A connectomics-driven analysis reveals novel characterization of border regions in mouse visual cortex

Abstract

AbstractLeveraging retinotopic maps to parcellate the visual cortex into its respective sub-regions has long been a canonical approach to characterizing the functional organization of visual areas in the mouse brain. However, with the advent of extensive connectomics datasets like MICrONS, we can now perform more granular analyses on biological neural networks, enabling us to better characterize the structural and functional profile of the visual cortex. In this work, we propose a statistical framework for analyzing the MICrONS dataset, focusing our efforts on the network encompassed by the retinotopically-induced V1, RL, and AL visual areas. In particular, we bridge the gap between connectomics and retinotopy by identifying several structural and functional differences between these regions. Most notably, by placing our attention on thebordersbetween these regions, we demonstrate how connectomics, in some ways, supersedes retinotopy, providing evidence for two major findings. One, by comparing the V1-RL and RL-AL border regions, we show that not all borders in the visual cortex are the same with respect to structure and function. Two, we propose a novel interpretation for the V1-RL border region in particular, motivating it as a subnetwork that possesses heightened synaptic connectivity and more synchronous neural activity. Going one step further, we analyze structure and function in tandem by measuring information flow along synapses, demonstrating that the V1-RL border serves as a bridge for communication between the V1 and RL visual areas, offering justification as to why it presents itself uniquely with respect to both structure and function.