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,
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,
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 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,
_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
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 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 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,
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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 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, _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail. _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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,
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu (mailto:greve@nmr.mgh.harvard.edu) Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html (http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html) Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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,
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu mailto:greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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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....) 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) 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,
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu (mailto:greve@nmr.mgh.harvard.edu) Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html (http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html) Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
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_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
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....) 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 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,
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
On 04/14/2014 10:35 AM, Pedro Rosa wrote:
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.
I would do it as a paired-test (see the wiki). You may have to re-run mris_preproc with the --paired-diff flag, then smooth by 10mm. Use this as the input to mri_glmfit. Set up the FSGD with the categorical and continuous variables (note that the FSGD file will have only a single entry per subject). Create your contrasts and run mri_glmfit. Overwrite the sig.mgh with the one from LME. Then run mri_glmfit-sim with the --cache option (not permutation)
I don't know what your contrast of interest is, so I can't help you there. In the end, it does not matter because you are overwriting the sig map anyway. You just need a contrast as a place holder.
doug
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:
- What does the FWHM procedure does?
- 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....) 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 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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The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu mailto:greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu mailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
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Hi, Doug. Thanks a lot! The command would look like this: mri_glmfit-sim --glmdir lh.thickness.Sch.glmdir --sim mc-z 10000 2 teste --sim-sign abs --overwrite --cache 1.3 abs It takes only a few seconds to run. I have a few questions about how to run it: 1) How the single entry for each subject look like in the FSGD file?
2) How to define the voxel/vertex-wise used to define clusters (after —sim, -log10( p)), and the voxel-wise threshold for the —cache option? What is the difference between them?
Thanks again, Pedro Rosa.
On Monday, April 14, 2014 at 3:29 PM, Douglas N Greve wrote:
On 04/14/2014 10:35 AM, Pedro Rosa wrote:
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.
I would do it as a paired-test (see the wiki). You may have to re-run mris_preproc with the --paired-diff flag, then smooth by 10mm. Use this as the input to mri_glmfit. Set up the FSGD with the categorical and continuous variables (note that the FSGD file will have only a single entry per subject). Create your contrasts and run mri_glmfit. Overwrite the sig.mgh with the one from LME. Then run mri_glmfit-sim with the --cache option (not permutation)
I don't know what your contrast of interest is, so I can't help you there. In the end, it does not matter because you are overwriting the sig map anyway. You just need a contrast as a place holder.
doug
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:
- What does the FWHM procedure does?
- 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....) 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) 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) mailto: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, > > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) > mailto:Freesurfer@nmr.mgh.harvard.edu > mailto:Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > The information in this e-mail is intended only for the person to > whom it is > addressed. If you believe this e-mail was sent to you in error and > the e-mail > contains patient information, please contact the Partners > Compliance HelpLine at > http://www.partners.org/complianceline . If the e-mail was sent to > you in error > but does not contain patient information, please contact the > sender and properly > dispose of the e-mail. > > > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) > mailto:Freesurfer@nmr.mgh.harvard.edu > mailto:Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) > mailto:Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >
-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu (mailto:greve@nmr.mgh.harvard.edu) mailto:greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html (http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html) http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
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The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
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-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu (mailto:greve@nmr.mgh.harvard.edu) Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html (http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html) Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
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On 04/15/2014 10:34 PM, Pedro Rosa wrote:
Hi, Doug. Thanks a lot! The command would look like this: mri_glmfit-sim --glmdir lh.thickness.Sch.glmdir --sim mc-z 10000 2 teste --sim-sign abs --overwrite --cache 1.3 abs It takes only a few seconds to run.
The command is fine, but remove --sim mc-z 10000 2 teste. This tells it to do the simulation, which you don't need to do. It is overridden by the --cache flag. If they had occurred in the other order, you would be waiting days for the simulation to run.
I have a few questions about how to run it:
- How the single entry for each subject look like in the FSGD file?
I'm not sure what you mean. Have you looked at the examples on the wiki?
- How to define the voxel/vertex-wise used to define clusters (after
—sim, -log10( p)), and the voxel-wise threshold for the —cache option? What is the difference between them?
They are the same doug
Thanks again, Pedro Rosa.
On Monday, April 14, 2014 at 3:29 PM, Douglas N Greve wrote:
On 04/14/2014 10:35 AM, Pedro Rosa wrote:
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.
