基于典型相关分析的脑网络研究方法综述
A Review of Brain Network Research Methods Based on Canonical Correlation Analysis
尹顺杰 1陈凯 1薛开庆 2尧德中 3徐鹏 3张涛4
作者信息
- 1. 西华大学理学院,成都 610039
- 2. 西华大学计算机与软件工程学院,成都 610039
- 3. 电子科技大学神经信息教育部重点实验室,成都 610054
- 4. 西华大学理学院,成都 610039;电子科技大学神经信息教育部重点实验室,成都 610054
- 折叠
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.
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.
关键词
典型相关分析/脑网络/功能连接/功能性磁共振成像(fMRI)Key words
canonical correlation analysis/brain network/functional connectivity/functional magnetic resonance imaging(fMRI)引用本文复制引用
基金项目
国家自然科学基金青年基金(62006197)
四川省科技厅科技项目(2018JY0526)
西华大学校重点科研基金(z1422614)
教育部"春晖计划"合作科研项目(z2017078)
出版年
2024
NETL
NSTL
万方数据