Functional Analysis of Continuous, High-Resolution Measures in Aging Research: A Demonstration Using Cerebral Oxygenation Data From the Irish Longitudinal Study on Aging - O’Connor, John D.; O’Connell, Matthew D. L.; Romero-Ortuno, Roman; Hernández, Belinda; Newman, Louise; Reilly, Richard B.; Kenny, Rose Anne; Knight, Silvin P. (Frontiers in Human Neuroscience, 2020)
Background: A shift towards dynamic measurement of physiologic resilience and improved technology incorporated into experimental paradigms in ageing research is producing high-resolution data. Identifying the most appropriate analysis method for this type of data is a challenge. In this work, functional principal component analysis was employed with the aim of demonstrating a data-driven approach to the analysis of high-resolution data in ageing research. Methods: Cerebral oxygenation during standing was measured in a large cohort (The Irish Longitudinal Study on Ageing). Functional principal component analysis (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 a 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 & fPC4) as well as a temporal shift in the location of the minimum TSI value (fPC5). Transition time was associated with components describing 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 summarise the inter-participant variation in TSI upon standing. A demonstration of principal component regression was provided in order to allow for continued use and development of data-driven approaches to high-resolution data analysis in ageing research.