Dataset Information

Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets.

ABSTRACT: Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise study sample differences contributing to differences in WMH variations across studies. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data.

SUBMITTER: Bordin V

PROVIDER: S-EPMC8285593 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets.

Bordin Valentina V Bertani Ilaria I Mattioli Irene I Sundaresan Vaanathi V McCarthy Paul P Suri Sana S Zsoldos Enikő E Filippini Nicola N Mahmood Abda A Melazzini Luca L Laganà Maria Marcella MM Zamboni Giovanna G Singh-Manoux Archana A Kivimäki Mika M Ebmeier Klaus P KP Baselli Giuseppe G Jenkinson Mark M Mackay Clare E CE Duff Eugene P EP Griffanti Ludovica L

NeuroImage 20210520

Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) ...[more]

PMID: 34022383

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Project description:PurposeThis study aimed to investigate the association between quantitative retinal vascular measurements and the risk of all-cause and premature mortality.MethodsIn this population-based cohort study using the UK Biobank data, we employed the Retina-based Microvascular Health Assessment System to assess fundus images for image quality and extracted 392 retinal vascular measurements per fundus image. These measurements encompass six categories of vascular features: caliber, density, length, tortuosity, branching angle, and complexity. Univariate Cox regression models were used to identify potential indicators of mortality risk using data on all-cause and premature mortality from death registries. Multivariate Cox regression models were then used to test these associations while controlling for confounding factors.ResultsThe final analysis included 66,415 participants. After adjusting for demographic, health, and lifestyle factors and genetic risk score, 18 and 10 retinal vascular measurements were significantly associated with all-cause mortality and premature mortality, respectively. In the fully adjusted model, the following measurements of different vascular features were significantly associated with all-cause mortality and premature mortality: arterial bifurcation density (branching angle), number of arterial segments (complexity), interquartile range and median absolute deviation of arterial curve angle (tortuosity), mean and median values of mean pixel widths of all arterial segments in each image (caliber), skeleton density of arteries in macular area (density), and minimum venular arc length (length).ConclusionsThe study revealed 18 retinal vascular measurements significantly associated with all-cause mortality and 10 associated with premature mortality. Those identified parameters should be further studied for biological mechanisms connecting them to increased mortality risk.Translational relevanceThis study identifies retinal biomarkers for increased mortality risk and provides novel targets for investigating the underlying biological mechanisms.

Dataset Information

Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets.

Publications

Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets.

Similar Datasets

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