Steven Grant wrote:
* I have now used MedX to make a simple correlation map of
* difference images vs.
* differences in the performance score. Specifically, I subtracted
* image from the task image for each subject. I then subtracted the
* performance score from the task performance score. The
* difference images and
* the difference scores were then correlated using the Functional
* toolbox module
* in MedX.
* The problem is whereas the MedX results produce high correlations
* in a priori
* predicted regions, the 0 0 1 type of Covariate analysis in SPM96
* shows nothing.
* Furthermore, the MedX results are consistent with an independent
* ROI analysis of
* the a priori regions.
* BUT, if I use a specific condition fit in SPM96 for the Covariates
* and specify
* the contrasts as 0 0 -1 1, then the SPM96 results _exactly_ match
* the MedX
* correlation map !
Could this be because the 0 0 1 contrast treats the repeat measures as
separate cases, i.e. suffers from the substantially larger (inter-subject)
error variance, whereas the 0 0 1 1 contrast explicitly throws out the
inter-subject variance and only tests for within-subject effects?
SPM99b seems to partition the variance in this fashion, presumably
SPM96 does, too. The 0 0 -1 1 contrast (used with your alternating
sign co-variate, see below) would then test for (positive) correlations
significantly different from zero between conditions, equivalent to
your (positive) correlation of differences with differences.
* What is even stranger is that the covariates in SPM96 were entered
* as specified
* by Andrew Holmes, i.e., the difference scores were mean centered,
* divided by 2,
* then multiplied by -1 for the control condition and +1 for the
* Therefore, a 0 0 -1 1 contrast would have inverted the signs of the
* covariate to produce the equivalent of a 0 0 1 1 contrast.
* Specifically -1 *
* (-1*Neutral_Covariate) + 1*(+1*Active_Covariate) =
* +1*Neutral_Covariate +
* 1*Active_Covariate. So there should not have been any subtraction
* at all.
* I am sorely confused by this outcome. Thanks for your help.
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If I am right about SPM96, this should explain it. Many thanks for
your examples !
Professor KP Ebmeier
University of Edinburgh
Royal Edinburgh Hospital
Edinburgh EH10 5HF
E-mail: [log in to unmask]