you would do it in fcseed-config by using -mean instead of -pca
On 7/20/18 4:06 PM, Eryilmaz, H. Hamdi wrote:
Thanks Doug!
One last question: If I wanted to regress out the average signal from
the ventricles and the white matter (instead of using the PCA output),
could I directly incorporate that into fcseedcor?
Best,
Hamdi
------------------------------------------------------------------------
*From:* freesurfer-boun...@nmr.mgh.harvard.edu
<freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of Douglas Greve
<dgr...@mgh.harvard.edu>
*Sent:* Friday, July 20, 2018 9:23:58 AM
*To:* freesurfer@nmr.mgh.harvard.edu
*Subject:* Re: [Freesurfer] fcseedcor -- pca output for vcsf.dat and
wm.dat
Hi Hamdi, sorry for the delay. Answers below.
On 6/26/18 2:50 PM, Eryilmaz, H. Hamdi wrote:
Hi Doug and Freesurfers,
I am using fcseedcor to compute the correlation between two time
courses for each subject in my group. The command that I run is as
follows:
fcseedcor -s $subject -fsd resting -seed seed1.dat -seed seed2.dat
-xreg global.waveform.dat 1 -xreg vcsf.dat 5 -xreg wm.dat 5 -xreg
mcprextreg 6 -hpf .01 -lpf .08 -nskip 4 -o cor_s1s2.dat
My first question is about the size of the vcsf.dat and wm.dat files.
They seem to be (N+1)xN matrices, where N is the number of timepoints
in the signal. Does this mean there are N+1 components for each time
point (i.e., N+1 potential regressors to add)? What do they exactly
correspond to and how do they relate to the average signal from the
ventricles, CSF, and white matter?
Each ROI consists of a matrix of size Ntp-by-Nvoxels. A PCA is
computed from this matrix. The the number of temporal components of
the PCA will be either Ntp or Nvoxels, which ever is less. In this
case, Nvoxels>Ntp, so you see the Ntp components (each Ntp long). The
components are whatever the PCA finds. The mean is removed, so it no
component represents the mean.
My other question is about the number of components to include for
vcsf.dat and wm.dat. I have seen 5 recommended in examples, however,
five components seem to explain very different amount of variance in
different subjects and if I change this number for a given subject, I
see substantial changes in the resulting correlation value. I would
appreciate any suggestion on how to select an unbiased value for
the number of components to include. Could including up to the
component at which fixed percentage of cumulative variance is
explained be a solution?
When I was doing experiments with this years ago, i did not find much
of a difference with different numbers of components, but I did the
experiments on whole brain resting state, so the ROI-wise correlation
may be different. It seemed like 5 components explained the bulk of
the variance within the ROI (WM or CSF). You could vary the number of
components for each subject. Sorry, I don't have any better guidance
on how to go about selecting the number.
doug
Many thanks for your help!
Best,
Hamdi
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