Heterochronicity shapes brain network formation.

Brain network formation unfolds on a nonuniform developmental timetable, with different cortical regions generating connections at different developmental phases. Generative network models (GNMs) aim to uncover the principles underpinning the organization of connectomes by creating synthetic networks according to simple computational rules. These models capture the connectome's topology, operationalized here as the cumulative distributions of network metrics (e.g., modularity, small-worldness, rich-club structure). However, GNMs typically ignore the differential timing of connectivity formation, and by omitting this temporal programme they often misplace topological features in physical space. Here, we add a heterochronous growth term to GNMs and use a model fitness function that weighs topology and topography equally. Topography refers to the spatial embedding of the network-the actual anatomical positions of the axonal tracts. With these advances, we can generate synthetic networks that more faithfully reproduce the spatial layout of diffusion-MRI connectomes from two independent adult cohorts. Compared with classical, temporally agnostic models, heterochronous simulations improve model fit, accurately locate cortical hubs and modules across individuals, and converge on a single caudal-to-rostral gradient of brain maturation. Overall, integrating heterochronicity makes GNMs more faithful to brain development, and allows for better identification of individual differences in network formation.