Statistical Brain Network Analysis: Recent Developments and Future Directions

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.