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Agree with Dianne - a more informative set of questions would be hard to find! I think the subtracting z-score maps should work fine - as you say, they are a measure of connectivity, so subtraction will yield change in connectivity maps, which can then feed straight into randomise. -Tom Centre for Integrative Neuroscience & Neurodynamics School of Psychology and CLS University of Reading Ph. +44 (0)118 378 7530 [log in to unmask] http://www.personal.reading.ac.uk/~sxs07itj/index.html On Thu, May 10, 2012 at 9:35 PM, Dianne Patterson <[log in to unmask]> wrote: > Bettyann, > I can't answer your questions, but I love them! > Absolutely clearheaded and useful to read. > Thankyou for taking the time to write them. > > -Dianne > > > On Thu, May 10, 2012 at 11:51 AM, bettyann <[log in to unmask]> wrote: >> >> 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 >> > > > > -- > Dianne Patterson, Ph.D. > Research Scientist > [log in to unmask] > University of Arizona > Speech and Hearing Science 314 > 1131 E 2nd Street, Building #71 > (Just East of Harvill) > 621-9877 > ============== > "I used to think that the brain was the most wonderful organ in my body. > Then I realized who was telling me this." > - Emo Phillips > ============== > If you write me (expecting an answer) and I don't respond within a > day, then the email may have been lost. > You can always write me at [log in to unmask] > ============== >