  Whole-brain network analyses remain the vanguard in neuroimaging research,
coming to prominence within the last decade. Network science approaches have
facilitated these analyses and allowed examining the brain as an integrated
system. However, statistical methods for modeling and comparing groups of
networks have lagged behind. Fusing multivariate statistical approaches with
network science presents the best path to develop these methods. Toward this
end, we propose a two-part mixed-effects modeling framework that allows
modeling both the probability of a connection (presence/absence of an edge) and
the strength of a connection if it exists. Models within this framework enable
quantifying the relationship between an outcome (e.g., disease status) and
connectivity patterns in the brain while reducing spurious correlations through
inclusion of confounding covariates. They also enable prediction about an
outcome based on connectivity structure and vice versa, simulating networks to
gain a better understanding of normal ranges of topological variability, and
thresholding networks leveraging group information. Thus, they provide a
comprehensive approach to studying system level brain properties to further our
understanding of normal and abnormal brain function.
