Graph Neural Networks for Brain Graph Learning: A Survey
Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Quan Z., Sheng, David McAlpine, Paul Sowman, Alexis Giral, Philip S. Yu

TL;DR
This survey reviews the application of graph neural networks in modeling brain structures from neuroimaging data, categorizing methods, datasets, and discussing future research directions in brain disorder analysis.
Contribution
It provides a systematic overview of current GNN-based brain graph learning methods, including modeling processes, categorization, datasets, and future insights.
Findings
Categorized brain graph learning methods based on graph types and research problems
Summarized representative GNN models and datasets used in brain disorder analysis
Identified future research directions in brain graph learning with GNNs
Abstract
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…
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Taxonomy
TopicsBrain Tumor Detection and Classification · Advanced Graph Neural Networks · Neural Networks and Applications
