This paper considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modeled trajectories of the multivariate variables using random effects and found the most correlated sets of linear combinations in the latent space. Our numerical simulations showed that the longitudinal canonical correlation analysis effectively recovers underlying correlation patterns between two high-dimensional longitudinal data sets. We applied the proposed LCCA to data from the Alzheimer's Disease Neuroimaging Initiative and identified the longitudinal profiles of morphological brain changes and amyloid cumulation.

翻译：本文考虑了两个纵向变量之间的典型相关分析，这两个变量可能以不规则时间网格在不同时间分辨率下采样。我们利用随机效应对多变量变量的轨迹进行建模，并在潜在空间中找出相关性最强的线性组合集。数值模拟表明，纵向典型相关分析能够有效恢复两个高维纵向数据集之间的潜在相关模式。我们将所提出的LCCA应用于阿尔茨海默病神经影像学倡议的数据，并识别了大脑形态变化与淀粉样蛋白累积的纵向特征。