Spacetime concordance in the primate cortex

The expanding scale and complexity of functional brain image datasets require space-time analytics. Spacetime concordance (STC) meets this need through an adaptive and robust framework optimized for high-speed analysis. At the core of STC, the Regional Functional Affinity (RFA) metric quantifies functional diversity and uniformity in primate connectomes using wakeful fMRI. This data-driven optimization achieves considerably faster processing, becoming broadly relevant to comparative neuroscience applications. We validated this approach using large-scale datasets from the Human Connectome Project (HCP) (N = 1,003) and the NIH Marmoset Brain Mapping Project (N = 26). Results demonstrate striking correspondence between parcellation boundaries of HCP atlas and functional heterogeneity, with boundary lines consistently aligning with regions of low RFA values. In humans, higher-order association networks exhibited lower RFA values indicating functional diversity, while primary sensorimotor networks displayed higher RFA values reflecting uniformity. Cross-species analysis revealed evolutionary conservation of this organizational principle alongside species-specific adaptations. The RFA metric successfully bridges discrete parcellation schemes and continuous models of brain function, offering new insights into primate brain organization and evolution.