I would do it as a paired-test (see the wiki). You may have to re-run mris_preproc with the --paired-diff flag, then smooth by 10mm. Use this as the input to mri_glmfit. Set up the FSGD with the categorical and continuous variables (note that the FSGD file will have only a single entry per subject). Create your contrasts and run mri_glmfit. Overwrite the sig.mgh with the one from LME. Then run mri_glmfit-sim with the --cache option (not permutation)
I don't know what your contrast of interest is, so I can't help you there. In the end, it does not matter because you are overwriting the sig map anyway. You just need a contrast as a place holder.
doug
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:
- What does the FWHM procedure does?
- 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....)
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 >> 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 >> mailto: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, >> >> _______________________________________________ >> Freesurfer mailing list >> Freesurfer@nmr.mgh.harvard.edu >> mailto:Freesurfer@nmr.mgh.harvard.edu >> mailto:Freesurfer@nmr.mgh.harvard.edu >> mailto:Freesurfer@nmr.mgh.harvard.edu >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >> >> >> The information in this e-mail is intended only for the person to >> whom it is >> addressed. If you believe this e-mail was sent to you in error and >> the e-mail >> contains patient information, please contact the Partners >> Compliance HelpLine at >> http://www.partners.org/complianceline . If the e-mail was sent to >> you in error >> but does not contain patient information, please contact the >> sender and properly >> dispose of the e-mail. >> >> >> >> _______________________________________________ >> Freesurfer mailing list >> Freesurfer@nmr.mgh.harvard.edu >> mailto:Freesurfer@nmr.mgh.harvard.edu >> mailto:Freesurfer@nmr.mgh.harvard.edu >> mailto:Freesurfer@nmr.mgh.harvard.edu >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >> >> >> >> >> _______________________________________________ >> Freesurfer mailing list >> Freesurfer@nmr.mgh.harvard.edu >> mailto:Freesurfer@nmr.mgh.harvard.edu >> mailto:Freesurfer@nmr.mgh.harvard.edu >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > -- > Douglas N. Greve, Ph.D. > MGH-NMR Center > greve@nmr.mgh.harvard.edu mailto:greve@nmr.mgh.harvard.edu > mailto:greve@nmr.mgh.harvard.edu > Phone Number: 617-724-2358 > Fax: 617-726-7422 > > Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting > FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 > www.nmr.mgh.harvard.edu/facility/filedrop/index.html > http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html > http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html > Outgoing: > ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu > mailto:Freesurfer@nmr.mgh.harvard.edu > mailto:Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
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-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu mailto:greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
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Thanks, Doug! I looked at the examples on the wiki, and the Paired Analysis wiki page as well. You mentioned I should include the categorical and continuous variables in the fsgd file for the mri_glmfit. It seems that one of my analyses includes few subjects, and then one of the class end up lacking a adequate range of continuous variables, and consequently the mri_glmfit finished with errors (this error no longer appears after I merged that problematic group with another). Therefore I ask whether it would be fine to run the mri_glmfit without some (or all) the continuous or/an categorical variables, while they would of course be included in the LME ran in MatLab. Further, I would like to ask what the FMHM outputs represents? Regards, Pedro Rosa.
On Wednesday, April 16, 2014 at 11:49 AM, Douglas N Greve wrote:
On 04/15/2014 10:34 PM, Pedro Rosa wrote:
Hi, Doug. Thanks a lot! The command would look like this: mri_glmfit-sim --glmdir lh.thickness.Sch.glmdir --sim mc-z 10000 2 teste --sim-sign abs --overwrite --cache 1.3 abs It takes only a few seconds to run.
The command is fine, but remove --sim mc-z 10000 2 teste. This tells it to do the simulation, which you don't need to do. It is overridden by the --cache flag. If they had occurred in the other order, you would be waiting days for the simulation to run.
I have a few questions about how to run it:
- How the single entry for each subject look like in the FSGD file?
I'm not sure what you mean. Have you looked at the examples on the wiki?
- How to define the voxel/vertex-wise used to define clusters (after
—sim, -log10( p)), and the voxel-wise threshold for the —cache option? What is the difference between them?
They are the same doug
Thanks again, Pedro Rosa.
On Monday, April 14, 2014 at 3:29 PM, Douglas N Greve wrote:
On 04/14/2014 10:35 AM, Pedro Rosa wrote:
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.
I would do it as a paired-test (see the wiki). You may have to re-run mris_preproc with the --paired-diff flag, then smooth by 10mm. Use this as the input to mri_glmfit. Set up the FSGD with the categorical and continuous variables (note that the FSGD file will have only a single entry per subject). Create your contrasts and run mri_glmfit. Overwrite the sig.mgh with the one from LME. Then run mri_glmfit-sim with the --cache option (not permutation)
I don't know what your contrast of interest is, so I can't help you there. In the end, it does not matter because you are overwriting the sig map anyway. You just need a contrast as a place holder.
doug
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:
- What does the FWHM procedure does?
