Ahh right. I have just understood which part of the output I needed to
look at. However, for the ROI GLM there are only 1-2 regions that were
significantly different between groups, while the SPSS ANCOVA showed
significant group differences for the majority of parcellated regions. I
have confirmed that I am using the exact same model for each, so it is
only the analysis method that differs. Do you know why these two methods
would produce such disparate results?
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
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On 5/02/2015 3:16 am, Douglas N Greve wrote:
I'm not sure that I understand what I'm looking at. If it is an ROI
analysis, then there is no surface, it should be about 40 numbers, one
for each ROI.
On 02/03/2015 06:26 PM, Bronwyn Overs wrote:
Hi Doug,
Thanks for your correction.
I have now completed the FDR for my case-control comparisons, and it
appears that none of the regions survived. This is again quite
confusing given the large number of parcellated regions that survived
FDR in the SPSS ANCOVA. Can you confirm that this screenshot of the
sig.mgh file from ROI analysis looks as you would expect (it looks
very strange to me):
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 4/02/2015 9:35 am, Douglas Greve wrote:
Yes, it should be .^
On 2/3/15 5:33 PM, Bronwyn Overs wrote:
Hi Doug,
I am having a problem with the line in Matlab 2014b:
p = 10^-abs(sigmat);
I keep getting the following error:
Error using ^
Inputs must be a scalar and a square matrix.
To compute elementwise POWER, use POWER (.^)
instead.
Do you know why this would be?
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 4/02/2015 2:22 am, Douglas Greve wrote:
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>
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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
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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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