Identification and classification of hubs in brain networks

• Published in: PloS one Vol. 2; no. 10; p. e1049
• Main Authors: Sporns, Olaf, Honey, Christopher J, Kötter, Rolf
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17-10-2007; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Brain; Brain - anatomy & histology; Brain - physiology; Brain Mapping; Cats; Cerebral cortex; Cerebral Cortex - anatomy & histology; Cerebral Cortex - physiology; Charts; Classification; Clustering; Clusters; Computer Simulation; Datasets; Embedding; Fingerprints; Gene expression; Hubs; Information flow; Macaca; Models, Anatomic; Models, Biological; Models, Statistical; Nerve Net; Nervous system; Networks; Neural Pathways; Neuroscience/Cognitive Neuroscience; Neuroscience/Theoretical Neuroscience; Protein Interaction Mapping; Rabies; Regional analysis; Species Specificity; Indiana; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0001049

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.