Hello experts,

I have been trying to understand the relationship between the first eigenvariate, mean and mean-beta value across a cluster.  For a between-groups analysis (two groups) I have made a t-contrast where group A >  group B. A cluster in medial vACC was found to be significantly different in the magnitude of correlation with the seed region between groups.  I extracted the first eigenvariate from this cluster, thus obtaining a value for each subject which I interpret as being the weighted-mean correlation across the cluster for each subject.  I then (using SPM12) extracted beta-values for the cluster, thus obtaining an rZ score for each voxel in the cluster for each group.  

Group-mean analysis for these two measures (first eigenvariate and mean-beta across the cluster) are virtually identical (e.g., group A: eigen = .3113; beta=.3094).  To obtain the actual mean, I used custom code to extract the mean value across a mask of the functional cluster from the t-con image.  Next, I went into the 1st level results folder and extracted mean values across the functional ROI for each subject.  For group A, the mean value was similar to the mean first eigenvariate from the 2nd level (.3386 vs .3114, respectively) but differed significantly for group B (mean = .2183, mean eigenvariate = .0315).  The mean value for groups A and B across the functional ROI was the same as found from the t-con analysis. However, mean values obtained from the two 2nd level beta images did not match the mean value from the t-con image... rather, they matched the mean beta values pulled from the 2nd level model.

Could someone please explain what is happening 'behind the curtain' here regarding the large difference between the mean, mean eigenvariate and mean-beta?  My understanding was that the t-con image was a linear combination of the beta images, with the spmT map representing (roughly) the relationship between the t-con image and the ResMS image.  If this is the case, then why do the beta images at the second level correspond more closely with the mean eigenvariate values rather than the mean values?

Sorry if convoluted, just trying to wrap my head around these measures.

All the best,
Patrick