Oh, yes, sorry. The reason that mri_glmfit_pcc is failing is because this is a one-sample group mean (design matrix a column of all 1s). What are you trying to compute the correlation between? Or do you just want to convert the p-values to a correlation coefficient?
On 10/09/2013 01:26 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, did you already have a chance to look into this? Thanks, Caspar2013/10/4 Caspar M. Schwiedrzik <cschwiedrz@rockefeller.edu <mailto:cschwiedrz@rockefeller.edu>>
<mailto:greve@nmr.mgh.harvard.edu>>
Done. Thank you very much for looking into this.
Caspar
2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu<mailto:greve@nmr.mgh.harvard.edu
Can you tar up you glmfit dir and drop it to me on our file drop?
On 10/04/2013 05:29 PM, Caspar M. Schwiedrzik wrote:
Hi Doug,
thank you very much for sending the Matlab function. When
I run this, it creates a pcc.mgh file for my osgm
contrast. However, the values seem strange. They range
from -630 to 36 for my particular dataset.
I was expecting something between -1 and 1.
Caspar
2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu>>>
I think you are conflating the 1st level and the 2nd
level. You
could get pcc out of the 2nd level regardless of what
you are
using for the input from the first level. I've
attached a matlab
script that will compute the pcc for mri_glmfit output
doug
On 10/04/2013 03:33 PM, Caspar M. Schwiedrzik wrote:
Hi Doug,
I guess it boils down to the question how to get a
group PCC
map after a RFX GLM?
Using -m PCC seems to only give me a map per
subject. Are you
calculating PCC from the t- values? Thanks,
Caspar
On Thursday, October 3, 2013, Caspar M.
Schwiedrzik wrote:
Hi Doug,
On Thursday, October 3, 2013, Douglas N Greve
wrote:
It sounds like two issues:
1. p-values not consistent with your
program. What did
you use
to compute? Did you do a two-sided (which
is what
fsfast uses)?
I used ttest in Matlab, two sided.
2. Using pcc maps. Why not use -m pcc?
Isn't that giving me a map per subject? How do
I get the
group map
that is consistent with the results of
mri_glmfit run on
ces.nii?
Thanks, Caspar
doug
On 10/03/2013 01:10 PM, Caspar M.
Schwiedrzik wrote:
Hi Doug,
I loaded the pcc.nii file that I got from
isxconcat-sess
into Matlab and then ran a t-test
against 0 over
the 4th
dimension. I converted the resulting
p-values to
-log10
and then compared them to the output of
mri_glmfit, namely
sig.vol.
This was the mri_glmfit command:
mri_glmfit \
--surf averagesubject hemisphere \
--y pcc.nii \
--no-cortex \
--osgm \
--glmdir analysisname
I was expecting the p-values to be the
same, which
apparently is not the case, unless I am
doing/understanding something wrong.
By now, I am actually more inclined to
use the
regression
coefficients instead. However, I'd
still like to
get pcc
maps from them, if there is a way to
do so in FSFAST.
Thanks, Caspar
2013/10/3 Douglas N Greve
<greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>><mailto:greve@nmr.mgh.harvard.edu <tel:617-724-2358 <tel:617-724-2358>><mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>>>>
On 10/03/2013 10:39 AM, Caspar M.
Schwiedrzik
wrote:
Hi Doug,
when I run a two-tailed t-test
against 0
in Matlab
on the Rs
in pcc.nii that I get out of
isxconcat-sess with
-m pcc, and
DOF from ffxdof.dat, I get
different -log10(p)
values than the
ones that come out of mri_glmfit.
I don't understand what you mean.
Can you
elaborate?
I am not sure why this is
happening.
In principle, I just want pcc
maps as
final output
to show
them on the surface (instead
of p-values).
So I'd
be happy to
follow your advice regarding
the biasing
effects
of noise and
autocorrelation and use the
regression
coefficients. However,
mri_glmfit (v5.1) does not
seem to output
pcc maps
of the
contrasts (contrary to
selxavg3-sess on
the single
subject
level). How would I get those?
Thanks, Caspar
2013/10/1 Douglas N Greve
<greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>>
<mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>>>
<mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>>
<mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>>>>>
On 10/01/2013 01:13 PM,
Caspar M.
Schwiedrzik
wrote:
> Hi Doug,
> it would be great if you
could give
me some
further
advise on the
> group analysis of functional
connectivity maps.
> Specifically, I am
trying to get PCC
maps
for certain
seeds, and am
> not planning any
comparison between
groups.
> Following your previous
advise, I am
running
isxconcat-sess with -m
> pcc to get the PCC maps.
> I would then run
>
> mri_glmfit \
> --surf averagesubject
hemisphere \
> --y pcc.nii \
> --no-cortex \
> --osgm \
> --glmdir analysisname
>
> *Could you please
provide some more
detail
on what kind of
analysis is
> performed when I provide
pcc.nii as
an input for
mri_glmfit? Is it a
> t-test of the
Fisher-transformed
r-values
against 0?
I just run a t-test of the
r-values. I
don't
have a
program to convert
them to z-values, however,
there are
z-values
that are
created in the
first level analysis.
These are
generated from the
p-values but I
bet it
would give you the same
thing. Use -m
z with
isxconcat-sess if you
want
to use the z.
> *Is the average r-value
or z-value saved
somewhere?
Which level? For
mri_glmfit, they are
not,
but it is not
hard to get
them with matlab.
> *Do you take the
autocorrelation into
account (as in
Vincent JL et
> al., 2007. Intrinsic
functional
architecture
in the
anaesthetized
> monkey brain. Nature.
447:83-86)?
Not usually, but it could
be done by
not including
-no-whiten when you
run mkanalysis-sess. I
usually use the
regression
coefficients instead
of correlation
coefficients because
that they
are at least
unbiased with
respect to noise level and
autocorrelation.
doug
> I'd also be happy to
look this up
but I'd
need to know
where I can
-- Douglas N. Greve, Ph.D.
MGH-NMR Center
greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>
Fax: 617-726-7422 <tel:617-726-7422> <tel:617-726-7422
<tel:617-726-7422>>
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--
Douglas N. Greve, Ph.D.
MGH-NMR Center
greve@nmr.mgh.harvard.edu
Phone Number: 617-724-2358
Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
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