Inferring brain network organization from connectomic reconstructions: a network analysis approach applied in d. melanogaster and d. rerio

The development of every-day more powerful techniques enables the generation of large and dense reconstructions of neuronal anatomy across a whole organism brain with a degree of resolution that can reach the synaptic detail. However, these reconstructions are still far from revealing the general connectivity patterns of the neuronal circuits across the brain and their functional organization in networks. Indeed, most frequently these datasets are analyzed following a bottom-up approach, starting from a “seed” neuron and then reconstructing its synaptic partners one after the other in order to reveal the organization of a single circuit component of interest. Even when the synaptic input/output relationship are known for all the neurons, there are not yet methods established to identify highly- connected neuronal communities and connectivity patterns. Here we describe a top-down approach that, combining graph theory methods with the information about the density of synaptic connections, allows to infer the most relevant neuronal connectivity patterns and with these the main functional networks within an organism brain. We applied this approach on two different datasets, the mesoscale light microscopy-based cell resolution atlas of the larval zebrafish brain and the electron microscopy reconstruction of the adult drosophila hemibrain, revealing architectural connectivity motifs aligned with their functional roles.