Hi, Zhang Lei-

Glad the QC is useful (again, I recommend using "-html_review_style pythonic" for the nicest version, which you might be using now...).

You can see the matrix/grid dimensions of a dataset with:

3dinfo -n4 -prefix MNI_avg152T1+tlrc.HEAD MNI*SSW*nii*
91	109	91	1	                   MNI_avg152T1
193	229	193	5	MNI152_2009_template_SSW.nii.gz

Both datasets have 1x1x1 mm**3 voxels, so indeed they have different fields of view (FOVs).

What atlas are you interested in, in particular?

Note that the matrix size of the output EPI dataset likely be different than the above, because it isn't resampled to such a fine size; the default output EPI will be about the same as input (maybe rounded slightly finer, but only very slightly). You can control the final output voxel size by using this afni_proc.py option:

-volreg_warp_dxyz 2.5

(for example, if you wanted 2.5 mm isotropic final voxels).

So, in general, you will always have to resample the atlas. There are better and worse ways of doing even this; Daniel Glen is the local expert on advising about this aspect. You can use "3dresample -rmode NN -input ATLAS_NAME -master FINAL_EPI_DSET ", for example and see how that looks as a starter.

But note also that there aaaare different MNI spaces, actually, where the brains match up approximately but not exactly-- so make sure to overlay your chosen atlas on the MNI reference base you use, to make sure they line up well.

Re. using "-regress_reml_exec": the stats files there will be called stats*REML*, and contains both "coef" and "tstat" subbricks, typically. Here is an example of at stats*REML* header (part of the "3dinfo stats*REML*HEAD" output) for an afni_proc.py-produced stats dset with that flag used:

Number of values stored at each pixel = 5
  -- At sub-brick #0 'Full_Fstat' datum type is float:            0 to       38.9125
     statcode = fift;  statpar = 2 227
  -- At sub-brick #1 'CONTROL#0_Coef' datum type is float:     -48.0433 to       47.5369
  -- At sub-brick #2 'CONTROL#0_Tstat' datum type is float:     -4.94164 to       5.79199
     statcode = fitt;  statpar = 227
  -- At sub-brick #3 'TASK#0_Coef' datum type is float:     -65.5111 to        37.002
  -- At sub-brick #4 'TASK#0_Tstat' datum type is float:     -5.80596 to       8.67473
     statcode = fitt;  statpar = 227

NB: this dset is part of the AFNI Bootcamp demo data, here: AFNI_demos/AFNI_pamenc/AFNI_02_pamenc/sub-10517/sub-10517.results
And the scripts for processing it are here: AFNI_demos/AFNI_pamenc/afni_scripts/, in particular this contains the afni_proc.py (AP) cmd: c.ss.3.AP.pamenc.

I think the idea is that you have asked for the 3dREMLfit version of modeling, so just that stats is output file of interest. It should have all the normal/usable properties of a stats file, just output via 3dREMLfit processing (so, using a generalized least squares approach instead of OLS, I think).

--pt