Dear FSL list users and staff,
I am a novice in using FSL as well as MRI imaging analysis. I am trying to figure out a design
matrix where I am looking at two-group average with a covariate.
So, I have two groups (one control group and one patient group), each subjects have completed a
MRI experiment where the subject was looking at 6 different visual stimuli. Each subject has also
completed a survey questionnaire that has a total score.
With out a covariate, my design matrix is as follows:
EV1 - EV12
EV1 : 1st visual stimulus - group1
EV2 : 2nd visual stimulus - group 1
EV3 : 3rd visual stimulus - group 1
EV4 : 4th visual stimulus - group 1
EV5 : 5th visual stimulus - group 1
EV6 : 6th visual stimulus - group 1
EV7 : 1st visual stimulus - group 2
EV8 : 2nd visual stimulus - group 2
EV9 : 3rd visual stimulus - group 2
EV10 : 4th visual stimulus - group 2
EV11 : 5th visual stimulus - group 2
EV12 : 6th visual stimulus -group 2
Each subject will have 6 cope files that will be used as input for each EVs.
i.e. for the subject’s 1st stimulus cope file (who is in group1), EV1 will have a value of 1 and the
rest of the EVs will have a values of 0 (zero).
6 contrasts and 1 F-test
6 contrast = 6 different visual stimulus
With all F-test turned on for all 6 contrasts
EV1 EV2 EV3 EV4 EV5 EV6 EV7 EV8 EV9 EV10 EV11 EV12
C1 1 0 0 0 0 0 -1 0 0 0 0 0
C2 0 1 0 0 0 0 0 -1 0 0 0 0
C3 0 0 1 0 0 0 0 0 -1 0 0 0
C4 0 0 0 1 0 0 0 0 0 -1 0 0
C5 0 0 0 0 1 0 0 0 0 0 -1 0
C6 0 0 0 0 0 1 0 0 0 0 0 -1
Now, I have this survey questionnaire that I would like to use it as a covariate that has a numerical
total score. But, I am not sure how to incorporate this covariate into the design matrix. I have gone
through the FSL online course, but only thing I can find was Unpaired Two-group difference
without covariate or one group with covariate.
I would appreciate it if anyone can suggest how to add in the covariate to my existing design
matrix or suggest alternative design matrix that would fit what I am trying to accomplish.
Thank you for all your help in advance.
Yours Sincerely,
Sang Lee
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