Simulation of robustness against lesions of cortical networks

Authors

  • Marcus Kaiser,

    1. School of Computing Science, University of Newcastle, Claremont Tower, Newcastle upon Tyne NE1 7RU, UK
    2. Henry Wellcome Building for Neuroecology, Institute of Neuroscience, University of Newcastle, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
    3. Jacobs University Bremen, School of Engineering and Science, Campus Ring 6, 28759 Bremen, Germany
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    • *

      M.K. and R.M. contributed equally to this paper.

  • Robert Martin,

    1. Henry Wellcome Building for Neuroecology, Institute of Neuroscience, University of Newcastle, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
    2. Neural Information Processing Group, Technische Universität Berlin, FR2-1, Franklinstr. 28/29, 10587 Berlin, Germany
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    • *

      M.K. and R.M. contributed equally to this paper.

  • Peter Andras,

    1. School of Computing Science, University of Newcastle, Claremont Tower, Newcastle upon Tyne NE1 7RU, UK
    2. Henry Wellcome Building for Neuroecology, Institute of Neuroscience, University of Newcastle, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
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  • Malcolm P. Young

    1. Henry Wellcome Building for Neuroecology, Institute of Neuroscience, University of Newcastle, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
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Dr M. Kaiser, as above.1
E-mail: m.kaiser@ncl.ac.uk

Abstract

Structure entails function, and thus a structural description of the brain will help to understand its function and may provide insights into many properties of brain systems, from their robustness and recovery from damage to their dynamics and even their evolution. Advances in the analysis of complex networks provide useful new approaches to understanding structural and functional properties of brain networks. Structural properties of networks recently described allow their characterization as small-world, random (exponential) and scale-free. They complement the set of other properties that have been explored in the context of brain connectivity, such as topology, hodology, clustering and hierarchical organization. Here we apply new network analysis methods to cortical interareal connectivity networks for the cat and macaque brains. We compare these corticocortical fibre networks to benchmark rewired, small-world, scale-free and random networks using two analysis strategies, in which we measure the effects of the removal of nodes and connections on the structural properties of the cortical networks. The structural decay of the brain networks is in most respects similar to that of scale-free networks. The results implicate highly connected hub-nodes and bottleneck connections as a structural basis for some of the conditional robustness of brain systems. This informs the understanding of the development of connectivity of the brain networks.

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