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
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