ArticleNetwork neuroscience (Cambridge, Mass.)2022
Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures.
Article in Network neuroscience (Cambridge, Mass.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AlloPool is a deep learning framework that infers protein allostery from molecular dynamics simulations.PLoS biology · 2026Article
- Tailored forecasting from short time series via meta-learning.Science advances · 2026Article
- Spatio-temporal dynamic functional brain network for mild cognitive impairment analysis.Frontiers in neuroscience · 2025Article
- PyMulSim: a method for computing node similarities between multilayer networks via graph isomorphism networks.BMC bioinformatics · 2024Article
- A benchmark of individual auto-regressive models in a massive fMRI dataset.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Network models to enhance the translational impact of cross-species studies.Nature reviews. Neuroscience · 2023Review
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Authors and funding
6 authors.
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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 spatiotemporal 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 multimodal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatiotemporal dynamics in brain networks.
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Registered trials
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