Forecasting Brain Activity Based on Models of Spatio-Temporal Brain Dynamics: A Comparison of Graph Neural Network Architectures. Wein, S., Schüller, A., Tomé, A. M., Malloni, W. M., Greenlee, M. W., & Lang, E. W. arXiv:2112.04266 [q-bio, stat], April, 2022. arXiv: 2112.04266![Forecasting Brain Activity Based on Models of Spatio-Temporal Brain Dynamics: A Comparison of Graph Neural Network Architectures [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 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 model 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 often 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 multi-modal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatio-temporal dynamics in brain networks.
@article{wein_forecasting_2022-1,
title = {Forecasting {Brain} {Activity} {Based} on {Models} of {Spatio}-{Temporal} {Brain} {Dynamics}: {A} {Comparison} of {Graph} {Neural} {Network} {Architectures}},
shorttitle = {Forecasting {Brain} {Activity} {Based} on {Models} of {Spatio}-{Temporal} {Brain} {Dynamics}},
url = {http://arxiv.org/abs/2112.04266},
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 model 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 often 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 multi-modal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatio-temporal dynamics in brain networks.},
urldate = {2022-05-02},
journal = {arXiv:2112.04266 [q-bio, stat]},
author = {Wein, Simon and Schüller, Alina and Tomé, Ana Maria and Malloni, Wilhelm M. and Greenlee, Mark W. and Lang, Elmar W.},
month = apr,
year = {2022},
note = {arXiv: 2112.04266},
keywords = {Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning},
}
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