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Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity
Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully un...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741721/ https://www.ncbi.nlm.nih.gov/pubmed/34633514 http://dx.doi.org/10.1007/s00429-021-02403-8 |
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author | Neudorf, Josh Kress, Shaylyn Borowsky, Ron |
author_facet | Neudorf, Josh Kress, Shaylyn Borowsky, Ron |
author_sort | Neudorf, Josh |
collection | PubMed |
description | Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. Here, we applied this model to predict functional connectivity from structural connectivity in a sample of 998 participants from the Human Connectome Project. Our results showed that the graph neural network accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. These results represent an important finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the model's performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for depicting connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00429-021-02403-8. |
format | Online Article Text |
id | pubmed-8741721 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-87417212022-01-20 Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity Neudorf, Josh Kress, Shaylyn Borowsky, Ron Brain Struct Funct Original Article Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. Here, we applied this model to predict functional connectivity from structural connectivity in a sample of 998 participants from the Human Connectome Project. Our results showed that the graph neural network accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. These results represent an important finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the model's performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for depicting connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00429-021-02403-8. Springer Berlin Heidelberg 2021-10-11 2022 /pmc/articles/PMC8741721/ /pubmed/34633514 http://dx.doi.org/10.1007/s00429-021-02403-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Neudorf, Josh Kress, Shaylyn Borowsky, Ron Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity |
title | Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity |
title_full | Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity |
title_fullStr | Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity |
title_full_unstemmed | Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity |
title_short | Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity |
title_sort | structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741721/ https://www.ncbi.nlm.nih.gov/pubmed/34633514 http://dx.doi.org/10.1007/s00429-021-02403-8 |
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