Dear Merina

Some thoughts on your questions below
Best
Alfonso


Originally posted by Merina Su:
I am not totally sure how the Y.mat file that you mention was generated, but assuming it contains the original values used in the second-level analysis (and given that in your case you are selecting a single condition and a single source) then yes, those should be the Fisher-transformed correlation coefficients for each subject between the selected source and region X or Y.

In case it is useful and in order to make these sort of explorations simpler, in the last version (CONN14c) I have added a small utility (in Tools->Calculator) that allows you to explore/display/analyze subject-level measures. In your case you would want to go to 'seed-to-voxel results explorer' for your original contrast (A, B, age, IQ, and gender with [1 -1 0 0 0] contrast) where you should see your region X, and the click on the 'import values' button. That will import the average connectivity within the X area for each subject as a new second-level covariate into the CONN toolbox. Importing the values averaged over the entire cluster is better than evaluating the values at the peak voxel since it will give produce considerably less bias. You can see what the corresponding values are in the Setup->Covariates->Second-level tab, and if you go to Tools->Calculator and select this new measure in the 'Measures' list (and then enter the same between-subject effects/contrast in the between-subject effects list as you did in the original analyses) it will display these values and their associated fitted effects.
Yes, exactly (again assuming that you were not testing any within-subject effects -e.g. between-conditions or between-sources)


Yes, but to actually assert that you need to perform additional analyses (you should not assert that from the post-hoc analyses alone, since those will certainly have some amount of bias). In particular I would run again seed-to-voxel analyses now looking at the simple main effects. For example select A, B, age, IQ, and gender and enter a [1 0 0 0 0] contrast to look at the connectivity in group A and a [0 1 0 0 0] contrast to look at the connectivity in group B. If the former is significant in region X as well then you can assert the 'positive connectivity in group A' portion of your phrase, and if the latter is significant (negative contrast) in region X as well then you can assert the 'negative connectivity in group B' portion. If you fail to assert some portion of this (e.g. you do not get significantly negative connectivity in region X for group B) then the negative values that you were seeing were likely due to selection bias (this bias is typical of post hoc analyses, and it will be greater if you select your values from the peak- response instead of averaged across the entire area of interest).

Hope this helps!
Alfonso