This paper outlines a detailed framework for creating highly granular, whole-brain connectomes at the single-neuron level within the mouse brain, aiming to surpass the resolution of prior macro- and mesoscale studies. The methodology utilizes a scalable approach that generated two probabilistic networks: the arbor-net (based on potential proximity between arbors) and the bouton-net (based on predicted synaptic sites). Cross-validation confirmed the strong statistical and spatial consistency between these models, which both highlighted pronounced modular distributions corresponding to functional areas. Notably, the derived single-neuron connections demonstrated a stronger correlation with gene coexpression patterns than the lower-resolution mesoscale connectomes previously established. Network analysis further revealed that these microscale maps are characterized by robust, nonrandom subnetwork patterns, confirming the structural complexity and functional diversity of the mouse brain's wiring.

References:

• Xiong F, Liu L, Peng H. Reconstruction of a connectome of single neurons in mouse brains by cross-validating multi-scale multi-modality data[J]. Nature Methods, 2025: 1-14.