Abstract
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.
Highlights
Large-scale cortical networks, comprising anatomically distinct regions and inter-regional pathways [1,2,3], exhibit specific nonrandom connection patterns [4]
Structural analyses have utilized a wide spectrum of graph theoretic measures [5,6] including clustering coefficients and the distributions of node degrees, path lengths and structural motifs
We show that the intersection of node degree, motif fingerprint, betweenness and closeness allows the identification of hub regions, many of which have previously been classified as polysensory or multimodal
Summary
Large-scale cortical networks, comprising anatomically distinct regions and inter-regional pathways [1,2,3], exhibit specific nonrandom connection patterns [4]. The structural (i.e. topological) features of large-scale cortical networks are of special interest as they may be linked to aspects of brain function. Brain networks have been found to exhibit high levels of clustering combined with short average path lengths, a pattern indicative of a small-world architecture [7,8,9,10,11]. Global structural parameters can reveal the organization of an entire network, but they cannot capture the contributions of individual network elements (e.g. brain regions). Network participation indices capturing some local statistics of degree distributions (density, transmission, and symmetry; [19]) revealed significant differences across brain regions in macaque cortex and highlighted the relations between their individual topological and functional characteristics. Some hub regions appeared to link multiple functional clusters (e.g. visual and sensorimotor) while others occupied central positions within a single functional cluster
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