Imaging data suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. This includes common imaging modalities, such as MRI, fMRI and DTI, as well as measurements derived from those modalities, for instance ROI volumes, RAVENS maps, cortical thickness measurements, connectome matrices, etc. To maximize statistical power, post-processing data harmonization is a powerful technique to remove unwanted variation when combining data across scanners and sites.

In two recent papers (harmonization of DTI data and harmonization of cortical thickness measurements) we have shown that ComBat, a popular batch-effect correction tool used in genomics, succesffuly removes inter-site technical variability while preserving inter-site biological variability. We showed that ComBat performs well for multi-site imaging studies that only have a few participants per site. We also showed that ComBat was robust to unbalanced studies, that is studies for which the biological covariate of interest is not balanced across sites.

We recommend to use the ComBat harmonization method after imaging processing, just right before the statistical analysis. The ComBat harmonization requires the imaging data to be represented in a matrix where rows are the imaging features (for instance voxels, ROIs or connectome edges) and columns are the participants. For example, for voxel-level analyses, this usually requires the images to be registered to a common template space.

The ComBat algorithm needs two mandatory inputs:

• The data matrix. Rows are features and columns are participants.
• The site, study or scanner variable. The algorithm can only handle one variable. You should provide the smallest unit of the study that you believe introduces unwanted variable. For instance, for a study with 2 sites and 3 scanners (1 site with 1 scanner, 1 site with 2 scanners), the variable for scanner should be used.

The ComBat algorithm also accepts an optional input:

• Biological variables. You can provide biological covariates, such as disease status, age, gender, to ensure that the harmonization technique does not remove the effects of those variables on the imaging data. The algorithm will take the variability associated with those variables in the estimation of the site/scanner effects.