ComBAT Harmonization for diffusion MRI: Challenges and Best Practices

TL;DR

This paper reviews the ComBAT harmonization method for diffusion MRI, analyzing its assumptions, limitations, and best practices to improve its reliability and reproducibility in clinical and research settings.

Contribution

It provides a detailed mathematical review of ComBAT, evaluates its performance under various conditions, and offers five practical recommendations for optimal use.

Findings

ComBAT's assumptions are critical for effective harmonization.

Population characteristics significantly influence ComBAT's performance.

Five key recommendations improve ComBAT's reliability and reproducibility.

Abstract

Over the years, ComBAT has become the standard method for harmonizing MRI-derived measurements, with its ability to compensate for site-related additive and multiplicative biases while preserving biological variability. However, ComBAT relies on a set of assumptions that, when violated, can result in flawed harmonization. In this paper, we thoroughly review ComBAT's mathematical foundation, outlining these assumptions, and exploring their implications for the demographic composition necessary for optimal results. Through a series of experiments involving a slightly modified version of ComBAT called Pairwise-ComBAT tailored for normative modeling applications, we assess the impact of various population characteristics, including population size, age distribution, the absence of certain covariates, and the magnitude of additive and multiplicative factors. Based on these experiments, we present five essential recommendations that should be carefully considered to enhance consistency and supporting reproducibility, two essential factors for open science, collaborative research, and real-life clinical deployment.