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Hi,
Correct.
On 26 Mar 2008, at 21:24, David Gutman wrote:
> Greetings fellow FSL users,
>
> I've recently been looking over an old thread about 3 group TBSS
> Comparisons and had some questions about the command syntax. I am
> doing a 3 group comparison, 2 groups are drug dependent (with the
> differentiating factor being whether they relapsed during the 6
> months to 1 year followup of our study), and the other group being
> controls.
>
> It seemed like in the original thread from ~ a month ago, in a
> message from Tim in response to a question by David K, it was
> recommended to set up the following contrasts
>
> 1 -1 0
> 1 0 -1
> 0 1 -1
>
> It seems like from my (comparatively limited) experience with TBSS,
> it appears the statistical output in the tbss_max_vox_stats* images
> are only one sided-- i.e. indicate voxels where A>B and so to
> actually do all the comparisons you would also need to add
>
> -1 1 0
> -1 0 1
> 0 -1 1
>
> to the design.mat file you set up in the GLM. Is that
> correct..........?
Sure - these are just simple contrasts that mean average_drug_user -
> Also I am a bit confused about the exact syntax to use for randomise
> and fear I used the wrong commands---- perhaps I am just confusing
> the nomenclature. I initially ran my 3 group comparison using
>
> randomise -i all_FA_skeletonised.nii.gz -o tbss -m
> mean_FA_skeleton_mask.nii.gz -d design_3groups.mat -t
> design_3groups.con -c 3
>
> I recently realized I need to add -f design_3groups.fts to the
> randomise command as well, but now that I think about it more, I am
> not sure if the "-c 3" is correct. Since there are 3 groups (i.e.
> F tests..?) do I actually want to use the -F option .... and if so
> what threshold would I use? In the contrats above I am just going t-
> tests between groups, but in my actual design matrix I am also
> intrasted in contrasts that compare between more than 2 groups (i.e.
> comparing simply ALL_DRUG_USERS - CONTROLS)
>
> i.e. something like ..
> 0.5 0.5 -1
> -0.5 -0.5 1
controls etc. and don't need f-tests.
For an f-test across 'all' comparisons you probably just need an f
across the top two contrasts, i.e
F
1 -1 0 x
1 0 -1 x
(you don't need the third contrast in this as it is a linear
combination of the first two)
Wrt choosing a threshold for the F option: as always, this is
arbitrary, you'll need to see what works reasonable by looking at the
raw fstat image.
Yes, that's fine.
>
>
> --------- FROM THE FSL WEB PAGE
> To use this option, use -c <thresh> for t contrasts and -F <thresh>
> for F contrasts, where the threshold is used to form supra-threshold
> clusters of voxels.
> ----------------------------------------------------
>
>
> Could anyone provide some clarification?
>
>
> FINALLY--- if I want to control for multiple comparisons, but want
> to exclude part of the brain a priori (there's a bad scanner
> artifact in some of my subjects in the back of the brain related to
> motion/scanner fibrations), so I just want to completely ignore a
> chunk of the occipital lobe. Can I just mask that out of my -m
> mean_FA_skeleton_mask.nii.gz, and so have it only correct for
> multiple comparisons across the regions of the brain that don't have
> obvious confounds like that.
Cheers.
>
>
> DG
>
>
> Thanks in advance,
>
> DG
>
>
> --
> David A Gutman, M.D. Ph.D.
> Department of Psychiatry & Behavioral Sciences
> Emory University School of Medicine
>
> On Fri, Mar 7, 2008 at 12:08 PM, Thomas Nichols <[log in to unmask]
> > wrote:
> David,
>
> I have two more questions re: -f design.fts.
> 1) Do I need to include both the -f design.fts and -t design.con,
> then, or just one? I would think both, but I just want to be sure.
> Again, I want the t-test and f-test.
>
> Yes, you need both, since the F tests are defined in terms of the t-
> tests. Ideally, if you didn't care about the t-tests, we'd have a
> way to have randomise to skip the t-tests, but we don't have such an
> option.
>
> 2) Can you tell me what one gains by including the f-tests if you
> already including the t-tests. To my understanding, the F-test will
> only tell you that there are differences between groups, but not
> where these differences are (that is the purpose of the t-test). So,
> by adding the -f design.fts command, what benefit will this give a
> person for results interpretation?
>
> F tests give a single answer to the 'Any Difference' question, while
> the t-tests give 6 answers (A<B,B<A,A<C,A>C,B<C,B<C, even though
> there's only really 4 linearly independent answers). Those multiple
> t-tests give multiple opportunities for false positives to occur,
> and require a Bonferroni or some other correction.
>
> Hence, if 'Any Difference' is the primary question, then the F is
> the standard answer. But, if you *did* discover any differences,
> you could use the t-tests to characterize the differences in a post
> hoc fashion (w/out formal control of false positives).
>
> -Tom
>
> ____________________________________________
> Thomas Nichols, PhD
> Director, Modelling & Genetics
> GlaxoSmithKline Clinical Imaging Centre
>
> Senior Research Fellow
> Oxford University FMRIB Centre
>
>
>
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Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
[log in to unmask] http://www.fmrib.ox.ac.uk/~steve
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