Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort
Background: The growth in multi-center neuroimaging studies generated a need for methods that mitigate the differences in hardware and acquisition protocols across sites i.e., scanner effects. ComBat harmonization methods have shown promise but have not yet been tested on all the data types commonly...
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| Format: | Article |
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Elsevier
2022-12-01
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| Series: | NeuroImage: Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666956022000605 |
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| author | Sophie Richter Stefan Winzeck Marta M. Correia Evgenios N. Kornaropoulos Anne Manktelow Joanne Outtrim Doris Chatfield Jussi P. Posti Olli Tenovuo Guy B. Williams David K. Menon Virginia F.J. Newcombe |
| author_facet | Sophie Richter Stefan Winzeck Marta M. Correia Evgenios N. Kornaropoulos Anne Manktelow Joanne Outtrim Doris Chatfield Jussi P. Posti Olli Tenovuo Guy B. Williams David K. Menon Virginia F.J. Newcombe |
| author_sort | Sophie Richter |
| collection | DOAJ |
| description | Background: The growth in multi-center neuroimaging studies generated a need for methods that mitigate the differences in hardware and acquisition protocols across sites i.e., scanner effects. ComBat harmonization methods have shown promise but have not yet been tested on all the data types commonly studied with magnetic resonance imaging (MRI). This study aimed to validate neuroCombat, longCombat and gamCombat on both structural and diffusion metrics in both cross-sectional and longitudinal data. Methods: We used a travelling subject design whereby 73 healthy volunteers contributed 161 scans across two sites and four machines using one T1 and five diffusion MRI protocols. Scanner was defined as a composite of site, machine and protocol. A common pipeline extracted two structural metrics (volumes and cortical thickness) and two diffusion tensor imaging metrics (mean diffusivity and fractional anisotropy) for seven regions of interest including gray and (except for cortical thickness) white matter regions. Results: Structural data exhibited no significant scanner effect and therefore did not benefit from harmonization in our particular cohort. Indeed, attempting harmonization obscured the true biological effect for some regions of interest. Diffusion data contained marked scanner effects and was successfully harmonized by all methods, resulting in smaller scanner effects and better detection of true biological effects. LongCombat less effectively reduced the scanner effect for cross-sectional white matter data but had a slightly lower probability of incorrectly finding group differences in simulations, compared to neuroCombat and gamCombat. False positive rates for all methods and all metrics did not significantly exceed 5%. Conclusions: Statistical harmonization of structural data is not always necessary and harmonization in the absence of a scanner effect may be harmful. Harmonization of diffusion MRI data is highly recommended with neuroCombat, longCombat and gamCombat performing well in cross-sectional and longitudinal settings. |
| format | Article |
| id | doaj-art-efea436ebb2d43e4b56a36af65ae9018 |
| institution | OA Journals |
| issn | 2666-9560 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Reports |
| spelling | doaj-art-efea436ebb2d43e4b56a36af65ae90182025-08-20T02:04:58ZengElsevierNeuroImage: Reports2666-95602022-12-012410013610.1016/j.ynirp.2022.100136Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohortSophie Richter0Stefan Winzeck1Marta M. Correia2Evgenios N. Kornaropoulos3Anne Manktelow4Joanne Outtrim5Doris Chatfield6Jussi P. Posti7Olli Tenovuo8Guy B. Williams9David K. Menon10Virginia F.J. Newcombe11Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK; Corresponding author.Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK; BioMedIA Group, Department of Computing, Imperial College London, London, UKMRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UKDiagnostic Radiology, Lund University, Lund, SwedenDivision of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UKDivision of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UKDivision of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UKTurku Brain Injury Center, Turku University Hospital & University of Turku, Turku, Finland; Department of Neurosurgery, Turku University Hospital, Turku, FinlandTurku Brain Injury Center, Turku University Hospital & University of Turku, Turku, FinlandWolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UKDivision of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UKDivision of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UKBackground: The growth in multi-center neuroimaging studies generated a need for methods that mitigate the differences in hardware and acquisition protocols across sites i.e., scanner effects. ComBat harmonization methods have shown promise but have not yet been tested on all the data types commonly studied with magnetic resonance imaging (MRI). This study aimed to validate neuroCombat, longCombat and gamCombat on both structural and diffusion metrics in both cross-sectional and longitudinal data. Methods: We used a travelling subject design whereby 73 healthy volunteers contributed 161 scans across two sites and four machines using one T1 and five diffusion MRI protocols. Scanner was defined as a composite of site, machine and protocol. A common pipeline extracted two structural metrics (volumes and cortical thickness) and two diffusion tensor imaging metrics (mean diffusivity and fractional anisotropy) for seven regions of interest including gray and (except for cortical thickness) white matter regions. Results: Structural data exhibited no significant scanner effect and therefore did not benefit from harmonization in our particular cohort. Indeed, attempting harmonization obscured the true biological effect for some regions of interest. Diffusion data contained marked scanner effects and was successfully harmonized by all methods, resulting in smaller scanner effects and better detection of true biological effects. LongCombat less effectively reduced the scanner effect for cross-sectional white matter data but had a slightly lower probability of incorrectly finding group differences in simulations, compared to neuroCombat and gamCombat. False positive rates for all methods and all metrics did not significantly exceed 5%. Conclusions: Statistical harmonization of structural data is not always necessary and harmonization in the absence of a scanner effect may be harmful. Harmonization of diffusion MRI data is highly recommended with neuroCombat, longCombat and gamCombat performing well in cross-sectional and longitudinal settings.http://www.sciencedirect.com/science/article/pii/S2666956022000605Up to 6): magnetic resonance imagingHarmonizationComBatTravelling subjectDiffusion tensor imaging |
| spellingShingle | Sophie Richter Stefan Winzeck Marta M. Correia Evgenios N. Kornaropoulos Anne Manktelow Joanne Outtrim Doris Chatfield Jussi P. Posti Olli Tenovuo Guy B. Williams David K. Menon Virginia F.J. Newcombe Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort NeuroImage: Reports Up to 6): magnetic resonance imaging Harmonization ComBat Travelling subject Diffusion tensor imaging |
| title | Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort |
| title_full | Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort |
| title_fullStr | Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort |
| title_full_unstemmed | Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort |
| title_short | Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort |
| title_sort | validation of cross sectional and longitudinal combat harmonization methods for magnetic resonance imaging data on a travelling subject cohort |
| topic | Up to 6): magnetic resonance imaging Harmonization ComBat Travelling subject Diffusion tensor imaging |
| url | http://www.sciencedirect.com/science/article/pii/S2666956022000605 |
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