Hello,

I am analyzing cortical thickness in a patient and control group. I have
two covariates: age and length of disease. I am using the model from:

http://surfer.nmr.mgh.harvard.edu/fswiki/Fsgdf2G2V

However, for the control group I put length of disease as 0 in my FSGD
file. When I run the GLM, I get the following error message:

matrix is ill-conditioned or badly scaled, condno = 1e+08

Can you please tell me what I am doing wrong? Below is the required
information.

1. Your command line:

    mri_glmfit --y lh.disease_length.thickness.20.mgz --fsgd
fsgd_disease_length.txt dods --glmdir lh.disease_length.thickness.20.glmdir
--surf fsaverage lh --C groupdiff_length_disease.mat

  2. The FSGD file

GroupDescriptorFile 1
Title Anorexia vs Controls
Class Control plus blue
Class Patient circle green
Variables Age Disease_Length

Input C07 Control 37 0
Input C28 Control 28 0
Input C39 Control 37 0
Input C54 Control 40 0
Input C57 Control 32 0
Input C10 Control 54 0
Input C33 Control 31 0
Input C29 Control 22 0
Input A01 Patient 39 20
Input A02 Patient 24 11
Input A03 Patient 35 15
Input A04 Patient 40 10
Input A05 Patient 35 20
Input A06 Patient 57 20
Input A07 Patient 31 16
Input A08 Patient 20 8

  3. And the design matrix

Design matrix ------------------
 1.000   0.000   37.000   0.000   0.000   0.000;
 1.000   0.000   28.000   0.000   0.000   0.000;
 1.000   0.000   37.000   0.000   0.000   0.000;
 1.000   0.000   40.000   0.000   0.000   0.000;
 1.000   0.000   32.000   0.000   0.000   0.000;
 1.000   0.000   54.000   0.000   0.000   0.000;
 1.000   0.000   31.000   0.000   0.000   0.000;
 1.000   0.000   22.000   0.000   0.000   0.000;
 0.000   1.000   0.000   39.000   0.000   20.000;
 0.000   1.000   0.000   24.000   0.000   11.000;
 0.000   1.000   0.000   35.000   0.000   15.000;
 0.000   1.000   0.000   40.000   0.000   10.000;
 0.000   1.000   0.000   35.000   0.000   20.000;
 0.000   1.000   0.000   57.000   0.000   20.000;
 0.000   1.000   0.000   31.000   0.000   16.000;
 0.000   1.000   0.000   20.000   0.000   8.000;
--------------------------------

Thank you,
Allie
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