A0444
Title: Analytical tools for whole-brain networks: Fusing statistics and network science to understand brain function
Authors: Sean Simpson - Wake Forest University School of Medicine (United States) [presenting]
Mohsen Bahrami - Wake Forest School of Medicine (United States)
Chal Tomlinson - University of North Carolina at Chapel Hill (United States)
Paul Laurienti - Wake Forest School of Medicine (United States)
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 analytical tools that allow relating system-level properties of brain networks to outcomes of interest. These tools serve as synergistic fusions of statistical approaches with network science methods, providing needed analytic foundations for whole-brain network data. We delineate two recent approaches--a mixed-modeling framework for dynamic network analysis and a regression framework for relating distances between brain network features to covariates of interest--that expand the suite of analytical tools for whole-brain networks and aid in providing complementary insight into brain function.
