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....) 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 <pedrogomesrosa@gmail.com mailto:pedrogomesrosa@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 <greve@nmr.mgh.harvard.edu mailto:greve@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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