Modeling Spatio-Temporal Dynamics in Brain Networks: A Comparison of Graph Neural Network Architectures

• URL: http://arxiv.org/abs/2112.04266v1
• Date: Wed, 8 Dec 2021 12:57:13 GMT
• Title: Modeling Spatio-Temporal Dynamics in Brain Networks: A Comparison of Graph Neural Network Architectures
• Authors: Simon Wein, Alina Sch\"uller, Ana Maria Tom\'e, Wilhelm M. Malloni, Mark W. Greenlee, Elmar W. Lang
• Abstract summary: Graph neural networks (GNNs) provide a possibility to interpret new structured graph signals. We show that by learning localized functional interactions on the substrate, GNN based approaches are able to robustly scale to large network studies.
• Score: 0.5033155053523041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatio-temporal GNN architectures and study their ability to replicate neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently predominantly used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multimodal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatio-temporal dynamics in brain networks.

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