  Understanding the human brain remains the Holy Grail in biomedical science,
and arguably in all of the sciences. Our brains represent the most complex
systems in the world (and some contend the universe) comprising nearly one
hundred billion neurons with septillions of possible connections between them.
The structure of these connections engenders an efficient hierarchical system
capable of consciousness, as well as complex thoughts, feelings, and behaviors.
Brain connectivity and network analyses have exploded over the last decade due
to their potential in helping us understand both normal and abnormal brain
function. Functional connectivity (FC) analysis examines functional
associations between time series pairs in specified brain voxels or regions.
Brain network analysis serves as a distinct subfield of connectivity analysis
in which associations are quantified for all time series pairs to create an
interconnected representation of the brain (a brain network), which allows
studying its systemic properties. While connectivity analyses underlie network
analyses, the subtle distinction between the two research areas has generally
been overlooked in the literature, with them often being referred to
synonymously. However, developing more useful analytic methods and allowing for
more precise biological interpretations requires distinguishing these two
complementary domains.
