Unveiling the Functional Connectivity of Astrocytic Networks with AstroNet, a Graph Reconstruction Algorithm Coupled to Image Processing

Astrocytes form extended intercellular networks, displaying complex calcium activity. However, the specific organization of these astrocytic networks and the precise extent of their functional connectivity in different brain areas remain unexplored. To unveil the functional architecture of astrocytic networks, we developed, using a data-driven methodology, a novel algorithm called AstroNet that uses two-photon calcium imaging to map temporal correlations in activation events among neighboring astro-cytes. Our approach involves reconstructing functional astrocytic networks by organizing individual astrocyte activation events chronologically. This chronological order creates activity paths that enable the extraction of local astrocyte functional correlations. Ultimately, by tallying the occurrences of direct co-activations between pairs of cells along these pathways, we construct a graph that mirrors the underlying astrocyte functional network. By applying this method to two distinct brain regions (CA1 hippocampus and motor cortex), we identified notable differences in local network organizations in sub-regions of around 20-40 astrocytes. Specifically, the cortex exhibited a lower connectivity, while astrocytes in the hippocampus displayed stronger connections. Moreover, we found that in both regions, astrocytic networks consist of smaller, tightly connected sub-networks embedded within a larger, more loosely connected one. Altogether, our innovative method enables the identification of activation paths among astrocytes, facilitates the characterization of local network functional connectivity, and quantifies distinct connectivity patterns among astrocytes from different brain regions. This approach sheds light on the heterogeneous functional organization of astrocytic networks within the brain, pointing to region-specific astrocyte connectivity.