Note: Refer to Example #9 of afni_proc.py for AFNI's most recent version of resting-state analysis.
Now that we've laid down some of the theory behind resting-state analyses, and have seen that it is nothing more than a glorified functional connectivity analysis, which in turn is nothing more than a glorified bivariate correlation, which in turn is something that I just made up, the time has now come to create the correlation maps and z-maps which we have so craved. I believe I have talked about correlation maps and their subsequent transmogrification into z-maps, but in the interest of redundancy* and also in the interest of showcasing a few ties that I picked up at Goodwill, I've created two more videos to show each of the steps in turn.
First, use 3dmaskave to extract the timecourse information from your ROI placed in the vmPFC:
3dmaskave -quiet -mask vmPFC+tlrc errts.{$subj}+tlrc > timeCourse.txt
This information is then used by 3dfim+ to generate a correlation map:
3dfim+ -input errts.{$subj}+tlrc -polort 0 -ideal_file timeCourse.txt -out Correlation -bucket vmPFC_Corr
Once those correlation maps are generated, use 3dcalc to convert them into z-maps:
3dcalc -a vmPFC_Corr+tlrc -expr 'log((1+a)/(1-a))/2' -prefix Corr_subj{$subj}_Z
N.B. In each of the above examples, {$subj} is a placeholder for the subject ID you are currently processing; with a few tweaks, you should be able to put this all into a script that automates these processes for each subject.
N.N.B. (I think that's how you do it): The original script that I uploaded had a couple of bugs; one of the placeholders should have been changed to a generic $subj variable, and also -giant_move option has been added to the align_epi_anat.py part of the script, since the anatomical and functional images actually start out quite far away from each other. If you haven't used it yet, downloading the new script should take care of those issues. Also, another hidden change I made was to increase the motion limit from 0.2 to 0.3mm; too many subjects were getting thrown out, and even though a more rigorous analysis would leave the motion threshold at a more conservative 0.2, I've raised it for now, for pedagogical purposes.
N.N.N.B. Find out what "N.B." means.
*Sponsored by the United States Department of Redundancy Department
Now that we've laid down some of the theory behind resting-state analyses, and have seen that it is nothing more than a glorified functional connectivity analysis, which in turn is nothing more than a glorified bivariate correlation, which in turn is something that I just made up, the time has now come to create the correlation maps and z-maps which we have so craved. I believe I have talked about correlation maps and their subsequent transmogrification into z-maps, but in the interest of redundancy* and also in the interest of showcasing a few ties that I picked up at Goodwill, I've created two more videos to show each of the steps in turn.
First, use 3dmaskave to extract the timecourse information from your ROI placed in the vmPFC:
3dmaskave -quiet -mask vmPFC+tlrc errts.{$subj}+tlrc > timeCourse.txt
This information is then used by 3dfim+ to generate a correlation map:
3dfim+ -input errts.{$subj}+tlrc -polort 0 -ideal_file timeCourse.txt -out Correlation -bucket vmPFC_Corr
Once those correlation maps are generated, use 3dcalc to convert them into z-maps:
3dcalc -a vmPFC_Corr+tlrc -expr 'log((1+a)/(1-a))/2' -prefix Corr_subj{$subj}_Z
N.B. In each of the above examples, {$subj} is a placeholder for the subject ID you are currently processing; with a few tweaks, you should be able to put this all into a script that automates these processes for each subject.
N.N.B. (I think that's how you do it): The original script that I uploaded had a couple of bugs; one of the placeholders should have been changed to a generic $subj variable, and also -giant_move option has been added to the align_epi_anat.py part of the script, since the anatomical and functional images actually start out quite far away from each other. If you haven't used it yet, downloading the new script should take care of those issues. Also, another hidden change I made was to increase the motion limit from 0.2 to 0.3mm; too many subjects were getting thrown out, and even though a more rigorous analysis would leave the motion threshold at a more conservative 0.2, I've raised it for now, for pedagogical purposes.
N.N.N.B. Find out what "N.B." means.
*Sponsored by the United States Department of Redundancy Department