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<p>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 <i>Pairwise-ComBAT</i> 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Pierre-Marc Jodoin,
  • Manon Edde,
  • Gabriel Girard,
  • Felix Dumais,
  • Guillaume Theaud,
  • Matthieu Dumont,
  • Jean-Christophe Houde,
  • Yoan David,
  • Maxime Descoteaux,
  • Michael W. Weiner,
  • Paul Aisen,
  • Ronald Petersen,
  • Clifford R. Jack Jr,
  • William Jagust,
  • Susan Landau,
  • Monica Rivera-Mindt,
  • Ozioma Okonkwo,
  • Leslie M. Shaw,
  • Edward B. Lee,
  • Arthur W. Toga,
  • Laurel Beckett,
  • Danielle Harvey,
  • Robert C. Green,
  • Andrew J. Saykin,
  • Kwangsik Nho,
  • Richard J. Perrin,
  • Duygu Tosun,
  • Pallavi Sachdev,
  • Erin Drake,
  • Tom Montine,
  • Cat Conti,
  • Rachel Nosheny,
  • Diana Truran-Sacrey,
  • Juliet Fockler,
  • Melanie J. Miller,
  • Catherine Conti,
  • Winnie Kwang,
  • Chengshi Jin,
  • Adam Diaz,
  • Miriam Ashford,
  • Derek Flenniken,
  • Adrienne Kormos,
  • Michael Rafii,
  • Rema Raman,
  • Gustavo Jimenez,
  • Michael Donohue,
  • Jennifer Salazar,
  • Andrea Fidell,
  • Virginia Boatwright,
  • Justin Robison,
  • Caileigh Zimmerman,
  • Yuliana Cabrera,
  • Sarah Walter,
  • Taylor Clanton,
  • Elizabeth Shaffer,
  • Caitlin Webb,
  • Lindsey Hergesheimer,
  • Stephanie Smith,
  • Sheila Ogwang,
  • Olusegun Adegoke,
  • Payam Mahboubi,
  • Jeremy Pizzola,
  • Cecily Jenkins,
  • Joel Felmlee,
  • Nick C. Fox,
  • Paul Thompson,
  • Charles DeCarli,
  • Arvin Forghanian-Arani,
  • Bret Borowski,
  • Calvin Reyes,
  • Caitie Hedberg,
  • Chad Ward,
  • Christopher Schwarz,
  • Denise Reyes,
  • Jeff Gunter,
  • John Moore-Weiss,
  • Kejal Kantarci,
  • Leonard Matoush,
  • Matthew Senjem,
  • Prashanthi Vemuri,
  • Robert Reid,
  • Ian Malone,
  • Sophia I. Thomopoulos,
  • Talia M. Nir,
  • Neda Jahanshad,
  • Alexander Knaack,
  • Evan Fletcher,
  • Duygu Tosun-Turgut,
  • Stephanie Rossi Chen,
  • Mark Choe,
  • Karen Crawford,
  • Paul A. Yushkevich,
  • Sandhitsu Das,
  • Laurel Beckett,
  • Naomi Saito,
  • Kedir Adem Hussen,
  • Ozioma Okonkwo,
  • Hannatu Amaza,
  • Mai Seng Thao,
  • Matt Glittenberg,
  • Isabella Hoang,
  • Joe Strong,
  • Trinity Weisensel,
  • Fabiola Magana,
  • Lisa Thomas,
  • Kaori Kubo Germano,
  • Sandra Talavera,
  • Vanessa Guzman,
  • Adeyinka Ajayi,
  • Joseph Di Benedetto,
  • Shaniya Parkins,
  • Omobolanle Ayo,
  • Victor Villemagne,
  • Brian LoPresti,
  • Robert A. Koeppe,
  • Gil Rabinovici,
  • John Morris,
  • Erin Franklin,
  • Nigel J. Cairns,
  • Lisa Taylor-Reinwald,
  • Virginia M. Y. Lee,
  • Magdalena Korecka,
  • Magdalena Brylska,
  • Yang Wan,
  • J. Q. Trojanowki,
  • Scott Neu,
  • Tatiana M. Foroud,
  • Taeho Jo,
  • Shannon L. Risacher,
  • Hannah Craft,
  • Liana G. Apostolova,
  • Kelly Nudelman,
  • Kelley Faber,
  • ZoA Potter,
  • Kaci Lacy,
  • Rima Kaddurah-Daouk,
  • Li Shen,
  • David Soleimani-Meigooni,
  • Renaud La Joie,
  • Konstantinos Chiotis,
  • Maison Abu Raya,
  • Agathe Vrillon,
  • Charles Windon,
  • Julien Lagarde,
  • Zoe Lin,
  • Aidyn Rose Hills,
  • Jason Karlawish,
  • Emily Largent,
  • Kristin Harkins,
  • Joshua Grill,
  • Zaven Kachaturian,
  • Richard Frank,
  • Peter J. Snyder,
  • Neil Buckholtz,
  • John K. Hsiao,
  • Laurie Ryan,
  • Susan Molchan,
  • Maria Carrillo,
  • William Potter,
  • Lisa Barnes,
  • Hector González,
  • Carole Ho,
  • Jonathan Jackson,
  • Eliezer Masliah,
  • Donna Masterman,
  • Nina Silverberg

摘要

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.