Identification and Classification of Hubs in Brain Networks

Department of Psychological and Brain Sciences and Program in Cognitive Science, Indiana University, Bloomington, Indiana, United States of America.
PLoS ONE (Impact Factor: 3.53). 02/2007; 2(10):e1049. DOI: 10.1371/journal.pone.0001049
Source: PubMed

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.

Download full-text


Available from: Olaf Sporns, Jul 06, 2015
1 Follower
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: After a brief introduction to principles of Cortical Learning Algorithm (CLA) and Hierarchical Temporal Memory (HTM) which frame the theory for NuPIC – a human neocortex-inspired neural network implementation, we focus on practical use-cases in context of Smart Homes: 1) processing bio-signals (ECG classification, prediction, anomaly detection) for the purpose of mobile assisted technologies. 2) prediction of sensory and location data in smart homes. Preliminary results, realized and in-progress projects are presented in this study as well as obstacles and some comparison to other solution for these ML tasks.
    Smart Homes, Prague, Czech republic; 11/2014
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Language production is a complex neural process that requires the interplay between multiple specialized cortical regions. We investigated modulations in large-scale cortical networks underlying preparation for speech production by contrasting cortico-cortical coherence for overt and silent picture naming in an all-to-all connectivity analysis. To capture transient, frequency-specific changes in functional connectivity we analyzed the magnetoencephalography data in two consecutive 300-ms time windows. Within the first 300 ms following picture onset beta frequency coherence was increased for overt naming in a network of regions comprising the bilateral parieto-temporal junction and medial cortices, suggesting that overt articulation modifies selection processes involved in speech planning. In the late time window (300–600 ms after picture onset) beta-range coherence was enhanced in a network that included the ventral sensorimotor and temporal cortices. Coherence in the gamma band was simultaneously reduced between the ventral motor cortex and supplementary motor area, bilaterally. The results suggest functionally distinct roles for beta (facilitatory) and gamma (suppressive) band interactions in speech production, with strong involvement of the motor cortex in both frequency bands. Overall, a striking difference in functional connectivity between the early and late time windows was observed, revealing the dynamic nature of large-scale cortical networks that support language and speech. Our results demonstrate that as the naming task evolves in time, the global connectivity patterns change, and that these changes occur (at least) on the time-scale of a few hundred milliseconds. More generally, these results bear implications for how we view large-scale neural networks underlying task performance. Hum Brain Mapp, 2014. © 2014 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
    Human Brain Mapping 11/2014; DOI:10.1002/hbm.22697 · 6.92 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We present in this paper a novel neuroinformatic platform, the BAMS2 Workspace (, designed for storing and processing information about gray matter region axonal connections. This de novo constructed module allows registered users to directly collate their data by using a simple and versatile visual interface. It also allows construction and analysis of sets of connections associated with gray matter region nomenclatures from any designated species. The Workspace includes a set of tools allowing the display of data in matrix and networks formats, and the uploading of processed information in visual, PDF, CSV, and Excel formats. Finally, the Workspace can be accessed anonymously by third party systems to create individualized connectivity networks. All features of the BAMS2 Workspace are described in detail, and are demonstrated with connectivity reports collated in BAMS and associated with the rat sensory-motor cortex, medial frontal cortex, and amygdalar regions. J. Comp. Neurol., 2014. © 2014 Wiley Periodicals, Inc.
    The Journal of Comparative Neurology 10/2014; 522(14). DOI:10.1002/cne.23592 · 3.51 Impact Factor