biostudies-literatureUnknown22(12)Xiong FBrain 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 distributionNature methods2670-2683https://www.ebi.ac.uk/biostudies/studies/S-EPMC12695645biostudies-literatureReconstruction of a connectome of single neurons in mouse brains by cross-validating multi-scale multi-modality data.PMC12695645Peng HLiu LXiong FfalseReconstruction of a connectome of single neurons in mouse brains by cross-validating multi-scale multi-modality data.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 distribution2025-01-01T00:00:00Z2025 Dec2026-06-06T00:27:54.204Z2026-05-24T03:11:19.222ZS-EPMC126956454085902110.1038/s41592-025-02784-2