Hi FSL users,


I am doing a cross-sectional and a longitudinal study on white matter of the brain in degenerative diseases. From what I learnt from the discussion forum is that there are two ways to compare longitudinal changes (please correct me if I get it wrong):

1) All diffusion data are registered to standard space, create mean FA and mean FA skeleton in TBSS and register all FA images onto the mean FA skeleton. Compare results with a paired t-test.

2) All diffusion data are registered to standard space. Then create a averaged group template of baseline images and register follow-up images onto baseline images using FLIRT. Voxelwise comparison done with GLM.

My questions are as follow:

a) Which of the above method will be more suitable or accurate for a longitudinal study of 40 people (three disease groups (n=11,7,7) and controls (n=15)) scanned at two different time points?

For method 2:
b)How do I create a white matter group average template for baseline images? (tbss_2_reg -n ?)

c) As in actual command lines, how should I incorporate it into what I have ran with my cross-sectional data? So I do the same eddy current correction, bet, dtifit, etc for all subjects, then run a separate TBSS for cross-sectional and longitudinal data?

To better illustration, these are the command lines I used for cross-sectional data, to process longitudinal data I will need to:

eddy_correct raw_dwi.nii.gz data.nii.gz 0
fslroi data.nii.gz nodif.nii.gz 0 1
bet nodif.nii.gz nodif_brain -m -g 0.2 -f 0.3
fslview nodif.nii.gz nodif_brain_mask.nii.gz
fslmaths nodif.nii.gz -mas nodif_brain_mask.nii.gz nodif_brain.nii.gz
dtifit --data=data.nii.gz --out=dti --mask=nodif_brain_mask.nii.gz --bvals=bvals --bvecs=bvecs
fslview dti_FA.nii.gz dti_V1.nii.gz
(use the same as processed for cross-sectional data)

tbss_1_preproc *.nii.gz
tbss_2_reg -T (change to tbss_2_reg -n ?)
tbss_3_postreg -S (skip)
cd stats
fslview all_FA.nii.gz
fslview mean_FA.nii.gz mean_FA_skeleton.nii.gz
tbss_4_prestats 0.2


Thank you for replying in advance !

Regards,
Bonnie