Harmonization of multi-site diffusion tensor imaging data
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Abstract
Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, may be counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.
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Reliable application of data harmonization requires adequate per-site sample sizes, detailed acquisition documentation, and standardized preprocessing to ensure feature consistency for valid statistical inference.
“Reliable application requires adequate per-site sample sizes, detailed acquisition documentation, and standardized preprocessing to ensure feature consistency for valid statistical inference (Fortin et al., 2017).”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Fortin et al., 2017
The Methods (§2.2, page unavailable) describe a consistent image-processing pipeline, and the evaluation framework (§2.4, page unavailable) links successful harmonization to removing site effects while preserving biological variability. But the paper does not state that detailed acquisition documentation or standardized preprocessing are requirements for reliable application. Its Results (§3.8, page unavailable) specifically report that ComBat is robust with 10 participants per site, so it does not establish a general requirement for an adequate per-site sample size. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe discrepancy between our findings and Fortin et al. (2017) may be due to several differences between studies: ADNI3 includes more sites and protocols; the number of ROIs is far less than the number of participants; and the age effects in the elderly populations tested here are stronger than the effects tested in adolescents in Fortin et al. (2017).
“This discrepancy between our findings and that of Fortin et al. (2017), may be due to several differences between studies: (1) ADNI3 includes more sites and protocols; (2) in contrast to the number of voxels, the number of ROIs is far less than the number of participants; and (3) the age effects in the elderly populations tested here are stronger than the effects tested in adolescents in Fortin et”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Fortin et al. (2017)
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Experimental — accuracy not yet measuredSite differences were corrected within the cross-validation scheme using ComBat.
“Furthermore, where applicable (ABCD, HBN, and HCPD), site differences were corrected within the cross-validation scheme using ComBat29–31.”
From Data leakage inflates prediction performance in connectome-based machine learning models (2024) · cites this paper as [30]
The gold standard model included covariate regression, site correction, and feature selection within the cross-validation scheme, with splits accounting for family structure.
“Our gold standard model included covariate regression, site correction29–31, and feature selection within the cross-validation scheme, which was split accounting for family structure.”
From Data leakage inflates prediction performance in connectome-based machine learning models (2024) · cites this paper as [30]
Correcting for site effects in the whole dataset prior to splitting data into training and test sets is a common example of leakage.
“A common example of leakage is correcting for site effects, such as with ComBat29–31, in the whole dataset prior to splitting data into training and test sets.”
From Data leakage inflates prediction performance in connectome-based machine learning models (2024) · cites this paper as [30]
ComBat was one of the most effective methods for harmonizing DTI measures in a recent comparison of such techniques.
“ComBat—originally developed to model and remove batch effects from genomic microarray data (Johnson et al., 2007)—was one of the most effective methods for harmonizing DTI measures in a recent comparison of such techniques (Fortin et al., 2017).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Fortin et al., 2017
ComBat uses an empirical Bayes framework to reduce unwanted variation in multi-site data due to differences in acquisition protocol, while preserving the desired biological variation in the data.
“ComBat uses an empirical Bayes framework to reduce unwanted variation in multi-site data due to differences in acquisition protocol, while preserving the desired biological variation in the data (Fortin et al., 2017).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Fortin et al., 2017
In Fortin et al. (2017), compared to other harmonization methods, ComBat increased the number of voxels where significant associations between age and FADTI or MDDTI were detected.
“In Fortin et al. (2017), compared to other harmonization methods, ComBat increased the number of voxels where significant associations between age and FADTI or MDDTI were detected.”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Fortin et al. (2017)
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- Authors
- Fortin, J.-P., Parker, D., Tunç, B., Watanabe, T., Elliott, M. A., Ruparel, K., Roalf, D. R., Satterthwaite, T. D., Gur, R. C., Gur, R. E., Schultz, R. T., Verma, R., & Shinohara, R. T.
- Affiliations
- University of Pennsylvania (US)
- Children's Hospital of Philadelphia (US)
- Corresponding author
- Shinohara, Russell T.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 161
- Pages
- 149-170
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC5736019
- OpenAlex
- W2950030754
- Cited by
- 1,261
- References
- 64
- Citations by year
- 2026: 197 · 2025: 227 · 2024: 192 · 2023: 182 · 2022: 166 · 2021: 126 · 2020: 92 · 2019: 50 · 2018: 23 · 2017: 3
- FWCI
- 35.23
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- NeuroImage (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS085211, R21NS093349)
- National Institute of Mental Health (R01MH092862, R01MH107703)
- National Institute of Child Health and Human Development (R01HD089390)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- diffusion tensor imaging, data harmonization, batch effect correction, fractional anisotropy, mean diffusivity, combat, white matter microstructure, confounding by age
- MeSH terms
- Autism Spectrum Disorder, Adolescent, Adult, Child, Female, Humans, Image Processing, Computer-Assisted, Male, Cohort Studies, Multicenter Studies as Topic, Young Adult, Diffusion Tensor Imaging, White Matter
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