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

A Two-Part Mixed-Effects Modeling Framework For Analyzing Whole-Brain Network Data