Evaluation of neuroCombat and deep learning harmonization for multi-site magnetic resonance neuroimaging in youth with prenatal alcohol exposure

Chloe Scholten; Elyssa M. McMaster; Adam M. Saunders; Michael E. Kim; Gaurav Rudravaram; Elias Levy; Bryce Geeraert; Lianrui Zuo; Simon Vandekar; Catherine Lebel; Bennett A. Landman

arXiv · 2604.00251

Evaluation of neuroCombat and deep learning harmonization for multi-site magnetic resonance neuroimaging in youth with prenatal alcohol exposure

Chloe Scholten·Elyssa M. McMaster·Adam M. Saunders·Michael E. Kim·Gaurav Rudravaram·Elias Levy·Bryce Geeraert·Lianrui Zuo·Simon Vandekar·Catherine Lebel·Bennett A. Landman

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Abstract

In cases of prevalent diseases and disorders, such as Prenatal Alcohol Exposure (PAE), multi-site data collection allows for increased study samples. However, multi-site studies introduce additional variability through heterogeneous collection materials, such as scanner and acquisition protocols, which confound with biologically relevant signals. Neuroscientists often utilize statistical methods on image-derived metrics, such as volume of regions of interest, after all image processing to minimize site-related variance. HACA3, a deep learning harmonization method, offers an opportunity to harmonize image signals prior to metric quantification; however, HACA3 has not yet been validated in a pediatric cohort. In this work, we investigate HACA3's ability to remove site-related variance and preserve biologically relevant signal compared to a statistical method, neuroCombat, and pair HACA3 processing with neuroCombat to evaluate the efficacy of multiple harmonization methods in a pediatric (age 7 to 21) population across three unique scanners with controls and cases of PAE with downstream MaCRUISE volume metrics. We find that HACA3 qualitatively improves inter-site contrast variations, but statistical methods reduce greater site-related variance within the MaCRUISE volume metrics following an ANCOVA test, and HACA3 relies on follow-up statistical methods to approach maximal biological preservation in this context.

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Chloe Scholten, Elyssa M. McMaster, Adam M. Saunders, Michael E. Kim, Gaurav Rudravaram, Elias Levy, Bryce Geeraert, Lianrui Zuo, Simon Vandekar, Catherine Lebel, Bennett A. Landman. 2026-03-31. Evaluation of neuroCombat and deep learning harmonization for multi-site magnetic resonance neuroimaging in youth with prenatal alcohol exposure. https://arxiv.org/abs/2604.00251

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