ComBatHarmonization | Harmonization of multi-site imaging data with ComBat

ComBatHarmonization is a R library. ComBatHarmonization has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

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, in which the biological covariate of interest is not balanced across sites. We recommend to use the ComBat harmonization method after imaging processing before downstream statistical analyses. 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 images to be registered to a common template space.