Thanks Mark. In that example, the interaction factor (EV2) seems to change alternations between -1 1 to 1 -1, to enable the subsequent comparison between the 2 groups. When having 3 groups though, how should this factor be built, in order to have all possible comparisons between the groups, and maintaining the sum of all its elements zero?
And what is the difference between doing the inference via FSL/GLM versus extracting grey matter volume values in fslstats and just doing a mixed-model ANOVA on them in SPSS? The latter method seems easier to do, but is it the case that it is not correct because it is skipping the permutations done by randomise?
- If YES: what exactly is being "permuted" by randomise but is not permuted in SPSS?
- If NO: should fslstats be applied to structurals as last processed by fslvbm_2_template, or do they also need to go through fslvbm_3_proc?
Also, few more questions:
1) If it the case that
all EVs should be demeaned, then how come EV3..EV6, in the
design you linked to, are not demeaned, i.e. sum of all elements across the entire EV (column) is not zero?
2) Where are the corresponding error/log files created after each FSLVBM command is run? I couldn't find any fslvbm*.e*or fslvbm*.o* files in my VBM directory – or is it the case that these files are only created in case the command exits with an error code?
3) (from the fslvbm page) "To be able to compare all the GM images on a voxelwise basis, they need to be transformed into a standard space, which involves the use of non-linear registration (..) You want all the structures across your subjects to match (that's why you use a non-linear registration), but not "too much" or you would not be able to see any difference, if all these structures were perfectly aligned across the subjects." – what does "not too much" mean, and how exactly is it avoided in fslvbm?
Thanks!!
On 9 June 2013 00:40, Mark Jenkinson
<[log in to unmask]> wrote:
Hi,
Look at:
and follow the advice for a randomise analysis.
All the best,
Mark
Hi everyone,
I have pre and post scans of 72 subjects divided into three groups, so 24 in each group and 144 scans altogether. I would like to do VBM to look for an effect of the "between" factor (group), as well as of the "within" factor (pre/post), on grey matter volume,
while co-varying out gender and age. I am at the stage of the VBM pipeline where I need to create my design.mat and design.con, and I'm a bit confused as to how these should be created.
From a general GLM perspective, I guess both questions, i.e. both the effect of group and the effect of scan time (pre/post),
could be answered by a single design; however I know that the order in which subjects are entered into design.mat has to be the same as the order in template_list, and I've named my structurals according to the template groupName_subjNr_preOrPost.nii.gz,
which makes it difficult to see how the two factors should be coded into the same design matrix.
Also, I know factor demeaning is sometimes a contentious issue - which factors (if any) should be demeaned, exactly? And should categorical factors such as gender be demeaned as well?
Regarding contrasts: assuming that only one design.mat is enough to test both factors, is it correct that the design.con for the within factor should have -1 for pre and 1 for post (or vice-versa), with all other columns in the matrix receiving a 0? And that
the contrast for the between factor should have a 2 for one group and -1 for each of the other two groups (with all three possible permutations of where the "2" goes)?
Also, what value should the covariate columns (age, gender) receive in the contrast?
Thanks very much in advance for any help, I really appreciate it!
Tudor
