Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar
On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote: > Hi Doug, > > On Thursday, October 3, 2013, Douglas N Greve wrote: > >> >> It sounds like two issues: >> 1. p-values not consistent with your program. What did you use to >> compute? Did you do a two-sided (which is what fsfast uses)? > > > I used ttest in Matlab, two sided. > > 2. Using pcc maps. Why not use -m pcc? > > > Isn't that giving me a map per subject? How do I get the group map that is > consistent with the results of mri_glmfit run on ces.nii? > > Thanks, Caspar > > > > > doug > > > On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote: > > Hi Doug, > I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and > then ran a t-test against 0 over the 4th dimension. I converted the > resulting p-values to -log10 and then compared them to the output of > mri_glmfit, namely sig.vol. > This was the mri_glmfit command: > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > I was expecting the p-values to be the same, which apparently is not the > case, unless I am doing/understanding something wrong. > > By now, I am actually more inclined to use the regression coefficients > instead. However, I'd still like to get pcc maps from them, if there is a > way to do so in FSFAST. > Thanks, Caspar > > > > > 2013/10/3 Douglas N Greve <gr...@nmr.mgh.harvard.edu <mailto: > gr...@nmr.mgh.harvard.edu>> > > > On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: > > Hi Doug, > > when I run a two-tailed t-test against 0 in Matlab on the Rs > in pcc.nii that I get out of isxconcat-sess with -m pcc, and > DOF from ffxdof.dat, I get different -log10(p) values than the > ones that come out of mri_glmfit. > > I don't understand what you mean. Can you elaborate? > > I am not sure why this is happening. > In principle, I just want pcc maps as final output to show > them on the surface (instead of p-values). So I'd be happy to > follow your advice regarding the biasing effects of noise and > autocorrelation and use the regression coefficients. However, > mri_glmfit (v5.1) does not seem to output pcc maps of the > contrasts (contrary to selxavg3-sess on the single subject > level). How would I get those? > > Thanks, Caspar > > > 2013/10/1 Douglas N Greve <gr...@nmr.mgh.harvard.edu > <mailto:gr...@nmr.mgh.harvard.edu> > <mailto:gr...@nmr.mgh.harvard.edu > <mailto:gr...@nmr.mgh.harvard.edu>>> > > > > On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > > Hi Doug, > > it would be great if you could give me some further > advise on the > > group analysis of functional connectivity maps. > > Specifically, I am trying to get PCC maps for certain > seeds, and am > > not planning any comparison between groups. > > Following your previous advise, I am running > isxconcat-sess with -m > > pcc to get the PCC maps. > > I would then run > > > > mri_glmfit \ > > --surf averagesubject hemisphere \ > > --y pcc.nii \ > > --no-cortex \ > > --osgm \ > > --glmdir analysisname > > > > *Could you please provide some more detail on what kind of > analysis is > > performed when I provide pcc.nii as an input for > mri_glmfit? Is it a > > t-test of the Fisher-transformed r-values against 0? > I just run a t-test of the r-values. I don't have a > program to convert > them to z-values, however, there are z-values that are > created in the > first level analysis. These are generated from the > p-values but I > bet it > would give you the same thing. Use -m z with > isxconcat-sess if you > want > to use the z. > > *Is the average r-value or z-value saved somewhere? > Which level? For mri_glmfit, they are not, but it is not > hard to get > them with matlab. > > *Do you take the autocorrelation into account (as in > Vincent JL et > > al., 2007. Intrinsic functional architecture in the > anaesthetized > > monkey brain. Nature. 447:83-86)? > Not usually, but it could be done by not including > -no-whiten when you > run mkanalysis-sess. I usually use the regression > coefficients instead > of correlation coefficients because that they are at least > unbiased with > respect to noise level and autocorrelation. > doug > > > > I'd also be happy to look this up but I'd need to know > where I can > >
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