- 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....) > > 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) mailto: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) mailto:greve@nmr.mgh.harvard.edu > > > mailto: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, > > > > > > > > > _______________________________________________ > > > Freesurfer mailing list > > > Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) > > > mailto:Freesurfer@nmr.mgh.harvard.edu > > > mailto:Freesurfer@nmr.mgh.harvard.edu > > > mailto:Freesurfer@nmr.mgh.harvard.edu > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > > > > > > > The information in this e-mail is intended only for the person to > > > whom it is > > > addressed. If you believe this e-mail was sent to you in error and > > > the e-mail > > > contains patient information, please contact the Partners > > > Compliance HelpLine at > > > http://www.partners.org/complianceline . If the e-mail was sent to > > > you in error > > > but does not contain patient information, please contact the > > > sender and properly > > > dispose of the e-mail. > > > > > > > > > > > > _______________________________________________ > > > Freesurfer mailing list > > > Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) > > > mailto:Freesurfer@nmr.mgh.harvard.edu > > > mailto:Freesurfer@nmr.mgh.harvard.edu > > > mailto:Freesurfer@nmr.mgh.harvard.edu > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > > > > > > > > > > > > > _______________________________________________ > > > Freesurfer mailing list > > > Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) > > > mailto:Freesurfer@nmr.mgh.harvard.edu > > > mailto:Freesurfer@nmr.mgh.harvard.edu > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > > > > > > > -- > > Douglas N. Greve, Ph.D. > > MGH-NMR Center > > greve@nmr.mgh.harvard.edu (mailto:greve@nmr.mgh.harvard.edu) mailto:greve@nmr.mgh.harvard.edu > > mailto:greve@nmr.mgh.harvard.edu > > Phone Number: 617-724-2358 > > Fax: 617-726-7422 > > > > Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting > > FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 > > www.nmr.mgh.harvard.edu/facility/filedrop/index.html (http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html) > > http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html > > http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html > > Outgoing: > > ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ > > > > _______________________________________________ > > Freesurfer mailing list > > Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) > > mailto:Freesurfer@nmr.mgh.harvard.edu > > mailto:Freesurfer@nmr.mgh.harvard.edu > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > > > > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu (mailto:Freesurfer@nmr.mgh.harvard.edu) > mailto:Freesurfer@nmr.mgh.harvard.edu > mailto:Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >
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The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
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-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu (mailto:greve@nmr.mgh.harvard.edu) mailto:greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html (http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html) http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
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-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu (mailto:greve@nmr.mgh.harvard.edu) Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html (http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html) Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
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On 4/26/14 2:11 PM, Pedro Rosa wrote:
Thanks, Doug! I looked at the examples on the wiki, and the Paired Analysis wiki page as well. You mentioned I should include the categorical and continuous variables in the fsgd file for the mri_glmfit. It seems that one of my analyses includes few subjects, and then one of the class end up lacking a adequate range of continuous variables, and consequently the mri_glmfit finished with errors (this error no longer appears after I merged that problematic group with another). Therefore I ask whether it would be fine to run the mri_glmfit without some (or all) the continuous or/an categorical variables, while they would of course be included in the LME ran in MatLab.
Yes, that is fine
Further, I would like to ask what the FMHM outputs represents?
The FWHM refers to the width of a gaussian. It is measured as the average correlation between the residuals of neighboring vertices (the correaltion coef get transformed into the FWHM). Smoothing with a gaussian of a certain FWHM will induce correlation, but there can be correlation from other sources so the FWHM you get out is not necessarily that which you used to smooth the data with.
doug
Regards, Pedro Rosa.
On Wednesday, April 16, 2014 at 11:49 AM, Douglas N Greve wrote:
On 04/15/2014 10:34 PM, Pedro Rosa wrote:
Hi, Doug. Thanks a lot! The command would look like this: mri_glmfit-sim --glmdir lh.thickness.Sch.glmdir --sim mc-z 10000 2 teste --sim-sign abs --overwrite --cache 1.3 abs It takes only a few seconds to run.
The command is fine, but remove --sim mc-z 10000 2 teste. This tells it to do the simulation, which you don't need to do. It is overridden by the --cache flag. If they had occurred in the other order, you would be waiting days for the simulation to run.
I have a few questions about how to run it:
- How the single entry for each subject look like in the FSGD file?
I'm not sure what you mean. Have you looked at the examples on the wiki?
- How to define the voxel/vertex-wise used to define clusters (after
---sim, -log10( p)), and the voxel-wise threshold for the ---cache option? What is the difference between them?
They are the same doug
Thanks again, Pedro Rosa.
