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

Wei Zhang; Qian Hong

doi:10.62762/TIS.2024.680959

Volume 1, Issue 2, ICCK Transactions on Intelligent Systematics

Volume 1, Issue 2, 2024

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Beihang University, China

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ICCK Transactions on Intelligent Systematics, Volume 1, Issue 2, 2024: 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: 10.62762/TIS.2024.680959

ARK: ark:/57805/tis.2024.680959

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

Cited by: 5 (Source: Web of Science), 6 (Source: ), 7 (Source: Google Scholar)

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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 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.

Graphical Abstract

Keywords

graph neural networks

brain functional networks

neuroimaging analysis

Data Availability Statement

Not applicable.

Funding

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

Conflicts of Interest

The authors declare no conflicts of interest.

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Not applicable.

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APA Style

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

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TY  - JOUR
AU  - Zhang, Wei
AU  - Hong, Qian
PY  - 2024
DA  - 2024/09/23
TI  - Modeling Brain Functional Networks Using Graph Neural Networks: A Review and Clinical Application
JO  - ICCK Transactions on Intelligent Systematics
T2  - ICCK Transactions on Intelligent Systematics
JF  - ICCK Transactions on Intelligent Systematics
VL  - 1
IS  - 2
SP  - 58
EP  - 68
DO  - 10.62762/TIS.2024.680959
UR  - https://www.icck.org/article/abs/TIS.2024.680959
KW  - graph neural networks
KW  - brain functional networks
KW  - neuroimaging analysis
AB  - 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 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.
SN  - 3068-5079
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Zhang2024Modeling,
  author = {Wei Zhang and Qian Hong},
  title = {Modeling Brain Functional Networks Using Graph Neural Networks: A Review and Clinical Application},
  journal = {ICCK Transactions on Intelligent Systematics},
  year = {2024},
  volume = {1},
  number = {2},
  pages = {58-68},
  doi = {10.62762/TIS.2024.680959},
  url = {https://www.icck.org/article/abs/TIS.2024.680959},
  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 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.},
  keywords = {graph neural networks, brain functional networks, neuroimaging analysis},
  issn = {3068-5079},
  publisher = {Institute of Central Computation and Knowledge}
}

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