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

Abstract

Leveraging 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 to better characterize the structure and function of the visual cortex. In this work, we propose a statistical framework for analyzing the MICrONS dataset, particularly the V1, RL, and AL visual areas. In addition to identifying several structural and functional differences between these regions, we focus on the borders between these regions. By comparing the V1-RL and RL-AL border regions, we show that different boundaries between visual regions are distinct in their structure and function. Additionally, we find that the V1-RL border region has greater synaptic connectivity and more synchronous neural activity than the V1 and RL regions individually. We further analyze structure and function in tandem by measuring information flow along synapses, observing that the V1-RL border appears to act as a bridge between the V1 and RL visual areas. Overall, we identify numerous measures that distinguish the V1-RL border from the larger V1-RL network, potentially motivating its characterization as a distinct region in the mouse visual cortex.