A new measure of centrality for brain networks.

Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central no...

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Autores principales:	Karen E Joyce, Paul J Laurienti, Jonathan H Burdette, Satoru Hayasaka
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d269ba62466e42e0971b7b25cc462fed
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spelling	oai:doaj.org-article:d269ba62466e42e0971b7b25cc462fed2021-11-18T06:36:02ZA new measure of centrality for brain networks.1932-620310.1371/journal.pone.0012200https://doaj.org/article/d269ba62466e42e0971b7b25cc462fed2010-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20808943/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood). Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network.Karen E JoycePaul J LaurientiJonathan H BurdetteSatoru HayasakaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 8, p e12200 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Karen E Joyce Paul J Laurienti Jonathan H Burdette Satoru Hayasaka A new measure of centrality for brain networks.
description	Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood). Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network.
format	article
author	Karen E Joyce Paul J Laurienti Jonathan H Burdette Satoru Hayasaka
author_facet	Karen E Joyce Paul J Laurienti Jonathan H Burdette Satoru Hayasaka
author_sort	Karen E Joyce
title	A new measure of centrality for brain networks.
title_short	A new measure of centrality for brain networks.
title_full	A new measure of centrality for brain networks.
title_fullStr	A new measure of centrality for brain networks.
title_full_unstemmed	A new measure of centrality for brain networks.
title_sort	new measure of centrality for brain networks.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/d269ba62466e42e0971b7b25cc462fed
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A new measure of centrality for brain networks.

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