I have run my resting data using afni_proc.py. However, I see the blur size was 4mm. So how to apply 8mm to the errts*+tlrc data without running the afni_proc.py again?
The best solution is to go back to the intermediate files and re-run the processing from there with the correct smoothing level. Filters are additive, so if you now applied a kernel of 4mm or 8mm, you’d end up with different values than you would have otherwise doing it in a single step.
One could make an argument of using 3dBlurToFWHM[/url], but I would say go back and do it again. You could modify the afni_proc command to start at a different point. Some indication of what you’d need to do are in [url=http://blog.cogneurostats.com/?p=255]this blog post. If you need more help, post your current script and we can try to modify it for you.
Thanks for your kind help. Running the fmri data using afni_proc.py is time intensive. Furthermore, I have too many subjects. I want to do follow steps, but I donot know which afni code can help me:
1, using FWHM=8mm, and generating results.
2,using results of 1 as master, then apply fwhm=4mm results to this master.
Post your current afni_proc.py script and we can go from there.
Just want to point out that if you smoothed at 8mm, you can’t really reduce the smoothing to 4mm. On the other hand, you could potentially go from 4mm to 8mm with 3dBlurtoFWHM, but as Peter recommends, I think redoing the analysis is the proper way to do this. If you have a cluster available to you, this would take less time than the series of posts in the thread…