Dear Gang,
This is a wonderful development! It seems that your process involves extracting preprocessed time-courses from a manageable number of seed regions of interest and calculating the unique predictive power of one region's time-course relative to another--once shared variance with potentially mediating seed regions is accounted for, or, more optimistically, once the mediating power of other potentially influential seed regions is uncovered.
Does the process you've developed do task-dependent GCA, resting-state GCA or both? Any chance of implementing a more exploratory yet less rigorous approach that calculates the predictive capacity of one seed region's time-course relative to the time-courses of all other voxels?
We have 20-participants worth of "resting-state" data that have been acquired with maximizing sensitivity of GCA in mind [TR = 1200 ms; 250 acquisitions; 18 5-mm slices covering, on average, the bottom of the temporal lobes to the top of the brain]. If you think that these data might be compatible with the current incarnation of your GCM process, then we'd be happy to take them for a test-drive.
Best regards,
Paul