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Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance
Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional co...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6039182/ https://www.ncbi.nlm.nih.gov/pubmed/29911970 http://dx.doi.org/10.7554/eLife.32696 |
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author | Keerativittayayut, Ruedeerat Aoki, Ryuta Sarabi, Mitra Taghizadeh Jimura, Koji Nakahara, Kiyoshi |
author_facet | Keerativittayayut, Ruedeerat Aoki, Ryuta Sarabi, Mitra Taghizadeh Jimura, Koji Nakahara, Kiyoshi |
author_sort | Keerativittayayut, Ruedeerat |
collection | PubMed |
description | Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding. |
format | Online Article Text |
id | pubmed-6039182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-60391822018-07-11 Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance Keerativittayayut, Ruedeerat Aoki, Ryuta Sarabi, Mitra Taghizadeh Jimura, Koji Nakahara, Kiyoshi eLife Neuroscience Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding. eLife Sciences Publications, Ltd 2018-06-18 /pmc/articles/PMC6039182/ /pubmed/29911970 http://dx.doi.org/10.7554/eLife.32696 Text en © 2018, Keerativittayayut et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Neuroscience Keerativittayayut, Ruedeerat Aoki, Ryuta Sarabi, Mitra Taghizadeh Jimura, Koji Nakahara, Kiyoshi Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance |
title | Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance |
title_full | Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance |
title_fullStr | Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance |
title_full_unstemmed | Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance |
title_short | Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance |
title_sort | large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6039182/ https://www.ncbi.nlm.nih.gov/pubmed/29911970 http://dx.doi.org/10.7554/eLife.32696 |
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