[Appendix D — MRI Harmonization]{#sec-mri-harmonization .quarto-section-identifier}

Appendix D — MRI Harmonization

Here, “harmonization” refers to the general effort to remove undesired effects related to sites/scanners in multisite data—in this case, the A2CPS imaging data. Part of imaging harmonization is the creation of “harmonized” MRI protocols across different scanners, a task that MCC1 carried out in the initial period of the A2CPS project, and that was also extended to MCC2 sites during their startup. On this page, the focus is on steps beyond that initial setup period.

There are several general ways in which to address site/scanner related effects:

Processing based standardization: the standard steps used for preprocessing will also harmonize to some extent, e.g. when normalizing scans into an MNI reference space and carrying out various steps like susceptibility distortion correction.
Statistical adjustments for site/scanner: once measures are extracted, they can be adjusted; for example, the ComBat method is widely used in omics, and has been used on scalar imaging measures. Linear mixed models can also be helpful, as can machine learning approaches.
Reference-related adjustments: by scanning “travelling subjects” across all the scanners, a dataset can be created that can be used to learn expected scanner specific variations; scans on phantoms can also be used. In A2CPS, our sample of traveling subjects (one and a half) is too limited to make this a viable option, and our phantom scans are collected with a different set of protocols than the study scans, also limiting their use.
Other scan data adjustments: a variation of the above adjustments is to not only account for, but also remove, site/scanner effects. One T1w-specific adjustment is RAVEL (Removal of Artificial Voxel Effect by Linear regression), which uses control voxels that are not expected to show biological variability as a basis to adjust all image voxels. Some machine learning approaches (e.g. deep learning) may be able to adjust any imaging data if given suitable reference samples, in essence transforming all scanner data into a “neutral” or “reference” scanner space, in ways beyond what can be modelled statistically.

The following bibliography includes results related to (2) and (4) above, and a separate bibliography related to (3) is also appended after this.

D.1 Bibliography

D.1.1 Reviews

Bayer, J. M. M., Thompson, P. M., Ching, C. R. K., Liu, M., Chen, A., Panzenhagen, A. C., Jahanshad, N., Marquand, A., Schmaal, L., & Samann, P. G. (2022). Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses. Frontiers in Neurology, 13, 923988. https://doi.org/10.3389/fneur.2022.923988

Bento, M., Fantini, I., Park, J., Rittner, L., & Frayne, R. (2022). Deep learning in large and multi-site structural brain MR imaging datasets. Frontiers in Neuroinformatics, 15, 805669. https://doi.org/10.3389/fninf.2021.805669

Chen, Z., Pawar, K., Ekanayake, M., Pain, C., Zhong, S., & Egan, G. F. (2023). Deep learning for image enhancement and correction in magnetic resonance imaging-state-of-the-art and challenges. Journal of Digital Imaging, 36(1), 204–230. https://doi.org/10.1007/s10278-022-00721-9

Hu, F., Chen, A. A., Horng, H., Bashyam, V., Davatzikos, C., Alexander-Bloch, A., Li, M., Shou, H., Satterthwaite, T. D., Yu, M., & Shinohara, R. T. (2023). Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. NeuroImage, 274, 120125. https://doi.org/10.1016/j.neuroimage.2023.120125

Pinto, M. S., Paolella, R., Billiet, T., Van Dyck, P., Guns, P.-J., Jeurissen, B., Ribbens, A., Dekker, A. J. den, & Sijbers, J. (2020). Harmonization of brain diffusion MRI: Concepts and methods. Frontiers in Neuroscience, 14, 396. https://doi.org/10.3389/fnins.2020.00396

Seoni, S., Shahini, A., Meiburger, K. M., Marzola, F., Rotunno, G., Acharya, U. R., Molinari, F., & Salvi, M. (2024). All you need is data preparation: A systematic review of image harmonization techniques in multi-center/device studies for medical support systems. Computer Methods and Programs in Biomedicine, 250, 108200. https://doi.org/10.1016/j.cmpb.2024.108200

Wen, G., Shim, V., Holdsworth, S. J., Fernandez, J., Qiao, M., Kasabov, N., & Wang, A. (2023). Machine learning for brain MRI data harmonisation: A systematic review. Bioengineering (Basel, Switzerland), 10(4), 397. https://doi.org/10.3390/bioengineering10040397

D.1.2 Other

These should all include MRI, but some might not be specific to the brain. See also the “other” section below for radiomics.

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An, L., Zhang, C., Wulan, N., Zhang, S., Chen, P., Ji, F., Ng, K. K., Chen, C., Zhou, J. H., Yeo, B. T. T., Alzheimer’s Disease Neuroimaging Initiative, & Australian Imaging Biomarkers and Lifestyle Study of Aging. (2024). DeepResBat: Deep residual batch harmonization accounting for covariate distribution differences. bioRxiv. https://doi.org/10.1101/2024.01.18.574145

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Nielson, D. M., Pereira, F., Zheng, C. Y., Migineishvili, N., Lee, J. A., Thomas, A. G., & Bandettini, P. A. (2018). Detecting and harmonizing scanner differences in the ABCD study - annual release 1.0. bioRxiv. https://doi.org/10.1101/309260

Ning, L., Bonet-Carne, E., Grussu, F., Sepehrband, F., Kaden, E., Veraart, J., Blumberg, S. B., Khoo, C. S., Palombo, M., Kokkinos, I., Alexander, D. C., Coll-Font, J., Scherrer, B., Warfield, S. K., Karayumak, S. C., Rathi, Y., Koppers, S., Weninger, L., Ebert, J., … Tax, C. M. W. (2020). Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results. NeuroImage, 221, 117128. https://doi.org/10.1016/j.neuroimage.2020.117128

Onicas, A. I., Ware, A. L., Harris, A. D., Beauchamp, M. H., Beaulieu, C., Craig, W., Doan, Q., Freedman, S. B., Goodyear, B. G., Zemek, R., Yeates, K. O., & Lebel, C. (2022). Multisite harmonization of structural DTI networks in children: An a-CAP study. Frontiers in Neurology, 13, 850642. https://doi.org/10.3389/fneur.2022.850642

Parekh, P., Vivek Bhalerao, G., John, J. P., Venkatasubramanian, G., & ADBS consortium. (2022). Sample size requirement for achieving multisite harmonization using structural brain MRI features. NeuroImage, 264, 119768. https://doi.org/10.1016/j.neuroimage.2022.119768

Pohl, K. M., Sullivan, E. V., Rohlfing, T., Chu, W., Kwon, D., Nichols, B. N., Zhang, Y., Brown, S. A., Tapert, S. F., Cummins, K., Thompson, W. K., Brumback, T., Colrain, I. M., Baker, F. C., Prouty, D., De Bellis, M. D., Voyvodic, J. T., Clark, D. B., Schirda, C., … Pfefferbaum, A. (2016). Harmonizing DTI measurements across scanners to examine the development of white matter microstructure in 803 adolescents of the NCANDA study. NeuroImage, 130, 194–213. https://doi.org/10.1016/j.neuroimage.2016.01.061

Pomponio, R., Erus, G., Habes, M., Doshi, J., Srinivasan, D., Mamourian, E., Bashyam, V., Nasrallah, I. M., Satterthwaite, T. D., Fan, Y., Launer, L. J., Masters, C. L., Maruff, P., Zhuo, C., Volzke, H., Johnson, S. C., Fripp, J., Koutsouleris, N., Wolf, D. H., … Davatzikos, C. (2020). Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. NeuroImage, 208, 116450. https://doi.org/10.1016/j.neuroimage.2019.116450

Radua, J., Vieta, E., Shinohara, R., Kochunov, P., Quide, Y., Green, M. J., Weickert, C. S., Weickert, T., Bruggemann, J., Kircher, T., Nenadic, I., Cairns, M. J., Seal, M., Schall, U., Henskens, F., Fullerton, J. M., Mowry, B., Pantelis, C., Lenroot, R., … ENIGMA Consortium collaborators. (2020). Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA. NeuroImage, 218, 116956. https://doi.org/10.1016/j.neuroimage.2020.116956

Ravano, V., Démonet, J.-F., Damian, D., Meuli, R., Piredda, G. F., Huelnhagen, T., Maréchal, B., Thiran, J.-P., Kober, T., & Richiardi, J. (2022). Neuroimaging harmonization using cGANs: Image similarity metrics poorly predict cross-protocol volumetric consistency. In Machine learning in clinical neuroimaging (Vol. 13596, pp. 83–92). Springer. https://doi.org/10.1007/978-3-031-17899-3_9

