TY - JOUR T1 - Harmonization of multi-site diffusion tensor imaging data JF - bioRxiv DO - 10.1101/116541 SP - 116541 AU - Jean-Philippe Fortin AU - Drew Parker AU - Birkan Tunç AU - Takanori Watanabe AU - Mark A. Elliott AU - Kosha Ruparel AU - David R. Roalf AU - Theodore D. Satterthwaite AU - Ruben C. Gur AU - Raquel E. Gur AU - Robert T. Schultz AU - Ragini Verma AU - Russell T. Shinohara Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/03/15/116541.abstract N2 - 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.ADNIAlzheimer’s Disease NeuroImaging Initiative;AXAxial diffusivityCATConcordance at the topComBatCombatting batch effects when combining batches of gene expression microarray dataCoVCoefficient of variationCSFCerebrospinal fluid; DTI: Diffusion 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 interest; SVA: Surrogate variable analysisSVDSingular value decompositionT1-wT1-weighted; TBSS: Tract-based spatial statisticsWMWhite matterWMPMWhite matter parcellation map ER -