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Dear Kersten,
Thank you so much for the quick reply! It was really helpful!! I still have a couple of doubts regarding the visualization of the results if you don't mind.
After running the last command *fs_write_Y(sided_pval,mri,'**spval_lh.mgh')*, I tried to visualize the results with *tksurfer fsaverage lh inflated -overlay spval_lh.mgh -fthresh 1.3* (1.3 was the pth value that came out from lme_mass_FDR2) but there were no clusters on the surface.
If I run mri_surfcluster with spval_lh.mgh as input it seems there are 15 clusters, here is a sample of the first clusters that come out: # ClusterNo Max VtxMax Size(mm^2) TalX TalY TalZ NVtxs WghtVtx 1 1.000 8 22284.41 -2.2 8.6 -3.3 48988 46268.86 2 1.000 140211 1542.44 -8.2 -16.7 58.6 3024 2633.36 3 1.000 44065 1510.18 -16.3 -71.7 -4.6 2078 1810.43 (...)
Am I doing something wrong?
I also tried the spatiotemporal mass-univariate approach that you suggested but matlab gets stuck with this command: [lhRgs,lhRgMeans] = lme_mass_RgGrow(lhsphere,lhRe,lhTh0,lhcortex,2,95);
In the terminal it says: "Starting region growing algorithm Computing seeds ..."
Do you have any suggestions on how to solve this?
Thanks again for the help!
Hi Tom,
basically most of it looks OK to me, just a few remarks:
I assume that you deliberately chose the plain mass-univariate approach instead of the potentially more powerful spatiotemporal mass-univariate approach.
For the interpretation of the intercepts, if desired, it may be advantageous to code sex as 0 and 1, and de-mean age. But that's not strictly necessary.
I suspect that it's just a typo, so just for the sake of completeness: for contrast estimation, I don't think that using CM.C1, CM.C2, etc. would work, as the program expects a 'C' substructure. So instead, one would use CM1.C, CM2.C etc for the different contrasts, if I am not mistaken.
About the mri_surfcluster command: if you want to do the FDR2 inference as suggested by the LME tutorial, just use the 'pth' value as returned by the lme_mass_FDR2 script for thresholding. I believe that you are mixing different kinds of inference (traditional FDR, clusterwise) otherwise.
Finally, if you want to make corrected inferences for the whole cortex, it's probably better to compute one (FDR2) threshold for both hemispheres at once (see the very last part of the tutorial) instead of treating them separately.
And regarding your specific questions:
The order is fine.
You can visualize the 'spval_lh.mgh' by first loading e.g. the 'lh.inflated' surface of the fsaverage template, and then adding 'spval_lh.mgh' as an overlay. Same with e.g. 'rh.inflated' for the 'spval_rh.mgh' file.
About the Monte Carlo correction for this type of analysis others may know better, sorry.
Best regards,
Kersten
Missatge de tom parker tomparker540@gmail.com del dia dg., 21 de jul. 2019 a les 20:50:
Dear all,
I am currently running a Mass-Univariate LME analysis in Freesurfer. I have some doubts about the last steps and I was wondering whether someone could help me figure them out and also check what I have done so far (sorry for the long email).
My study has 2 time points (baseline, follow-up), 3 groups (controls, patients without cognitive problems, patients with cognitive problems) and 2 covariates of no interest (age, sex). I am interested in looking at the interactions between group and time (mainly whether the 2 patient groups show more cortical thinning over time compared to controls).
These are the commands I have ran so far through terminal or in matlab:
mris_preproc --qdec-long long.qdec.table.dat --target fsaverage --hemi lh --meas thickness --out lh.thickness.mgh mris_preproc --qdec-long long.qdec.table.dat --target fsaverage --hemi rh --meas thickness --out rh.thickness.mgh
mri_surf2surf --hemi lh --s fsaverage --sval lh.thickness.mgh --tval lh.thickness_sm15.mgh --fwhm-trg 15 --cortex --noreshape mri_surf2surf --hemi rh --s fsaverage --sval rh.thickness.mgh --tval rh.thickness_sm15.mgh --fwhm-trg 15 --cortex --noreshape
[Y,mri] = fs_read_Y('lh.thickness_sm15.mgh'); [Y,mri] = fs_read_Y('rh.thickness_sm15.mgh');
lhsphere = fs_read_surf('fsaverage/surf/lh.sphere') rhsphere = fs_read_surf('fsaverage/surf/rh.sphere') lhcortex = fs_read_label('fsaverage/label/lh.cortex.label') rhcortex = fs_read_label('fsaverage/label/rh.cortex.label')
- Create X matrix in matlab, here is a sample:
intercept time group2 group2*time group3 group3*time age sex 1 0 0 0 0 0 41 1 1 2.42 0 0 0 0 41 1 1 0 1 0 0 0 53 1 1 2.5 1 2.5 0 0 53 1 1 0 0 0 0 0 54 2 1 5.25 0 0 0 0 54 2
lhstats = lme_mass_fit_vw(X, [1], Y, ni, lhcortex); rhstats = lme_mass_fit_vw(X, [1], Y, ni, rhcortex);
I entered 3 contrasts separately to check the interaction group*time. CM.C1 = [0 0 0 1 0 0 0 0]; this contrast will show if group 1 and 2 show a different cortical thinning pattern over time CM.C2 = [0 0 0 0 0 1 0 0]; this contrast will show if group 1 and 3 show a different cortical thinning pattern over time CM.C3 = [0 0 0 -1 0 1 0 0]; this contrast will show if group 2 and 3 show a different cortical thinning pattern over time
For each of the previous contrasts, i did the following:
F_lhstats = lme_mass_F(lhstats, CM); F_rhstats = lme_mass_F(rhstats, CM); fs_write_fstats(F_lhstats, mri,' sigL.mgh', 'sig'); fs_write_fstats(F_rhstats, mri,' sigR.mgh', 'sig');
mri.volsz(4) = 1 [detvtx,sided_pval,pth] = lme_mass_FDR2(F_lhstats.pval,F_lhstats.sgn,lhcortex,0.05,0); [detvtx,sided_pval,pth] = lme_mass_FDR2(F_rhstats.pval,F_rhstats.sgn,rhcortex,0.05,0);
fs_write_Y(sided_pval,mri,'spval_lh.mgh'); fs_write_Y(sided_pval,mri,'spval_rh.mgh');
mri_surfcluster --subject fsaverage --hemi lh --in spval_lh.mgh --cwpvalthresh 0.05 --fdr 0.05 --sign pos --o lh_time_cluster --sum lh_time_cluster_sum mri_surfcluster --subject fsaverage --hemi rh --in spval_rh.mgh --cwpvalthresh 0.05 --fdr 0.05 --sign pos --o rh_time_cluster --sum rh_time_cluster_sum
*My questions are:*
- Is the order of the steps (1-8) correct?
- How can I visualize the significant FDR-corrected results?
- Is there any way I can apply a Monte Carlo correction using the
generated files?
Thank you so much!