Identification and Classification of Hubs in Brain Networks

University of Newcastle, United Kingdom
PLoS ONE (Impact Factor: 3.23). 02/2007; 2(10):e1049. DOI: 10.1371/journal.pone.0001049
Source: PubMed


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.

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Available from: Olaf Sporns
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    • "Thus, small-world networks are characterized by a high cluster coefficient 231 (C real ) and a short path length (L real ) compared to a random network (C rand , L rand ) [Bullmore 232 and Sporns, 2009]. Mathematically, small-worldness is defined by the small-world indices γ 233 (C real /C rand ), λ (L real /L rand ) and σ (λ/γ) [Humphries et al., 2006; Sporns et al., 2007]. C and L 234 were calculated with the Matlab-based Brain Connectivity Toolbox (http://www.brain- "
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    • "Hubs are nodes with a degree at least one standard deviation above the network mean. Thanks to this peculiarity, hubs play a significant role on the neural dynamics (Sporns et al., 2007). In the scale-free network, the probability that a generic node i has k connections is given by a power law relationship: p k ∝ k −γ (2) where γ is the characteristic exponent which ranges experimentally from 1.3 (slice recordings, Bonifazi et al., 2009) to 2 (fMRI recordings, Eguíluz et al., 2005). "
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    • "In the wider scope of graph theory, we can also regard node degree as one measure that characterizes centrality, or the extent to which a node is located near a central position in its relationships to other nodes. In this generalization, it is also possible to use other centrality measures instead of degree to characterize the central position of hubs in network organization (Sporns et al. 2007; Newman 2009). Furthermore, we can potentially detect " connector hubs " between different communities, which may play important roles for mediating communications between nodes participating in different communities . "

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