Reynolds, M., Chaudhary, T., Eshaghzadeh Torbati, M., Tudorascu, D. L., Batmanghelich, K., & Alzheimer’s Disease Neuroimaging Initiative. (2023). ComBat harmonization: Empirical bayes versus fully bayes approaches. NeuroImage. Clinical, 39, 103472. https://doi.org/10.1016/j.nicl.2023.103472

Roca, V., Kuchcinski, G., Pruvo, J.-P., Manouvriez, D., Leclerc, X., & Lopes, R. (2023). A three-dimensional deep learning model for inter-site harmonization of structural MR images of the brain: Extensive validation with a multicenter dataset. Heliyon, 9(12), e22647. https://doi.org/10.1016/j.heliyon.2023.e22647

Roca, V., Kuchcinski, G., Pruvo, J.-P., Manouvriez, D., & Lopes, R. (2024). IGUANe: A 3D generalizable CycleGAN for multicenter harmonization of brain MR images. arXiv. https://doi.org/10.48550/arXiv.2402.03227

Roffet, F., Delrieux, C., & Patow, G. (2022). Assessing multi-site rs-fMRI-based connectomic harmonization using information theory. Brain Sciences, 12(9), 1219. https://doi.org/10.3390/brainsci12091219

Saponaro, S., Giuliano, A., Bellotti, R., Lombardi, A., Tangaro, S., Oliva, P., Calderoni, S., & Retico, A. (2022). Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset. NeuroImage. Clinical, 35, 103082. https://doi.org/10.1016/j.nicl.2022.103082

Schilling, K. G., Tax, C. M. W., Rheault, F., Hansen, C., Yang, Q., Yeh, F.-C., Cai, L., Anderson, A. W., & Landman, B. A. (2021). Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow. NeuroImage, 242, 118451. https://doi.org/10.1016/j.neuroimage.2021.118451

Serra, G., Mainas, F., Golosio, B., Retico, A., & Oliva, P. (2023). Effect of data harmonization of multicentric dataset in ASD/TD classification. Brain Informatics, 10(1), 32. https://doi.org/10.1186/s40708-023-00210-x

Sinha, S., Thomopoulos, S. I., Lam, P., Muir, A., & Thompson, P. M. (2021). Alzheimer’s disease classification accuracy is improved by MRI harmonization based on attention-guided generative adversarial networks. Proceedings of SPIE–the International Society for Optical Engineering, 12088, 120880L. https://doi.org/10.1117/12.2606155

St-Jean, S., Viergever, M. A., & Leemans, A. (2020). Harmonization of diffusion MRI data sets with adaptive dictionary learning. Human Brain Mapping, 41(16), 4478–4499. https://doi.org/10.1002/hbm.25117

Sun, D., Rakesh, G., Haswell, C. C., Logue, M., Baird, C. L., O’Leary, E. N., Cotton, A. S., Xie, H., Tamburrino, M., Chen, T., Dennis, E. L., Jahanshad, N., Salminen, L. E., Thomopoulos, S. I., Rashid, F., Ching, C. R. K., Koch, S. B. J., Frijling, J. L., Nawijn, L., … Morey, R. A. (2022). A comparison of methods to harmonize cortical thickness measurements across scanners and sites. NeuroImage, 261, 119509. https://doi.org/10.1016/j.neuroimage.2022.119509

Tax, C. M., Grussu, F., Kaden, E., Ning, L., Rudrapatna, U., John Evans, C., St-Jean, S., Leemans, A., Koppers, S., Merhof, D., Ghosh, A., Tanno, R., Alexander, D. C., Zappala, S., Charron, C., Kusmia, S., Linden, D. E., Jones, D. K., & Veraart, J. (2019). Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms. NeuroImage, 195, 285–299. https://doi.org/10.1016/j.neuroimage.2019.01.077

Thieleking, R., Zhang, R., Paerisch, M., Wirkner, K., Anwander, A., Beyer, F., Villringer, A., & Witte, A. V. (2021). Same brain, different look?-the impact of scanner, sequence and preprocessing on diffusion imaging outcome parameters. Journal of Clinical Medicine, 10(21), 4987. https://doi.org/10.3390/jcm10214987

