On 07/17/2013 04:48 PM, Joseph Dien wrote:
The single nuisance regressor models the difference between the merged
runs. So if the first run had a mean of 90 and the second run had a
mean of 110 then the merged run mean would be 100. The nuisance
regressor has 1s for the volumes of the first run and -1s for the
volumes of the second run so it ends up with a beta value of 10, thus
accounting for the difference between these two sets of volumes. I
think it does make sense.
Do you do this in a single regressor? So you would have a +1 -1 pattern
repeated 4 times? I think to make it work, you would need 4 regressors.
In any event, FSFAST will do the right thing, so maybe it is not important.
Looking at the X.X file, it did create four nuisance regressors. I'm really impressed with how well FSFAST handles all this! :)
In any case, while it was necessary to do so for my original SPM
analyses since it uses a separate covariates for each run, after
working through the FSFAST procedures with your help I see that is not
the case for FSFAST (a single regressor models a given condition in
all the runs). I'll try doing as you suggest to see what difference
it makes.
It is indeed cognitive areas and the manipulations are subtle social
cognition manipulations so perhaps not surprising after all.
I'll send you the paradigm file separately.
Thanks for taking the time to look into this!
Joe
On Jul 17, 2013, at 4:38 PM, Douglas N Greve
<greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> wrote:
It is not necessary or beneficial to combine the runs in this way.
FSFAST will do all this for you and keep account of all the runs and
transitions. FSFAST will put in regressors to fit each of the run means.
The single regressor you have is not the right way to hand this (at
least I don't understand how it works). It could be that the low %
signal change is related to the colinearity between the task waveform
and the mean regressors. Can you set things up in the way that FSFAST
expects them and don't use any nuisance regressors?
Also, in what area are you looking at the percent change? .02% sounds
very small, but may be if it is in some cognitive area, maybe it is ok.
If it is in visual cortex, then it looks way too low.
Also, can you send the paradigm file?
doug
On 07/17/2013 04:27 PM, Joseph Dien wrote:
It's a little complicated. Basically there were eight runs,
comprising four conditions (me, we, you1, you2) each with two
adjoining runs. For the analysis, I merged each of the pairs into a
single run and added a nuisance regressor to account for the
difference in run means. There were a total of four different kinds
of boxcars (AR, CS, EM, MP). So 4x4=16 conditions. There was also a
covariate of non-interest to mark the switch point for each boxcar,
one for each run, so 20 total.
The 7 nuisance regressors are six movement covariates plus one to
account for merging eight runs into four (it consists of 1 for the
first half and -1 for the second, so the difference in the run means).
I'm using the movement covariates from a prior SPM run since
ARTdetect (for detecting bad volumes) isn't set up for AFNI style
data. From all published accounts the different movement detection
routines yield similar enough results that it shouldn't be a problem
(consistent with what I found when I compared them for this dataset).
You're thinking that collinearity could have reduced the effect sizes?
When I correlate the X.X regressor matrix, the 20 predictors don't
correlate by more than about .2 at worst. I do see greater
correlations with some of the nuisance regressors (as high as the .4
range). Are my betas unusually small for FSFAST analyses? They did
come up clusterwise significant at least. Or should I not worry? I'm
not sure what to expect from FSFAST analyses.
Thanks!
Joe
On Jul 17, 2013, at 3:58 PM, Douglas N Greve
<greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu>> wrote:
why do you have 20 conditions? And what are the 7 nuisance regressors?
On 07/17/2013 03:54 PM, Joseph Dien wrote:
It's a boxcar design so 20.265.
mkanalysis-sess -fsd bold -analysis CPA.sm05.lh -surface fsaverage lh
-fwhm 5 -event-related-paradigm CPA1.par -nconditions 20 -spmhrf 0 -TR
2 -refeventdur 20.265 -polyfit 2 -per-run -force -nuisreg nuisreg2.dat
7 -tpexclude tpexclude.dat
On Jul 17, 2013, at 3:50 PM, Douglas N Greve s.
<greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu>
<mailto:greve@nmr.mgh.harvard.edu>> wrote:
when you ran mkanalysis-sess, what did you set --refeventdur to?
On 07/17/2013 02:50 PM, Joseph Dien wrote:
then I get on the order of .02% difference between the contrasted
conditions.
The run mean values are in my expected ballpark of about 100 or so.
The condition betas are just very very small.
Or perhaps this is typical of FSFAST analyses?
On Jul 17, 2013, at 2:00 PM, 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>> wrote:
The beta's have already been scaled. What do you get if you just
beta/runmean ?
On 07/17/2013 01:45 PM, Joseph Dien wrote:
I implemented the ROI percent signal change formula following the
MarsBaR FAQ (http://marsbar.sourceforge.net/faq.html) but the
values
I'm getting seem too small (on the order of .0002%).
Basically the
formula is the (beta * peak absolute value of the canonical HRF
regressor * 100)/(run mean). No derivatives in this case as
it is a
boxcar design.
I took the mean across all the runs since FSFAST uses the same
regressor across the entire experiment (unlike SPM).
I used the X.runflac(1).flac.ev(m).Xirf values for the
canonical HRF
as you suggested (where m equals the condition+1).
Is it possible that I'm missing something in the scaling here?
Especially with a boxcar design, the signal change should be much
larger than this for a significant cluster, I think. For
example, the
peak HRF value for one of the conditions is 0.0092. If the
betas are
already scaled according to the peak value, then it would come
out as
.02%, which is more reasonable, although still too small.
Thanks for your help with this!
Joe
On May 31, 2013, at 5:02 PM, 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>> wrote:
Oh, right, it is probably not there for subcortical. I don't know
what I
would have to do to write it out. It won't be something that
happens
before I get back from HBM. Can you remind me after HBM?
doug
On 05/31/2013 04:44 PM, Joseph Dien wrote:
It looks like the corrected vertex p-values
(ex: cache.th13.abs.sig.voxel.nii.gz) are only available for the
surface-based lh and rh spaces. For the subcortical
volume-based
analysis I don't see the corresponding corrected voxel p-values
being
available?
On May 31, 2013, at 2:46 PM, Joseph Dien <jdien07@mac.com
<mailto:jdien07@mac.com>
<mailto:jdien07@mac.com>
<mailto:jdien07@mac.com> <mailto:jdien07@mac.com>
<mailto:jdien07@mac.com>
<mailto:jdien07@mac.com>> wrote:
On May 31, 2013, at 12:11 PM, 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>> wrote:
On 05/31/2013 01:49 AM, Joseph Dien wrote:
I was able to make more progress so I'm mostly good at this
point but
I have a remaining question:
I assume the contents of sig.nii.gz (which I assume are the
vertex
p-values) are not FWE corrected. Is it possible to get
FWE-corrected
vertex p-values? Or are only clusterwise corrections
available?
There should be something like cache.th13.abs.sig.voxel.mgh
which is
corrected on a voxelwise basis (the th13 is just part of the
name
but it
should be the same regardless of the threshold you choose)
doug
Excellent! Thanks! :)
Thanks again for your patience!
Joe
On May 30, 2013, at 4:37 PM, Joseph Dien <jdien07@mac.com
<mailto:jdien07@mac.com>
<mailto:jdien07@mac.com>
<mailto:jdien07@mac.com> <mailto:jdien07@mac.com>
<mailto:jdien07@mac.com>
<mailto:jdien07@mac.com>
<mailto:jdien07@mac.com>> wrote:
Just to make sure I'm doing this right, I'm going to
summarize what
I've taken away from your answers and to ask some new
questions. In
order to present the results, I need two things:
1) A set of histograms (with error bars) for each cluster
figure to
show the % signal change for each of the four contrasts of
interest.
The cache.th20.pos.y.ocn.dat file only gives it for the
condition
where the cluster was significant so I can't use that.
So I could use mri_label2vol to convert
cache.th20.neg.sig.ocn.annot
from the group level analysis to generate a mask for each
cluster of
interest.
