I am currently conducting a brain network analysis on my diffusion MRI data, using the connectomestats TFNBS algorithm. The final results that I get after running TFNBS are: beta0, beta1, enhanced, fwe_1mpvalue, null_contributions, null_dist, tvalue, uncorrected_pvalue, Zstat, abs_effect, cond, std_dev and std_effect.
Out of these results, I gathered that the relevant subnetwork could be identified using the fwe_1mpvalue file. Because having identical FWE-corrected p values demonstrate catagorisation by emergent effects in TFNBS, I was wondering if the edges represented by different, discrete p-values in the fwe-1mpvalue file can be separated out as independent subnetworks. For example, will all the edges with a p value equal to 0.0002 be a part of Subnetwork A, while all the edges with a p value equal to 0.0004 be a part of Subnetwork B?
Or, would it be more statistically accurate to look at the aggregate of all edges with a fwe-1m pvalue under a certain, higher threshold such as p<0.05? As an extension of the previous example, will all the edges with a p value equal to 0.0002 and 0.0004 be aggregated together into a single subnetwork, under a given p value threshold?
Furthermore, if we were to use TFNBS results in predicting the presence of a network correlating with a certain variable, would it be possible we use more statistically significant fwe-corrected p value thresholds such as p < 0.0005?
Thank you so much and have a nice day!