Estimating whole brain dynamics using spectral clustering

Understand

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.