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Comprehending the interplay between spatial and temporal characteristics ofneural dynamics can contribute to our understanding of information processingin the human brain. Graph neural networks (GNNs) provide a new possibility tointerpret graph structured signals like those observed in complex brainnetworks. In our study we compare different spatio-temporal GNN architecturesand study their ability to model neural activity distributions obtained infunctional MRI (fMRI) studies. We evaluate the performance of the GNN models ona variety of scenarios in MRI studies and also compare it to a VAR model, whichis currently often used for directed functional connectivity analysis. We showthat by learning localized functional interactions on the anatomical substrate,GNN based approaches are able to robustly scale to large network studies, evenwhen available data are scarce. By including anatomical connectivity as thephysical substrate for information propagation, such GNNs also provide amulti-modal perspective on directed connectivity analysis, offering a novelpossibility to investigate the spatio-temporal dynamics in brain networks.

Forecasting Brain Activity Based on Models of Spatio-Temporal Brain Dynamics: A Comparison of Graph Neural Network Architectures