Dear Freesurfers,

My experimental design includes 2 discrete factors and 3 continuous variables:

Discrete factors:

-       Diagnosis with three levels (A, B, C)

-       Gender with two levels (Males, Females)

Continuous variables:

-       Mean cortical thickness in the left hemisphere (CTLH)

-       Age

-       Performance in a memory test (TASK)

The FSGD file would be as follows:

GroupDescriptorFile 1

Title Thickness-subj

Class AMale

Class AFemale

Class BMale

Class BFemale

Class CMale

Class CFemale

variables ZCTLH ZAGE

Input control_01 AMale -1.04398 0.24789

Input control_02 AMale 1.22853 -0.72343

Input control_03 AFemale .13773 -1.37097

Input control_04 AFemale .41043 0.24789

Input mci_01 BMale  -.77128 0.24789

Input mci_02 BMale -2.40749 0.73354

Input mci_03 BFemale -.86218 -1.0472

Input mci_04 BFemale .50133 0.23777

Input ad_01 CMale  -.75678 0.22789

Input ad_02 CMale -2.88749 0.63047

Input ad_03 CFemale -.96015 -1.4452

Input ad_04 CFemale .80712 -0.37567 …

We are assuming different offsets and different slopes (DDOS).

The following contrast would test the null hypothesis whether there is a difference between B and C (T-test) regressing out the effect of the remaining variables.

0 0 0.5 0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 0 0 0 0

Could you help me to build the contrast for testing interactions in the above experimental design? In the FSwiki examples, you add one column when you have two factors with two levels each, and one continuous variable. I am not sure how many columns should I add in our particular case and why.

Null hypothesis: is there an interaction between diagnosis (B > C) and gender regressing out the two remaining variables? Assuming that you add one column per variable that is regressed out, the contrast would be as follows:

0 0 0.5 0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Is this correct? Any help will be very appreciated.

Many thanks in advance.

Best regards,

Jose