Identification and classification of hubs in brain networks.

Brain regions in the mammalian cerebral cortex are linked by a complex network of fiber bundles. These inter-regional networks have previously been analyzed in terms of their node degree, structural motif, path length and clustering coefficient distributions. In this paper we focus on the identifica...

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Autores principales:	Olaf Sporns, Christopher J Honey, Rolf Kötter
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2007
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a570f364572f420f83f74d2642b3a55e
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spelling	oai:doaj.org-article:a570f364572f420f83f74d2642b3a55e2021-11-25T06:10:40ZIdentification and classification of hubs in brain networks.1932-620310.1371/journal.pone.0001049https://doaj.org/article/a570f364572f420f83f74d2642b3a55e2007-10-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0001049https://doaj.org/toc/1932-6203Brain regions in the mammalian cerebral cortex are linked by a complex network of fiber bundles. These inter-regional networks have previously been analyzed in terms of their node degree, structural motif, path length and clustering coefficient distributions. In this paper we focus on the identification and classification of hub regions, which are thought to play pivotal roles in the coordination of information flow. We identify hubs and characterize their network contributions by examining motif fingerprints and centrality indices for all regions within the cerebral cortices of both the cat and the macaque. Motif fingerprints capture the statistics of local connection patterns, while measures of centrality identify regions that lie on many of the shortest paths between parts of the network. Within both cat and macaque networks, we find that a combination of degree, motif participation, betweenness centrality and closeness centrality allows for reliable identification of hub regions, many of which have previously been functionally classified as polysensory or multimodal. We then classify hubs as either provincial (intra-cluster) hubs or connector (inter-cluster) hubs, and proceed to show that lesioning hubs of each type from the network produces opposite effects on the small-world index. Our study presents an approach to the identification and classification of putative hub regions in brain networks on the basis of multiple network attributes and charts potential links between the structural embedding of such regions and their functional roles.Olaf SpornsChristopher J HoneyRolf KötterPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 2, Iss 10, p e1049 (2007)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Olaf Sporns Christopher J Honey Rolf Kötter Identification and classification of hubs in brain networks.
description	Brain regions in the mammalian cerebral cortex are linked by a complex network of fiber bundles. These inter-regional networks have previously been analyzed in terms of their node degree, structural motif, path length and clustering coefficient distributions. In this paper we focus on the identification and classification of hub regions, which are thought to play pivotal roles in the coordination of information flow. We identify hubs and characterize their network contributions by examining motif fingerprints and centrality indices for all regions within the cerebral cortices of both the cat and the macaque. Motif fingerprints capture the statistics of local connection patterns, while measures of centrality identify regions that lie on many of the shortest paths between parts of the network. Within both cat and macaque networks, we find that a combination of degree, motif participation, betweenness centrality and closeness centrality allows for reliable identification of hub regions, many of which have previously been functionally classified as polysensory or multimodal. We then classify hubs as either provincial (intra-cluster) hubs or connector (inter-cluster) hubs, and proceed to show that lesioning hubs of each type from the network produces opposite effects on the small-world index. Our study presents an approach to the identification and classification of putative hub regions in brain networks on the basis of multiple network attributes and charts potential links between the structural embedding of such regions and their functional roles.
format	article
author	Olaf Sporns Christopher J Honey Rolf Kötter
author_facet	Olaf Sporns Christopher J Honey Rolf Kötter
author_sort	Olaf Sporns
title	Identification and classification of hubs in brain networks.
title_short	Identification and classification of hubs in brain networks.
title_full	Identification and classification of hubs in brain networks.
title_fullStr	Identification and classification of hubs in brain networks.
title_full_unstemmed	Identification and classification of hubs in brain networks.
title_sort	identification and classification of hubs in brain networks.
publisher	Public Library of Science (PLoS)
publishDate	2007
url	https://doaj.org/article/a570f364572f420f83f74d2642b3a55e
work_keys_str_mv	AT olafsporns identificationandclassificationofhubsinbrainnetworks AT christopherjhoney identificationandclassificationofhubsinbrainnetworks AT rolfkotter identificationandclassificationofhubsinbrainnetworks
_version_	1718414122394058752

Identification and classification of hubs in brain networks.

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