Canalization of neural dynamics by δ-protocadherins in the developing zebrafish optic tectum

Brain dynamics are constrained by the underlying topology of neuronal networks. How genes collaborate to organize these neural networks during development remains an enduring mystery. In humans, large numbers of genes have been implicated in neurodevelopmental disorders that are characterized by variable and overlapping phenotypes. The complexity of the brain and the heterogeneity of the disorders makes understanding the relationships between genes, development and neural function challenging (Lee PH, Anttila V, Won H, et al. Cell. 179. p. 1469-82.e11. 2019, Hyman SE. Philos Trans R Soc Lond B Biol Sci. 373(1742). p. 20170031. 2018, de Masfrand S, Cogné B, Nizon M, et al. Eur J Med Genet. 69. p. 104932. 2024). Beginning in the 1940s, Waddington suggested the concept of canalization to describe the role of genes as buffering developmental trajectories against genetic and environmental variation, leading to precise outcomes (Waddington CH. Nature. 150(3811). p. 563-5. 1942). Here, we show that members of the δ-protocadherin family of homophilic cell adhesion molecules, Protocadherin-19 and Protocadherin-17, contribute to developmental canalization of neural dynamics in the visual system of larval zebrafish. We provided oriented visual stimuli to zebrafish larvae and performed in vivo 2-photon calcium imaging in the optic tectum. The latent dynamics resulting from the population activity were remarkably conserved among different wild type larvae, allowing quantitative comparisons within and among genotypes. In both Protocadherin-19 and Protocadherin-17 mutants, the latent dynamics diverged stochastically from wild type, suggesting that the loss of these adhesion molecules leads to stochastic phenotypic variability and introduced disruptions of circuit organization that varied among individual mutants. These results are consistent with the developmental canalization of a vertebrate neural circuit, and suggest a framework for understanding the observed variability in complex brain disorders.