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.
Highlights
The increasing availability of brain MRI datasets through multicentre studies, consortia, and data sharing platforms, along with the increased power of computational resources, allows for the possibility of merging datasets and achieving unprecedent statistical power (Smith and Nichols, 2018)
It is worth noting that the worst agreements were observed for subjects characterised by very low white matter hyperintensity (WMH) loads
Comparing segmentation performance within scanner, we observed a significant increase in the overall segmentation accuracy after bias field correction (BC), with higher Dice Similarity Index (DI) values for both which 513 (WH1) and WH2 datasets (Fig. 3)
Summary
The increasing availability of brain MRI datasets through multicentre studies, consortia, and data sharing platforms, along with the increased power of computational resources, allows for the possibility of merging datasets and achieving unprecedent statistical power (Smith and Nichols, 2018). This has greatly increased the range of research questions that can be tackled. Our goal was to find the best combination of processing approaches to minimise non-biological variability in WMH measures extracted from these datasets This would help providing a comprehensive protocol to successfully reduce biases and promote data integration
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