| # ComBat Harmonization for Multi-Site Neuroimaging | |
| ComBat (Johnson et al. 2007, adapted to MRI by Fortin et al. 2017, 2018) | |
| is the de-facto standard for removing scanner / acquisition-site bias | |
| from multi-center neuroimaging studies. | |
| ## How it works | |
| ComBat models per-site location (mean) and scale (variance) parameters | |
| using an empirical-Bayes hierarchical framework. It estimates these | |
| parameters jointly across all sites and shrinks them toward a global | |
| prior — small-N sites are pulled toward the global mean, preventing | |
| overfitting. | |
| ## Site-gap reduction | |
| A typical demonstration: the per-site mean of a hippocampus volume | |
| feature can vary by 5+ standard deviations across hospitals. ComBat | |
| typically collapses this gap to <0.005 — a 1000x+ reduction — while | |
| preserving within-site biological variance (age, sex, diagnosis). | |
| ## When it fails | |
| ComBat requires at least 2 sites with overlapping covariate | |
| distributions. Single-site data, or sites with completely disjoint | |
| populations (e.g., one site only-pediatric, another only-elderly), | |
| produce unreliable harmonization. | |