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Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data
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Abstract
While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.
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Cites
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Cited by
Deep learning-based unlearning of dataset bias for mri harmonisation and confound removal
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Dynamic Memory to Alleviate Catastrophic Forgetting in Continual Learning with Medical Imaging
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White matter abnormalities and cognition in aging and Alzheimer disease
Peter, C., Sathe, A., Shashikumar, N., Pechman, K. R., Workmeister, A. W., Jackson, T. B., Huo, Y., Mukherjee, S., Mez, J., Dumitrescu, L. C., et al. (2025) · JAMA Neurology · doi:10.1001/jamaneurol.2025.1601 · Cited by 59
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Metadata
- Authors
- Beer, J. C., Tustison, N. J., Cook, P. A., Davatzikos, C., Sheline, Y. I., Shinohara, R. T., & Linn, K. A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 220
- Pages
- 117129
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W3038220767
- Cited by
- 319
- References
- 8
- FWCI
- 18.19
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
- Funders
- Genentech
- Johnson and Johnson
- Merck
- Janssen Research and Development
- H. Lundbeck A/S
- National Institute of Biomedical Imaging and Bioengineering
- IXICO
- U.S. Department of Defense (W81XWH-12-2-0012)
- Alzheimer's Disease Neuroimaging Initiative
- National Institute of Mental Health (T32 MH106442, U01 MH109991)
- Northern California Institute for Research and Education
- National Institute on Aging
- Fujirebio US
- National Institute of Neurological Disorders and Stroke (R01 NS060910, R01 NS085211)
- University of Southern California
- National Institutes of Health (U01 AG024904)
- GE Healthcare
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