Functional Analysis of Continuous, High-Resolution Measures in Ageing Research: A Demonstration using Cerebral Oxygenation Data from The Irish Longitudinal Study on Ageing
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2020Access:
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O'Connor JD, O'Connell MD, Romero-Ortuno R, Hernandez B, Newman L, Reilly R, Kenny RA, Knight SP, Functional Analysis of Continuous, High-Resolution Measures in Ageing Research: A Demonstration using Cerebral Oxygenation Data from The Irish Longitudinal Study on Ageing, Frontiers in Human Neuroscience, section Cognitive Neuroscience, 14, 261, 2020Abstract:
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.
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http://people.tcd.ie/siknighthttp://people.tcd.ie/hernandb
http://people.tcd.ie/lonewman
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Journal ArticleSeries/Report no:
Frontiers in Human Neuroscience, section Cognitive Neuroscience14
261
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Data-driven analysis, Functional principal component analysis, Near infrared spectroscopy, Cerebraloxygenation, Ageing, Orthostatic hypotensionSubject (TCD):
Ageing , Next Generation Medical Devices , ANALYSIS OF VARIANCE , Aging/Gerontology , Analysis & Functional Analysis , Automated Clinical Analysis , Complex and functional analysis , Gerontology , NIR SPECTROSCOPY , PRINCIPAL COMPONENT ANALYSISDOI:
https://doi.org/10.3389/fnhum.2020.00261Metadata
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