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>> 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>> 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>> 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>> 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>> 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>> 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>> 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
greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu>
Phone Number: 617-724-2358
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E-mail: jdien07@mac.com <mailto:jdien07@mac.com> <mailto:jdien07@mac.com>
Phone: 202-297-8117
http://joedien.com//

--
Douglas N. Greve, Ph.D.
MGH-NMR Center
greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>
Phone Number: 617-724-2358
Fax: 617-726-7422

Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2
www.nmr.mgh.harvard.edu/facility/filedrop/index.html
Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/

_______________________________________________
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--------------------------------------------------------------------------------

Joseph Dien,
Senior Research Scientist
University of Maryland

E-mail: jdien07@mac.com <mailto:jdien07@mac.com>
Phone: 202-297-8117
http://joedien.com//

--
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
FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2
www.nmr.mgh.harvard.edu/facility/filedrop/index.html
Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/