You would have a design matrix with two columns and rows equal to n+1, eg
1 0
0 1
0 1
0 1
0 1
... n times
you would then permute the design matrix
On 7/23/2020 12:06 PM, Xiaojiang Yang wrote:
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Hi Doug,
For the first question, you answered "It is unusual, though it should
work". Could you please briefly describe the way FS used to permute
(based on my notation v0, v1, ... vn)? Or, the usual way to permute?
The way I described seems to be the only way I can think of. Looking
forward to your help here. Thanks a lot!
Xiao
Douglas N. Greve
<https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from:%22Douglas+N.+Greve%22>Thu,
23 Jul 2020 07:37:13 -0700
<https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date:20200723>
On 7/21/2020 11:45 AM, Xiaojiang Yang wrote:
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Hi Doug,
For your questions:
1. "Not sure what you mean by in the ROI. Are you trying to
permute across space? That generally does not work because the
points are not exchangeable across space."
By "In the ROI", I mean for all vertices listed in the label file. Here, I
am
talking about a test subject and n control subjects, they are all
considered in the same
reference subject space - fsaverage. A ROI is defined by a label file for
the subject
fsaverage. So, I am comparing the test subject and control group on the
same ROI region,
vertex by vertex. I want to permute points in the test subject and points
in all subjects
in control groups.
I am not talking about permuting vertex locations; I am talking about
permuting
values (thickness in my case) from subjects on each vertex. For example, for
vertex i, I have one value (v0) from test subject, and n values (v1,
v2,...vn)
from control subjects:
test subject control subjects
v0 v1, v2, ...... vn
One way of permutation would be:
test subject control subjects
v1 v0, v2, ...... vn
Is this a reasonable way to do permutation?
It is unusual, though it should work.
2. "Not sure. You cannot discriminate between the groups when you
are doing permutation"
By doing the permutation many (say 1000) times, I want to get the
probability
distribution of the sampling (observed or test) data inside the ROI area, so
that I can decide if I should reject or accept null hypothesis based on
cluster-wise significance level. What problems do you think I have in this
idea?
That should work. I'm not sure what my original concern was
Thanks a lot!
On 7/21/2020 1:12 AM, Xiaojiang Yang wrote:
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Dear FS experts,
Instead of using mri_glmfit-sim, I am trying to implement a
customized multiple comparison correction algorithm using
permutation. Before I implement my own, I want to make sure my
permutation idea is correct. So I was looking at how
mri_glmfit-sim does the permutation. The link here
http://freesurfer.net/fswiki/FsTutorial/MultipleComparisonsV6.0Perm has
<http://freesurfer.net/fswiki/FsTutorial/MultipleComparisonsV6.0Perm%C2%A0has>
<http://freesurfer.net/fswiki/FsTutorial/MultipleComparisonsV6.0Perm%C2%A0has>
a simple description for how to do permutation, but I don't quite
understand the 1st step: Permute the design matrix. To me, permute
the design matrix means permute the matrix rows here, but still
hard to understand why permuting matrix rows does the trick.
This is pretty standard in permutation. I think Tom Nichols has
some basic tutorials on how permutations work.
Anyway, I will not use any design matrix in my customized
implementation, so it does not matter for now. My problem can be
described as follows: if I have a ROI (a label file) on fsaverage,
and I have a test subject and a group of control subjects whose
thickness values on every vertex in this label are all known. (The
test subject and control subjects are all using fsaverage
reference space). I want to compare this test subject's thickness
within the ROI to a control group of subjects (within the same
ROI). This is a multiple-comparison problem, so I want to use
permutation to get less FP rate. My question is: How do I permute
the test subject's points and control subjects' points in ROI?
Not sure what you mean by in the ROI. Are you trying to permute
across space? That generally does not work because the points are
not exchangeable across space.
My understanding is that: for each point in the label, I randomly
re-assign all (1+n) values from (1+n) subjects to these (n+1)
subjects (where n is the number of subjects in control group). And
when all points in the label are done, this is only 1 permutation.
I will need at least 1000 times of permutation to get the
comparison statistics.
Is my understanding right?
Not sure. You cannot discriminate between the groups when you are
doing permutation
Thank you!
Xiao
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