Re: [Freesurfer] Monte Carlo simulation - Longitudinal Pipeline

Mon, 14 Apr 2014 07:36:43 -0700

Dear Doug and Jorge,
I tried what you suggested and I think it work, although I have some concerns.
I am working with a longitudinal study with two time-points for all subjects, three categorical variables (group, substance abuse / dependence and gender) and three continuous variables (interval between scans, age and medication intake).
I generated a contrast with intercept + 7 betas for the LME, ran it without any problem and saved the sig.mgh using fs_write_fstats(F_lhstats,mri,’sig.mgh’,’sig’).
For the mri_glmfit I entered the same output from mri_surf2surf I used for the LME (smoothed at 10mm), but I did not know how exactly to enter the categorical and continuous variables, or which contrast to use.

The commmand was: mri_glmfit —y pval.mgh —sim perm 10000 0.05 sch

I just tested creating a matrix with 24 columns (Nclasses*(Nvariables+1) as suggested for DODS).
Afterwards I ran the mri_glmfit-sim (mri_glmfit-sim --glmdir Sch-glmdir --sim mc-full 5 2 teste --sim-sign abs, and it finished apparently without errors.
I attached the logs for both mri_glmfit and mri_glmfit-sim.

That said, I have the following questions:

1) What does the FWHM procedure does?
2) How should I decide which contrast to test if the mri_glmfit does not consider the longitudinal design? 
3) Will the mri_glmfit-sim consider only the FMHM output from mri_glmfit and sig.mgh from the LME, or also other outputs from the mri_glmfit?
4) Does the FWHM rely only on the images, and not on variables and contrasts?

Thank you very much!
Pedro Rosa.

On Monday, March 31, 2014 at 10:53 PM, Pedro Rosa wrote:

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

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


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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MGH-NMR Center
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Attachment: teste.mri_glmfit-sim.log
Description: Binary data

Attachment: mri_glmfit.log
Description: Binary data

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