Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-08-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811921004663 |
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doaj-10926e7daf43424a9667360e45cba9e9 |
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Article |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Valentina Bordin Ilaria Bertani Irene Mattioli Vaanathi Sundaresan Paul McCarthy Sana Suri Enikő Zsoldos Nicola Filippini Abda Mahmood Luca Melazzini Maria Marcella Laganà Giovanna Zamboni Archana Singh-Manoux Mika Kivimäki Klaus P Ebmeier Giuseppe Baselli Mark Jenkinson Clare E Mackay Eugene P Duff Ludovica Griffanti |
| spellingShingle |
Valentina Bordin Ilaria Bertani Irene Mattioli Vaanathi Sundaresan Paul McCarthy Sana Suri Enikő Zsoldos Nicola Filippini Abda Mahmood Luca Melazzini Maria Marcella Laganà Giovanna Zamboni Archana Singh-Manoux Mika Kivimäki Klaus P Ebmeier Giuseppe Baselli Mark Jenkinson Clare E Mackay Eugene P Duff Ludovica Griffanti Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets NeuroImage Harmonisation MRI White matter hyperintensities UK Biobank |
| author_facet |
Valentina Bordin Ilaria Bertani Irene Mattioli Vaanathi Sundaresan Paul McCarthy Sana Suri Enikő Zsoldos Nicola Filippini Abda Mahmood Luca Melazzini Maria Marcella Laganà Giovanna Zamboni Archana Singh-Manoux Mika Kivimäki Klaus P Ebmeier Giuseppe Baselli Mark Jenkinson Clare E Mackay Eugene P Duff Ludovica Griffanti |
| author_sort |
Valentina Bordin |
| title |
Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets |
| title_short |
Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets |
| title_full |
Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets |
| title_fullStr |
Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets |
| title_full_unstemmed |
Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets |
| title_sort |
integrating large-scale neuroimaging research datasets: harmonisation of white matter hyperintensity measurements across whitehall and uk biobank datasets |
| publisher |
Elsevier |
| series |
NeuroImage |
| issn |
1095-9572 |
| publishDate |
2021-08-01 |
| description |
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. |
| topic |
Harmonisation MRI White matter hyperintensities UK Biobank |
| url |
http://www.sciencedirect.com/science/article/pii/S1053811921004663 |
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doaj-10926e7daf43424a9667360e45cba9e92021-07-03T04:44:25ZengElsevierNeuroImage1095-95722021-08-01237118189Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasetsValentina Bordin0Ilaria Bertani1Irene Mattioli2Vaanathi Sundaresan3Paul McCarthy4Sana Suri5Enikő Zsoldos6Nicola Filippini7Abda Mahmood8Luca Melazzini9Maria Marcella Laganà10Giovanna Zamboni11Archana Singh-Manoux12Mika Kivimäki13Klaus P Ebmeier14Giuseppe Baselli15Mark Jenkinson16Clare E Mackay17Eugene P Duff18Ludovica Griffanti19Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, ItalyWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, ItalyWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, ItalyWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UKWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UKWellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK; Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UKWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK; Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UKWellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UKDepartment of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UKWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, ItalyIRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, ItalyWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, ItalyINSERM U1153, Epidemiology of Ageing and Neurodegenerative diseases, Université de Paris, Paris, France; Department of Epidemiology and Public Health, University College London, London, UKDepartment of Epidemiology and Public Health, University College London, London, UKDepartment of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UKDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, ItalyWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UKWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK; Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK; Oxford Health NHS Foundation Trust, Oxford, UKWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Paediatrics, University of Oxford, Oxford, UKWellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK; Corresponding author at: Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Warneford Ln, Oxford, Headington OX3 7JX, UK.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.http://www.sciencedirect.com/science/article/pii/S1053811921004663HarmonisationMRIWhite matter hyperintensitiesUK Biobank |