That worked out great! Many thanks again for everything.
I've run into one other wrinkle. Part of what sent me on this path was that I wanted to use similar assessments of model fit for my analysis in AFNI as for my analyses in FSL, as it is the same data set. I had moved to AFNI to conduct a different piece of the analysis examining task compliance using activation in the superior sagittal sinus.
As I mentioned, FSL has an output of two graphs: 1) the peak voxel timeseries against the fitted model, and 2) an averaged timeseries across clusters of significant voxels against the fitted model.
The first graph, using your suggestions, worked out very well. For the second graph, however, I am trying to figure out how to apply in AFNI the same clustering and thresholding applied in FSL. I felt the best way to do this, to ensure I was comparing apples to apples, was to apply clustering and thresholding that resulted in the timeseries being an average across approximately the same number of voxels as the FSL analysis. Thus, in both analyses, I would be selecting for the 10,000 voxels that showed the most activation, for example, which should be close enough to qualitatively assess whether the model fit the data to a similar degree in both programs.
However, in examining this option, I found out that FSL had averaged across a subsample of the brain that had more voxels than the entire brain in AFNI. To me, this suggests that the voxel size in the FSL analysis is somehow smaller than the voxel size in the AFNI analysis. The preprocessing of the data in each case was relatively similar. The brain was extracted in the FSL analysis, but this should make the brain in FSL have fewer voxels if anything, not more. Do you have any thoughts about how I might go about ensuring both programs are defining a voxel similarly, even just from the AFNI side of things?