Harmonization of cortical thickness measurements across scanners and sites
Open accessPublished version of a preprint
Abstract
With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, which we refer to as "scanner effects", can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners. We propose a set of tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.
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Site 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 [31]
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 [31]
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 [31]
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References 46 · Cited by 1,580
Research works printed but not linked: 3
- Bartlett Maurice S Properties of sufficiency and statistical tests Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences 268 282 1937
- Wang Hongzhi Suh Jung W Das Sandhitsu R Pluta John B Craige Caryne Yushkevich Paul A Multi-atlas segmentation with joint label fusion IEEE transactions on pattern analysis and machine intelligence 35 3 611 623 2013b 22732662 PMC3864549
- Westfall Peter H Young S Stanley Resampling-based multiple testing: Examples and methods for p-value adjustment 279 John Wiley & Sons 1993
- Authors
- 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., McInnis, M., Phillips, M. L., Trivedi, M. H., Weissman, M. M., & Shinohara, R. T.
- Affiliations
- University of Pennsylvania (US)
- Vanderbilt University (US)
- Columbia University (US)
- The University of Texas Southwestern Medical Center (US)
- University of Michigan (US)
- University of Pittsburgh (US)
- New York State Psychiatric Institute (US)
- Corresponding author
- Shinohara, Russell T.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 167
- Pages
- 104-120
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC5845848
- OpenAlex
- W2950086191
- Cited by
- 1,580
- References
- 46
- Citations by year
- 2026: 304 · 2025: 335 · 2024: 248 · 2023: 227 · 2022: 197 · 2021: 134 · 2020: 80 · 2019: 40 · 2018: 13 · 2017: 1
- FWCI
- 28.13
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Columbia Academic Commons (Columbia University)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS085211, R21NS093349)
- National Institute of Mental Health (R01MH112847)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- cortical thickness, scanner effects, harmonization, combat, multi-site neuroimaging, statistical power, reproducibility
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Cerebral Cortex, Data Interpretation, Statistical, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Models, Theoretical, Multicenter Studies as Topic, Young Adult, Datasets as Topic
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