Statistical Brain Network Analysis: Recent Developments and Future Directions

This seminar was delivered on 22 June 2026 at the Charles Perkins Centre, University of Sydney. For more information: https://spds.sydney.edu.au/ Speaker: Prof Sean L. Simpson, Wake Forest University Abstract: The recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless, many statistical challenges remain to be able to fully realize the promise of this field. Here we touch on a few of these challenges, briefly survey three complementary statistical frameworks that we have developed to attempt to address a subset of these needs—a mixed modeling framework, a distance regression framework, and a hidden semi-Markov modeling framework—and discuss potential future avenues of research. About the speaker: Prof Sean L. Simpson is a biostatistician in the Department of Biostatistics and Data Science, with joint appointments in Biomedical Engineering and Neuroscience, and an Affiliate appointment with the Maya Angelou Center for Healthy Communities (MARCH) at Wake Forest University School of Medicine. His main research focus has been on the development of novel fusions of statistical tools with network science methods for the analysis of whole-brain network data. Studying the brain as a whole and statistically accounting for the inherent complexity in the way various regions of the brain interact will engender a more biologically meaningful approach to understanding the root causes of a number of brain diseases and disorders.