so you want the mean ppc over subjects? That would be gamma.mgh
On 10/09/2013 03:10 PM, Caspar M. Schwiedrzik wrote:
Hi Doug,
I would ideally like something that is as close as possible to the pcc map that comes out of selxavg3 (the single subject analysis). How is that computed?
Thanks, Caspar
On Wednesday, October 9, 2013, Douglas N Greve wrote:
If it is just an effect size you can compute gamma.mgh/rstd.mgh
doug
On 10/09/2013 01:39 PM, Caspar M. Schwiedrzik wrote:
Hi Doug,
this is coming from a seed-based resting state analysis. I
would like to show effect size instead of p-values.
I thought you calculate them from the slopes or the t-values?
Caspar
2013/10/9 Douglas N Greve <greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>>
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, Caspar
2013/10/4 Caspar M. Schwiedrzik
<cschwiedrz@rockefeller.edu
<mailto:cschwiedrz@rockefeller.edu>
<mailto:cschwiedrz@rockefeller.edu
<mailto:cschwiedrz@rockefeller.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>
<mailto: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
<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>>>>
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:gr <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 10/03/2013
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>>
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<mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>>>
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<mailto:greve@nmr.mgh.harvard.edu>>
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<mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>>>>
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<mailto:greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>>
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<mailto:greve@nmr.mgh.harvard.edu>>>
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<mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu
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-- Douglas N. Greve, Ph.D.
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