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Abstract：
Neurological disorders (NDs), such as Alzheimer\'s disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects, thus becoming one of the best deep learning models in the diagnosis of ND. Some researchers have investigated the application of GNN in the medical field, but the scope is broad, and its application to NDs is less frequent and not detailed enough. This review focuses on the research progress of GNNs in the diagnosis of ND. Firstly, we systematically investigated the GNN framework of ND, including graph construction, graph convolution, graph pooling, and graph prediction. Secondly, we investigated common NDs using the GNN diagnostic model in terms of data modality, number of subjects, and diagnostic accuracy. Thirdly, we discussed some research challenges and future research directions. The results of this review may be a valuable contribution to the ongoing intersection of artificial intelligence technology and brain imaging.
摘要：
神经系统疾病（ND），如老年痴呆症,已经威胁到全世界的人类健康。结合人工智能技术和脑成像技术对诊断ND具有重要意义。图神经网络（GNN）可以对大脑进行建模和分析，从形态学成像，解剖结构,功能特征，和其他方面，从而成为诊断ND的最佳深度学习模型之一。一些研究人员研究了GNN在医学领域的应用,但是范围很广，其在ND中的应用不太频繁，也不够详细。本文就GNNs在ND诊断中的研究进展作一综述。首先,我们系统地研究了ND的GNN框架，包括图形构造，图卷积，图池化,和图形预测。其次,我们使用GNN诊断模型从数据模态的角度研究了常见的ND，科目数,和诊断的准确性。第三，我们讨论了一些研究挑战和未来的研究方向。这篇综述的结果可能对人工智能技术和脑成像的持续交叉做出有价值的贡献。
