RT Journal Article SR Electronic T1 Harmonization of multi-site diffusion tensor imaging data JF bioRxiv FD Cold Spring Harbor Laboratory SP 116541 DO 10.1101/116541 A1 Jean-Philippe Fortin A1 Drew Parker A1 Birkan Tunç A1 Takanori Watanabe A1 Mark A. Elliott A1 Kosha Ruparel A1 David R. Roalf A1 Theodore D. Satterthwaite A1 Ruben C. Gur A1 Raquel E. Gur A1 Robert T. Schultz A1 Ragini Verma A1 Russell T. Shinohara YR 2017 UL http://biorxiv.org/content/early/2017/03/22/116541.abstract AB Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, is counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.AbbreviationsADNIAlzheimer’s Disease NeuroImaging InitiativeAXAxial diffusivityCATConcordance at the topComBatCombatting batch effects when combining batches of gene expression microar-ray dataCoVCoefficient of variationCSFCerebrospinal fluidDTIDiffusion tensor imagingEBEmpirical BayesFAFractional anisotropyGMGrey matterGSGlobal scalingIBMAImage-based meta analysisIPWInverse probability weightingMDMean diffusivityMRIMagnetic resonance imagingOLSOrdinary least squaresRADRadial diffusivityRAVELRemoval of artificial voxel effect by linear regressionRISHRotation invariant spherical harmonicROIRegion of interestSVASurrogate variable analysisSVDSingular value decomposition T1-wT1-weightedTBSSTract-based spatial statisticsWMWhite matterWMPMWhite matter parcellation map;