Connectivity allometry is a robust organizing principle of the human functional connectome

Abstract

A defining feature of the human brain is its multiscale organization, in which regional processes are integrated into coherent whole-brain network architecture. How local functional organization adapts to variation in this global architecture, however, remains largely unknown. Here, we addressed this question by applying an allometric scaling framework to resting-state functional magnetic resonance imaging data from four large-scale datasets spanning different cultures, cognitive states, developmental stages, and clinical conditions. We found that human functional networks were organized according to a robust and spatially heterogeneous connectivity allometry, whereby regional functional connectivity scales nonlinearly with whole-brain connectivity strength. This scaling architecture closely followed the cortical sensorimotor-association hierarchy, with lower-order systems exhibiting supralinear scaling and higher-order systems exhibiting sublinear scaling. Connectivity allometry was further associated with multiscale neurobiological organization, including regional metabolic properties, neurotransmitter receptor distributions, and transcriptomic landscapes, indicating that local-global functional scaling is embedded within fundamental biological gradients of cortical organization. Moreover, the scaling pattern and its biological associations were highly reproducible across independent cross-cultural cohorts. Importantly, connectivity allometry remained remarkably stable across working memory conditions regardless of cognitive load, emerged by late childhood, and was broadly preserved in children and adolescents with autism spectrum disorder despite region-specific alterations. Together, these findings establish connectivity allometry as a previously unrecognized organizational principle of the human functional connectome and provide a quantitative framework linking regional functional organization to whole-brain network architecture in health and disease.