Bayesian change-point detection for dynamic functional brain networks

Abstract

Brain function relies on a precisely coordinated and dynamic balance between the functional integration and segregation of distinct networks. Characterizing the way in which brain regions reconfigure their interactions to give rise to distinct but hidden brain states remains an open challenge. In this thesis, novel Bayesian methods are developed for identifying the states and transitions of dynamic functional brain networks and estimating the community structure of the group-level discrete brain states.