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Hi,

I think one way you can use is performing one-sample t-test (on the zFC*.img) within each group. Then create a mask (only including the voxels with significant positive correlations) for each group. And then make a union of these two masks for between-group analysis.

One concern about this way is a multiple comparison issue. The union mask brought some information to the between-group analysis. For a voxel inside this mask, it's significant in one group, but could be either significant or insignificant in other group. E.g., for those voxels is significant (positive) in group 1 but not significant in group 2, it's more easily find significant higher activity in group 1 than group 2. By bringing such an information into between-group analysis, there should be a multiple comparison issue. I am not very sure how to address such an issue appropriately, probably other experts could provide more thoughts.

Best,

Chao-Gan



On Fri, Aug 24, 2012 at 10:46 AM, KimMJ <[log in to unmask]> wrote:
Dear experts..

I'm analyzing seed-based functional connectivity in resting-state data using SPM8 and REST software.
I've found that some published studies have compared only positive correlation maps (and excluding negative correlations) between the groups, because there are much controversy with regard to negative or anti-correlated networks (? regressing out global signals).

And my question is: 
How can I make each zFC*.img (z-trasnformed FC map) that contain only positive correlations ?

Thank you in advance for your help.
P. Kim