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<p>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.</p>

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Reconstruction of a connectome of single neurons in mouse brains by cross-validating multi-scale multi-modality data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月26日

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  • Feng Xiong,
  • Lijuan Liu,
  • Hanchuan Peng

摘要

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.