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Estimating whole-brain dynamics by using spectral clustering
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Cribben, Ivor and Yu, Yi (2017) Estimating whole-brain dynamics by using spectral clustering. Journal of the Royal Statistical Society: Series C (Applied Statistics), 66 (3). pp. 607-627. doi:10.1111/rssc.12169 ISSN 00359254.
An open access version can be found in:
Official URL: http://dx.doi.org/10.1111/rssc.12169
Abstract
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.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
Journal or Publication Title: | Journal of the Royal Statistical Society: Series C (Applied Statistics) | ||||||||
Publisher: | Wiley | ||||||||
ISSN: | 00359254 | ||||||||
Official Date: | April 2017 | ||||||||
Dates: |
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Volume: | 66 | ||||||||
Number: | 3 | ||||||||
Page Range: | pp. 607-627 | ||||||||
DOI: | 10.1111/rssc.12169 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Related URLs: | |||||||||
Open Access Version: |
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