Convergent and divergent spatial topographies of individualized brain functional networks and their developmental origins
The human brain is intrinsically organized into canonical functional networks with distinct spatial topographies. While precision functional mapping delineates individualized topographies of single networks, the spatial coordination among networks and its developmental origin remains largely unknown. Here, we propose functional topography covariance analysis (FOCA), a novel framework that quantifies convergent and divergent spatial alignments across individualized functional networks and further delineates their internetwork relationships, neurobiological basis, ontogenetic layouts, and cognitive outcomes. Across two well-established task-free functional MRI (fMRI) datasets encompassing both conventional and densely sampled scans, FOCA consistently revealed self-clustered hierarchies in the coordination of functional topographies, closely aligned with existing functional gradients and characterized by convergent couplings within primary systems and divergent couplings in higher-order systems. Such pattern was well predicted by fundamental neurobiological attributes, especially aerobic glycolysis. In a large public neonatal cohort, FOCA matrix exhibited adult-inverted hierarchical couplings and marked changes in auditory and action-mode networks, primarily driven by prominent redistributions of negative couplings. Moreover, neonatal FOCA profiles in the primary visual system significantly predicted neurodevelopmental outcomes at 18 months. Finally, compared with conventional functional connectivity (FC), FOCA proved more robust to global signals and more sensitive to the maturation of negative couplings. These findings highlight the critical role of negative connectivity in brain functional reorganization and advance our understanding of the cooperative and competitive relationships among functional systems and their developmental origins.