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Dear all, I realize that this post is actually off-topic as it really concerns (very basic) general statistics and not FSL specifically, I just happen to be using FSL for the analysis. My apologies for this. I am comparing fMRI results from a group of subjects with a condition that may or may not affect the results, and a group of individually age- sex matched control subjects. I would like to test if the presence of the condition affects the fMRI results, and if/how this effect depends on the severity of the condition. The condition is expected to be associated with subtle changes of the results only. I cannot rule out, that other subject-specific parameters, such as BMI, cerebral volume or IQ, could affect the results. I would like to do as sensitive an analysis as possible. Would it be OK to apply a paired/longitudinal design for this purpose? Consider the two groups of subjects: A - subjects with condition B - matched subjects without condition Group A subj scan_results severity age gender 1 X1 3 29 2 2 X2 5 25 1 3 X3 4 33 2 4 X4 7 28 2 where, in the gender column, 2 is female, 1 is male, and Group B subj scan_results severity age gender 1 Y1 0 29 2 2 Y2 0 25 1 3 Y3 0 33 2 4 Y4 0 28 2 I imagine doing testing the differences between groups in a paired fashion: Differences scan_diff severity_diff age_diff gender_diff X1-Y1 3 0 0 X2-Y2 5 0 0 X3-Y3 4 0 0 X4-Y4 7 0 0 To do this, I would use a GLM like this: scan_diff = mean + severity_diff Could I even control for the (small) age differences by adding this as another confound: scan_diff = mean + severity_diff + age_diff ? Another approach would be to do an unpaired design, in which the dependent variable would be a column of scan results from all subjects: scan_results severity age gender X1 3 29 2 X2 5 25 1 X3 4 33 2 X4 7 28 2 Y1 0 29 2 Y2 0 25 1 Y3 0 33 2 Y4 0 28 2 And to correct for differences due to age and gender, the model would be: scan_results = mean + severity + age + gender What are the principal differences between these two approaches and which one would you consider most appropriate and most sensitive for this purpose? Best, Anders