Estimating whole-brain dynamics by using spectral clustering

• Published in: Journal of the Royal Statistical Society Series C: Applied Statistics Vol. 66; no. 3; pp. 607 - 627
• Main Authors: Cribben, Ivor, Yu, Yi
• Format: Journal Article
• Language: English
• Published: Oxford John Wiley & Sons Ltd 01.04.2017; Oxford University Press
• Subjects: Brain; Change point analysis; Dimensional changes; Dynamics; Functional magnetic resonance imaging; Magnetic resonance imaging; Multivariate analysis; Network change points; Networks; NMR; Nuclear magnetic resonance; Resting state data; Simulation; Spectra; Spectral clustering; Stationary bootstrap; Studies; Time series
• ISSN: 0035-9254; 1467-9876
• DOI: 10.1111/rssc.12169

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.