Torbati, M. E., Minhas, D. S., Laymon, C. M., Maillard, P., Wilson, J. D., Chen, C.-L., Crainiceanu, C. M., DeCarli, C. S., Hwang, S. J., & Tudorascu, D. L. (2023). MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data. Medical Image Analysis, 89, 102926. https://doi.org/10.1016/j.media.2023.102926

Torbati, M. E., Tudorascu, D. L., Minhas, D. S., Maillard, P., DeCarli, C. S., & Hwang, S. J. (2021). Multi-scanner harmonization of paired neuroimaging data via structure preserving embedding learning... IEEE International Conference on Computer Vision Workshops. IEEE International Conference on Computer Vision, 2021, 3277–3286. https://doi.org/10.1109/ICCVW54120.2021.00367

Wada, A., Akashi, T., Hagiwara, A., Nishizawa, M., Shimoji, K., Kikuta, J., Maekawa, T., Sano, K., Kamagata, K., Nakanishi, A., & Aoki, S. (2024). Deep learning-driven transformation: A novel approach for mitigating batch effects in diffusion MRI beyond traditional harmonization. Journal of Magnetic Resonance Imaging, 60(2), 510–522. https://doi.org/10.1002/jmri.29088

Wang, Y.-W., Chen, X., & Yan, C.-G. (2023). Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion. NeuroImage, 274, 120089. https://doi.org/10.1016/j.neuroimage.2023.120089

Wrobel, J., Martin, M. L., Bakshi, R., Calabresi, P. A., Elliot, M., Roalf, D., Gur, R. C., Gur, R. E., Henry, R. G., Nair, G., Oh, J., Papinutto, N., Pelletier, D., Reich, D. S., Rooney, W. D., Satterthwaite, T. D., Stern, W., Prabhakaran, K., Sicotte, N. L., … NAIMS Cooperative. (2020). Intensity warping for multisite MRI harmonization. NeuroImage, 223, 117242. https://doi.org/10.1016/j.neuroimage.2020.117242

Xia, Y., & Shi, Y. (2024). Diffusion MRI harmonization via personalized template mapping. Human Brain Mapping, 45(5), e26661. https://doi.org/10.1002/hbm.26661

Xu, H., Hao, Y., Zhang, Y., Zhou, D., Karkkainen, T., Nickerson, L. D., Li, H., & Cong, F. (2023). Harmonization of multi-site functional MRI data with dual-projection based ICA model. Frontiers in Neuroscience, 17, 1225606. https://doi.org/10.3389/fnins.2023.1225606

Xu, H., Newlin, N. R., Kim, M. E., Gao, C., Kanakaraj, P., Krishnan, A. R., Remedios, L. W., Khairi, N. M., Pechman, K., Archer, D., Hohman, T. J., Jefferson, A. L., Isgum, I., Huo, Y., Moyer, D., Schilling, K. G., Landman, B. A., & BIOCARD Study Team. (2024). Evaluation of mean shift, ComBat, and CycleGAN for harmonizing brain connectivity matrices across sites. ArXiv.

Yu, M., Linn, K. A., Cook, P. A., Phillips, M. L., McInnis, M., Fava, M., Trivedi, M. H., Weissman, M. M., Shinohara, R. T., & Sheline, Y. I. (2018). Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Human Brain Mapping, 39(11), 4213–4227. https://doi.org/10.1002/hbm.24241

Zhang, R., Chen, L., Oliver, L. D., Voineskos, A. N., & Park, J. Y. (2024). SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites. Human Brain Mapping, 45(7), e26692. https://doi.org/10.1002/hbm.26692

Zhang, R., Oliver, L. D., Voineskos, A. N., & Park, J. Y. (2023). RELIEF: A structured multivariate approach for removal of latent inter-scanner effects. Imaging Neuroscience (Cambridge, Mass.), 1, 1–16. https://doi.org/10.1162/imag_a_00011

Zhao, S., Zhang, T., Zhang, W., Pan, T., Zhang, G., Feng, S., Zhang, X., Nie, B., Liu, H., Shan, B., & Alzheimer’s Disease Neuroimaging Initiative. (2024). Harmonizing T1-weighted images to improve consistency of brain morphology among different scanner manufacturers in alzheimer’s disease. Journal of Magnetic Resonance Imaging, 59(4), 1327–1340. https://doi.org/10.1002/jmri.28887

