NeuroCarta: An Automated and Quantitative Approach to Mapping Cellular Networks in the Mouse Brain

Abstract

Understanding the structural organization of the brain is essential for deciphering how complex functions emerge from neural circuits. The Allen Mouse Brain Connectivity Atlas (AMBCA) has revolutionized our ability to quantify anatomical connectivity at a mesoscale resolution, bridging the gap between microscopic cellular interactions and macroscopic network organization. To leverage AMBCA for automated network construction and analysis, here we introduce NeuroCarta, an open-source MATLAB toolbox designed to extract, process, and analyze brain-wide connectivity networks. NeuroCarta generates directed and weighted connectivity graphs, computes key network metrics, and visualizes topological features of brain circuits. As an application example, using NeuroCarta on viral tracer data from the AMBCA, we demonstrate that the mouse brain exhibits a densely connected architecture, with a degree of separation of approximately four synapses, suggesting an optimized balance between local specialization and global integration. We identify attractor nodes that may serve as key convergence points in brain-wide neural computations and show that NeuroCarta facilitates comparative network analyses, revealing regional variations in projection patterns. While the toolbox is currently constrained by the resolution and coverage of the AMBCA dataset, it provides a scalable and customizable framework for investigating brain network topology, interregional communication, and anatomical constraints on mesoscale circuit organization.