Brain connectome from neuronal morphology Open Access

Single-subject morphological brain networks derived from cross-feature correlation of macroscopic MRI-derived morphological measures provide an important means for studying the brain connectome. However, the validity of this approach remains to be confirmed at the microscopic level. Here, we constructed morphological brain networks at the single-cell level by extending features from macroscopic morphological measures to microscopic descriptions of neuronal morphology. We demonstrated the feasibility and generalizability of the method using neurons in the somatosensory cortex of a rat, neurons over the whole brain of a mouse, and neurons in the middle temporal gyrus (MTG) of a human. We found that interneuron morphological similarity was higher for intra- than interclass connections, depended on cytoarchitectonic, chemoarchitectonic, and laminar classification of neurons (rat), differed between regions with different evolutionary timelines (mouse), and correlated with neuronal axonal projections (mouse). Furthermore, highly connected hub neurons were disproportionately from superficial layers (rat), inhibitory neurons (rat), and subcortical regions (mouse), and exhibited unique morphology. Finally, we demonstrated a more segregated, less integrated, and economic network architecture with worse resistance to targeted attacks for neurons in human MTG than neurons in a mouse’s primary visual cortex. Overall, our method provides an alternative avenue to study neuronal wiring diagrams in brains.

The brain is a highly complex network spanning multiple spatial scales, yet the organization of brain networks at the single-cell level remains poorly understood. Here, we constructed microscopic morphological brain networks by assessing interneuron similarity based on neuronal morphology for different species. We found that interneuron morphological similarity was correlated with neuronal axonal projections, dependent on neuronal affiliation with respect to cellular and molecular architecture, laminar positioning, and brain area location, and capable of uncovering cross-species differences. Our method complements existing methodology aimed at mapping wiring diagrams in brains at the microscopic level.