dc.contributor.author | Knight, Silvin | en |
dc.contributor.author | Hernández, Belinda | en |
dc.contributor.author | Newman, Louise | en |
dc.date.accessioned | 2021-01-14T18:46:05Z | |
dc.date.available | 2021-01-14T18:46:05Z | |
dc.date.issued | 2020 | en |
dc.date.submitted | 2020 | en |
dc.identifier.citation | 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, 2020 | en |
dc.identifier.other | Y | en |
dc.identifier.uri | http://hdl.handle.net/2262/94686 | |
dc.description | PUBLISHED | en |
dc.description.abstract | 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. | en |
dc.language.iso | en | en |
dc.relation.ispartofseries | Frontiers in Human Neuroscience, section Cognitive Neuroscience | en |
dc.relation.ispartofseries | 14 | en |
dc.relation.ispartofseries | 261 | en |
dc.rights | Y | en |
dc.subject | Data-driven analysis | en |
dc.subject | Functional principal component analysis | en |
dc.subject | Near infrared spectroscopy | en |
dc.subject | Cerebraloxygenation | en |
dc.subject | Ageing | en |
dc.subject | Orthostatic hypotension | en |
dc.title | Functional Analysis of Continuous, High-Resolution Measures in Ageing Research: A Demonstration using Cerebral Oxygenation Data from The Irish Longitudinal Study on Ageing | en |
dc.type | Journal Article | en |
dc.type.supercollection | scholarly_publications | en |
dc.type.supercollection | refereed_publications | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/siknight | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/hernandb | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/lonewman | en |
dc.identifier.rssinternalid | 217201 | en |
dc.identifier.doi | https://doi.org/10.3389/fnhum.2020.00261 | en |
dc.rights.ecaccessrights | openAccess | |
dc.subject.TCDTheme | Ageing | en |
dc.subject.TCDTheme | Next Generation Medical Devices | en |
dc.subject.TCDTag | ANALYSIS OF VARIANCE | en |
dc.subject.TCDTag | Aging/Gerontology | en |
dc.subject.TCDTag | Analysis & Functional Analysis | en |
dc.subject.TCDTag | Automated Clinical Analysis | en |
dc.subject.TCDTag | Complex and functional analysis | en |
dc.subject.TCDTag | Gerontology | en |
dc.subject.TCDTag | NIR SPECTROSCOPY | en |
dc.subject.TCDTag | PRINCIPAL COMPONENT ANALYSIS | en |
dc.identifier.rssuri | http://hdl.handle.net/2262/92927 | en |
dc.identifier.orcid_id | 0000-0003-1245-4870 | en |
dc.status.accessible | N | en |