Two Groups (One Factor/Two Levels), No Covariates
This models the input as two separate offsets/intercepts/means, one for each group. The two groups can be thought of as two levels of a single discrete factor. There is not continuous covariate. These results hold for DODS or DOSS.
FSGD File (g2v0.fsgd)
GroupDescriptorFile 1 Title OSGM Class Group1 Class Group2 Input subject1 Group1 Input subject2 Group2
Nclasses = 2
Nvariables = 0
Nregressors = Nclasses*(Nvariables+1) = 2*(0+1) = 2
Regressor1: ones for subjects in Group 1, 0 otherwise. Codes intercept/mean for Group 1
Regressor2: ones for subjects in Group 2, 0 otherwise. Codes intercept/mean for Group 2
The number of columns in each contrast matrix must be the same as the number of regressors (Nregressors). If there is only one row in the contrast matrix, then the result will be a t-test and will have a sign. Reversing the signs in the contrast matrix will only change the sign of the output, not its magnitude. If there is more than one row, the result will be an F-test and will be unsigned.
Contrast 1 group.diff.mtx
Null Hypothesis: is there a difference between the group intercepts? Is there a difference between groups?
This is a t-test with Group1>Group2 being positive (red/yellow).
Contrast 2 group1.mtx
Null Hypothesis: is there a main effect of Group1? Does the mean of Group1 equal 0?
This is a t-test with Group1>0 being positive (red/yellow).
Contrast 3 group2.mtx
Null Hypothesis: is there a main effect of Group2? Does the mean of Group2 equal 0?
This is a t-test with Group2>0 being positive (red/yellow).
Contrast 4 g1g2.intercept.mtx
Null Hypothesis: does mean of the group means differ from 0?
This is a t-test with (Group1+Group2)/2 > 0 being positive (red/yellow). If the mean is < 0, then it will be displayed in blue/cyan.
This is an example invocation of mri_glmfit. Depending upon your application, you may have other options as well.
mri_glmfit \ --glmdir g2v0 \ --y y.mgh \ --fsgd g2v0.fsgd \ --C group.diff.mtx \ --C group1.mtx \ --C g1g2.intercept.mtx \