The dsign.con looks something like this:
--------------cut here -------------------
%! VEST-Waveform File
/NumWaves 2
/NumContrasts 2
/PPheights 1.000000e+00 1.000000e+00
/Matrix
1.000000e+00 -1.000000e+00
-1.000000e+00 1.000000e+00
------------- cut here -------------------
This file can be created automatically by the Feat GUI when running the
group (gfeat) mode. Alternatively, you can create it by hand. Each row
in the matrix just corresponds to a contrast vector.
Be aware that permutation/randomization cannot be used on all designs
-- they must be orthogonal, which basically means that you cannot use
nuisance regressors. I think this means that your design is not
appropriate for this procedure :). Most of the time, permutation is
used to test for a difference between classes (with no nuisance
variables).
doug
[EMAIL PROTECTED] wrote:
Hi Doug, we are eager to try the FSL randomise program on a FS data set we
have. However, we have one (stupid, maybe) question: is the file
design.con something that we make manually, or is it generated by some of
the previous processes?
If we are going to make it ourselves, how should it be?
In our GLM, we have two classes (males, females) and two variables (memory
score, hippocampal volume), and we want to assess the relationship between
the memory score and thickness when gender and hippocampal volume is
regressed out), e.g. DOSS:
0 0 1 0
Thanks,
- Anders
FYI, FSL has a nice site documenting the randomise program :)
http://www.fmrib.ox.ac.uk/fsl/randomise/index.html
Doug Greve wrote:
mris_glm does not correct for multiple comparisons itself. However,
you can use fdr inside of tksurfer, or ...
Steve Smith and I just worked out how to use the FSL randomise program
to compute the vertex-wise threshold. Randomise implements permutation
testing, which is much less conservative than FDR or GRF.
When you run mris_glm, make sure to specify the --y output (something
like --y y-lh.mgh). then run mri_surf2surf to convert it to nifit,
something like:
mri_surf2surf --srcsubject average7 --trgsubject average7 \
--srcsurfval y-lh.mgh --src_type mgh \
--trg_type nii --trgsurfval y-lh.nii --hemi lh
You will also need to convert the design matrix produced by mris_glm
(something like y.X.mat) into ascii. This can be done in matlab with
something like:
load y.X.mat
save('X.asc','X','-ascii')
Then run:
randomise -i y-lh -o y-lh \
-d X.asc -t design.con -n 5000 -V
Where design.con has your contrasts
The output will be something like: y-lh_max_tstat1.mgh, which you can
view with tksurfer with something like:
tksurfer average7 lh inflated -overlay y-lh_max_tstat1.mgh
We're still working out the details on this (obviously:), you may have
to play with this a little to get the command lines exactly correct.
Note that randomise program cannot do cluster-based thresholding
because it is not aware that these values are really on the surface
(not in a volume), but the max stat will work.
doug
Antao Du wrote:
Hi,
I am running mris_glm to compare the cortical thickness between two
groups.
I have a question, which method is used for correcting multiple
comparison
in the analysis? Thanks,
Antao
_______________________________________________
Freesurfer mailing list
Freesurfer@nmr.mgh.harvard.edu
https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
--
Douglas N. Greve, Ph.D.
MGH-NMR Center
[EMAIL PROTECTED]
Phone Number: 617-724-2358
Fax: 617-726-7422
_______________________________________________
Freesurfer mailing list
Freesurfer@nmr.mgh.harvard.edu
https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
--
Douglas N. Greve, Ph.D.
MGH-NMR Center
[EMAIL PROTECTED]
Phone Number: 617-724-2358
Fax: 617-726-7422
|
