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Estimating whole‐brain dynamics by using spectral clustering
Author(s) -
Cribben Ivor,
Yu Yi
Publication year - 2017
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12169
Subject(s) - computer science , data mining , cluster analysis , series (stratigraphy) , node (physics) , spectral clustering , data set , artificial intelligence , set (abstract data type) , time series , multivariate statistics , pattern recognition (psychology) , algorithm , machine learning , paleontology , structural engineering , engineering , biology , programming language
Summary 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.