Zhong, J., Wang, Y., Li, J., Xue, X., Liu, S., Wang, M., Gao, X., Wang, Q., Yang, J., & Li, X. (2020). Inter-site harmonization based on dual generative adversarial networks for diffusion tensor imaging: Application to neonatal white matter development. Biomedical Engineering Online, 19(1), 4. https://doi.org/10.1186/s12938-020-0748-9

Zhou, X., Sakaie, K. E., Debbins, J. P., Narayanan, S., Fox, R. J., & Lowe, M. J. (2018). Scan-rescan repeatability and cross-scanner comparability of DTI metrics in healthy subjects in the SPRINT-MS multicenter trial. Magnetic Resonance Imaging, 53, 105–111. https://doi.org/10.1016/j.mri.2018.07.011

Zuo, L., Liu, Y., Xue, Y., Dewey, B. E., Remedios, S. W., Hays, S. P., Bilgel, M., Mowry, E. M., Newsome, S. D., Calabresi, P. A., Resnick, S. M., Prince, J. L., & Carass, A. (2023). HACA3: A unified approach for multi-site MR image harmonization. Computerized Medical Imaging and Graphics, 109, 102285. https://doi.org/10.1016/j.compmedimag.2023.102285

D.1.3 Denoising

Denoising outside the context of harmonization, including retrospective motion correction. Currently includes reviews/overviews only.

Nazir, N., Sarwar, A., & Saini, B. S. (2024). Recent developments in denoising medical images using deep learning: An overview of models, techniques, and challenges. Micron (Oxford, England, 180, 103615. https://doi.org/10.1016/j.micron.2024.103615

Spieker, V., Eichhorn, H., Hammernik, K., Rueckert, D., Preibisch, C., Karampinos, D. C., & Schnabel, J. A. (2024). Deep learning for retrospective motion correction in MRI: A comprehensive review. IEEE Transactions on Medical Imaging, 43(2), 846–859. https://doi.org/10.1109/TMI.2023.3323215

D.1.4 Specifics of Scanner Differences

There is some work in the literature on head coil differences, though not specific to the 32 vs. 64 channel models used in A2CPS; also, papers that are not comparatively recent will predate multiband imaging.

Panman, J. L., To, Y. Y., Ende, E. L. van der, Poos, J. M., Jiskoot, L. C., Meeter, L. H. H., Dopper, E. G. P., Bouts, M. J. R. J., Osch, M. J. P. van, Rombouts, S. A. R. B., Swieten, J. C. van, Grond, J. van der, Papma, J. M., & Hafkemeijer, A. (2019). Bias introduced by multiple head coils in MRI research: An 8 channel and 32 channel coil comparison. Frontiers in Neuroscience, 13, 729. https://doi.org/10.3389/fnins.2019.00729

D.1.5 Radiomics, etc.

Radiomics is technically quantitative imaging, but often concerned with things like tumors that may not translate to our study? Introduction to Radiomics However, these articles do all include MRI, and some (e.g. Ma et al. 2023) may even use familiar techniques like VBM or similar. “Preclinical” is sometimes used as a way to describe animal imaging.

Bhagavatula, S., Cabeen, R., Harris, N. G., Grohn, O., Wright, D. K., Garner, R., Bennett, A., Alba, C., Martinez, A., Ndode-Ekane, X. E., Andrade, P., Paananen, T., Ciszek, R., Immonen, R., Manninen, E., Puhakka, N., Tohka, J., Heiskanen, M., Ali, I., … EpiBioS4Rx investigators. (2023). Image data harmonization tools for the analysis of post-traumatic epilepsy development in preclinical multisite MRI studies. Epilepsy Research, 195, 107201. https://doi.org/10.1016/j.eplepsyres.2023.107201

Carre, A., Battistella, E., Niyoteka, S., Sun, R., Deutsch, E., & Robert, C. (2022). AutoComBat: A generic method for harmonizing MRI-based radiomic features. Scientific Reports, 12(1), 12762. https://doi.org/10.1038/s41598-022-16609-1

Clark, K. A., O’Donnell, C. M., Elliott, M. A., Tauhid, S., Dewey, B. E., Chu, R., Khalil, S., Nair, G., Sati, P., DuVal, A., Pellegrini, N., Bar-Or, A., Markowitz, C., Schindler, M. K., Zurawski, J., Calabresi, P. A., Reich, D. S., Bakshi, R., Shinohara, R. T., & NAIMS Cooperative. (2023). Intersite brain MRI volumetric biases persist even in a harmonized multisubject study of multiple sclerosis. Journal of Neuroimaging, 33(6), 941–952. https://doi.org/10.1111/jon.13147

