Reconstruction of a connectome of single neurons in mouse brains by cross-validating multi-scale multi-modality data
Year: 2025
Type: article
Abstract: Brain networks, or connectomes, have inspired research at macro-, meso- and micro-scales. However, the rise of single-cell technologies necessitates inferring connectomes consisting of individual neurons projecting throughout the brain. Her, we present a scalable approach to map single-neuron connectivity at the whole-brain scale using two complementary methods. We first generated an arbor-net by probabilistically pairing dendritic and axonal arbors of 20,247 neurons registered to the Allen Brain Atlas. We also produced a bouton-net based on 2.57 million putative axonal boutons from 1,877 fully reconstructed neurons and probabilistic pairing of these full-morphology datasets. Cross-validation of both networks showed statistical consistency in spatially and anatomically modular distributions of neuronal connections, corresponding to functional modules in the mouse brain. We found that single-neuron connections correlated more strongly with gene coexpression than the full-brain mesoscale connectome. Our network analysis, comparing the connectomes with alternative brain architectures, identified nonrandom subnetwork patterns. Overall, our data indicate rich granularity and strong modular diversity in mouse brain networks. This Resource presents a scalable method to build a connectome from light-microscopy-based reconstructions of mouse neurons and inferred synaptic connections that were derived from the overlap of axonal and dendritic arbors or boutons and dendritic arbors. The inferred connectome has been validated with additional multi-modal datasets.
Source: Nature Methods
Authors: Feng Xiong, Lijuan Liu, Hanchuan Peng
Institutions: Fudan University, Southeast University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $12,690
OpenAlex ID: W4413706353