On 2/3/15 4:21 AM, Schweren, LJS (med) wrote:
Thank you Doug!
One more question for clarification:
“You would remove the quadratic term from the FSGD file, then create
an image of quadradic values for each veretx and pass that with --pvr
to glmfit”.
My quadratic value (age-squared) is a constant per participant. Do you
mean I should create an image file per subject, where each vertex has
the same value, namely age-squared of that subject? Next I should
concatenate these files in FSGD-order to create a stacked mgz file, right?
Yes. I assumed from your question that you would set some of the
vertices = 0 in the case where you thought that having a quad term was
not helpful.
Second question: is there a way to hierarchically order the pvr-s,
such that pvr 1 can only be included when pvr 2 is also included?
No, it is an all-or-nothing kind of thing.
doug
Thanks again,
Lizanne
*From:*freesurfer-boun...@nmr.mgh.harvard.edu
[mailto:freesurfer-boun...@nmr.mgh.harvard.edu] *On Behalf Of *Douglas
Greve
*Sent:* maandag 2 februari 2015 17:19
*To:* freesurfer@nmr.mgh.harvard.edu
*Subject:* Re: [Freesurfer] vertexwise statistical modeling
On 2/2/15 3:56 AM, Schweren, LJS (med) wrote:
Dear freesurfer experts,
I investigate case-control differences in cortical thickness in a
group of 11 to 30 year olds. I ran vertex-wise analyses with
glmfit, with covariates age and age-squared, to take into account
confounding by age as we have a very wide age range. I use
monte-carlo simulation testing. What results is the following:
-some clusters of between-group difference (which I was looking
for, no worries here)
-a cluster covering almost the entire cortex being negatively
correlated to the linear age term (which I understand, overall
decreasing CT with increasing age)
-2 small clusters correlated to the quadratic age term
Given these results, if I want to model age optimally, I should
include the quadratic age-term only in some vertices but not in
others, right? I have two questions:
1.Is there a way to include different covariates in different
vertices, still doing the vertex-wise analyses of the entire brain?
You can use the per-vertex regressor option (--pvr) to mri_glmfit. So
you would remove the quadratic term from the FSGD file, then create an
image of quadradic values for each veretx and pass that with --pvr to
glmfit.
2.I apply a cluster-size threshold for the initial results, because I
want to be sure about the case-control clusters. However for the
covariates, if I can optimize the model per vertex, I would rather not
use this threshold to be more sensitive. How could I go about this?
Uncorrected results are stored in the sig.mgh file
doug
Thank you, your help is very much appreciated!
Best wishes,
Lizanne
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