Static pair-wise functional connectivity has been widely used in the neuroimaging field. In contrast, higher-order functional interactions among brain networks and their temporal dynamic transition patterns have been rarely explored. This paper presents a novel dynamic Bayesian variable partition model (DBVPM) that simultaneously considers and models high-order functional interactions and their dynamics via a unified Bayesian framework. Then, we modeled and characterized these temporal state transitions as finite-state machines, and quantitatively compared their transition patterns between post-traumatic stress disorder (PTSD) patients and healthy controls. We found that these interaction patterns are hopping among a finite number of states, and PTSD patients have a different functional interaction state-space and their temporal transition patterns are substantially different in comparison with healthy controls. This work discovered interesting phenomena that cannot be revealed by static pair-wise functional connectivity, thus offering novel opportunities for deciphering the working mechanisms of brain networks in the future.