Functional interactivity in fMRI using multiple seeds' correlation analyses--novel methods and comparisons.

This paper presents novel statistical methods for estimating brain networks from fMRI data. Functional interactions are detected by simultaneously examining multi-seed correlations via multiple correlation coefficients. Spatially structured noise in fMRI is also taken into account during the identification of functional interconnection networks through non-central F hypothesis tests. Furthermore, partial multiple correlations are introduced and formulated to measure any additional task-induced but not stimulus-locked relation over brain regions so that we can take the analysis of functional connectivity closer to the characterization of direct functional interactions of the brain. Evaluation for accuracy and advantages of the new approaches and comparison with the existing single-seed method were performed extensively using both simulated data and real fMRI data.