ComBat Harmonization: Empirical Bayes versus fully Bayes approaches.

ABSTRACT: Studying small effects or subtle neuroanatomical variation requires large-scale sample size data. As a result, combining neuroimaging data from multiple datasets is necessary. Variation in acquisition protocols, magnetic field strength, scanner build, and many other non-biologically related factors can introduce undesirable bias into studies. Hence, harmonization is required to remove the bias-inducing factors from the data. ComBat is one of the most common methods applied to features from structural images. ComBat models the data using a hierarchical Bayesian model and uses the empirical Bayes approach to infer the distribution of the unknown factors. The empirical Bayes harmonization method is computationally efficient and provides valid point estimates. However, it tends to underestimat

SUBMITTER: Reynolds M 

PROVIDER: S-EPMC10412957 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature