Dear DTI Experts,
I have run the following TBSS pipeline in order to get a whole-brain FA skeleton for a cohort of 4-8 year old children:
#Run TBSS Preprocessing (must navigate to the folder with all subjects' FA maps)
tbss_1_preproc *.nii.gz
#TBSS Nonlinear registration: Register to standard space
tbss_2_reg -T
#Get study-specific FA skeleton. Run from TBSS directory (directory before FA folder)
tbss_3_postreg -T
#TBSS Threshold for FA values
@Linda Hoffman You can read the User Guide on the website of TBSS to get other scalars (MD, AD RD):
Using non-FA Images in TBSS
It is straightforward to apply TBSS to other diffusion-derived data than FA images. For example, you may be interested in how MD (mean diffusivity) or the first diffusion tensor eigenvalue varies between different subjects.
To achieve this we recommend using the FA images to achieve the nonlinear registration and skeletonisation stages, and also to estimate the projection vectors from each individual subject onto the mean FA skeleton. The nonlinear warps and skeleton projection can then also be applied to other images such as the second eigenvalue. The following instructions assume that you want to run TBSS on the second eigenvalue, named L2 by dtifit:
- Run the full TBSS analysis (see all steps above) on your FA data.
- Create a new directory called L2 (or any other name) in your TBSS analysis directory (the one that contains the existing origdata, FA and stats directories from the FA analysis). Type: mkdir L2
- Copy your L2 images into this new directory, making sure that they are named exactly the same as the original FA images were (look in origdata to check the original names - and keep them exactly the same, even if they include FA, which can be confusing; e.g. if there is an image origdata/subj005_FA.nii.gz then you need an image L2/subj005_FA.nii.gz and this file should contain the L2 data, even though it has FA in the name).
- Now, making sure that you are in your top working TBSS directory (the one that now contains FA, stats and L2 subdirectories) and run the tbss_non_FA script, telling it that the alternate data is called L2. This will apply the original nonlinear registration to the L2 data, merge all subjects' warped L2 data into a 4D file stats/all_L2, project this onto the original mean FA skeleton (using the original FA data to find the projection vectors), resulting in the 4D projected data stats/all_L2_skeletonised. Run: tbss_non_FA L2
- You can now run voxelwise stats on the projected 4D data all_L2_skeletonised in the same manner as described above.
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