I'm not sure what you are asking. What do you mean you want to compare the correlation maps between the two groups? If you just want to look at them, then set up contrasts for each.
On 10/28/2019 3:36 PM, Nillo, Ryan Michael R wrote:
External Email - Use CautionThanks, Doug. One more question: I want to see how correlated my covariate ROI is with LGI and compare the correlation maps between the two groups. Is my glmfit command the correct setup? I have tried running glmfit on a different fsgd and contrast matrix with only one of the two groups and have a pcc.mgh for that analysis. When I compare that to the pcc.mgh I get from my original analysis for that same group, the value at each vertex is slightly different.
Ryan Michael Nillo UCSF Department is Radiology and Biomedical Imaging Lab for Precision Neuroimaging Staff Research Associate 1
On Oct 28, 2019, at 11:17, Greve, Douglas N.,Ph.D. DGREVE@mgh.harvard.edu wrote:
Yes
On 10/28/19 1:04 PM, Nillo, Ryan Michael R wrote: Am I correct in assuming this results in z-scoring/normalizing the covariates?
Ryan Michael Nillo Staff Research Associate I University of California San Francisco Department of Radiology and Biomedical Imaging
On Oct 28, 2019, at 7:33 AM, Greve, Douglas N.,Ph.D. <DGREVE@mgh.harvard.edu mailto:DGREVE@mgh.harvard.edu> wrote:
My guess is that your covariates have almost no range to them and so look like constant (similar to columns 1 and 2). You can try demeaning and rescaling the covariates. You can do this by hand and create a new FSGD file or you can add the following lines anywhere in the fsgd: RescaleFlag 1 DemeanFlag 1
On 10/26/19 10:45 PM, Nillo, Ryan Michael R wrote:
Hello,
I ran mri_glmfit with the following fsgd file:
GroupDescriptorFile 1 Title CJD
Class MM1_MV1 Class MV2_VV2
Variablesfrontal
Input7693MV2_VV22.930667 Input11985MV2_VV22.410333 Input13082MV2_VV22.529333 Input13425MV2_VV22.593667 Input13667MM1_MV12.628667 Input13900MM1_MV12.675333 Input14094MM1_MV12.549333 Input15566MV2_VV22.666667 Input15999MM1_MV12.641 Input16714MV2_VV22.643 Input16848MV2_VV22.63 Input16952MV2_VV22.516 Input7693aMV2_VV23.054 Input11985aMV2_VV22.42 Input13082aMV2_VV22.454 Input13425aMV2_VV22.602 Input13667aMM1_MV12.604333 Input13900aMM1_MV12.601333 Input14094aMM1_MV12.523333 Input15566aMV2_VV22.713333 Input15999aMM1_MV12.584333 Input16714aMV2_VV22.673333 Input16848aMV2_VV22.533333 Input16952aMV2_VV22.486
My two contrast matrices are: one.mtx = [ 0 0 1 0 ] two.mtx = [ 0 0 0 1 ]
When running the mri_glmfit command "*mri_glmfit --glmdir new.frontal.lgi --fsgd new.frontal.fsgd.txt --fwhm 30 --surf fsaverage lh --y lh.pial_lgi.fwhm0.mgh --C one.mtx --C two.mtx*”, I get the following output:
INFO: gd2mtx_method is dods Saving design matrix to new.frontal.lgi/Xg.dat Computing normalized matrix Normalized matrix condition is 12820.7 Design matrix ------------------ 0.00000 1.00000 0.00000 2.93067; 0.00000 1.00000 0.00000 2.41033; 0.00000 1.00000 0.00000 2.52933; 0.00000 1.00000 0.00000 2.59367; 1.00000 0.00000 2.62867 0.00000; 1.00000 0.00000 2.67533 0.00000; 1.00000 0.00000 2.54933 0.00000; 0.00000 1.00000 0.00000 2.66667; 1.00000 0.00000 2.64100 0.00000; 0.00000 1.00000 0.00000 2.64300; 0.00000 1.00000 0.00000 2.63000; 0.00000 1.00000 0.00000 2.51600; 0.00000 1.00000 0.00000 3.05400; 0.00000 1.00000 0.00000 2.42000; 0.00000 1.00000 0.00000 2.45400; 0.00000 1.00000 0.00000 2.60200; 1.00000 0.00000 2.60433 0.00000; 1.00000 0.00000 2.60133 0.00000; 1.00000 0.00000 2.52333 0.00000; 0.00000 1.00000 0.00000 2.71333; 1.00000 0.00000 2.58433 0.00000; 0.00000 1.00000 0.00000 2.67333; 0.00000 1.00000 0.00000 2.53333; 0.00000 1.00000 0.00000 2.48600;
ERROR: matrix is ill-conditioned or badly scaled, condno = 12820.7
Possible problem with experimental design: Check for duplicate entries and/or lack of range of continuous variables within a class. If you seek help with this problem, make sure to send:
- Your command line:
mri_glmfit --glmdir new.frontal.lgi --fsgd new.frontal.fsgd.txt--fwhm 30 --surf fsaverage lh --y lh.pial_lgi.fwhm0.mgh --C one.mtx --C two.mtx 2. The FSGD file (if using one) 3. And the design matrix above Attempting to diagnose further SumSq: Min=2.828427 (col 1), Max=10.485683 (col 4) ... could not determine the cause of the problem
Can anyone give me some insight into what might be going wrong?
Thanks so much,
Ryan Michael Nillo Staff Research Associate I University of California San Francisco Department of Radiology and Biomedical Imaging
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