School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China.
Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
Hum Brain Mapp. 2021 Jun 1;42(8):2374-2392. doi: 10.1002/hbm.25373. Epub 2021 Feb 24.
Canonical correlation analysis (CCA), a multivariate approach to identifying correlations between two sets of variables, is becoming increasingly popular in neuroimaging studies on brain-behavior relationships. However, the CCA stability in neuroimaging applications has not been systematically investigated. Although it is known that the number of subjects should be greater than the number of variables due to the curse of dimensionality, it is unclear at what subject-to-variable ratios (SVR) and at what correlation strengths the CCA stability can be maintained. Here, we systematically assessed the CCA stability, in the context of investigating the relationship between the brain structural/functional imaging measures and the behavioral measures, by measuring the similarity of the first-mode canonical variables across randomly sampled subgroups of subjects from a large set of 936 healthy subjects. Specifically, we tested how the CCA stability changes with SVR under two different brain-behavior correlation strengths. The same tests were repeated using an independent data set (n = 700) for validation. The results confirmed that both SVR and correlation strength affect greatly the CCA stability-the CCA stability cannot be guaranteed if the SVR is not sufficiently high or the brain-behavior relationship is not sufficiently strong. Based on our quantitative characterization of CCA stability, we provided a practical guideline to help correct interpretation of CCA results and proper applications of CCA in neuroimaging studies on brain-behavior relationships.
典型相关分析(CCA)是一种用于识别两组变量之间相关性的多元方法,在脑-行为关系的神经影像学研究中越来越受欢迎。然而,CCA 在神经影像学应用中的稳定性尚未得到系统研究。尽管众所周知,由于维度的诅咒,受试者的数量应该大于变量的数量,但尚不清楚在什么受试者-变量比(SVR)和什么相关强度下可以保持 CCA 的稳定性。在这里,我们通过从一大组 936 名健康受试者中随机抽取受试者的子组来测量第一模态典型变量之间的相似性,系统地评估了 CCA 在研究大脑结构/功能影像学测量与行为测量之间关系的背景下的稳定性。具体来说,我们测试了在两种不同的脑-行为相关强度下,SVR 如何影响 CCA 的稳定性。使用另一个独立数据集(n = 700)进行了相同的测试以进行验证。结果证实,SVR 和相关强度都会极大地影响 CCA 的稳定性——如果 SVR 不够高或脑-行为关系不够强,则不能保证 CCA 的稳定性。基于我们对 CCA 稳定性的定量描述,我们提供了一个实用的指南,以帮助正确解释 CCA 结果,并正确应用 CCA 于脑-行为关系的神经影像学研究。