ComBAT Harmonization for diffusion MRI: Challenges and Best Practices
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918027700731904 |
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| author | Jodoin, Pierre-Marc Edde, Manon Girard, Gabriel Dumais, Félix Theaud, Guillaume Dumont, Matthieu Houde, Jean-Christophe David, Yoan Descoteaux, Maxime |
| author_facet | Jodoin, Pierre-Marc Edde, Manon Girard, Gabriel Dumais, Félix Theaud, Guillaume Dumont, Matthieu Houde, Jean-Christophe David, Yoan Descoteaux, Maxime |
| contents | Over the years, ComBAT has become the standard method for harmonizing MRI-derived measurements, with its ability to compensate for site-related additive and multiplicative biases while preserving biological variability. However, ComBAT relies on a set of assumptions that, when violated, can result in flawed harmonization. In this paper, we thoroughly review ComBAT's mathematical foundation, outlining these assumptions, and exploring their implications for the demographic composition necessary for optimal results.
Through a series of experiments involving a slightly modified version of ComBAT called Pairwise-ComBAT tailored for normative modeling applications, we assess the impact of various population characteristics, including population size, age distribution, the absence of certain covariates, and the magnitude of additive and multiplicative factors. Based on these experiments, we present five essential recommendations that should be carefully considered to enhance consistency and supporting reproducibility, two essential factors for open science, collaborative research, and real-life clinical deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ComBAT Harmonization for diffusion MRI: Challenges and Best Practices Jodoin, Pierre-Marc Edde, Manon Girard, Gabriel Dumais, Félix Theaud, Guillaume Dumont, Matthieu Houde, Jean-Christophe David, Yoan Descoteaux, Maxime Applications Computer Vision and Pattern Recognition Machine Learning Medical Physics Over the years, ComBAT has become the standard method for harmonizing MRI-derived measurements, with its ability to compensate for site-related additive and multiplicative biases while preserving biological variability. However, ComBAT relies on a set of assumptions that, when violated, can result in flawed harmonization. In this paper, we thoroughly review ComBAT's mathematical foundation, outlining these assumptions, and exploring their implications for the demographic composition necessary for optimal results. Through a series of experiments involving a slightly modified version of ComBAT called Pairwise-ComBAT tailored for normative modeling applications, we assess the impact of various population characteristics, including population size, age distribution, the absence of certain covariates, and the magnitude of additive and multiplicative factors. Based on these experiments, we present five essential recommendations that should be carefully considered to enhance consistency and supporting reproducibility, two essential factors for open science, collaborative research, and real-life clinical deployment. |
| title | ComBAT Harmonization for diffusion MRI: Challenges and Best Practices |
| topic | Applications Computer Vision and Pattern Recognition Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2505.14722 |