Inferring Neuron-level Brain Circuit Connection via Graph Neural Network Amidst Small Established Connections

MotivationReconstructing neuron-level brain circuit network is a universally recognized formidable task. A significant impediment involves discerning the intricate interconnections among multitudinous neurons in a complex brain network. However, the majority of current methodologies only rely on learning local visual synapse features while neglecting the incorporation of comprehensive global topological connectivity information. In this paper, we consider the perspective of network connectivity and introduce graph neural networks to learn the topological features of brain networks. As a result, we propose Neuronal Circuit Prediction Network (NCPNet), a simple and effective model to jointly learn node structural representation and neighborhood representation, constructing neuronal connection pair feature for inferring neuron-level connections in a brain circuit network. ResultsWe use a small number of connections randomly selected from a single brain circuit network as training data, expecting NCPNet to extrapolate known connections to unseen instances. We evaluated our model on Drosophila connectome and C. elegans worm connectome. The numerical results demonstrate that our model achieves a prediction accuracy of 91.88% for neuronal connections in the Drosophila connectome when utilizing only 5% of known connections. Similarly, under the condition of 5% known connections in C. elegans, our model achieves an accuracy of 93.79%. Additional qualitative analysis conducted on the learned representation vectors of Kenyon cells indicates that NCPNet successfully acquires meaningful features that enable the discrimination of neuronal sub-types. Our project is available at https://github.com/mxz12119/NCPNet.