Hi Patrick,

I am not sure I am understanding correctly, could you please clarify the following?

a) when you say "fed those seeds back into the conn model to explore potential connectivity paths", I am assuming you entered the mask of significant clusters from your original seed-to-voxel analyses (looking at between-group differences in connectivity with a R&L seed) as new ROIs into CONN, and then performed ROI-to-ROI first-level analyses using bivariate correlations and only these new ROIs as sources. In your second-level analyses you then looked at average (across both groups) ROI-to-ROI effects to determine significant connections between these ROIs (roi1-roi2, roi1-roi3, and roi2-roi4). Am I interpreting correctly?

b) not sure what you mean by "extracted single subject eigenvariates across each cluster to determine pattern of correlation in each group", are you referring to the step above (but now using single-group contrasts in your second-level analyses) or are you talking about a different set of analyses?

c) the second paragraph is clearer I believe, just rephrasing to make sure I am interpreting correctly: you then run a new set of first-level ROI-to-ROI analyses using your roi1 to roi4 ROIs as sources, and now using bivariate regression measures, and then looked at second-level within-group effects, limiting your results to only those ROI-to-ROI connections of interest (those where you found in the step (a) above significant across-group connectivity effects; i.e. roi1-roi2, roi1-roi3, etc.). In addition, for the subset of connections where you found bi-directional significant effects in this newer (bivariate-regression) set of analyses you then extracted the connectivity strengths for each subject and performed paired t-tests to explore directionality effects (e.g. higher roi1-roi2 vs. roi2-roi1 effects). Let me know if I am misinterpreting/misrepresenting anything here.

Thanks!
Alfonso
 
Originally posted by Patrick McConnell: