Howdy-
OK, so there are 2 zeros there, meaning that of the 5 grid properties tested, the 2 are not matched between the dsets.
From the help of this option in 3dinfo:
-same_grid: Output 1 if the grid is identical between two dsets
0 otherwise.
For -same_grid to be 1, all of -same_dim, -same_delta,
-same_orient, -same_center, and -same_obl must return 1
-same_dim: 1 if dimensions are the same between dset pairs
-same_delta: 1 if voxels sizes are the same between dset pairs
-same_orient: 1 if orientation is the same between dset pairs
-same_center: 1 if geometric center is the same between dset pairs
-same_obl: 1 if obliquity is the same between dset pairs
-same_all_grid: Equivalent to listing all of -same_dim -same_delta
-same_orient, -same_center, and -same_obl on the
command line.
... we can see that "-same_dim" and "-same_delta" are not True, meaning that the matrix dimensions and the size of the voxels are not matched.
Can you please post your 3dAllineate commands? Those sounds like the right kind of steps for using 3dAllineate, but I wonder if the "-master" option wasn't used in the application of the affine parameters, in the 2nd part.
Also, it depends how close of alignment/matching you want between the datasets, but you would likely have a much better alignment quality if you used nonlinear alignment, rather than just 3dAllineate. 3dAllineate will do a pretty good job aligning 2 different brains (e.g., your subj and the MNI template), but there is only so much it can do with 12 DOF applied globally---we typically use it to align datasets from the same subj to each other (e.g., EPI to anatomical). For between-subject processing, nonlinear gives a much better alignment result. You can evaluate if 3dAllineate's output is good enough for your current purposes by overlaying the aligned dset to the base.
--pt
