You can use
labelconvert to do this. I show how to use this for the HCPMMP1 atlas in the tutorial. The procedure for the AAL atlas is very similar to this (section 8.1 in the appendix). However, a few things are different for the AAL atlas:
The AAL is – unlike the surface-based HCPMMP1 atlas – a volumetric atlas. This makes things easier for you since you do not have to do the FreeSurfer anatomical preprocessing in this case. However, you still have to register the AAL to the highresolution native T1 space in a first step (the AAL atlas is in standard space: MNI_152_2mm). There are several ways to do this, e.g. with MRtrix or FSL commands. I like to use FSL’s
flirt tool for this. Here are a couple of commands with which you can achieve this (assuming you have done FSL’s anatomical preprocessing, which gave you a T1_biascorr_brain.nii.gz, and the AAL file in standard space is called “AAL.nii”):
flirt –in T1_biascorr_brain.nii.gz –ref $FSL_DIR/standard/MNI152_2mm_brain.nii.gz –omat highres2standard.mat –interp nearestneighbour –datatype int -inverse
convert_xfm –omat standard2highres.mat –inverse highres2standard.mat
flirt –in AAL.nii –ref T1_biascorr_brain.nii.gz –out aal2highres.nii.gz –applyxfm –init standard2highres.mat –interp nearestneighbour
You can then use labelconvert with the resultant file, e.g.
labelconvert aal2highres.nii.gz aalLUT_90_orig.txt aalLUT_90_ordered.txt aal2highres.mif
(In case you do not have these files, I uploaded the following three files on the OSF website: aalLUT_90_orig.txt, aalLUT_90_orig.txt, and AAL.nii. You can find those in the Supplementary Files directory here: https://osf.io/fkyht/files/. However, please note that the color lookup tables I provide are for the 90 parcellation AAL atlas, and I ask you to verify it before using it).
Regarding your second question,
I think using the AAL to identify the CST is not the way to go. I’m not an expert here, but aren’t there any atlases of white matter pathways? Alternatively, you could try to identify the CST in every of your subjects by tracking through a ROI (in the white matter) through which you know the CST must pass (e.g. how in section 4.4 in the tutorial, which shows how to use
tckedit to identify the CST). However, this is a subjective procedure, so I recommend this only as an exploratory analysis. An atlas would be the preferrable option for a standardized analysis.
Hope this helps.