Abstract The segregation and integration of infant brain networks undergo tremendous changes due to the rapid development of brain function and organization. In this paper, we introduce a novel approach utilizing Bayesian modeling to analyze the dynamic development of functional modules in infants over time. This method retains inter-individual variability and, in comparison with conventional group averaging techniques, more effectively detects modules, taking into account the stationarity of module evolution. Furthermore, we explore gender differences in module development under awake and sleep conditions by assessing modular similarities. Our results show that female infants demonstrate more distinct modular structures between these 2 conditions, possibly implying relative quiet and restful sleep compared with male infants.