【摘要】 脑网络分析在研究大脑的认知活动、探究大脑的信息处理模式和辅助精神类疾病的诊断等方面都起着重要作用。近年来，基于多变量数据集的脑网络研究方法得到了普遍关注。典型相关分析(CCA)作为一种基于数据驱动的多元统计方法，能够有效捕捉多变量数据间的隐含关系，被广泛地应用于脑网络研究。综述CCA在脑网络研究中的作用、具体应用模式、存在的优势和局限性。首先，对传统的CCA其及常见变体的算法原理进行归纳总结；然后，阐述基于CCA分析方法在脑网络构建、脑网络分析、脑网络标记物识别方面的研究现状；最后，对基于CCA的脑网络研究方法进行总结并探讨未来研究的方向。更多还原

【Abstract】 Brain network analysis plays important roles in studying the cognitive activity of brain, including exploring the information processing mode of the brain and assisting the diagnosis of mental diseases. In recent years, brain network research methods based on multivariate datasets have attracted great attention. Canonical correlation analysis(CCA), as a data-driven multivariate statistical method, can effectively capture the implicit relationship between multivariate data and is widely used in brain network research. This article reviewed the roles of CCA in the brain network research, specific application modes, and advantages and limitations. Firstly, the algorithm principles of traditional CCA and its common variants were summarized. Next, the research status of CCA-based analysis methods in the brain network construction, brain network analysis, and brain network marker identification were described. At last, the methods of brain network research based on CCA were summarized and the future research directions were discussed.更多还原