This study further explored the phenomenon by taking advantage of the inter- and intra-subject variations of the time delay and correlation coefficient of the signal timeseries in each region using the global mean signal as the reference signal. Consistent with the hypothesis based on the empirical and theoretical findings, the time lag and correlation, which have consistently been proven to represent local hemodynamic status, were shown to organize networks equivalent to RSNs. The results not only provide further evidence that the local hemodynamic status could be the direct source of the RSNs’ spatial patterns but also explain how the regional variations in the hemodynamics, combined with the changes in the global events’ power spectrum, lead to the observations. While the findings pose challenges to interpretations of rsfMRI studies, they further support the view that rsfMRI can offer detailed information related to global neurophysiological phenomena as well as local hemodynamics that would have great potential as biomarkers. Frontiers Media S.A. 2023-08-17 /pmc/articles/PMC10469869/ /pubmed/37662100 http://dx.doi.org/10.3389/fnins.2023.1220848 Text en Copyright © 2023 Amemiya, Takao, Hanaoka and Abe. https://creativecommons.org/licenses/by/4.0/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) and the copyright owner(s) 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
Amemiya, Shiori
Takao, Hidemasa
Hanaoka, Shouhei
Abe, Osamu
Resting-state networks representation of the global phenomena
title Resting-state networks representation of the global phenomena
title_full Resting-state networks representation of the global phenomena
title_fullStr Resting-state networks representation of the global phenomena
title_full_unstemmed Resting-state networks representation of the global phenomena
title_short Resting-state networks representation of the global phenomena
title_sort resting-state networks representation of the global phenomena
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469869/
https://www.ncbi.nlm.nih.gov/pubmed/37662100
http://dx.doi.org/10.3389/fnins.2023.1220848
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