Dear FSL users,
I have used FSL for DTI analysis, but I am not sure whether all the steps in
my procedure are appropriate. So I would appreciate you very much if
someone can help me to check it. There are two groups in my experiment and
25 subjects in each. My research purpose is to compare the difference
between the two groups in FA and skeletonised FA. Please see my procedures
for this purpose as follow.
#Section 1, tensor estimation for each subject in his/her own directory
eddy_correct DTI_raw.nii.gz data 0
bet data.nii.gz nodif_brain_mask
dtifit -k data -o dti -m nodif_brain_mask -r *.nii.bvec -b *.nii.bval
# estimation end
# Section 2, TBSS analysis
# copy and rename dti_FA.nii.gz from each subject`s folder to the same dir.
e.g. &./TBSS*
# cd to ./TBSS
tbss_1_preproc *FA*.nii.gz
tbss_2_reg -T
tbss_3_postreg -S
tbss_4_prestats 0.2
cd stats
design_ttest2 design 25 25
randomise -i all_FA_skeletonised -o tbss -m mean_FA_skeleton_mask -d
design.mat -t design.con -c 1.6839 每V
# mean difference at the significant level of p < 0.05 is expected. Option 每c
is used with a value of 1.6839 by looking up t-value tables, t0.05(42-2)
=1.6839. Is this setting correct?
# TBSS end
# Section 3, statistic for raw FA in &TBSS/stas* directory
# could I compare FA in the whole brain between groups directly with
randomise command as flow?
# threshold FA data by 0.2
fslmaths mean_FA.nii.gz 每thr 0.2 mean_FA_mask_2.nii.gz fslmaths
# make a binary mask
mean_FA_mask_2.nii.gz 每bin mean_FA_mask_2_bin.nii.gz
#make inference, is this appropriate as follow?
randomise -i all_FA -o FA2 -m mean_FA_mask_2_bin -d design.mat -t
design.con -c 1.6839 每V
# statistic for FA end
Any suggestion is appreciated!
All the best!
Yuzheng.Hu
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