Hi Stef,

The files_ACF files show the autocorrelation values at different radii. Column 0 is the radius, and columns 1-3 are the computed ACF, the modeled ACF (so these should be close), and the Gaussian ACF (the old style). There is one file per run and per type (errts or epits, say), along the corresponding png files that show the same functions as an image.

For example, you could run either of these to plot them for run 1 errts:

1dplot -one "files_ACF/out.3dFWHMx.ACF.errts.r01.1D[1..3]"
aiv files_ACF/out.3dFWHMx.ACF.errts.r01.1D.png

Yes, there should be a mask. Look for the -mask option in the 3dFWHMx command in the actual proc script. It is probably full_mask or mask_epi_anat.


The per-subject ACF parameters are first stored in the blur_est*.1D file, which is then captured in the @ss_review_basic output, which is store in the file out.ss_review*.txt. For example, run:

grep ACF out.ss_review*.txt

If you have many subject directories (sub*, such as sub-001), and the afni_proc.py *.results directories under there, you can average all of the ACF parameters as is done in our complete processing example, AFNI_demos/AFNI_pamenc/AFNI_02_pamenc/global_process_outline.txt.

grep -h ACF sub*/*.results/out.ss*.txt | awk -F: '{print $2}'   \
        | 3dTstat -mean -prefix - 1D:stdin\'

Or just to see, start with:

grep -h ACF sub*/*.results/out.ss*.txt

For going after a list of pre-defined ROIs, it might be okay to use the same smoothness values. They are approximated as being global, and I am not sure how reasonable it would be to compute them over multiple small regions. If you are interested only in the ROIs, maybe a full brain analysis is not the most reasonable choice. You could consider one of Gang's new Bayesian approaches.

- rick