Dear Mark,
many thanks for your answers. I would have thought that even in cases where
the denominator of the pc1 (i.e. GM values of the pre image) is small, the
numerator of the fraction would be proportionately small (since pre and
post are expected to not be orders of magnitude apart), and so the division
would not produce abberantly high values. But either way, how can results
be recasted in terms of percentage change outside of (after) the statstical
inference process? I am guessing that it'd still be based on the pc1 image
I calculated, though not sure it's quite that simple.
A few follow-ups:
1) I actually ran randomise directly on the difference image, as I thought
that the merged images that it subtracted were already smoothed by
fslvbm_3_proc. I realise now that in fact fslvbm_3_proc probably only
smoothes the GM_mod_merg, but since here I am using a"custom-made"
GM_mod_merg, I guess I need to smooth my difference image manually, so now
I gave the command:
fslmaths all_diff_struc_GM_to_template_GM_mod.nii.gz -kernel gauss 2
-fmean all_diff_struc_GM_to_template_GM_mod_s2.nii.gz
and am currently re-running randomise on that.
2) Is there any point then to aim for an equal number of males&females in
each group, as long as gender still has to be modelled (and thus "cost" in
terms of statistical power)? Surely in that case, the bias towards a gender
being more present than the other would be accounted for by the gender EV
anyway, so there'd be no downside?
4) And what about non-contiguous clusters (with "black holes") - should
those be reported based on the size of their "envelope", i.e. ignoring the
black voxels, or should each contiguous (no holes) cluster be reported
separately instead, no matter how small? I don't really know how large a
cluster would have to be in order for it to be biologically meaningful in
the case of this study - although the effect size I'm expecting is on the
small side.
Best wishes,
Tudor
On 18 August 2014 06:57, Mark Jenkinson <[log in to unmask]>
wrote:
> Hi,
>
> 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:
>
> http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomise/UserGuide#Getting_Cluster_and_Peak_Information_from_Randomise_Output
>
> All the best,
> Mark
>
>
>
> Many thanks in advance for your help!
>
> Best wishes,
> Tudor
>
>
>
