A shrinkage method for causal network detection of brain regions

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 ( 2007 ), 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. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 140–146, 2013