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> wrote:
On May 31, 2013, at 12:11 PM, Douglas N Greve <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> 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:
- 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.
- 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> 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> 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-cal...) > > 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 >>> >>> >>> >>>