Title: Mixed modeling frameworks for analyzing whole-brain network data
Authors: Sean Simpson - Wake Forest School of Medicine (United States) [presenting]
Abstract: Brain network analyses have exploded in recent years, and hold great potential in helping us understand normal and abnormal brain function. Network science approaches have facilitated these analyses and our understanding of how the brain is structurally and functionally organized. However, the development of statistical methods that allow relating this organization to health outcomes has lagged behind. We have attempted to address this need by developing mixed-modeling frameworks that allow relating system-level properties of brain networks to outcomes of interest. These frameworks serve as a synergistic fusion of multivariate statistical approaches with network science, providing a needed analytic foundation for whole-brain network data. Here we delineate these approaches that have been developed for single-task, multitask, and dynamic brain network data.