biostudies-literatureJodoin PMACUITY/CQDM consortiumSherbrooke Neuroimaging research chairNIA NIH HHSMITACS AccelerationNSERC Discovery grant41508https://www.ebi.ac.uk/biostudies/studies/S-EPMC12644733biostudies-literatureUnknown15(1)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 covaScientific reportsChallenges and best practices when using ComBAT to harmonize diffusion MRI data.PMC12644733U01 AG024904Fox NCLacy KShen LCrawford KLoPresti BKaddurah-Daouk RHoang IWan YLin ZSaito NStrong JTosun-Turgut DSenjem MMiller MJKarlawish JCarrillo MJin CVillemagne VOkonkwo ODeCarli CTruran-Sacrey DGunter JSilverberg NParkins SSalazar JLee VMYMasliah EKnaack AMontine TYushkevich PAShaffer ETaylor-Reinwald LSachdev PPizzola JFidell AFrank RGermano KKReyes CJenkins CReyes DNudelman KMatoush LMoore-Weiss JVrillon AWalter SWindon CTosun DGrill JGonzalez HDescoteaux MFletcher ELee EBRobison JMolchan SSchwarz CCairns NJMalone IThao MSAyo ONeu SBuckholtz NToga AWLagarde JThomas LMasterman DConti CCabrera YNir TMRisacher SLFelmlee JPerrin RJBoatwright VDumont MAisen PClanton TMahboubi PWebb CForoud TMSnyder PJGuzman VHarkins KRaman RGlittenberg MTalavera SChiotis KZimmerman CTheaud GCraft HJo TForghanian-Arani AApostolova LGBeckett LHedberg CJack CRBorowski BLandau SFockler JKwang WAjayi AKoeppe RAAshford MHussen KAWeisensel TJodoin PMGreen RCAmaza HSoleimani-Meigooni DKachaturian ZBarnes LRaya MAHo CLargent EChen SRHergesheimer LPotter ZRabinovici GHoude JCWeiner MWPotter WNho KKorecka MHarvey DWard CDiaz AEdde MFranklin EDumais FRyan LDas SChoe MDrake EKormos AReid RBenedetto JDAlzheimer’s Disease Neuroimaging InitiativeNosheny RAdegoke ODavid YRivera-Mindt MRafii MOgwang SMorris JFlenniken DBrylska MJackson JJagust WSaykin AJLa Joie RHsiao JKMagana FJahanshad NTrojanowki JQHills ARDonohue MJimenez GFaber KThompson PSmith SThomopoulos SIGirard GShaw LMVemuri PPetersen RKantarci KfalseChallenges and best practices when using ComBAT to harmonize diffusion MRI data.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 cova2025-01-01T00:00:00Z2025 Nov2026-06-06T15:55:35.186Z2026-06-02T03:09:45.587ZS-EPMC126447334128582810.1038/s41598-025-25400-x