• Login
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    A Topological Criterion for Filtering Information in Complex Brain Networks. 
    •   QMRO Home
    • Queen Mary University of London
    • College Publications
    • A Topological Criterion for Filtering Information in Complex Brain Networks.
    •   QMRO Home
    • Queen Mary University of London
    • College Publications
    • A Topological Criterion for Filtering Information in Complex Brain Networks.
    ‌
    ‌

    Browse

    All of QMROCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    ‌
    ‌

    Administrators only

    Login
    ‌
    ‌

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    A Topological Criterion for Filtering Information in Complex Brain Networks.

    Volume
    13
    Pagination
    e1005305 - ?
    DOI
    10.1371/journal.pcbi.1005305
    Journal
    PLoS Comput Biol
    Issue
    1
    Metadata
    Show full item record
    Abstract
    In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to a fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections. The structural properties of the resulting network depend on how much of the inferred connectivity is eventually retained. However, how to objectively fix this threshold is still an open issue. We introduce a criterion, the efficiency cost optimization (ECO), to select a threshold based on the optimization of the trade-off between the efficiency of a network and its wiring cost. We prove analytically and we confirm through numerical simulations that the connection density maximizing this trade-off emphasizes the intrinsic properties of a given network, while preserving its sparsity. Moreover, this density threshold can be determined a-priori, since the number of connections to filter only depends on the network size according to a power-law. We validate this result on several brain networks, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering methods. ECO advances our ability to analyze and compare biological networks, inferred from experimental data, in a fast and principled way.
    Authors
    De Vico Fallani, F; Latora, V; Chavez, M
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/22485
    Collections
    • College Publications [5168]
    Language
    eng
    Licence information
    This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    Copyright statements
    © 2017 De Vico Fallani et al
    Twitter iconFollow QMUL on Twitter
    Twitter iconFollow QM Research
    Online on twitter
    Facebook iconLike us on Facebook
    • Site Map
    • Privacy and cookies
    • Disclaimer
    • Accessibility
    • Contacts
    • Intranet
    • Current students

    Modern Slavery Statement

    Queen Mary University of London
    Mile End Road
    London E1 4NS
    Tel: +44 (0)20 7882 5555

    © Queen Mary University of London.