Structural and genetic determinants of zebrafish functional brain networks

Abstract

Network science has significantly advanced our understanding of brain networks across species, revealing universal connectivity principles. While human studies based on magnetic resonance imaging (MRI) have established several network principles at macroscopic scales, recent breakthroughs, including high-amplitude regional co-activation patterns and spatially contiguous functional gradients, remain unexplored at cellular resolution in animal models. Here, we employ whole-brain functional imaging at cellular resolution in larval zebrafish, combined with anatomical and spatial genetic expression profile databases, to investigate the structural and genetic basis of functional brain networks. We show that mesoscopic functional connectivity (FC) is a robust measure of brain activity that captures the individuality of larvae. Using a public dataset of thousands of single-neuron reconstructions, we reveal a strong coupling between FC and structural connectivity (SC). Numerous properties of the connectome that account for indirect pathways and diffusion mechanisms individually and collectively predict interregional correlations. The hierarchical modular structure of SC and FC significantly overlaps in space, and modules identified within the connectome constrain the shape of both spontaneous and stimulus-driven activity patterns. Using visual stimuli and tail monitoring, we identify a functional network gradient that maps onto the sensorimotor function of brain regions. Finally, we identify a set of genes whose co-expression in brain regions significantly predicts regional FC. Our findings reproduce several key features of mammalian brain networks in zebrafish, demonstrating the potential for studying large-scale network phenomena in smaller, optically accessible vertebrate brains.