1(School of Science, Xihua University, Chengdu 610039, China) 2(School of Computer Science and Software Engineering, Xihua University, Chengdu 610039, China) 3(Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China,Chengdu 610054, China)
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.
Yin Shunjie,Chen Kai,Xue Kaiqing, et al. A Review of Brain Network Research Methods Based on Canonical Correlation Analysis[J]. Chinese Journal of Biomedical Engineering, 2024, 43(2): 240-251.
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