Hi Patrick,

It is relatively standard to perform these form of post-hoc analyses in order to better interpret, for example, the actual connectivity strengths in each group after a between-group comparison. Beyond this (aiding in the interpretation of your results) there is not much that you should be doing with those values since, due to "double-dipping", the actual effect sizes extracted will typically be inflated, and any statistics derived from these values will be invalid (liberal p-values). This inflation will always occur as long as the contrast used in your original analyses (the one defining the clusters of interest) and the contrast that you want to look at in your post-hoc analyses are not orthogonal. The amount of inflation depends on multiple factors, but most importantly on the power of your original analyses (underpowered designs tend to produce significant inflation of effect sizes, while the amount of inflation in well-powered designs is considerably smaller). If your intention is to obtain Cohen's d values from a pilot study to be used for power analyses (to help you design future studies) I would recommend using a cross-validation approach in order to better estimate the expected strength of the between-group differences in the population. 

Hope this helps
Alfonso
Originally posted by Patrick McConnell: