I am working on a research project attempting to compare two groups of subjects' (call them A and B) brains based on MRI results.

Let's say in my data set I have split each subject's brain into 50 regions, and then classified each region as either "healthy" or "not healthy" i.e. assigned a 1 or 0 as a binary variable.

If I compare the total number of "not healthy" brain regions for each subject across these two groups, I see they are different (p<.05 using either two-sample t-test or a non-parametric rank-sum test) and would like to explore if a subset of particular regions is driving this group difference. Let's say Group A tends to have fewer total "healthy" regions than Group B.

Is the appropriate test now to do a two sample chi-square test for each region? If so, wouldn't I need to correct my p-values for multiple comparisons using either Bonferroni or FDR and need a p<.001 (i.e. 0.05 / 50)?

Is it legitimate to only look at the regions with lower average healthy % in group A and do a multiple comparisons correction across that number (which should be less than 50 presumably)?

Thanks in advance,

-mnd12