Then I could extract the value of the voxels from each
subject's cespct file for each contrast, average them
across the
cluster ROI, then average them across each subject, to
generate the
histogram?
This would suffice to give me the %age signal change?
I would be doing these computations in Matlab using MRIread.
2) A results table with the headings:
Cluster p (FWE corrected)
Cluster size
Peak Voxel p (FWE corrected)
Peak Voxel T
Peak Voxel Coords
BA
Anatomical Landmark
I can get the first two from
the cache.th20.pos/neg.sig.cluster.summary files from the
group
level
analysis.
I can get the peak voxel coordinates from the summary files
as well.
I can use this to get the peak voxel p from the group
level sig.nii.gz file. Is this FWE corrected? If not,
how can
I get
this information?
I can use these coordinates to get the peak voxel T by
getting the
value from the group level F.nii.gz file and taking its
square root.
How can I get the sign of the T statistic?
I can use the Lancaster transform to convert the MNI305 peak
voxel
coordinates into the Atlas coordinates to look up the
putative
BA and
landmarks (unless there is a better way with Freesurfer?
I'm seeing
some references to some BA labels in the forum but it
doesn't look
like this is a complete set yet?).
Sorry for all these questions! I got some nice results from
FSFAST
and would like to get them written up.
Cheers!
Joe
On May 29, 2013, at 10:53 PM, Douglas 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>> wrote:
On 5/29/13 10:42 PM, Joseph Dien wrote:
On May 29, 2013, at 11:40 AM, 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>> wrote:
Hi Joe,
On 05/29/2013 01:00 AM, Joseph Dien wrote:
I need to extract the beta weights from a cluster
identified
with
FS-Fast in order to compute percentage signal change.
1) I see a file called beta.nii.gz that appears to have
the beta
weight information. It has a four dimensional structure
and the
fourth dimension appears to be the beta weights. Is
there an
index
somewhere as to which beta weight is which? Or if
not, how
are they
organized?
For the first level analysis, the first N beta weights
correspond
to the
N conditions in the paradigm file. The rest are nuisance
variables.
Ah, very good! In order to compute the percent signal
change
statistic (I'm following the MarsBaR approach:
http://marsbar.sourceforge.net/faq.html#how-is-the-percent-signal-change-calculated)
I'm also going to need the beta weights for the
session mean
regressors. How are the nuisance regressors organized?
You can just use the meanfunc.nii.gz. Also, each
contrasts is
computed as the simple contrast (ces) and as a percent
of the
baseline at the voxel (cespct, cesvarpct).
2) In order to extract the cluster, it looks like I
would
use mri_label2vol to convert
cache.th20.neg.sig.ocn.annot into a
volume where the voxels are tagged with the number
of the
corresponding cluster.
Is that from a group analysis?
Yes, that's right.
I could then use that to generate masks to extract the
information I
need for each cluster from beta.nii.gz.
If this is from a group analysis, then there should
already be
a file
there (something.y.ocn.dat) that has a value for each
subject
in the
rows and a value for each cluster in the columns.
I see it. Are these values already scaled as percent
signal
change? If so, that would be wonderful! :)
Only if you specified it when you ran isxconcat-sess. Note
that the
"non-scaled" values are actually scaled to percent of grand
mean
intensity.
Is that correct?
3) The final information that I would need is the
canonical hrf
shape
generated by FSFAST for a single event. I guess I could
generate
that
by setting up a dummy analysis run with a single event
of the
desired
duration and then look in the X variable in the
resulting
X.mat file?
try this
plot(X.runflac(1).flac.ev(2).tirf,
X.runflac(1).flac.ev(2).Xirf)
Perfect! :)
Sorry for all the questions!
Joe
--
Douglas N. Greve, Ph.D.
MGH-NMR Center
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MGH-NMR Center
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MGH-NMR Center
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<mailto:jdien07@mac.com>
Phone: 202-297-8117
http://joedien.com//
--
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MGH-NMR Center
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Phone: 202-297-8117
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--
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MGH-NMR Center
greve@nmr.mgh.harvard.edu
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