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

Interdisciplinary Center for Dynamics of Complex Systems, University of Potsdam Potsdam, Germany.
Frontiers in Neuroinformatics (Impact Factor: 3.26). 03/2010; 4:1. DOI: 10.3389/neuro.11.001.2010
Source: PubMed

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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Available from: Gorka Zamora-López, Sep 28, 2015
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    • "We use this representation of nodeʼs roles in order to show that the structure of neuronal networks is optimized to process information in a way that combines specialization and integration [22] [23]. Those are two features that coexist thanks to the combination of modular differentiation and highly interconnected hubs [24] [25] [26]. Finally, we study the transcriptional regulatory network of the Mycobacterium tuberculosis [27] [28] to gain understanding on the relationship between functional classes of genes. "
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    ABSTRACT: The analysis of complex networks is devoted to the statistical characterization of the topology of graphs at different scales of organization in order to understand their functionality. While the modular structure of networks has become an essential element to better apprehend their complexity, the efforts to characterize the mesoscale of networks have focused on the identification of the modules rather than describing the mesoscale in an informative manner. Here we propose a framework to characterize the position every node takes within the modular configuration of complex networks and to evaluate their function accordingly. For illustration, we apply this framework to a set of synthetic networks, empirical neural networks, and to the transcriptional regulatory network of the Mycobacterium tuberculosis. We find that the architecture of both neuronal and transcriptional networks are optimized for the processing of multisensory information with the coexistence of well-defined modules of specialized components and the presence of hubs conveying information from and to the distinct functional domains.
    New Journal of Physics 12/2014; 16(12):125006. DOI:10.1088/1367-2630/16/12/125006 · 3.56 Impact Factor
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    • "Each node in the network corresponds to a cytoarchitecturally defined area and each link (or edge) represents a physical connection promoted by the axonal pathways between two areas. Connectivity data of this kind allow us to investigate properties of the high-level processes taking place in the cortex, such as communication efficiency [7], integration of information [29] [27], modular organization [17], robustness against lesion [12], and the effect of diseases in connectivity [4]. A recent study [15] has used quantitative anatomical tract tracing to map the interareal connectivity of the macaque monkey cerebral cortex with unprecedented richness of detail. "
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    ABSTRACT: Over the past years, network science has proven invaluable as a means to better understand many of the processes taking place in the brain. Recently, interareal connectivity data of the macaque cortex was made available with great richness of detail. We explore new aspects of this dataset, such as a correlation between connection weights and cortical hierarchy. We also look at the link-community structure that emerges from the data to uncover the major communication pathways in the network, and moreover investigate its reciprocal connections, showing that they share similar properties.
    11/2014; DOI:10.1017/nws.2015.19
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    • "At first glance, subsystems with high functional integration are also expected to display high functional segregation. The fact that Connectome subsystems have relatively high values of both Subsystem Integration and Subsystem-Environment MI suggests that they may balance a trade-off between two important information-processing functions: accessing information from large areas of the brain and integrating it efficiently across a network of hub regions (Zamora-López et al., 2010). We investigated this question by looking at particular values of Subsystem Integration and Subsystem-Environment MI for subsystems of size of 11 (~5% of one hemisphere) (Figure 7A). "
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    ABSTRACT: The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales.
    Frontiers in Neuroinformatics 06/2014; doi: 10.3389/fninf.2014.00066. DOI:10.3389/fninf.2014.00066 · 3.26 Impact Factor
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