Biostatistics Weekly Seminar

Regression Frameworks for Brain Network Distance Metrics

Sean L. Simpson, PhD
Department of Biostatistics and Data Science
Wake Forest University

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 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. Here we delineate these approaches 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.

Virtual: Zoom Link to Follow
21 February 2024

Speaker Itinerary

Topic revision: r2 - 25 Jan 2024, CierraStreeter

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