TY - JOUR
T1 - Integrating large-scale neuroimaging research datasets
T2 - Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets
AU - Bordin, Valentina
AU - Bertani, Ilaria
AU - Mattioli, Irene
AU - Sundaresan, Vaanathi
AU - McCarthy, Paul
AU - Suri, Sana
AU - Zsoldos, Enikő
AU - Filippini, Nicola
AU - Mahmood, Abda
AU - Melazzini, Luca
AU - Laganà, Maria Marcella
AU - Zamboni, Giovanna
AU - Singh-Manoux, Archana
AU - Kivimäki, Mika
AU - Ebmeier, Klaus P.
AU - Baselli, Giuseppe
AU - Jenkinson, Mark
AU - Mackay, Clare E.
AU - Duff, Eugene P.
AU - Griffanti, Ludovica
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2021/8/15
Y1 - 2021/8/15
N2 - 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.
AB - 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.
KW - Harmonisation
KW - MRI
KW - UK Biobank
KW - White matter hyperintensities
UR - http://www.scopus.com/inward/record.url?scp=85107081382&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2021.118189
DO - 10.1016/j.neuroimage.2021.118189
M3 - Article
C2 - 34022383
AN - SCOPUS:85107081382
SN - 1053-8119
VL - 237
JO - NeuroImage
JF - NeuroImage
M1 - 118189
ER -