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> wrote:

On May 31, 2013, at 12:11 PM, Douglas N Greve <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>> 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>> 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>> 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