Function aligns with geometry in locally connected neuronal networks

The geometry of the brain imposes fundamental constraints on its activity and function. However, the mechanisms linking its shape to neuronal dynamics remain elusive. Here, we investigate how geometric eigenmodes relate to functional connectivity gradients within three-dimensional structures using numerical simulations and calcium imaging experiments in larval zebrafish. We show that functional connectivity gradients arising from network activity closely match the geometric eigenmodes of the network’s spatial embedding when neurons are locally connected. By systematically varying network parameters such as the connectivity radius and the prevalence of long-range connections introduced via edge swaps, we reveal a robust geometry-function correspondence that progressively deteriorates as local connectivity is disrupted. Additionally, we demonstrate that spatial filtering can artificially imprint geometric patterns on functional gradients. To support our computational results, we conduct volumetric calcium imaging experiments at cellular resolution in the optic tectum of zebrafish larvae, uncovering functional gradients that closely align with geometric eigenmodes. Furthermore, the eigenmode-gradient mapping exhibits a cutoff at a spatial wavelength that precisely reflects the size of neuronal arborizations measured from single-neuron reconstructions, as predicted by our simulations. Our findings demonstrate how short-range anatomical connectivity anchors large-scale functional connectivity gradients to the brain’s geometry.