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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
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Small sample inference for fixed effects from restricted maximum likelihood
Kenward, M. G., & Roger, J. H. (1997) · Biometrics · doi:10.2307/2533558 · Cited by 4,552 · cited 1×
“To test for additive site or scanner effects, we also fit models omitting the site or scanner fixed effects and used the package pbkrtest (Halekoh and Højsgaard, 2014) to carry out tests of their joint significance using the Kenward-Roger (KR) approach (Kenward and Roger, 1997).”
MethodThe Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Harmonization of cortical thickness measurements across scanners and sites
Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., Adams, P., Cooper, C., Fava, M., McGrath, P. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.024 · Cited by 1,580
Self-citationHan, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Czanner, S., Busa, E., Pacheco, J., Albert, M., Killiany, R., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.051 · Cited by 1,508
Halekoh, U., & Højsgaard, S. (2014) · Journal of Statistical Software · doi:10.18637/jss.v059.i09 · Cited by 1,324 · cited 2×
“To test for additive site or scanner effects, we also fit models omitting the site or scanner fixed effects and used the package pbkrtest (Halekoh and Højsgaard, 2014) to carry out tests of their joint significance using the Kenward-Roger (KR) approach (Kenward and Roger, 1997).”
MethodWeiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Cedarbaum, J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.11.001 · Cited by 348
Research works printed but not linked: 20
- Bakkour, 2009, The cortical signature of prodromal AD: regional thinning predicts mild AD dementia, Neurology, 72, 1048, 10.1212/01.wnl.0000340981.97664.2f · DOI · Paper page
- Conover, 1981, A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data, Technometrics, 23, 351, 10.1080/00401706.1981.10487680 · DOI · Paper page
- Dewey, 2019, DeepHarmony: a deep learning approach to contrast harmonization across scanner changes, Magn. Reson. Imag., 64, 160, 10.1016/j.mri.2019.05.041 · DOI
- Dickerson, 2008, The cortical signature of Alzheimer’s disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals, Cerebr. Cortex, 19, 497, 10.1093/cercor/bhn113 · DOI · Paper page
- Donix, 2010, Longitudinal changes in medial temporal cortical thickness in normal subjects with the apoe-4 polymorphism, Neuroimage, 53, 37, 10.1016/j.neuroimage.2010.06.009 · DOI
- Erus, 2018, Longitudinally and inter-site consistent multi-atlas based parcellation of brain anatomy using harmonized atlases, Neuroimage, 166, 71, 10.1016/j.neuroimage.2017.10.026 · DOI
- Fortin, 2017, Harmonization of multi-site diffusion tensor imaging data, Neuroimage, 161, 149, 10.1016/j.neuroimage.2017.08.047 · DOI · Paper page
- Gunter, 2009, Measurement of MRI scanner performance with the ADNI phantom, Med. Phys., 36, 2193, 10.1118/1.3116776 · DOI
- Jovicich, 2006, Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data, Neuroimage, 30, 436, 10.1016/j.neuroimage.2005.09.046 · DOI · Paper page
- Klein, 2012, 101 labeled brain images and a consistent human cortical labeling protocol, Front. Neurosci., 6, 171, 10.3389/fnins.2012.00171 · DOI · Paper page
- Lee, 2019, Estimating and accounting for the effect of MRI scanner changes on longitudinal whole-brain volume change measurements, Neuroimage, 184, 555, 10.1016/j.neuroimage.2018.09.062 · DOI
- Müller, 2016, Removing batch effects from longitudinal gene expression-quantile normalization plus ComBat as best approach for microarray transcriptome data, PloS One, 11, 10.1371/journal.pone.0156594 · DOI
- Orlhac, 2018, A postreconstruction harmonization method for multicenter radiomic studies in PET, J. Nucl. Med., 59, 1321, 10.2967/jnumed.117.199935 · DOI · Paper page
- Patterson, 1971, Recovery of inter-block information when block sizes are unequal, Biometrika, 58, 545, 10.1093/biomet/58.3.545 · DOI · Paper page
- Pomponio, 2020, Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan, Neuroimage, 208, 116450, 10.1016/j.neuroimage.2019.116450 · DOI · Paper page
- Shinohara, 2017, Volumetric analysis from a harmonized multisite brain MRI study of a single subject with multiple sclerosis, Am. J. Neuroradiol., 38, 1501, 10.3174/ajnr.A5254 · DOI
- Takao, 2011, Effect of scanner in longitudinal studies of brain volume changes, J. Magn. Reson. Imag., 34, 438, 10.1002/jmri.22636 · DOI
- Tustison, 2019, Longitudinal mapping of cortical thickness measurements: an Alzheimer’s Disease Neuroimaging Initiative-based evaluation study, J. Alzheim. Dis., 71, 165, 10.3233/JAD-190283 · DOI
- Venkatraman, 2015, Region of interest correction factors improve reliability of diffusion imaging measures within and across scanners and field strengths, Neuroimage, 119, 406, 10.1016/j.neuroimage.2015.06.078 · DOI
- Yu, 2018, Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data, Hum. Brain Mapp., 39, 4213, 10.1002/hbm.24241 · DOI · Paper page
Cited by
Deep learning-based unlearning of dataset bias for mri harmonisation and confound removal
Dinsdale, N. K., Jenkinson, M., & Namburete, A. I. L. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117689 · Cited by 178
Dynamic Memory to Alleviate Catastrophic Forgetting in Continual Learning with Medical Imaging
Perkonigg, M., Hofmanninger, J., Herold, C. J., Brink, J. A., Pianykh, O., Prosch, H., & Langs, G. (2021) · Nature Communications · doi:10.1038/s41467-021-25858-z · Cited by 106
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
Self-citationBakas, S., Vollmuth, P., Galldiks, N., Booth, T. C., Aerts, H. J. W. L., Bi, W. L., Wiestler, B., Tiwari, P., Pati, S., Baid, U., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00315-2 · Cited by 36
Self-citationWhitman, E. T., Elliott, M. L., Knodt, A. R., Abraham, W. C., Anderson, T. J., Cutfield, N. J., Hogan, S., Ireland, D., Melzer, T. R., Ramrakha, S., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00897-z · Cited by 37
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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