Da-Ano, R., Lucia, F., Masson, I., Abgral, R., Alfieri, J., Rousseau, C., Mervoyer, A., Reinhold, C., Pradier, O., Schick, U., Visvikis, D., & Hatt, M. (2021). A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets. PloS One, 16(7), e0253653. https://doi.org/10.1371/journal.pone.0253653

Da-Ano, R., Masson, I., Lucia, F., Dore, M., Robin, P., Alfieri, J., Rousseau, C., Mervoyer, A., Reinhold, C., Castelli, J., De Crevoisier, R., Ramee, J. F., Pradier, O., Schick, U., Visvikis, D., & Hatt, M. (2020). Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies. Scientific Reports, 10(1), 10248. https://doi.org/10.1038/s41598-020-66110-w

Da-Ano, R., Visvikis, D., & Hatt, M. (2020). Harmonization strategies for multicenter radiomics investigations. Physics in Medicine and Biology, 65(24), 24TR02. https://doi.org/10.1088/1361-6560/aba798

Hajianfar, G., Hosseini, S. A., Bagherieh, S., Oveisi, M., Shiri, I., & Zaidi, H. (2024). Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques: A phantom study. Medical & Biological Engineering & Computing, 62(8), 2319–2332. https://doi.org/10.1007/s11517-024-03071-6

Ma, H., Zhang, D., Wang, Y., Ding, Y., Yang, J., & Li, K. (2023). Prediction of early improvement of major depressive disorder to antidepressant medication in adolescents with radiomics analysis after ComBat harmonization based on multiscale structural MRI. BMC Psychiatry, 23(1), 466. https://doi.org/10.1186/s12888-023-04966-8

Mali, S. A., Ibrahim, A., Woodruff, H. C., Andrearczyk, V., Muller, H., Primakov, S., Salahuddin, Z., Chatterjee, A., & Lambin, P. (2021). Making radiomics more reproducible across scanner and imaging protocol variations: A review of harmonization methods. Journal of Personalized Medicine, 11(9), 842. https://doi.org/10.3390/jpm11090842

Orlhac, F., Lecler, A., Savatovski, J., Goya-Outi, J., Nioche, C., Charbonneau, F., Ayache, N., Frouin, F., Duron, L., & Buvat, I. (2021). How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. European Radiology, 31(4), 2272–2280. https://doi.org/10.1007/s00330-020-07284-9

Parekh, P., Vivek Bhalerao, G., John, J. P., Venkatasubramanian, G., & ADBS consortium. (2022). Sample size requirement for achieving multisite harmonization using structural brain MRI features. NeuroImage, 264, 119768. https://doi.org/10.1016/j.neuroimage.2022.119768

Shinohara, R. T., Oh, J., Nair, G., Calabresi, P. A., Davatzikos, C., Doshi, J., Henry, R. G., Kim, G., Linn, K. A., Papinutto, N., Pelletier, D., Pham, D. L., Reich, D. S., Rooney, W., Roy, S., Stern, W., Tummala, S., Yousuf, F., Zhu, A., … NAIMS Cooperative. (2017). Volumetric analysis from a harmonized multisite brain MRI study of a single subject with multiple sclerosis. AJNR. American Journal of Neuroradiology, 38(8), 1501–1509. https://doi.org/10.3174/ajnr.A5254

Stamoulou, E., Spanakis, C., Manikis, G. C., Karanasiou, G., Grigoriadis, G., Foukakis, T., Tsiknakis, M., Fotiadis, D. I., & Marias, K. (2022). Harmonization strategies in multicenter MRI-based radiomics. Journal of Imaging, 8(11), 303. https://doi.org/10.3390/jimaging8110303

Tafuri, B., Lombardi, A., Nigro, S., Urso, D., Monaco, A., Pantaleo, E., Diacono, D., De Blasi, R., Bellotti, R., Tangaro, S., & Logroscino, G. (2022). The impact of harmonization on radiomic features in parkinson’s disease and healthy controls: A multicenter study. Frontiers in Neuroscience, 16, 1012287. https://doi.org/10.3389/fnins.2022.1012287

