Just call your thickness file some other name, eg,
lh.xformed.thickness.mgz, the just specify --meas xformed.thickness.mgz
On 5/11/2020 3:52 PM, Xiaojiang Yang wrote:
External Email - Use Caution
Thank you Douglas!
If I apply my transform to both the healthy controls and the patient,
I guess I would lose the convenience of using mri_glmfit, which
eventually loses the convenience of using mri-glmfit-sim (I don't care
not using mri_glmfit, but I want to use mri_glmfit-sim to do multiple
comparisons correction). That is because mri_glmfit uses the prepared
output from mris_preproc, which I have no control over to use the
transformed subject. The only way I can circumvent this is to use my
transformed thickness file to overwrite the FS calculated thickness
file, but that is what I am hesitant to do. If you have good
suggestions regarding this, please let me know. Thanks!
Xiao
On Mon, May 11, 2020 at 12:25 PM Douglas N. Greve
<dgr...@mgh.harvard.edu <mailto:dgr...@mgh.harvard.edu>> wrote:
I guess you could apply your transform to both the healthy
controls and to your patient, can then create an FSGD file with
two classes as before but no continuous variable. Then just have a
contrast that computes the diff between the patient and the mean
of the controls.
On 5/8/2020 11:49 AM, Xiaojiang Yang wrote:
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Hi Douglas,
Could you please reply my last email (sent to
freesurfer@nmr.mgh.harvard.edu
<mailto:freesurfer@nmr.mgh.harvard.edu>) regarding my question
about the weighted age? I am eager to listen to your
answer/opinion to my question. For your convenience, I also
include the whole email chain below this email.
Thank you very much!
Sincerely,
Xiao
<https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from
:%22Douglas+N.+Greve%22> Douglas N. Greve
<https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date
:20200507> Thu, 07 May 2020 14:56:08 -0700
sorry, please include the previous email chain so that I know
what you are
referring to
On 5/7/2020 5:33 PM, Xiaojiang Yang wrote:
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Douglas,
Just as I mentioned in the initial email, subjects in the healthy
group have
different ages. Because average cortical thickness is believed to be
declined as the age gets older, I want to "normalize" the group by
multiplying each subject's thickness with an age-dependent scalar
(pre-calculated constant). Suppose I take age 30 as the
"standard" age, then
subjects with ages greater than 30 will have scalars greater than
1 (depend
on age); subjects with ages smaller than 30 will have scalars
less than 1.
In this way, all subjects in the group looks like to be at the
same age
(30).
Of course, I also suspect that there is no need to do the above
mentioned
normalization if I use mri_glmfit. But my initial intention was using
non-linear scalars, and mri_glmft can only use linear fit. Please
give me
your opinion. Thank you!
Xiao
<https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from
:%22Douglas+N.+Greve%22> Douglas N. Greve
<https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date
:20200507> Thu, 07 May 2020 14:06:27 -0700
I don't know what weight is. Can you elaborate?
On 5/7/2020 4:54 PM, Xiaojiang Yang wrote:
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Hi Douglas,
Thank you very much! Could you please help me with more detailed
information?
1)Should I use "Variables age" or "Variables age weight" in the
fsgd file?
If I can use either of them, which one is better?
I don't know what weight is. Can you elaborate?
2)For the contrast file, the content "1 -1 0" you mentioned, it
corresponds
to the "Variables age" fsgd file, correct?
Yes
3)If I use the "Variables age weight" fsgd file, what will be the
content of
the contrast file? Is it "1 -1 0 0"?
Yes
Thank you again!
Xiao
<https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from
:%22Douglas+N.+Greve%22> Douglas N. Greve
<https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date
:20200507> Thu, 07 May 2020 11:55:27 -0700
I think it will work properly if you use the raw data as input
and create an
FSGD file with two classes: (1) the individual subject and (2)
healthy
subjects. Also include age as a covariate. Use DOSS instead of
DODS. Then
set your contrast to be 1 -1 0 and run mri_glmfit. I think that
should
properly account for age.
On 5/7/2020 2:44 PM, Xiaojiang Yang wrote:
External Email - Use Caution
Dear FS experts,
I have a group of healthy subjects. Given a new individual
subject, I want
to compare its cortical thickness to the healthy group so that I
can find
where the thickness is abnormal (thickening or thinning).
I use fsaverage as the template subject to calculate mean and std
of the
healthy group. Since subjects in the healthy group have different
ages, I do
a "normalization" process to all the subjects in the group BEFORE
calculating the mean and std. Thus the normalized healthy group
can be
regarded as all subjects having the same "standard" age. The
normalization
process is just multiplying some pre-obtained scale (varied by
age, but not
linearly ) to the file *?h.thickness.fwhm0.fsaverage.mgh* of each
subject
that are already calculated by "recon-all --qcache" command. So
the mean and
std of the group are actually weighted in the sense of Freesurfer's
recon-all results.
My questions are:
1)If I do vertex-wise t-tests by simply comparing individual
subject's
thickness to the group using mris_calc but WITHOUT using
mri_glmfit, can I
still use mri_glmfit-sim to do multiple comparisons correction?
If yes, how?
2)If the answer is no for the above question, how should I use
mris_glmft to
implement my above thoughts so that I can use use mri_glmfit-sim?
Specifically, how can I use the weighted mean for the group?
3)Take a step back, if I consider the weight is linearly
correlated with the
age, how to design the fsdg and contrast file?
Thank you very much!
Xiao
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