Thanks, Doug!  
Should I run the mri_preproc and and smooth the output using mri_surf2surf 
with, let’s say, 10mm, and than run the LME normally in MatLab?
Would this be problematic with a different smoothing procedure in mri_glmfit?
How will mri_glmfit deal with the longitudinal design? Does this matter, or the 
FWHM would only be estimated on a average image of all time-points for all 
subjects?
Regards,
Pedro Rosa.


On Sunday, March 30, 2014 at 3:51 PM, Douglas Greve wrote:

>  
> I think I would just run mri_glmfit on your data to get the proper directly 
> structure and estimate of FWHM, then copy the sig file from the mixed fx 
> analysis into the glmfit folder for one of the contrasts. Then run 
> mri_glmfit-sim.
>  
> doug
>  
>  
> On 3/29/14 10:29 AM, Pedro Rosa wrote:
> > Dear Doug and Jorge,  
> > Thank you very much for your help.
> > I found another message in the list 
> > (https://mail.nmr.mgh.harvard.edu/pipermail//freesurfer/2013-November/034649.html)
> >  in which you suggested a way of using MC in mri_glmfit-sim by creating 
> > “fake files”, which would not be read by the script. In this case, only the 
> > simulation would be run, and not the full statistics. The command would be 
> > something like this:
> > - mri_glmfit-sim --glmdir $SUBJECTS_DIR --sim mc-full 5 2 teste --sim-sign 
> > abs
> > I created a “fake” mri_glmfit.log, fwhm.dat and mask.mgh files as suggested 
> > by the older post. This would be fine, I believe, if only sig.mgh is read 
> > by the script.
> > However, I get this message after running the command:
> > [server:Long-T0-T2-Posproc/Vertex/Sch] pedrogomesrosa% mri_glmfit-sim 
> > --glmdir $SUBJECTS_DIR --sim mc-full 5 2 teste --sim-sign abs
> > if: Expression Syntax.
> >  
> > Is it possible to do what I am trying to do? Does the residual errors at 
> > each location included in the sig.mgh, and, if necessary, how to compute it 
> > into image FWHM?  
> > Regards,
> >  
> > Pedro Rosa.
> >  
> >  
> > On Friday, March 28, 2014 at 2:38 PM, Douglas N Greve wrote:
> >  
> > > Jorge, do you output the FWHM?
> > > doug
> > >  
> > > On 03/27/2014 03:14 PM, jorge luis wrote:
> > > > Hi Pedro
> > > >  
> > > > Sorry, right now the only multiple comparisons corrections implemented  
> > > > in lme are the original Benjamini and Hochberg (1995) FDR procedure  
> > > > (lme_mass_FDR) and a more recent and powerful two-stage FDR procedure  
> > > > (lme_mass_FDR2):
> > > >  
> > > > Benjamini, Y., Krieger, A.M., Yekutieli, D. (2006). Adaptive linear  
> > > > step-up procedures that control the false discovery rate. Biometrika,  
> > > > 93, 491-507.
> > > >  
> > > > In my experience, this procedure is as powerful to detect effects in  
> > > > neuroimage data as alternative corrections with strong control of the  
> > > > family-wise error rate (FWE). However it would be great if we could  
> > > > use an implementation of any multiple comparisons correction with  
> > > > strong control of the FWE (MC, RFT, ect...) for lme (FDR procedures  
> > > > only provide weak control). The residual errors at each location  
> > > > required to compute an estimate of the image FWHM can be obtained from  
> > > > the lme output. But an actual FWHM estimate is not currently saved.
> > > >  
> > > > Best  
> > > > -Jorge
> > > >  
> > > >  
> > > > El Martes 25 de marzo de 2014 8:15, Pedro Rosa  
> > > > <pedrogomesr...@gmail.com (mailto:pedrogomesr...@gmail.com)> escribió:
> > > >  
> > > > Dear Doug,  
> > > > Thank you very much!
> > > > I will try what you suggested, although I am not sure if Jorge's
> > > > stream outputs the FMHM, or if I would need to run the statistics
> > > > from the beggining using in the terminal, and not in MatLab.
> > > > Do you think Jorge could comment on this issue?
> > > > Regards,
> > > > Pedro Rosa.
> > > >  
> > > > On Mar 24, 2014, at 12:44 PM, Douglas Greve  
> > > > <gr...@nmr.mgh.harvard.edu (mailto:gr...@nmr.mgh.harvard.edu) 
> > > > <mailto:gr...@nmr.mgh.harvard.edu>> wrote:
> > > >  
> > > >  
> > > > In theory, it should be possible. I have not used Jorge's stream,  
> > > > so I
> > > > don't know that much about it. Does it save an estimate of the
> > > > FWHM? If
> > > > so, then you can run mri_surfcluster passing it the p-value (ie,
> > > > -log10(p)) map, the FWHM, the mask, and a voxel-wise threshold.
> > > > This is
> > > > what mri_glmfit-sim does, so you might check that script for
> > > > mri_surfcluster command line options
> > > >  
> > > > doug  
> > > >  
> > > >  
> > > > > On 3/22/14 11:03 PM, Pedro Rosa wrote:  
> > > > > Dear list,
> > > > > I ran the recon-all and the Freesurfer 5.1 longitudinal pipeline
> > > > in a structural MRI dataset and I would like to use Monte Carlo as
> > > > the method for correction for multiple comparisons. However, the
> > > > longitudinal LME tutorial includes only FDR correction
> > > > (lme_mass_FDR2).
> > > > > Is it possible to use Monte Carlo correction for longitudinal
> > > > data? Can I input the outputs from MatLab (fstats =
> > > > lme_mass_F(?h,CM): stats.F / pval / sgn / df) into mri_glmfit and
> > > > then run Monte Carlo?
> > > > > If not, do you have any other suggestions of how I use Monte
> > > > Carlo in longitudinal analyses?
> > > > > Thanks in advance,
> > > >  
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> > >  
> > >  
> > > --  
> > > Douglas N. Greve, Ph.D.
> > > MGH-NMR Center
> > > gr...@nmr.mgh.harvard.edu (mailto:gr...@nmr.mgh.harvard.edu)
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