Brain connectome from neuronal morphology

Morphological brain networks derived from macroscopic structural magnetic resonance imaging have become prevalent, yet lack microscopic validation. Here, we proposed a method to construct morphological brain networks at the single-cell level by estimating inter-neuron similarity for rat, mouse, and human. We demonstrated the feasibility and generalizability of the method by showing that inter-neuron morphological similarity was correlated with neuronal axonal projections, was higher for intra- than inter-class connections, depended on cytoarchitectonic, chemoarchitectonic, and laminar structures of neurons, and differed between regions with different evolutionary timelines. Furthermore, highly connected hub neurons were disproportionately located in superficial layers, inhibitory neurons, and subcortical regions, and exhibited unique morphology. Finally, we demonstrated a more segregated, less integrated, and economic network architecture with worse resistance to targeted attacks for the human than mouse. Overall, our findings provide microscopic support for using structural magnetic resonance imaging-based morphological brain networks to study the wiring patterns in brains.