Aaaand, we're back! Thanks (in advanced) for your thoughts on Round 1:
z-score v beta weight as a measure of functional connectivity
Here we are with ...
Round 2 for US$500 / 310 GBP / 386 EUR:
Assessing change in functional connectivity between pre- and post-conditions using randomise.
You may recall from Round 1 that I have a better intuitive feeling for using z-score as a measure of functional connectivity. I don't yet understand the advantages of using beta weights instead.
Now I would like to assess the *change* in functional connectivity between a pre- and post-condition.
I have set up a paired t-test design where the lower-level FEAT directories are the results from GLM analysis that produced the functional connectivity maps to my seed region's time course, two per subject (one from the pre-condition; the other from the post-condition).
'Ah,' you ask, 'but what are these input functional connectivity maps?' I ask the same thing. Am I correct in thinking that both the cope's (beta weights) and varcope's (variance) will be combined in some statistically sound way to give me a measure of change in functional connectivity (since I am using a repeated measures / paired t-test design where the inputs themselves are functional connectivity maps).
The result of this paired t-test produces z-scores, beta weights (copes) and variances (varcopes). I won't repeat my question from Round 1 here. No, instead I want to ask about using randomise for inference analysis.
I am unsure of how best to use randomise in a repeated measures fashion. I can deal with the repeated measures part by subtracting pre-condition from post-condition resulting in a difference map, one per subject.
Given my current understanding that z-scores reflect correlation, I am leaning toward subtracting z-score (zstat1) volumes to create a zstat-difference, one per subject. I would then feed these zstat-difference volumes into randomise. are z-score differences meaningful? I tell myself the differences are meaningful because these z-scores do reflect correlation. (But I tell myself a lot of things.)
Again I am concerned that I'm not comprehending the strength and beauty of beta weights. Maybe I should be using the difference in beta weights. But what about noise ... some of these measurements are noisy, which is uncontrolled (?) in the betas.
At this point, I am worried that I've become biased about z-scores. And that I'm missing something important about beta weights. Add into the mix the idea of 'difference' and 'change in functional connectivity'.
Thoughts? Comments?
Thanks for playing,
Thanks for all,
* ba
|