There is not a way to do it from the command line. You can do it in
matlab, something like
sig = MRIread('sig.mgh');
sigmat = fast_vol2mat(sig);
p = 10^-abs(sigmat);
fdr = .05;
pthresh = fast_fdrthresh(p,fdr);
ind = find(p < pthresh); % This will be a list of ROI indices that
survive FDR
doug
On 2/2/15 10:26 PM, Bronwyn Overs wrote:
Hi Doug,
That makes perfect sense. Just one more thing then, how do you conduct
an FDR via the command line for an ROI mri_glmfit analysis?
Kind regards,
Bronwyn Overs
Research Assistant
Neuroscience Research Australia
Neuroscience Research Australia
Margarete Ainsworth Building
Barker Street Randwick Sydney NSW 2031 Australia
*M* 0411 308 769 *T* +61 2 9399 1883 *F* +61 2 9399 1265
neura.edu.au <http://neura.edu.au>
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On 3/02/2015 2:00 pm, Douglas Greve wrote:
This is not something you would run the MC sim on because there is no
clustering, it is just a list of ROIs with their p-values. The
p-values are uncorrected. You can do bonferoni correction across all
the ROIs (or just the ones you are interested in). You could do FDR too.
doug
On 2/2/15 9:39 PM, Bronwyn Overs wrote:
Thanks Doug, that worked well.
However, is it possible to run a monte-carlo simulation with this
GLM ROI analysis? I attempted to run it using the following command...
mri_glmfit-sim --glmdir DesikanROIAnal_case-control.thick.lh.glmdir
--cache 1.3 abs --cwpvalthresh 0.05 --2spaces
and received the following error:
ERROR: could not determine file for
DesikanROIAnal_case-control.thick.lh.glmdir/mask
Kind regards,
Bronwyn Overs
Research Assistant
Neuroscience Research Australia
Neuroscience Research Australia
Margarete Ainsworth Building
Barker Street Randwick Sydney NSW 2031 Australia
*M* 0411 308 769 *T* +61 2 9399 1883 *F* +61 2 9399 1265
neura.edu.au <http://neura.edu.au>
Follow @neuraustralia on twitter
<https://twitter.com/neuraustralia>Follow NeuRA on facebook
<https://www.facebook.com/NeuroscienceResearchAustralia>Subscribe to
the NeuRA Magazine <http://www.neura.edu.au/help-research/subscribe>
On 3/02/2015 11:03 am, Douglas Greve wrote:
Even easier. Run aparcstats2table, then run mri_glmfit passing the
output of aparcstats2table with --table (instead of --y). There's
something on the wiki about it, also look for the ROI tutorial.
doug
On 2/2/15 6:20 PM, Bronwyn Overs wrote:
Hi Doug,
I am not sure how to run an ROI analysis using mri_glmfit. Is
there a wiki page detailing this method (I was unable to find
one)? Is the first step to map lh.aparc.label and rh.aparc.label
from fsaverage to each of my individual subjects using
mri_label2label? When I do so and then view the mapped label for
an individual subject in freeview, it appears to be a continuous
label for all of the parcellated regions combined. Is this correct?
Kind regards,
Bronwyn Overs
Research Assistant
Neuroscience Research Australia
Neuroscience Research Australia
Margarete Ainsworth Building
Barker Street Randwick Sydney NSW 2031 Australia
*M* 0411 308 769 *T* +61 2 9399 1883 *F* +61 2 9399 1265
neura.edu.au <http://neura.edu.au>
Follow @neuraustralia on twitter
<https://twitter.com/neuraustralia>Follow NeuRA on facebook
<https://www.facebook.com/NeuroscienceResearchAustralia>Subscribe
to the NeuRA Magazine
<http://www.neura.edu.au/help-research/subscribe>
On 3/02/2015 3:31 am, Douglas Greve wrote:
First, I would run the ROI analysis in mri_glmfit to see if you
get the same results as in SPSS. In the handfull of these cases,
no one has been able to correctly replicate the FS design matrix
in SPSS, so I suspect that is part of the discrepancy. The other
thing is that ROI and vertex-wise analyses are simply different.
As an extreme example, if some of the vertices are pos and some
are neg then they would cancel out when you average them in an
ROI but individually could be significant at the vertex level. If
you analyze the average over the cluster then that should come
out as significant.
doug
On 2/1/15 11:36 PM, Bronwyn Overs wrote:
Dear FreeSurfer Mailing List,
I have a sample of schizophrenia and control subjects for whom I
have run a case-control analysis of cortical thickness using two
separate methods (GLM vertex-wise analysis in freesurfer,
repeated measures ANCOVA analysis of parcellated data in SPSS).
However, for each methods of analysis I am getting extremely
different results. For the GLM in Freesurfer I have only 1 small
cluster in the frontal lobe that differs between cases and
controls (controlling for all other IVs, FWMH = 10mm,
cluster-forming threshold= .05, cluster-wise pval=.05), while
for the ANCOVA method all but 8 of the parcellated regions
differ significantly between groups (p<.05). For both methods I
have used the same model of predictors (independent variables =
gender, group, scanning site; covariate = age) and exactly the
same sample of participants. I have also replicated the GLM
analysis using the QDEC GUI to ensure that I had no made any
mistakes.
Can you provide any insight into why I would be seeing such
different results for each method using the same data set? My
findings using the ANCOVA analysis make much more sense to me,
given previous findings of reduced cortical thickness in
schizophrenia subjects. I was surprised not to find the same
pattern of effects using the GLM analysis.
--
Kind regards,
Bronwyn Overs
Research Assistant
Neuroscience Research Australia
Neuroscience Research Australia
Margarete Ainsworth Building
Barker Street Randwick Sydney NSW 2031 Australia
*M* 0411 308 769 *T* +61 2 9399 1883 *F* +61 2 9399 1265
neura.edu.au <http://neura.edu.au>
Follow @neuraustralia on twitter
<https://twitter.com/neuraustralia>Follow NeuRA on facebook
<https://www.facebook.com/NeuroscienceResearchAustralia>Subscribe to
the NeuRA Magazine
<http://www.neura.edu.au/help-research/subscribe>
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