  The segregation and integration of infant brain networks undergo tremendous
changes due to the rapid development of brain function and organization.
Traditional methods for estimating brain modularity usually rely on
group-averaged functional connectivity (FC), often overlooking individual
variability. To address this, 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
to 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 two
conditions, possibly implying relative quiet and restful sleep compared with
male infants.
