Hi Gabriel,
for mri_glmfit, W = diag(sqrt(1./yffxvar)), ie, it is a diagonal matrix where each element is the square root of the inverse of the variance from the lower level. This changes the equation from a simple ordinary least squares into a weighted least squares (though not quite a true mixed-effects analysis).
There are not explicitly nuisance regressors in the B matrix. The B is the matrix of regression coefficients ("betas") based on the solution to the forward model y=X*B (B=inv(X'*X)*X'*y). So the frames in B depend on the design matrix which depend on how you've set up your FSGD (or if you've entered the X matrix explicitly).
I don't have another manuscript, but there is a lot of detail in the tutorial slides MailScanner has detected a possible fraud attempt from "surfer.nmr.mgh.harvard.edu" claiming to be ftp://s pdb.set_trace()
urfer.nmr.mgh.harvard.edu/pub/docs/freesurfer.groupanalysis.ppt
doug
Gabriel A. Tobon wrote:
------------------------------------------------------------------------Hello Freesurfers
I would like to run a specialized group GLM analysis on the volume using estimates from the individual subject analysis in Freesurfer as summary statistics. I would like to learn a little more about how this estimation is done before doing so. I have looked at the documentation for "mri_glmfit" and the following tutorial:
http://surfer.nmr.mgh.harvard.edu/fswiki/FsFastTutorial
I also have your 1999/2003 manuscripts on "Signal Processing and Statistical Analysis for Event-Related fMRI" and "fMRI Analysis 101 - Univariate Analysis," but I am not sure if the method has been updated since then.
* I noticed there is a weight matrix (W) described for "mri_glmfit" command. How is this derived and used? I can't find more information about it in the documentation/mailing list.
* How are the nuisance regressors organized in the B matrix? I assume the conditions are first? What are the following regressors?
* Do you have a similar manuscript that describes just how group analysis is implemented in FsFast?
Thank you for your time and help!
Gabriel
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