【摘要】 神经精神疾病严重影响患者脑解剖结构、神经系统功能及心理健康，其早期识别与诊断对患者的治疗及康复具有重要意义。基于神经影像数据构建复杂的脑网络，可用于定量化分析神经精神疾病患者的脑结构及功能异常，为研究神经精神疾病的神经影像生物标记物提供重要参考。近年来，图神经网络具有处理非欧几里得数据、能充分利用节点与连边的拓扑结构和属性等优势，被广泛应用于神经精神疾病的辅助诊断研究。本文对图卷积神经网络的基本原理和神经精神疾病的最新研究进展进行了总结和分析，并展望了动态脑网络、大样本与多中心、可视化与可解释性等研究热点。更多还原

【Abstract】 Neuropsychiatric diseases seriously affect the anatomical structure of the brain, nervous system function, and mental health of patients. Early identification and diagnosis are of great significance for the treatment and rehabilitation of patients with neuropsychiatric diseases. The construction of complex brain networks based on neuroimage data can be used to quantitatively analyze the brain structure and function abnormalities in patients with neuropsychiatric diseases, and provide an important reference for the development of neuroimaging biomarkers for neuropsychiatric diseases. In recent years, graph neural network has been widely used in the diagnosis of neuropsychiatric diseases because of their advantages of processing non-Euclidean data and making full use of the topological structure and attributes of nodes and connected edges. We summarize the basic principles of graph convolutional network and the latest research progress in neuropsychiatric diseases, and look forward to research hotspots such as dynamic brain network, large sample and multi center, visualization and interpretability.更多还原