Challenges and best practices when using ComBAT to harmonize diffusion MRI data.

Pierre-Marc Jodoin^1,2, Manon Edde^3,4, Gabriel Girard³, Felix Dumais⁵, Guillaume Theaud⁶, Matthieu Dumont⁶, Jean-Christophe Houde⁶, Yoan David⁵, Maxime Descoteaux^3,6,

¹VitaLab, Dep of Computer Science, University of Sherbrooke, Sherbrooke, Qc, J1K 2R1, Canada. Pierre-Marc.Jodoin@usherbrooke.ca.

²Imeka Solutions Inc., Sherbrooke, Qc, J1H 4A7, Canada. Pierre-Marc.Jodoin@usherbrooke.ca.

³Sherbrooke Connectivity Imaging Lab (SCIL), Dep of Computer Science, University of Sherbrooke, Sherbrooke, Qc, J1K 2R1, Canada.

⁴StoP-AD Centre, Douglas Mental Health University Institute, McGill University, Montréal, Qc, H4H 1R3, Canada.

⁵VitaLab, Dep of Computer Science, University of Sherbrooke, Sherbrooke, Qc, J1K 2R1, Canada.

⁶Imeka Solutions Inc., Sherbrooke, Qc, J1H 4A7, Canada.

PubMed PMC DOI PDF

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, outlin…

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