Modeling Brain Functional Networks Using Graph Neural Networks: A Review and Clinical Application

Wei Zhang; Qian Hong

doi:10.62762/TIS.2024.680959

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Volume 1, Issue 2, IECE Transactions on Intelligent Systematics

Volume 1, Issue 2, 2024

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Sheng Hong

Beihang University, China

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IECE Transactions on Intelligent Systematics, 2024, Volume 1, Issue 2: 58-68

Free to Read | Review Article | 23 September 2024

Modeling Brain Functional Networks Using Graph Neural Networks: A Review and Clinical Application

Wei Zhang 1,2

Qian Hong 3 *

1 Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore

2 Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore

3 Department of Obstetrics and Gynecology, Jiangxi Fifth People's Hospital, Nanchang 330046, China

* Corresponding Author: Qian Hong, [email protected]

DOI: https://doi.org/10.62762/TIS.2024.680959

Received: 20 August 2024, Accepted: 14 September 2024, Published: 23 September 2024

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Abstract

The integration of graph neural networks (GNNs) with brain functional network analysis is an emerging field that combines neuroscience and machine learning to enhance our understanding of complex brain dynamics. We first briefly introduce the fundamentals of brain functional networks, followed by an overview of Graph Neural Network principles and architectures. The review then focuses on the applications of these networks and address current challenges in the field, such as the need for interpretable models and effective integration of multi-modal neuroimaging data. We also highlight the potential of GNNs in clinical perimenopausal areas such as perimenopausal depression research, demonstrating the broad applicability of this approach. The review concludes by outlining future research directions, including the development of more sophisticated architectures for large-scale, heterogeneous brain graphs, and the exploration of causal inference in brain networks. By synthesizing recent advances and identifying key research directions, this review aims to summarize the focal points of brain functional network analysis and GNNs, explore the potential of their integration, and provide a reference for advancing this interdisciplinary field.

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Keywords

graph neural networks

brain functional networks

neuroimaging analysis

Funding

This work was supported by funding from the Cognitive Neuroimaging Centre, NTU Shared Research Facility under Grant D821/CoNiC.

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APA Style

Zhang, W. & Hong, Q. (2024). Modeling Brain Functional Networks Using Graph Neural Networks: A Review and Clinical Application. IECE Transactions on Intelligent Systematics, 1(2), 58–68. https://doi.org/10.62762/TIS.2024.680959

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