I have a structural (T1-weighted) data set with 2 time points (pre&post training) and 3 groups, and want to see, for each group, where there are changes in grey matter volume from pre to post, and also where these changes differ between the groups (i.e.
time x group interaction), while controlling for age and gender.
For that, I computed the 4D post minus pre "difference" image with
fslmaths all_d5_struc_GM_to_template_GM_mod.nii.gz –sub all_d1_struc_GM_to_template_GM_mod.nii.gz all_diff_struc_GM_to_template_GM_mod.nii.gz
This looks fine, as long as you incorporate the smoothing later on.
However, I thought that another meaningful dependent variable would be obtained by dividing this difference by each subject's initial ("pre") image. This would give the percentage change from scan 1 to scan 2, a measure also used by other software such
as FreeSurfer (which refers to it as "pc1", i.e. percentange change from scan 1). I did this with:
fslmaths all_diff_struc_GM_to_template_GM_mod.nii.gz -div all_d1_struc_GM_to_template_GM_mod.nii.gz pc1.nii.gz
I then did whole-brain VBM on both the "diff" and the "pc1" images.
I would not recommend running the statistics on such an image, because any voxels which have very small amounts of GM in timepoint 1 would end up as a very small divisor and massively amplify the effects of noise in these voxels. It would have a very
bad effect on the smoothed results too. Recasting results in terms of percentage change is not uncommon, but it is not usual to run the statistics on such images due to the highly variable noise characteristics that this generates.
My questions:
1) I wanted to check if my design and contrasts are correct. Simplifying to 2 subjects per group, the design I used is:
<14-08-2014, 18.49.08.png>
Examples of each category of contrast that I have defined (are these correct?) :
- increased GMV, i.e. post>pre, for G2: (0,1,0,0)
- decreased GMV, i.e. post<pre, for G3: (0,0,-1,0)
- greater pre-to-post increase in GMV for G1 than for G2: (1,-1,0,0)
- (..and other similar contrasts..)
These all look correct.
2) I have an equal number of males and females in each of the 3 groups, specifically 12 males and 12 females per group. I reckon there is, thus, no need for gender as a covariate, but to be sure I had one design with and one without, which both lead to
very similar corrp maps. Is it correct to do any subsequent analyses only with the design that
doesn't have gender as an EV?
Just because you have an equal number in each group doesn't mean there is no effect. I would keep your gender EV in the model so that you are sure that you are taking the effect of gender into account.
3) The corrp maps for the two dependent variables (diff and pc1), using the same design for both, are quite different. Specifically, for some of the group-wise pre-to-post change contrasts, e.g. (G1,G2,G3,age) = (-1,0,0,0), the corrp map (limited at 0.95-1)
shows me almost the entire brain as significant when the DV is pc1 but not when it is just the difference. Which of the two DVs (diff or pc1) makes more sense to use, if I want to see what effect my manipulation (group) had on the pre-to-post difference?
Ignore the pc1 results, as explained above.
4) I ran randomise with the TFCE (-T) option, which, if I understand correctly, means that any cluster that survives the multiple comparisons correction is valid, no matter how small. Still, some of the surviving (1-p > .95) voxels in my corrp maps are
quite isolated (e.g. "clusters" formed of 1 or 2 voxels). Sometimes, several near-by (but non-adjacent) voxels seem to form a cluster which thus has lots of "black holes". Can I really trust that any voxel (isolated or clustered) as a valid result worthy of
being reported or should further criteria be imposed?
You are right that technically the results, even for small clusters of 1-2 voxels, are statistically valid. However, statistically valid does not mean it is definitely true biologically. Is just means that there is a known false positive rate. So you
should still interpret results in the light of their biological plausibility. If it does not make sense for such a very small cluster to exist then you should explain this in your discussion.
5) For each contrast's corrp map, I have been checking for significant clusters manually by limiting brightness of corrp maps to (0.95-1) and advancing through saggital slices from L to R. But because of the issue mentioned above (arbitrarily small clusters,
which are easy to miss), I am wondering if this operation is perhaps better performed with a command (the "cluster" command, I guess) that lists all clusters in text mode? I did not find any information about this in the FSL documentation.
This information is in the wiki - under randomise. Look here:
All the best,
Mark
Many thanks in advance for your help!
Best wishes,
Tudor
