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Publication - Dr Yi Yu

    Estimating whole-brain dynamics by using spectral clustering

    Citation

    Cribben, I & Yu, Y, 2017, ‘Estimating whole-brain dynamics by using spectral clustering’. Journal of the Royal Statistical Society: Series C, vol 66., pp. 607-627

    Abstract

    The estimation of time varying networks for functional magnetic
    resonance imaging data sets is of increasing importance and interest. We
    formulate the problem in a high dimensional time series framework and
    introduce a data-driven method, namely network change points detection,
    which detects change points in the network structure of a multivariate
    time series, with each component of the time series represented by a
    node in the network. Network change points detection is applied to
    various simulated data and a resting state functional magnetic resonance
    imaging data set. This new methodology also allows us to identify
    common functional states within and across subjects. Finally, network
    change points detection promises to offer a deep insight into the
    large-scale characterizations and dynamics of the brain.

    Full details in the University publications repository