  In systems and network neuroscience, many common practices in brain
connectomic analysis are often not properly scrutinized. One such practice is
mapping a predetermined set of sub-circuits, like functional networks (FNs),
onto subjects' functional connectomes (FCs) without adequately assessing the
information-theoretic appropriateness of the partition. Another practice that
goes unchallenged is thresholding weighted FCs to remove spurious connections
without justifying the chosen threshold. This paper leverages recent
theoretical advances in Stochastic Block Models (SBMs) to formally define and
quantify the information-theoretic fitness (e.g., prominence) of a
predetermined set of FNs when mapped to individual FCs under different fMRI
task conditions. Our framework allows for evaluating any combination of FC
granularity, FN partition, and thresholding strategy, thereby optimizing these
choices to preserve important topological features of the human brain
connectomes. By applying to the Human Connectome Project with Schaefer
parcellations at multiple levels of granularity, the framework showed that the
common thresholding value of 0.25 was indeed information-theoretically valid
for group-average FCs despite its previous lack of justification. Our results
pave the way for the proper use of FNs and thresholding methods and provide
insights for future research in individualized parcellations.
