Dear Prof. Smith,
Thanks for your reply. I think I did not express my idea clearly about raw FA
statistic in the last email.
......
>> # Section 3, statistic for raw FA in TBSS/stas?directory
>> # threshold FA data by 0.2
>> fslmaths mean_FA.nii.gz thr 0.2 mean_FA_mask_2.nii.gz fslmaths
>> # make a binary mask
>> mean_FA_mask_2.nii.gz 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 -V
>> # statistic for FA end
>
>You mean you want to compare the mean-across-space values in the
>groups? Not quite, not, I would apply the mask (that's already done
>for you hence doesn't appear below) and then summarise over space, e.g.
No, My purpos is to compare non-skeletonised FA for all voxels in the brain
across all subjects between two groups. Though TBSS can deal with alignment
issue, there have been a great may papers used GRF theory to make
inference for non-skeletonised FA ,e.g. FA maps got from DTIFit, between
groups. To compare with results from GRF, I wonder if I can fed a 4D non-
skeletonised FA map without spatial smooth (the fourth demension is subject
ID) to randomise to obtain inference for FA? If this makes sense, my procedure
above is appropriate? shall I still use TFCE option?
I have another question. I have used --T2 option in TBSS. i.e.
randomise -i all_FA_skeletonised -o tbss -m mean_FA_skeleton_mask -d
design.mat -t design.con --T2 ¨CV
It produced (1-p) maps with multiple comarsion corrected, pleaes see the
attachment tbss_tfce_corrp_tstat2.gif. The histogram of
tbss_tfce_corrp_tstat2.nii has many sharp peaks. Is this distribution
reasonable?
Thanks a lot.
Yuzheng
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