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Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks
Brain function is often characterized by the connections and interactions between highly interconnected brain regions. Pathological disruptions in these networks often result in brain dysfunction, which manifests as brain disease. Typical analysis investigates disruptions in network connectivity bas...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5410606/ https://www.ncbi.nlm.nih.gov/pubmed/28507502 http://dx.doi.org/10.3389/fnins.2017.00238 |
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author | Sohn, William S. Lee, Tae Young Yoo, Kwangsun Kim, Minah Yun, Je-Yeon Hur, Ji-Won Yoon, Youngwoo Bryan Seo, Sang Won Na, Duk L. Jeong, Yong Kwon, Jun Soo |
author_facet | Sohn, William S. Lee, Tae Young Yoo, Kwangsun Kim, Minah Yun, Je-Yeon Hur, Ji-Won Yoon, Youngwoo Bryan Seo, Sang Won Na, Duk L. Jeong, Yong Kwon, Jun Soo |
author_sort | Sohn, William S. |
collection | PubMed |
description | Brain function is often characterized by the connections and interactions between highly interconnected brain regions. Pathological disruptions in these networks often result in brain dysfunction, which manifests as brain disease. Typical analysis investigates disruptions in network connectivity based correlations between large brain regions. To obtain a more detailed description of disruptions in network connectivity, we propose a new method where functional nodes are identified in each region based on their maximum connectivity to another brain region in a given network. Since this method provides a unique approach to identifying functionally relevant nodes in a given network, we can provide a more detailed map of brain connectivity and determine new measures of network connectivity. We applied this method to resting state fMRI of Alzheimer's disease patients to validate our method and found decreased connectivity within the default mode network. In addition, new measure of network connectivity revealed a more detailed description of how the network connections deteriorate with disease progression. This suggests that analysis using key relative network hub regions based on regional correlation can be used to detect detailed changes in resting state network connectivity. |
format | Online Article Text |
id | pubmed-5410606 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-54106062017-05-15 Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks Sohn, William S. Lee, Tae Young Yoo, Kwangsun Kim, Minah Yun, Je-Yeon Hur, Ji-Won Yoon, Youngwoo Bryan Seo, Sang Won Na, Duk L. Jeong, Yong Kwon, Jun Soo Front Neurosci Neuroscience Brain function is often characterized by the connections and interactions between highly interconnected brain regions. Pathological disruptions in these networks often result in brain dysfunction, which manifests as brain disease. Typical analysis investigates disruptions in network connectivity based correlations between large brain regions. To obtain a more detailed description of disruptions in network connectivity, we propose a new method where functional nodes are identified in each region based on their maximum connectivity to another brain region in a given network. Since this method provides a unique approach to identifying functionally relevant nodes in a given network, we can provide a more detailed map of brain connectivity and determine new measures of network connectivity. We applied this method to resting state fMRI of Alzheimer's disease patients to validate our method and found decreased connectivity within the default mode network. In addition, new measure of network connectivity revealed a more detailed description of how the network connections deteriorate with disease progression. This suggests that analysis using key relative network hub regions based on regional correlation can be used to detect detailed changes in resting state network connectivity. Frontiers Media S.A. 2017-05-01 /pmc/articles/PMC5410606/ /pubmed/28507502 http://dx.doi.org/10.3389/fnins.2017.00238 Text en Copyright © 2017 Sohn, Lee, Yoo, Kim, Yun, Hur, Yoon, Seo, Na, Jeong and Kwon. http://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) 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 Sohn, William S. Lee, Tae Young Yoo, Kwangsun Kim, Minah Yun, Je-Yeon Hur, Ji-Won Yoon, Youngwoo Bryan Seo, Sang Won Na, Duk L. Jeong, Yong Kwon, Jun Soo Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks |
title | Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks |
title_full | Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks |
title_fullStr | Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks |
title_full_unstemmed | Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks |
title_short | Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks |
title_sort | node identification using inter-regional correlation analysis for mapping detailed connections in resting state networks |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5410606/ https://www.ncbi.nlm.nih.gov/pubmed/28507502 http://dx.doi.org/10.3389/fnins.2017.00238 |
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