Sure, any simulation can’t be exhaustive, but my understanding, chatting with Anderson, is that their 2014 paper simulated a quite large space of designs and Freedman-Lane is appropriate for the vast majority of the GLMs that we would encounter in neuroimaging.
Also, again per Anderson, I believe that method implemented in mri_glmfit-sim is that of Manly for those interested in linking what mri_glmfit-sim is doing to the results in Winkler (e.g., Table 7).
cheers, -MH
-- Michael Harms, Ph.D.
----------------------------------------------------------- Conte Center for the Neuroscience of Mental Disorders Washington University School of Medicine Department of Psychiatry, Box 8134 660 South Euclid Ave.Tel: 314-747-6173 St. Louis, MO 63110Email: mharms@wustl.edu
On 8/30/16, 8:33 AM, "freesurfer-bounces@nmr.mgh.harvard.edu on behalf of Douglas Greve" <freesurfer-bounces@nmr.mgh.harvard.edu on behalf of greve@nmr.mgh.harvard.edu> wrote:
By "wrong" I meant that permutation no longer gives exact p-values in expectation with non-orthogonal designs. There is no theory to characterize the accuracy of Freeman-Lane of the other methods. In this sense, they are not approximations but ad hoc methods that people hope do a better job than parametric methods. Anderson tested them on a wide range of designs, but it was, of course, not exhaustive. The accuracy of his results may not extend to other designs, so it is a buyer-beware situation (as with all neuroimaging).
On 8/29/16 10:16 PM, Harms, Michael wrote:
Hi, I wouldn’t say that non-orthogonal designs are “wrong” to use with permutation. Rather, there are different approaches to handling that situation and produce approximate p-values. See Table 2 in Winkler’s 2014 paper, and the results therein comparing the various approaches:
http://www.ncbi.nlm.nih.gov/pubmed/24530839
PALM actually gives you control over the method used, with the default (and recommended) approach being that of “Freedman-Lane", which is the same approach used by FSL’s ‘randomise’ tool to handle correlated covariates.
cheers, -MH
-- Michael Harms, Ph.D.
Conte Center for the Neuroscience of Mental Disorders Washington University School of Medicine Department of Psychiatry, Box 8134 660 South Euclid Ave.Tel: 314-747-6173 St. Louis, MO 63110Email: mharms@wustl.edu
On 8/29/16, 7:49 PM, "freesurfer-bounces@nmr.mgh.harvard.edu on behalf of Matt Glasser" <freesurfer-bounces@nmr.mgh.harvard.edu on behalf of matt@ma-tea.com> wrote:
PALM handles GIFTI and CIFTI data.
Peace,
Matt.
On 8/29/16, 6:21 PM, "Douglas N Greve" <freesurfer-bounces@nmr.mgh.harvard.edu on behalf of greve@nmr.mgh.harvard.edu> wrote:
Does PALM do surface-based? Also, there is no way to appropriately handle this. For permutation, non-orthogonal designs are wrong. There are ways to try to compensate for it, which is what PALM is doing. Sorry to be nit-picky!
On 08/29/2016 06:12 PM, Harms, Michael wrote:
Hi Maaike, Why not just use PALM? Then you don¹t have to worry about this (since PALM appropriately handles the situation of correlated covariates).
cheers, -MH
-- Michael Harms, Ph.D.
Conte Center for the Neuroscience of Mental Disorders Washington University School of Medicine Department of Psychiatry, Box 8134 660 South Euclid Ave.Tel: 314-747-6173 St. Louis, MO 63110Email: mharms@wustl.edu
On 8/29/16, 4:45 PM, "freesurfer-bounces@nmr.mgh.harvard.edu on behalf of Douglas N Greve" <freesurfer-bounces@nmr.mgh.harvard.edu on behalf of greve@nmr.mgh.harvard.edu> wrote:
It is hard to say. Since the subjects are not exchangeable, the permutation is technically not appropriate. Check the winkler paper, I think he talks about what happens if you just don't do anything.
On 08/29/2016 11:07 AM, maaike rive wrote:
Hi all,
Is using forced permutation for non-orthogonal design matrices wrong or is it allowed to do this instead of using tools like palm (what happens eg with the covariates when using forced permutation)? I used forced permutation and it seemed to work, results were (partly) comparable to what I found with monte carlo simulations.
Thanks, Maaike
*Van:* freesurfer-bounces@nmr.mgh.harvard.edu freesurfer-bounces@nmr.mgh.harvard.edu namens Harms, Michael mharms@wustl.edu *Verzonden:* vrijdag 26 augustus 2016 01:00:13 *Aan:* Freesurfer support list *Onderwerp:* Re: [Freesurfer] mri_glmfit-sim permutation testing running after 3 days!
Hi, You might want to check out FSL¹s PALM tool, which has a bit more sophisticated permutation framework, and allows for permutation in the context of non-orthogonal covariates.
cheers, -MH
-- Michael Harms, Ph.D.
Conte Center for the Neuroscience of Mental Disorders Washington University School of Medicine Department of Psychiatry, Box 8134 660 South Euclid Ave.Tel: 314-747-6173 St. Louis, MO 63110Email: mharms@wustl.edu
From: <freesurfer-bounces@nmr.mgh.harvard.edu mailto:freesurfer-bounces@nmr.mgh.harvard.edu> on behalf of Ajay Kurani <dr.ajay.kurani@gmail.com mailto:dr.ajay.kurani@gmail.com> Reply-To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu mailto:freesurfer@nmr.mgh.harvard.edu> Date: Thursday, August 25, 2016 at 4:13 PM To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu mailto:freesurfer@nmr.mgh.harvard.edu> Subject: Re: [Freesurfer] mri_glmfit-sim permutation testing running after 3 days!
