Graph neural network approach for inferring neuronal connectivity

Understanding the connectivity of neuronal networks is essential for understanding brain function. Current anatomical mapping techniques, such as serial section microscopy and viral tracing, are resource-intensive and cannot capture the efficacy of connectivity. A complementary approach is to infer effective connectivity from single-neuron-resolution population activity. We developed a graph neural network (GNN)-based tool, CircuitGNN, that accurately infers neural effective connectivity from population activity. CircuitGNN accurately inferred the connectivity of several small circuits and detected both the strengthening and weakening of connections, such as those that occur during learning. CircuitGNN outperformed other methods when applied to simulated population activity generated by an invertebrate central pattern-generating circuit and a cortical microcircuit. CircuitGNN was evaluated on networks of varying size, recording duration, and proportion of hidden neurons. Although performance improved with longer recordings, CircuitGNN achieved high precision with recordings as short as 4 min, scaled to large circuits of up to ~750 neurons, and remained robust when 20% or 40% of neurons were hidden. These results establish CircuitGNN as an accurate and robust technique for inferring neural connectivity from activity.