Colloquium: Analytical tools for whole-brain networks: fusing statistics and network science to understand brain function

Dr. Sean L. Simpson, Department of Biostatistics and Data Science
Wake Forest University School of Medicine

Analytical tools for whole-brain networks: fusing statistics
and network science to understand brain function

Tuesday, April 4, 11 :00AM
Location: 302 Divinity & Religious Studies Building

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. Here 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.