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      Cortical Hubs Form a Module for Multisensory Integration on Top of the Hierarchy of Cortical Networks

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          Abstract

          Sensory stimuli entering the nervous system follow particular paths of processing, typically separated (segregated) from the paths of other modal information. However, sensory perception, awareness and cognition emerge from the combination of information (integration). The corticocortical networks of cats and macaque monkeys display three prominent characteristics: (i) modular organisation (facilitating the segregation), (ii) abundant alternative processing paths and (iii) the presence of highly connected hubs. Here, we study in detail the organisation and potential function of the cortical hubs by graph analysis and information theoretical methods. We find that the cortical hubs form a spatially delocalised, but topologically central module with the capacity to integrate multisensory information in a collaborative manner. With this, we resolve the underlying anatomical substrate that supports the simultaneous capacity of the cortex to segregate and to integrate multisensory information.

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          Most cited references34

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          The structure and function of complex networks

          M. Newman (2003)
          Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
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            Identification and Classification of Hubs in Brain Networks

            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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              A measure for brain complexity: relating functional segregation and integration in the nervous system.

              In brains of higher vertebrates, the functional segregation of local areas that differ in their anatomy and physiology contrasts sharply with their global integration during perception and behavior. In this paper, we introduce a measure, called neural complexity (CN), that captures the interplay between these two fundamental aspects of brain organization. We express functional segregation within a neural system in terms of the relative statistical independence of small subsets of the system and functional integration in terms of significant deviations from independence of large subsets. CN is then obtained from estimates of the average deviation from statistical independence for subsets of increasing size. CN is shown to be high when functional segregation coexists with integration and to be low when the components of a system are either completely independent (segregated) or completely dependent (integrated). We apply this complexity measure in computer simulations of cortical areas to examine how some basic principles of neuroanatomical organization constrain brain dynamics. We show that the connectivity patterns of the cerebral cortex, such as a high density of connections, strong local connectivity organizing cells into neuronal groups, patchiness in the connectivity among neuronal groups, and prevalent reciprocal connections, are associated with high values of CN. The approach outlined here may prove useful in analyzing complexity in other biological domains such as gene regulation and embryogenesis.
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                Author and article information

                Journal
                Front Neuroinformatics
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Research Foundation
                1662-5196
                10 June 2009
                19 March 2010
                2010
                : 4
                : 1
                Affiliations
                [1] 1simpleInterdisciplinary Center for Dynamics of Complex Systems, University of Potsdam Potsdam, Germany
                [2] 2simpleDepartment of Physics, Centre for Nonlinear Studies, Hong Kong Baptist University Hong Kong, China
                [3] 3simpleThe Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems, Hong Kong Baptist University Hong Kong, China
                [4] 4simpleTransdisciplinary concepts and methods, Potsdam Institute for Climate Impact Research Potsdam, Germany
                [5] 5simpleInstitute of Physics, Humboldt University Berlin, Germany
                Author notes

                Edited by: Claus C. Hilgetag, Jacobs University Bremen, Germany

                Reviewed by: Steven Bressler, Florida Atlantic University, USA; David Meunier, University of Cambridge, UK

                *Correspondence: Gorka Zamora-López, Interdisciplinary Center for Dynamics of Complex Systems, University of Potsdam, Komplex II – Golm (Haus 28) , Karl-Liebknecht-Str. 24, D-14476 Potsdam, Germany. e-mail: gorka@ 123456agnld.uni-potsdam.de
                Article
                10.3389/neuro.11.001.2010
                2859882
                20428515
                0f502187-2429-47cd-9f29-d74992828ee8
                Copyright © 2010 Zamora-López, Zhou and Kurths.

                This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited

                History
                : 06 April 2009
                : 02 February 2010
                Page count
                Figures: 11, Tables: 1, Equations: 10, References: 50, Pages: 13, Words: 9027
                Categories
                Neuroscience
                Original Research

                Neurosciences
                cortical hubs,segregation,multisensory integration,corticocortical networks,integration

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