Cluster-span threshold

An unbiased threshold for binarising weighted complete networks in functional connectivity analysis

Keith Smith, Hamed Azami, Mario Parra Rodriguez, John M. Starr, Javier Escudero

    Research output: Contribution to conferenceOther

    Abstract

    We propose a new unbiased threshold for network analysis named the Cluster-Span Threshold (CST). This is based on the clustering coefficient, C, following logic that a balance of ?clustering? to ?spanning? triples results in a useful topology for network analysis and that the product of complementing properties has a unique value only when perfectly balanced. We threshold networks by fixing C at this balanced value, rather than fixing connection density at an arbitrary value, as has been the trend. We compare results from an electroencephalogram data set of volunteers performing visual short term memory tasks of the CST alongside other thresholds, including maximum spanning trees. We find that the CST holds as a sensitive threshold for distinguishing differences in the functional connectivity between tasks. This provides a sensitive and objective method for setting a threshold on weighted complete networks which may prove influential on the future of functional connectivity research.
    Original languageEnglish
    Pages2840-2843
    Number of pages4
    Publication statusPublished - 27 Aug 2015

    Fingerprint

    functional analysis
    thresholds
    network analysis
    fixing
    electroencephalography
    logic
    topology
    trends

    Keywords

    • connectivity measurements
    • nonlinear coupling of biomedical signals

    Cite this

    Smith, K., Azami, H., Parra Rodriguez, M., Starr, J. M., & Escudero, J. (2015). Cluster-span threshold: An unbiased threshold for binarising weighted complete networks in functional connectivity analysis. 2840-2843.
    Smith, Keith ; Azami, Hamed ; Parra Rodriguez, Mario ; Starr, John M. ; Escudero, Javier. / Cluster-span threshold : An unbiased threshold for binarising weighted complete networks in functional connectivity analysis. 4 p.
    @conference{0d59c35447554ea7919d8edeb8206bce,
    title = "Cluster-span threshold: An unbiased threshold for binarising weighted complete networks in functional connectivity analysis",
    abstract = "We propose a new unbiased threshold for network analysis named the Cluster-Span Threshold (CST). This is based on the clustering coefficient, C, following logic that a balance of ?clustering? to ?spanning? triples results in a useful topology for network analysis and that the product of complementing properties has a unique value only when perfectly balanced. We threshold networks by fixing C at this balanced value, rather than fixing connection density at an arbitrary value, as has been the trend. We compare results from an electroencephalogram data set of volunteers performing visual short term memory tasks of the CST alongside other thresholds, including maximum spanning trees. We find that the CST holds as a sensitive threshold for distinguishing differences in the functional connectivity between tasks. This provides a sensitive and objective method for setting a threshold on weighted complete networks which may prove influential on the future of functional connectivity research.",
    keywords = "connectivity measurements, nonlinear coupling of biomedical signals",
    author = "Keith Smith and Hamed Azami and {Parra Rodriguez}, Mario and Starr, {John M.} and Javier Escudero",
    note = "Date of acceptance: 31/05/2015",
    year = "2015",
    month = "8",
    day = "27",
    language = "English",
    pages = "2840--2843",

    }

    Smith, K, Azami, H, Parra Rodriguez, M, Starr, JM & Escudero, J 2015, 'Cluster-span threshold: An unbiased threshold for binarising weighted complete networks in functional connectivity analysis' pp. 2840-2843.

    Cluster-span threshold : An unbiased threshold for binarising weighted complete networks in functional connectivity analysis. / Smith, Keith; Azami, Hamed; Parra Rodriguez, Mario; Starr, John M.; Escudero, Javier.

    2015. 2840-2843.

    Research output: Contribution to conferenceOther

    TY - CONF

    T1 - Cluster-span threshold

    T2 - An unbiased threshold for binarising weighted complete networks in functional connectivity analysis

    AU - Smith, Keith

    AU - Azami, Hamed

    AU - Parra Rodriguez, Mario

    AU - Starr, John M.

    AU - Escudero, Javier

    N1 - Date of acceptance: 31/05/2015

    PY - 2015/8/27

    Y1 - 2015/8/27

    N2 - We propose a new unbiased threshold for network analysis named the Cluster-Span Threshold (CST). This is based on the clustering coefficient, C, following logic that a balance of ?clustering? to ?spanning? triples results in a useful topology for network analysis and that the product of complementing properties has a unique value only when perfectly balanced. We threshold networks by fixing C at this balanced value, rather than fixing connection density at an arbitrary value, as has been the trend. We compare results from an electroencephalogram data set of volunteers performing visual short term memory tasks of the CST alongside other thresholds, including maximum spanning trees. We find that the CST holds as a sensitive threshold for distinguishing differences in the functional connectivity between tasks. This provides a sensitive and objective method for setting a threshold on weighted complete networks which may prove influential on the future of functional connectivity research.

    AB - We propose a new unbiased threshold for network analysis named the Cluster-Span Threshold (CST). This is based on the clustering coefficient, C, following logic that a balance of ?clustering? to ?spanning? triples results in a useful topology for network analysis and that the product of complementing properties has a unique value only when perfectly balanced. We threshold networks by fixing C at this balanced value, rather than fixing connection density at an arbitrary value, as has been the trend. We compare results from an electroencephalogram data set of volunteers performing visual short term memory tasks of the CST alongside other thresholds, including maximum spanning trees. We find that the CST holds as a sensitive threshold for distinguishing differences in the functional connectivity between tasks. This provides a sensitive and objective method for setting a threshold on weighted complete networks which may prove influential on the future of functional connectivity research.

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    KW - nonlinear coupling of biomedical signals

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    SP - 2840

    EP - 2843

    ER -