Tixier, F., Jaouen, V., Hognon, C., Gallinato, O., Colin, T., & Visvikis, D. (2021). Evaluation of conventional and deep learning based image harmonization methods in radiomics studies. Physics in Medicine and Biology, 66(24). https://doi.org/10.1088/1361-6560/ac39e5

D.1.6 Traveling Subject

Harmonization via traveling subjects would have been a great option to consider in the design stage, but it seems likely to require more travelers than we have (our often non-standard travelling human scans due to their use as prototype cases might also be an impediment). However, it’s possible new public resources might help here? (e.g. Warrington et al. 2025)

Kurokawa, R., Kamiya, K., Koike, S., Nakaya, M., Uematsu, A., Tanaka, S. C., Kamagata, K., Okada, N., Morita, K., Kasai, K., & Abe, O. (2021). Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition. NeuroImage, 245, 118675. https://doi.org/10.1016/j.neuroimage.2021.118675

Maikusa, N., Zhu, Y., Uematsu, A., Yamashita, A., Saotome, K., Okada, N., Kasai, K., Okanoya, K., Yamashita, O., Tanaka, S. C., & Koike, S. (2021). Comparison of traveling-subject and ComBat harmonization methods for assessing structural brain characteristics. Human Brain Mapping, 42(16), 5278–5287. https://doi.org/10.1002/hbm.25615

Richter, S., Winzeck, S., Correia, M. M., Kornaropoulos, E. N., Manktelow, A., Outtrim, J., Chatfield, D., Posti, J. P., Tenovuo, O., Williams, G. B., Menon, D. K., & Newcombe, V. F. J. (2022). Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort. Neuroimage. Reports, 2(4), None. https://doi.org/10.1016/j.ynirp.2022.100136

Saito, Y., Kamagata, K., Andica, C., Maikusa, N., Uchida, W., Takabayashi, K., Yoshida, S., Hagiwara, A., Fujita, S., Akashi, T., Wada, A., Irie, R., Shimoji, K., Hori, M., Kamiya, K., Koike, S., Hayashi, T., & Aoki, S. (2023). Traveling subject-informed harmonization increases reliability of brain diffusion tensor and neurite mapping. Aging and Disease, 15(6), 2770–2785. https://doi.org/10.14336/AD.2023.1020

Tian, D., Zeng, Z., Sun, X., Tong, Q., Li, H., He, H., Gao, J.-H., He, Y., & Xia, M. (2022). A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset. NeuroImage, 257, 119297. https://doi.org/10.1016/j.neuroimage.2022.119297

Treit, S., Stolz, E., Rickard, J. N., McCreary, C. R., Bagshawe, M., Frayne, R., Lebel, C., Emery, D., & Beaulieu, C. (2022). Lifespan volume trajectories from non-harmonized T1-weighted MRI do not differ after site correction based on traveling human phantoms. Frontiers in Neurology, 13, 826564. https://doi.org/10.3389/fneur.2022.826564

D.1.7 Statistics

Options that are not conventional harmonization but touch on similar issues.

Honnorat, N., & Habes, M. (2022). Covariance shrinkage can assess and improve functional connectomes. NeuroImage, 256, 119229. https://doi.org/10.1016/j.neuroimage.2022.119229

Kia, S. M., Huijsdens, H., Dinga, R., Wolfers, T., Mennes, M., Andreassen, O. A., Westlye, L. T., Beckmann, C. F., & Marquand, A. F. (2020). Hierarchical bayesian regression for multi-site normative modeling of neuroimaging data. In Medical image computing and computer assisted intervention - MICCAI 2020 (Vol. 12267, pp. 699–709). Springer. https://doi.org/10.1007/978-3-030-59728-3_68

Ren, Z., Sadil, P., & Lindquist, M. (2026). MV-ComBat and MV-CovBat: Multivariate frameworks for joint harmonization of multi-metric neuroimaging data. bioRxiv, 2026–2002. https://doi.org/10.64898/2026.02.05.704069

Shan, Y., Huang, C., Li, Y., & Zhu, H. (2024). Merging or ensembling: Integrative analysis in multiple neuroimaging studies. Biometrics, 80(1), ujae003. https://doi.org/10.1093/biomtc/ujae003

D.2 Search Notes

Good starting point