Hello,
I am interested in comparing two groups of subjects in terms of functional connectivity with
respect to a seed voxel X. Evidently it would be in line with common practice to perform this
comparison by
1) running a general linear model with the voxel X time series as a predictor,
and
2) testing a 2nd-level contrast representing the difference between the groups' average
coefficients for that predictor.
However, this approach, by substituting regression coefficients for correlations as the measure of
functional connectivity, appears to run into a serious problem: if, say, the model finds a between-
groups difference at voxel Y, this could mean either that the groups differ in terms of X-Y
correlation (i.e. the type of difference we're interested in), *or* that the groups differ in terms
of the amount of signal at Y (but not in terms of X-Y correlation).
It seems to me that this problem could be removed by scaling each voxel's time series (including
that of the seed voxel) to a common variance, which would more or less eliminate the difference
between regression coefficients and correlations. But I'm very inexperienced with FSL, so I'd
appreciate it very much if anyone could comment on either my diagnosis of the problem or my
proposed solution.
Thanks very much!
Regards,
Phil Reiss
P.S. My much more FSL-savvy colleague Jeanette Mumford has proposed the procedure below to
implement the above suggestion. If anyone has any thoughts on this procedure's feasibility, or if
anyone has tried anything similar, I'd very much appreciate hearing about it.
-----------------------------
[from JM:]
First, simply fix the seed voxel time series in the design matrix (divide by its sd), then estimate the
first level model the usual way. Before feeding the first level copes into the second level you
could first copy the original copes and varcopes under a different name and then create the
properly weighted cope/varcope images and save using the original cope/varcope number. The
reason you'd have to be sneaky is because I think there are more than just the cope/varcope files
that the next level of feat will be looking for.
1) Create the weighting image using avwmaths dir.feat/filtered_func_data -Tstd
sd_filtered_func_data
2) Copy copes and varcopes cp cope# cope#_copy
3) Create new cope/varcope avwmaths cope#_copy -div
../sd_filtered_func_data cope#
avwmaths varcope#_cope -div
../sd_filtered_func_data -div ../sd_filtered_func_data varcope#
I think simply dividing twice for the varcope should do the trick. Oh, if you're using the newest
fsl, then avwmaths is fslmaths.
I've never tried switching out the original cope/varcope files before, but it seems like it would
work.
------------------------------
Philip Reiss, Ph.D.
Associate Research Scientist
New York University Child Study Center
215 Lexington Ave., 16th floor
New York, NY 10016
phone: 212-263-3669
fax: 212-263-2476
e-mail: [log in to unmask]
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