Hi Doug, Thanks for the help! I think I figured out the issue based on your response.
- I created a template to use for this group and named it fsaverage
(including creating monte carlo simulations) for simplicity of integrating with freesurfer as I am newer to it. This is why the sizes didn't match up as you expected but the mri_glmfit still ran.
- I deleted the folder and restarted without background processes.
The error became apparent. Of my covariates (2 fix factors and 3 quantitative), not all were orthogonal. In looking at the error more, it seems that i need to add the --perm-force if I wanted the simulation to run, however the background processes were not aware of this error and kept polling as you mentioned.
This brings me to a new but related issue. From what I have read in other freesurfer posts, it is statistically incorrect to use --perm-force for non-orthogonal covariates (or continuous covariates). I am unsure how to proceed. a) If I ran permutation testing (to overcome the issue of incorrect smoothness estimations from the gaussian distribution assumption), then I run into the issue of non-orthogonal covariates. Is there a way to orthogonalize the data in freesurfer, or a solution to this issue?
b) If orthogonalizing is difficult to implement, another option is running Qdec with the montecarlo simulation at a more conservative p value (p< 0.001). From your previous posts, the testing at this p value for 10mm seems to meet the 5% FPR. One question is if the non-orthogonal data affects this analysis as well for this model?
Thanks, Ajay
On Thu, Aug 25, 2016 at 12:18 PM, Ajay Kurani <dr.ajay.kurani@gmail.com mailto:dr.ajay.kurani@gmail.com> wrote:
Hi Freesurfer Experts, I am trying to use freesurfer's mri_glmfit-sim tool to run permutation testing on cortical thickness data (as recommendedby Doug in my previous post:
http://www.mail-archive.com/freesurfer%40nmr.mgh.harvard.edu/msg48653.h t ml
http://www.mail-archive.com/freesurfer%40nmr.mgh.harvard.edu/msg48653. h tm l )
Most of the tutorials I found were not related to permutation testing so the subsequent steps may be incorrect. Please let me know where I go wrong... 1) I first ran QDec to generate a folder for the analysis which would create the subsequent fsgd and y files needed my mri_glmfit-sim. I am running both left and right hemisphere cortical thickness analysis with 10mm smoothing. The followingis for just the left hemisphere. Note I am doing a 3 group comparison, but for this 2 group ttest I manually centered the data based on the 3 group mean for age and education.
2) I ran the following command: /mri_glmfit-sim --glmdir ./HCvsPAT_lh_thickness_10mm/ --sim perm 10000 2 perm.abs.2 --sim-sign abs --bg 16 Prior to running the command above, from the y.fsdg file Ideleted the fwhm estimate of 13mm since this was not correctly estimated (ACF with long tails). I assumed that by removing this estimate, it would force the permutation test to calculate based on the data but when looking at the log output I see the following which says fwhm 0:
cmdline mri_glmfit.bin --C./HCvsPAT_lh_thickness_10mm//tmp.mri_glmfit-sim-19468/lh-Avg-Intercept- t hi ckness.mtx --C
./HCvsPAT_lh_thickness_10mm//tmp.mri_glmfit-sim-19468/lh-Diff-Male-Fema l e- Intercept-thickness.mtx --C
./HCvsPAT_lh_thickness_10mm//tmp.mri_glmfit-sim-19468/lh-Diff-PD-MCI-In t er cept-thickness.mtx --C
./HCvsPAT_lh_thickness_10mm//tmp.mri_glmfit-sim-19468/lh-X-Gender-Group
In tercept-thickness.mtx --sim perm 625 2 ./HCvsPAT_lh_thickness_10mm//csd/perm.abs.2.j013 --y
/home/akurani/Documents/PPMI/FS_Final/qdec/HCvsPAT_lh_thickness_10mm/y. m gh --mask ./HCvsPAT_lh_thickness_10mm//mask.mgh --sim-sign abs --fwhm 0 --fsgd ./HCvsPAT_lh_thickness_10mm//y.fsgd dods --surf fsaverage lh white --sim-done ./HCvsPAT_lh_thickness_10mm//csd/poll/done.perm.abs.2.j013
3)I started this a few days ago on a 16 core machine and it is still running in the terminal. I have 150 subjects in the analysis and specified 10000 iterations. In the terminal I assumed when I reach Poll 10000 it would be complete butcurrently I am at : Poll 13341 job 1 Thu Aug 25 12:03:51 CDT 2016
Questions: a) I am curious, is this going to run 10,000 simulations X 150 patients or does the Poll number not have anything to do withthe number of iterations it is on?
b) Did I run this procedure correctly? Was I incorrect indeleting the fwhm estimate from y.fsgd file generated by Qdec even though we know the estimate is incorrect since smoothness assumed a gaussian distribution as opposed to gaussian with heavy tails
c) I noticed in the logfile the following warning: INFO: gd2mtx_method is dods Computing normalized matrix Normalized matrix condition is 5.65727 Matrix condition is 935.597 Found 136777 voxels in mask Reshaping mriglm->mask... search space = 89675.729228 ERROR: design matrix is not orthogonal, cannot be used with permutation. If this something you really want to do, run with --perm-force Poll 2 job 1 Tue Aug 23 22:58:18 CDT 2016 I am not sure if this means my simulation is incorrect? Thanks, Ajay
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