Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures.
Brain connectivity
Directed connectivity
Graph neural networks
Structure-function relationship
Journal
Network neuroscience (Cambridge, Mass.)
ISSN: 2472-1751
Titre abrégé: Netw Neurosci
Pays: United States
ID NLM: 101719149
Informations de publication
Date de publication:
Jul 2022
Jul 2022
Historique:
received:
10
12
2021
accepted:
02
05
2022
entrez:
6
1
2023
pubmed:
7
1
2023
medline:
7
1
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36607180
doi: 10.1162/netn_a_00252
pii: netn_a_00252
pmc: PMC9810370
doi:
Types de publication
Journal Article
Langues
eng
Pagination
665-701Informations de copyright
© 2022 Massachusetts Institute of Technology.
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