B0491
Title: Regression frameworks for brain network distance metrics
Authors: Sean Simpson - Wake Forest University 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 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. The attempt is to address this need by developing regression frameworks for brain network distance metrics that allow relating system-level properties of brain networks to outcomes of interest. These frameworks serve as synergistic fusions of statistical approaches with network science methods, providing needed analytic foundations for whole-brain network data. These approaches are delineated that have been developed for single-task, multi-task/multi-session, and multilevel brain network data. These tools help expand the suite of analytical tools for whole-brain networks and aid in providing complementary insight into brain function.
