State-dependent signal flow hierarchy in the human cerebral cortex

Understanding how information flows across distributed brain networks is central to linking brain structure, dynamics and function. Here we present a neuroimaging framework that combines integrated effective connectivity (iEC) and unconstrained signal flow mapping for data-driven identification of human cerebral functional hierarchies. Simulations and empirical validation show that iEC recovers connectome directionality and aligns with histologically defined feedforward and feedback pathways. The iEC-derived hierarchy exhibits a monotonically increasing level along the axis where the sensorimotor, association and paralimbic areas are sequentially ordered, consistent with predictions from the structural model of laminar connectivity. This hierarchy is not fixed but flexibly reorganizes across brain states; it becomes flatter during externally oriented processing and steeper during internally focused conditions, reflecting increased engagement of interoceptive regions. Our study indicates that macroscale directed functional connectivity can reveal biologically grounded, state-dependent principles of signal flow in the human brain.