Background: A shift towards the dynamic measurement of physiologic resilience and improved technology incorporated into experimental paradigms in aging research is producing high-resolution data. Identifying the most appropriate analysis method for this type of data is a challenge. In this work, the functional principal component analysis (fPCA) was employed to demonstrate a data-driven approach to the analysis of high-resolution data in aging research. Methods: Cerebral oxygenation during standing was measured in a large cohort [The Irish Longitudinal Study on Aging (TILDA)]. FPCA was performed on tissue saturation index (TSI) data. A regression analysis was then conducted with the functional principal component (fPC) scores as the explanatory variables and transition time as the response. Results: The mean ± SD age of the analysis sample was 64 ± 8 years. Females made up 54% of the sample and overall, 43% had tertiary education. The first PC explained 96% of the variance in cerebral oxygenation upon standing and was related to a baseline shift. Subsequent components described the recovery to before-stand levels (fPC2), drop magnitude and initial recovery (fPC3 and fPC4) as well as a temporal shift in the location of the minimum TSI value (fPC5). Transition time was associated with components describing the magnitude and timing of the nadir. Conclusions: Application of fPCA showed utility in reducing a large amount of data to a small number of parameters which summarize the inter-participant variation in TSI upon standing. A demonstration of principal component regression was provided to allow for continued use and development of data-driven approaches to high-resolution data analysis in aging research.

中文翻译：

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衰老研究中连续、高分辨率测量的功能分析：使用来自爱尔兰衰老纵向研究的脑氧合数据的演示

背景：向生理弹性的动态测量转变，并将改进的技术纳入衰老研究的实验范式，正在产生高分辨率数据。为此类数据确定最合适的分析方法是一项挑战。在这项工作中，函数主成分分析 (fPCA) 被用来展示一种数据驱动的方法来分析衰老研究中的高分辨率数据。方法：在一个大型队列中测量站立期间的脑氧合 [爱尔兰老龄化纵向研究 (TILDA)]。FPCA 是对组织饱和度指数 (TSI) 数据进行的。然后以函数主成分 (fPC) 分数作为解释变量和转换时间作为响应进行回归分析。结果：分析样本的平均 ± SD 年龄为 64 ± 8 岁。女性占样本的 54%，总体而言，43% 受过高等教育。第一个 PC 解释了站立时脑氧合变化的 96%，并且与基线变化有关。随后的组件描述了恢复到站立前水平 (fPC2)、下降幅度和初始恢复 (fPC3 和 fPC4) 以及最小 TSI 值 (fPC5) 位置的时间偏移。过渡时间与描述最低点幅度和时间的分量有关。结论：fPCA 的应用显示了将大量数据减少到少数参数的效用，这些参数总结了站立时 TSI 的参与者间变化。