bioRxiv2023
Functional brain activity is supported by specific circuit wiring diagrams. When the synaptic annotation is available, the analysis of high-resolution connectomes allows for unraveling the circuit architectures. Despite the continuous technological and computational improvements, obtaining a whole-brain bauplan based on synaptic annotation remains a demanding effort. As an alternative, we present here an approach to extract an approximated brain connectome starting from libraries of single cell anatomical reconstructions aligned on the same anatomical reference brain. Our approach relies on the identification of neurite terminal nodes based on Strahler numbering of the cell morphology, and the adoption of a proximity range criterion, so as to infer, in the absence of synapse information, approximated brain network architectures. As an initial benchmark we used information theory metrics to confront our approach against a synaptically-annotated EM dataset of Drosophila melanogaster hemibrain. The comparison with our approach revealed a general agreement in the organization of the extracted connectivity modules measured in terms of Normalized Mutual Information, Adjusted Rand Index and Pearson correlation. Moreover, we show that the modules identified, along with their organization, can capture known circuit motives. We then applied this approach to a light microscopy dataset of the zebrafish larval brain composed of about 3000 neuronal skeletonizations. We show that the approximated connectome and the resulting modular organization is capable of capturing specific and topographically organized connection patterns as well as known functional circuit architectures. In conclusion, we present a scalable, from-circuit-to-brain range, approach to reveal approximated neuronal architectures supporting brain mechanisms, potentially suitable for hypothesis generation and for guiding the exploration of and integration with EM connectomes. Author SummaryUnderstanding how the brain works requires detailed maps of its neural connections, known as connectomes. While advanced techniques like electron microscopy (EM) can map these connections at the level of individual synapses, they are time consuming and resource intensive, limiting their use. As an alternative, we developed a computational method that approximates brain connectivity using existing datasets of neuron shapes (morphologies) without requiring synaptic annotations. Our approach identifies potential connections between neurons based on the proximity of their terminal branches regions where synapses are likely to form within a shared 3D reference brain. We validated our method using a high-resolution EM connectome of the fruit fly brain, demonstrating that it captures broad organizational patterns, such as clusters of densely interconnected neurons, despite moderate agreement at the synaptic level. Applying the method to a light microscopy dataset of the zebrafish larva brain ([~]3,000 neurons), we successfully reconstructed large-scale networks that recapitulated known functional circuits. This approach offers a scalable way to extract brain-wide connectivity principles from existing datasets, bridging the gap between cellular anatomy and circuit function. It can guide targeted experiments and complement future EM studies, making neuroscience data more accessible for hypothesis generation.