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.