Journal
NEUROIMAGE
Volume 32, Issue 1, Pages 228-237Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2005.12.057
Keywords
functional MRI; functional brain interactivity; motor network; partial correlation; functional connectivity; effective connectivity; Bayesian analysis
Ask authors/readers for more resources
Examination of functional interactions through effective connectivity requires the determination of three distinct levels of information: (1) the regions involved in the process and forming the spatial support of the network, (2) the presence or absence of interactions between each pair of regions, and (3) the directionality of the existing interactions. While many methods exist to select regions (Step 1), very little is available to complete Step 2. The two main methods developed so far, structural equation modeling (SENI) and dynamical causal modeling (DCM), usually require precise prior information to be used, while such information is sometimes lacking. Assuming that Step I was successfully completed, we here propose a data-driven method to deal with Step 2 and extract functional interactions from fMRI datasets through partial correlations. Partial correlation is more closely related to effective connectivity than marginal correlation and provides a convenient graphical representation for functional interactions. As an instance of brain interactivity investigation, we consider how simple hand movements are processed by the bihemispheric cortical motor network. In the proposed framework, Bayesian analysis makes it possible to estimate and test the partial statistical dependencies between regions without any prior model on the underlying functional interactions. We demonstrate the interest of this approach on real data. (c) 2006 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available