Hi Loveall,
I see the article's references #31 and #32 are related to a thing I've been studying the last week.
They contain mention of connetivity, or FC (Functional Connectivity).
I want to contribute with two publications and drawing some lines:
https://www.ncbi.nlm.nih...mc/articles/PMC3848787/ Bayesian network models in brain functional connectivity analysis
https://www.ncbi.nlm.nih...mc/articles/PMC4346370/ Bayesian Models for fMRI Data Analysis
It seem Bayesian Networks (my latest object of study) are a powerful mechanisms which help in performing inference and working with conditional probabilities.
A Bayesian network (also known as a Bayes network, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).
https://en.wikipedia.org/wiki/Bayesian_network
It may elude some readers that to be able to hold any reasonable correlation between topics of such high complexity, all but working with such networks of scales of tousands of nodes can prove effective.
These networks are also used in genome analysis, for example I read some studies about segregation of gene polymorphism of TPH1 (Tryptophan hydroxylase) which was linked to depression with high confidence, but these probabilities are not as simple as it seems, and often Markov Models, BN (Bayesian Networks) or other PGM are developed to aid in understanding.
Good to see science on Nexus!