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Open Access 01-01-2022 | Original Article

Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity

Authors: Josh Neudorf, Shaylyn Kress, Ron Borowsky

Published in: Brain Structure and Function | Issue 1/2022

Abstract

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).

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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
Authors: Josh Neudorf
Shaylyn Kress
Ron Borowsky
Publication date: 01-01-2022
Publisher: Springer Berlin Heidelberg
Published in: Brain Structure and Function / Issue 1/2022
Print ISSN: 1863-2653
Electronic ISSN: 1863-2661
DOI: https://doi.org/10.1007/s00429-021-02403-8

At a glance: The STEP trials

Springer Medicine

Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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