So I had a question regarding multiple comparisons. I was looking at 4 brain networks, with regions of interest (ROIs) that were defined (ranging from 3 to 7 ROIs) to study depression. I extracted these ROIs from ICA (independent component analysis). I then extracted the IC that most matched the network of interest. Then I set a threshold to get individual ROIs within the network. I then did a pairwise statistical analysis using a t-test for each pair of ROIs within each network (I did not perform between network analysis). So I ended up with a matrix of "functional connectivity values" for each network (i.e. 7x7 matrix). Now, I was wondering if it was necessary to perform a multiple comparisons test on my data? I know some literature performs a whole brain analysis randomly and I see why they would need to adjust for the type I error, but seeing that I am not doing that, would that still apply or could it be arguable? Is there some way I can justify not performing a multiple comparisons correction? Also I was wondering what your thoughts about this article are? https://www.graphpad.com/support/faq/when-to-not-correct-for-multiple-comparisons--startfragment---endfragment-/
Another question..If I am performing a t-test for each pair of ROI's (6 ROIs in a network) would I adjust the p-value to be 0.05/15? Or would I break up the bonferroni correction to only be adjusted for all the ROI pairs matching with ROI1? What I mean by that is, in a matrix I'll have
1 2 3 4 5 6
That way I'll only correct for the ROI1 duplicates, instead of all 6 ROI pairwise combinations. I hope that makes sense.