Identification and Classification of Hubs in Brain Networks

By Olaf Sporns, Christopher J. Honey, Rolf Kötter. PLoS ONE, 2007

1,175 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b07597da6ccfe2770a158441ab63d9c099d6d8e5

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Citations in the corpus

Recurrent Processing during Object Recognition (2013, background): “stream (as well as many other neural systems) are bidirectional (Felleman & Van Essen, 1991; Scannell et al., 1995; Sporns et al., 2007; Sporns & Zwi, 2004).”
Graph-Based Network Analysis of Resting-State Functional MRI (2010, positive): “A node with high centrality is considered a hub in the network (Sporns et al., 2007; He et al., 2008; Buckner et al., 2009).”
Rich-Club Organization of the Human Connectome (2011, background): “…in nonhuman species reporting on the tendency of hubs in the macaque and cat cortex to form “hub complexes” of high interconnectivity density (Sporns et al., 2007; Zamora-López et al., 2009) and reinforces the results of a recent study suggesting a hierarchical assortative organization in…”
Comparison of Characteristics between Region- and Voxel-Based Network Analyses in Resting-State fMRI Data (2009, positive): “The nervous systems of mammals such as cats and monkeys have been shown to exhibit small-world network properties based on histologically identified regional connections (Hilgetag, et al. 2000; Honey and Sporns 2008; Sporns, et al. 2007; Sporns and Zwi 2004).”
Structural Properties of the Caenorhabditis elegans Neuronal Network (2009, background): “We also present the beginnings of a theoretical phase to conn e tomics [28], [29], pursuing an understanding of function.”
Network-Level Structure-Function Relationships in Human Neocortex (2016, background): “Due to their dense connectivity, hubs are hypothesized to act as bridges for signal traffic, facilitating the spread and integration of information in brain networks (Sporns et al. 2007; ZamoraLopez et al. 2010; van den Heuvel and Sporns 2013).”
Whole-brain anatomical networks: Does the choice of nodes matter? (2010, background): “The issue of nodal choice first arose when brain networks of mammalia such as the cat and macaque were analyzed (Felleman and Van Essen, 1991; Hilgetag et al., 2000a,b; Sporns et al., 2007; Young, 1993).”
Resting state fMRI: A review of methods and clinical applications (2012, neutral): “39,45 40-42 Another method used to analyze RS-fMRI data is clustering algorithms.”
The Human Thalamus Is an Integrative Hub for Functional Brain Networks (2016, methodology): “Graph-theoretic measures can be used to quantify topographic properties of each brain region and make inferences on each region's network functions (Sporns et al., 2007).”
Fast transient networks in spontaneous human brain activity (2014, background): “While there is strong evidence for hub-like behavior of the PCC/precuneus in terms of structural (Sporns et al., 2007; Hagmann et al., 2008) and functional (Buckner et al.”