纵向典型相关分析
Longitudinal Canonical Correlation Analysis
摘要：
本文考虑了两个纵向变量的典型相关分析，这两个纵向变量可能是在不同的时间分辨率和不规则的网格采样。我们使用随机效应对多变量的轨迹进行建模，并在潜在空间中找到最相关的线性组合集。我们的数值模拟表明，纵向典型相关分析有效地恢复了两个高维纵向数据集之间的相关模式。我们将LCCA应用于阿尔茨海默病神经影像学倡议的数据，并确定了脑形态变化和淀粉样蛋白累积的纵向特征。
英文摘要：
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.
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