  The estimation of time-varying networks for functional Magnetic Resonance
Imaging (fMRI) data sets is of increasing importance and interest. In this
work, we formulate the problem in a high-dimensional time series framework and
introduce a data-driven method, namely Network Change Points Detection (NCPD),
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. NCPD is applied to various simulated data and a resting-state fMRI
data set. This new methodology also allows us to identify common functional
states within and across subjects. Finally, NCPD promises to offer a deep
insight into the large-scale characterisations and dynamics of the brain
