A shrinkage method for causal network detection of brain regions

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We present a computationally as well as statistically efficient method of inferring causal networks for the brain regions. It is based on James-Stein-type shrinkage estimation of covariance matrix, suggested by (Opgen-Rhein and Strimmer, BMC Syst Biol 1 (), 37-40), among different brain regions of interest of the functional magnetic resonance imaging (fMRI) experiment, that enhance the accuracy of vector autoregressive (VAR) model coefficient estimates. We have shown that this approach is well suited for the small number of samples in time and large number of brain regions encountered in real fMRI experiments of seventeen healthy individuals. (c) 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 140146, 2013
Publisher
WILEY-BLACKWELL
Issue Date
2013-06
Language
English
Article Type
Article
Keywords

GRANGER CAUSALITY; DECISION-MAKING; TIME-SERIES; FUNCTIONAL CONNECTIVITY; CORTICAL INTERACTIONS; NEURAL SYSTEMS; PATH DIAGRAMS; FMRI DATA; MODELS; IDENTIFICATION

Citation

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, v.23, no.2, pp.140 - 146

ISSN
0899-9457
DOI
10.1002/ima.22047
URI
http://hdl.handle.net/10203/174016
Appears in Collection
MA-Journal Papers(저널논문)EE-Journal Papers(저널논문)
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