Hi,
I am new to AFNI and don't have a local expert to get me started, so I have a very basic question.

I used 3dDeconvolve on each individual data set to create a bucket of several contrasts for each individual (using the 3dvolreg output as regressors in the baseline model). I then used 3dmerge to create thresholded and spatially smoothed maps. Now I am trying to determine which voxels are active across the entire cohort. My understanding is that I should use 3dttest, which computes the mean of each voxel across all subjects. To keep things simple, I decided to start with a group of 2 subjects. I typed:

3dttest -prefix contrast1 -base1 0 -set2 subject1/merged-data+tlrc subject2/merged-data+tlrc

The resulting map was very odd-looking. Although there were some small clusters in regions that corresponded to activated regions in subject 1 or 2, there were also several large Jackson Pollack-ish swaths of color in regions that were not activated in either subject 1 or 2, and regions were missing from the "group map" that should have been there.

I thought it might be because I used the output from 3dmerge, so I tried it again, using the output from 3dDeconvolve:

3dttest -prefix contrast1 -base1 0 \
-set2 'subject1/my-deconvolution+tlrc[2]' 'subject2/mydeconvolution+tlrc[2]'

The results were again quite odd - the map was covered with very small, confetti-like "activations", again in regions where neither individual subject was active.

Then I tried using the baseline sub-briks as set1:

3dttest -prefix contrast1 -paired -set1 'subject1/my-deconvolution+tlrc[0]' 'subject2/mydeconvolution+tlrc[0]' -set2 'subject1/my-deconvolution+tlrc[2]' 'subject2/mydeconvolution+tlrc[2]'

which produced large clusters, but mostly in regions that were not active in either of the two individuals.

Am I mis-understanding how to use 3dttest? Why should the output from the 3 things I tried look so radically different from each other? What do I need to do to get a map that looks something like the average of the 2 individual maps?

Thank you!
Sara