Graph Neural Networks for Brain Graph Learning: A Survey
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arXiv
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| Format: | Preprint |
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2024
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| author | Luo, Xuexiong Wu, Jia Yang, Jian Xue, Shan Beheshti, Amin Sheng, Quan Z. McAlpine, David Sowman, Paul Giral, Alexis Yu, Philip S. |
| author_facet | Luo, Xuexiong Wu, Jia Yang, Jian Xue, Shan Beheshti, Amin Sheng, Quan Z. McAlpine, David Sowman, Paul Giral, Alexis Yu, Philip S. |
| contents | Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology, a novel approach has emerged that involves modeling the human brain as a graph-structured pattern, with different brain regions represented as nodes and the functional relationships among these regions as edges. Moreover, graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. Developing GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. However, there is a lack of systematic survey work summarizing current research methods in this domain. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs. We first introduce the process of brain graph modeling based on common neuroimaging data. Subsequently, we systematically categorize current works based on the type of brain graph generated and the targeted research problems. To make this research accessible to a broader range of interested researchers, we provide an overview of representative methods and commonly used datasets, along with their implementation sources. Finally, we present our insights on future research directions. The repository of this survey is available at \url{https://github.com/XuexiongLuoMQ/Awesome-Brain-Graph-Learning-with-GNNs}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02594 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Networks for Brain Graph Learning: A Survey Luo, Xuexiong Wu, Jia Yang, Jian Xue, Shan Beheshti, Amin Sheng, Quan Z. McAlpine, David Sowman, Paul Giral, Alexis Yu, Philip S. Machine Learning Artificial Intelligence 68T07 (Primary) 68T30 (Secondary) Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology, a novel approach has emerged that involves modeling the human brain as a graph-structured pattern, with different brain regions represented as nodes and the functional relationships among these regions as edges. Moreover, graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. Developing GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. However, there is a lack of systematic survey work summarizing current research methods in this domain. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs. We first introduce the process of brain graph modeling based on common neuroimaging data. Subsequently, we systematically categorize current works based on the type of brain graph generated and the targeted research problems. To make this research accessible to a broader range of interested researchers, we provide an overview of representative methods and commonly used datasets, along with their implementation sources. Finally, we present our insights on future research directions. The repository of this survey is available at \url{https://github.com/XuexiongLuoMQ/Awesome-Brain-Graph-Learning-with-GNNs}. |
| title | Graph Neural Networks for Brain Graph Learning: A Survey |
| topic | Machine Learning Artificial Intelligence 68T07 (Primary) 68T30 (Secondary) |
| url | https://arxiv.org/abs/2406.02594 |