Dear AFNI experts,
I am new to AFNI and looking for guidance regarding putting individual subjects in MNI space in the proc_py script.
As per the the documentation for proc_py, ‘volreg_tlrc_warp’ can be used to put the images in Talariach space. How can I indicate in proc_py that I want the epi and anatomical to be put in MNI space?
Thank you for your help,
The main tool for building an FMRI pipeline (task, rest or other) is afni_proc.py; in the help file for that program
… there are several useful examples:
… and depending on what specifically you want to do, you might want to look at Ex. 11/11b for resting state, and something like #6/6b for task (though, there are soooo many varieties of task designs…).
Here is also a link to some of the Bootcamp recordings of us teaching:
This might be helpful for you to look at to learn more about AFNI programs (starting from the GUI, through basic analysis, to group analysis, alignment and visualization capabilities), and specifically using afni_proc.py (videos #11-15).
Thank you so much! These resources are great and I was able to find the answer to my specific question pretty quickly.
So just to confirm, using -tlrc_base MNI152_T1_2009c+tlrc would specify that we want the output files to be registered to the MNI 2009c template?
Note, though, that an additional important piece of information is how you want the warping between the anatomical volume and the template volume to be calculated: with linear affine or with nonlinear warping? The former is default in the program (because it takes a lot less time, and so is more convenient in teaching circumstances), but the latter is what we strongly recommend for real analyses. So, while you are “getting to know” afni_proc.py, it might be fine to just use linear affine alignment to the template (that is what we use during the Bootcamp, for example, for the sake of time), but then you might want to consider using our current recommendation: @SSwarper to perform both skullstripping (the “SS” part of the name) and nonlinear warping to a template-- for this program, you have to use one of a set of templates versions we have made from standard templates:
… but you seem to want MNI, anyways. The outputs of this program then get input to afni_proc.py, as described in the programs’ help file.
And actually, for a comparison (plus methodology on how to combine the programs), you can look to the most recent Bootcamp dataset: in AFNI_data6/FT_analysis/, there are two particular scripts for afni_proc.py:
s05.ap.uber : example single subject analysis with linear affine registration to a template (-> what we run in class, takes about 10mins to run)
s05.ap.uber.NL : same as s05.ap.uber but with nonlinear warping to a template, using results from @SSwarper, which was run in the manner shown in AFNI_data6/FT_analysis/Qwarp/s00.warper