Sure.
By that I mean that you have to centre your ev around
its mean. You need to demean (remove the mean) both
your data - which is done by the -D option - AND your
covariate. Assuming that the vector y is your
covariate, then you just need to do y-mean(y), which
gives you the demeaned covariate:
22
-108
-118
-48
-68
132
102
-38
52
72
Cheers,
Gwen
--- Bhargav Kumar Errangi <[log in to unmask]> a
écrit :
> Thank you for your response.
>
> I am not sure that I know what is meant by "demean".
> We did use the -D
> option, but it seems you are suggesting we do
> something additional with the
> covariate in the design matrix. Can you please
> describe what we should do?
>
> Thank you very much,
>
> Bhargav
>
> Gwenaëlle DOUAUD <[log in to unmask]> wrote:
>
> Hi Matt,
>
> it seems that you have not demeaned your ev in
> your
> stats.mat. You should do so and run exactly the
> same
> command you've used without forgetting the -D
> option.
>
> Cheers,
> Gwenaelle
>
> --- Matt Glasser a écrit :
>
> > Tom and others,
> >
> >
> >
> > I am helping Bhargav with this project. We
> tried
> > using the -D option,
> > however now all of our t statistics are very
> small
> > (between -1 and 1, with
> > most very close to zero). This doesn't make
> sense,
> > however, as we would
> > expect at least some voxels to show t
> statistics
> > greater than 1, just by
> > chance. Our randomize commandline, our design
> > matrix and our contrast file
> > follow this message. We have verified that our
> 4D
> > FA skeleton file and mean
> > skeleton mask files are okay. Does anyone know
> why
> > we are getting such low
> > t values? Would it be possible to get r values
> > instead using something
> > other than randomise?
> >
> >
> >
> > Thanks,
> >
> >
> >
> > Matt.
> >
> >
> >
> > randomise commandline:
> >
> > randomise -i all_FA_skeletonised -o tbss -m
> > mean_FA_skeleton_mask -d
> > stats.mat -t stats.con -n 5000 -c 3 -D
> >
> >
> >
> > stats.mat:
> >
> >
> >
> > /NumWaves 1
> >
> > /NumPoints 10
> >
> > /PPheights 7.800000e+02
> >
> >
> >
> > /Matrix
> >
> > 6.700000e+02
> >
> > 5.400000e+02
> >
> > 5.300000e+02
> >
> > 6.000000e+02
> >
> > 5.800000e+02
> >
> > 7.800000e+02
> >
> > 7.500000e+02
> >
> > 6.100000e+02
> >
> > 7.000000e+02
> >
> > 7.200000e+02
> >
> >
> >
> > stats.con:
> >
> >
> >
> > /ContrastName1 Positive
> >
> > /ContrastName2 Negative
> >
> > /NumWaves 1
> >
> > /NumContrasts 2
> >
> > /PPheights 7.800000e+02
> > 7.800000e+02
> >
> > /RequiredEffect 3.726 3.726
> >
> >
> >
> > /Matrix
> >
> > 1.000000e+00
> >
> > -1.000000e+00
> >
> >
> >
> > _____
> >
> > From: FSL - FMRIB's Software Library
> > [mailto:[log in to unmask]] On Behalf
> > Of Thomas Nichols
> > Sent: Wednesday, October 10, 2007 5:10 AM
> > To: [log in to unmask]
> > Subject: Re: [FSL] TBSS-voxelwise correlations
> > between FA and Test scores
> >
> >
> >
> > Bhargav,
> >
> > Did you include an intercept in the model? Or
> > specify the -D option and use
> > a centered covariate? That might well explain
> the
> > large t values (without
> > an intercept you may be testing if the FA
> values are
> > non-zero, not a very
> > interesting question).
> >
> > randomise uses the general linear model and
> > contrasts to test effects of
> > interest with t-tests and so doesn't produce
> > correlation coefficients.
> >
> > You can threshold on the basis of corrected or
> > uncorrected P-values with
> > fslmaths/avwmaths, the -thr option, and the
> > corresponding One-Minus-P-value
> > images; see the randomise help page
> > http://www.fmrib.ox.ac.uk/fsl/randomise/ for
> the
> > exact filename extensions
> > for each of these types of images.
> >
> > Hope this helps!
> >
> > -Tom
> >
> > On 10/9/07, Bhargav Kumar Errangi
> > wrote:
> >
> > Hello,
> >
> > We are using TBSS to look for voxel-wise
> > correlations between FA and test
> > scores. Could you please answer the following
> > questions for us?
> >
> > 1) Using the GLM tool, we specificy 1
> covariate and
> > provide values for each
> > of 10 subjects in the EV tab. Then in the
> contrast
> > tab, we specify 2
> > contrasts (1 and -1)
> > 2) The output is a t statistic image. We were
> > exepcting an r statistic map.
> > Does FSL calculate the t from the r?
> > 3) Will the "1" contrast yield positive
> correlations
> > and the "-1" contrast
> > yield negative correlations?
> > 4) The t statistic values are very, very
> large. We
> > only get reasonbable maps
> > when we threshold at t>20. Do you know why
> this
> > might be?
> > 5) Is there a way to threshold the map based
> on
> > p-values rather than t
> > statistics?
> >
> >
> >
> > ____________________________________________
> > Thomas Nichols, PhD
> > Director, Modelling & Genetics
> > GlaxoSmithKline Clinical Imaging Centre
> >
> > Senior Research Fellow
> > Oxford University FMRIB Centre
>
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