Since MOCO means motion-corrected NOMOCO, you are asking
afni_proc.py to perform motion correction a second time.
It is not clear why this would be useful, unless you think
the other software did not do it correctly.

Why would you expect the activation to be different at all?
Are you sure the test is not to SKIP motion correction?
That would be a real MOCO vs NOMOCO test. This is more like
MOCOx2 vs MOCOx1.


So what is the point of this study, if the only difference
is one set of motion correction vs two?. The main effects
will be to add additional blur, and possible additional
noise due to the secondary motion correction.

And since the original volumes were resampled the last time
motion correction was run, the subsequent "corrections" are
non-zero only because of the resampling (and method diffs).
The only reason one might think this would not ADD motion
noise is because the blur effect will probably be much
stronger than the inappropriate motion "correction".


Regarding your to3d command, are you sure the alphabetical
ordering of the IMA files is correct? I suggest you use
Dimon to create the AFNI datasets:

Dimon -infile_pattern '*.IMA' -dicom_org -gert_create_dataset -sp alt+z2

See if the volume order is different from your original one.
Dimon will say whether -dicom_org was useful.


In a later comment, you say you do not want to use motion
correction. Maybe you do not want a 'volreg' block at all.
But in that case, it might be difficult to get afni_proc.py
working to deal with the other transformations.


Yes, the 0.2 value for censor is basically in millimeters/
degrees. They are considered to be in the same range, since
a 1 degree rotation is about 1 mm maybe 2/3 of the way to the
cortex.

But your 3mm limit is probably a cumulative limit for dropping
subjects in the other software. The limit you are giving to
afni_proc.py is a per-time point one, which is very different.
Maybe the other packages do not censor?


Before you try to understand the activations, try to understand
the processing. Without that, there is no context to understand
a difference in activations.

It is possible that NOMOCO gives lower activations because it
uses less blur, now that you blurring MOCO more, due to the
extra motion "correction" resampling.

- rick