Hi all,

I have a dataset with eight conditions (we’ll call them conditions A, B, C, D, E, F, G, and H). I need to construct a single general linear test that will identify voxels where there is a difference between at least two conditions. In other words, I need to find voxels where at least one condition is different from at least one other condition, as identified by the F-test on a single general linear test. The full matrix for describing such a contrast would look like this:
A B C D E F G H
____________________________________________
1 -1 0 0 0 0 0 0
1 0 -1 0 0 0 0 0
1 0 0 -1 0 0 0 0
1 0 0 0 -1 0 0 0
1 0 0 0 0 -1 0 0
1 0 0 0 0 0 -1 0
1 0 0 0 0 0 0 -1
0 1 -1 0 0 0 0 0
0 1 0 -1 0 0 0 0
0 1 0 0 -1 0 0 0
0 1 0 0 0 -1 0 0
0 1 0 0 0 0 -1 0
0 1 0 0 0 0 0 -1
0 0 1 -1 0 0 0 0
0 0 1 0 -1 0 0 0
0 0 1 0 0 -1 0 0
0 0 1 0 0 0 -1 0
0 0 1 0 0 0 0 -1
0 0 0 1 -1 0 0 0
0 0 0 1 0 -1 0 0
0 0 0 1 0 0 -1 0
0 0 0 1 0 0 0 -1
0 0 0 0 1 -1 0 0
0 0 0 0 1 0 -1 0
0 0 0 0 1 0 0 -1
0 0 0 0 0 1 -1 0
0 0 0 0 0 1 0 -1
0 0 0 0 0 0 1 -1

The problem with this matrix, of course, is that there are WAY more rows than columns and 3dDeconvolve can’t find the inverse matrix. Does anyone have any suggestions as to how to code this contrast so that it won’t crash 3dDeconvolve and still show the voxels where a difference exists between at least two conditions?

Thanks,
Kyle