Convergent and divergent spatial topographies of individualized brain functional networks and their developmental origins.

Background: The human brain is intrinsically organized as canonical functional networks with distinct spatial topographies. While precision functional mapping studies have delineated individualized topographies of single networks, the spatial coordination among these networks and its developmental origin remains largely unknown. Methods: Utilizing three well-established task-free functional magnetic resonance imaging (fMRI) datasets encompassing both conventional and densely sampled scans across neonatal and adult cohorts, we proposed functional topography covariance analysis (FOCA), a novel framework that quantifies convergent and divergent spatial alignments across individualized functional networks and further delineated their internetwork relationships, neurobiological basis, ontogenetic layouts, and cognitive outcomes. Results: In adults, FOCA consistently revealed self-clustered and gradient-distributed functional hierarchies 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 prominent changes in auditory and action-mode networks, driven primarily by 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, FOCA demonstrated greater robustness to the global signal and higher sensitivity to the maturation of negative couplings. Conclusions: These findings highlight the critical role of negative functional connectivity and deepen our understanding of the cooperative-competitive interactions among functional systems and their developmental origins.