Estimating whole-brain dynamics by using spectral clustering

Ivor Cribben, Yi Yu

Research output: Contribution to journalArticle (Academic Journal)peer-review

39 Citations (Scopus)
336 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)607-627
Number of pages21
JournalJournal of the Royal Statistical Society: Series C
Issue number3
Early online date1 Sept 2016
Publication statusPublished - Apr 2017


  • Change point analysis
  • Functional magnetic resonance imaging
  • Network change points
  • Resting state data
  • Spectral clustering
  • Stationary bootstrap


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