Thanks, Alfonso!

The approach I took was initially a hypothesis driven bivariate correlation (between-groups), seed-voxel analysis with a a R & L seed.  I thresholded results in SPM at p<.001 and p<.05 cluster FWE and made functional ROIs from significant results and fed those seeds back into the conn model to explore potential connectivity paths.  I ended up with a path from roi1 to roi2 and roi3, and from roi2 to roi4.  I extracted single subject eigenvariates across each cluster to determine the pattern of correlation in each group (e.g., +/-, +/+, -/-) and to get an indication of effect size.  

To explore potential effective connectivity between these functionally defined regions, I ran a bivariate regression (within-group), ROI-ROI analysis using those paths shown to be significant, finding all of them to be bidirectionally significant in one group but not the other (p <.001, p< .05 FDR), although the magnitude of t-stat varied by direction.  To explore this further, I went in and extracted regression coefficients for each subject (only where significant bidirectional effects were observed) and ran paired-samples t-tests to see if the magnitude of t-stat was significantly larger for one direction (e.g., roi1-->roi2 vs. roi2-roi1) than the other.  

So,

1) Is my approach statistically valid, or "double-dipping"?
2) Is it appropriate to infer directionality of effective connectivity based on the above approach?

Thanks!!!

-Patrick