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Neurons exhibit complex geometry in their branched networks of neurites whichis essential to the function of individual neuron but also brings challenges totransport a wide variety of essential materials throughout their neuritenetworks for their survival and function. While numerical methods likeisogeometric analysis (IGA) have been used for modeling the material transportprocess via solving partial differential equations (PDEs), they require longcomputation time and huge computation resources to ensure accurate geometryrepresentation and solution, thus limit their biomedical application. Here wepresent a graph neural network (GNN)-based deep learning model to learn theIGA-based material transport simulation and provide fast material concentrationprediction within neurite networks of any topology. Given input boundaryconditions and geometry configurations, the well-trained model can predict thedynamical concentration change during the transport process with an averageerror less than 10% and 120~330 times faster compared to IGA simulations. Theeffectiveness of the proposed model is demonstrated within several complexneurite networks.

Deep learning of material transport in complex neurite networks