On Monday, April 14, 2014 at 3:29 PM, Douglas N Greve wrote:
On 04/14/2014 10:35 AM, Pedro Rosa wrote:
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.
I would do it as a paired-test (see the wiki). You may have to re-run mris_preproc with the --paired-diff flag, then smooth by 10mm. Use this as the input to mri_glmfit. Set up the FSGD with the categorical and continuous variables (note that the FSGD file will have only a single entry per subject). Create your contrasts and run mri_glmfit. Overwrite the sig.mgh with the one from LME. Then run mri_glmfit-sim with the --cache option (not permutation)
I don't know what your contrast of interest is, so I can't help you there. In the end, it does not matter because you are overwriting the sig map anyway. You just need a contrast as a place holder.
doug
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:
- What does the FWHM procedure does?
- 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....) >> >> >> 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 >>>> mailto: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 >>>> mailto:greve@nmr.mgh.harvard.edu >>>> mailto: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, >>>> >>>> _______________________________________________ >>>> Freesurfer mailing list >>>> Freesurfer@nmr.mgh.harvard.edu >>>> mailto:Freesurfer@nmr.mgh.harvard.edu >>>> mailto:Freesurfer@nmr.mgh.harvard.edu >>>> mailto:Freesurfer@nmr.mgh.harvard.edu >>>> mailto:Freesurfer@nmr.mgh.harvard.edu >>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>>> >>>> >>>> The information in this e-mail is intended only for the person to >>>> whom it is >>>> addressed. If you believe this e-mail was sent to you in >>>> error and >>>> the e-mail >>>> contains patient information, please contact the Partners >>>> Compliance HelpLine at >>>> http://www.partners.org/complianceline . If the e-mail was >>>> sent to >>>> you in error >>>> but does not contain patient information, please contact the >>>> sender and properly >>>> dispose of the e-mail. >>>> >>>> >>>> >>>> _______________________________________________ >>>> Freesurfer mailing list >>>> Freesurfer@nmr.mgh.harvard.edu >>>> mailto:Freesurfer@nmr.mgh.harvard.edu >>>> mailto:Freesurfer@nmr.mgh.harvard.edu >>>> mailto:Freesurfer@nmr.mgh.harvard.edu >>>> mailto:Freesurfer@nmr.mgh.harvard.edu >>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>>> >>>> >>>> >>>> >>>> _______________________________________________ >>>> Freesurfer mailing list >>>> Freesurfer@nmr.mgh.harvard.edu >>>> mailto:Freesurfer@nmr.mgh.harvard.edu >>>> mailto:Freesurfer@nmr.mgh.harvard.edu >>>> mailto:Freesurfer@nmr.mgh.harvard.edu >>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>> >>> -- >>> Douglas N. Greve, Ph.D. >>> MGH-NMR Center >>> greve@nmr.mgh.harvard.edu mailto:greve@nmr.mgh.harvard.edu >>> mailto:greve@nmr.mgh.harvard.edu >>> mailto:greve@nmr.mgh.harvard.edu >>> Phone Number: 617-724-2358 >>> Fax: 617-726-7422 >>> >>> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting >>> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 >>> www.nmr.mgh.harvard.edu/facility/filedrop/index.html >>> http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html >>> http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html >>> http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html >>> Outgoing: >>> ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ >>> >>> _______________________________________________ >>> Freesurfer mailing list >>> Freesurfer@nmr.mgh.harvard.edu >>> mailto:Freesurfer@nmr.mgh.harvard.edu >>> mailto:Freesurfer@nmr.mgh.harvard.edu >>> mailto:Freesurfer@nmr.mgh.harvard.edu >>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >> >> >> >> _______________________________________________ >> Freesurfer mailing list >> Freesurfer@nmr.mgh.harvard.edu >> mailto:Freesurfer@nmr.mgh.harvard.edu >> mailto:Freesurfer@nmr.mgh.harvard.edu >> mailto:Freesurfer@nmr.mgh.harvard.edu >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu > mailto:Freesurfer@nmr.mgh.harvard.edu > mailto:Freesurfer@nmr.mgh.harvard.edu > mailto:Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > The information in this e-mail is intended only for the person to > whom it is > addressed. If you believe this e-mail was sent to you in error and > the e-mail > contains patient information, please contact the Partners Compliance > HelpLine at > http://www.partners.org/complianceline . If the e-mail was sent to > you in error > but does not contain patient information, please contact the sender > and properly > dispose of the e-mail.
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-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu mailto:greve@nmr.mgh.harvard.edu mailto:greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
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-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu mailto:greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
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Douglas Greve -
Douglas N Greve
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jorge luis -
Pedro Rosa