Abstract
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.
Abbreviations
- ADNI
- Alzheimer’s Disease NeuroImaging Initiative
- AX
- Axial diffusivity
- CAT
- Concordance at the top
- ComBat
- Combatting batch effects when combining batches of gene expression microar-ray data
- CoV
- Coefficient of variation
- CSF
- Cerebrospinal fluid
- DTI
- Diffusion tensor imaging
- EB
- Empirical Bayes
- FA
- Fractional anisotropy
- GM
- Grey matter
- GS
- Global scaling
- IBMA
- Image-based meta analysis
- IPW
- Inverse probability weighting
- MD
- Mean diffusivity
- MRI
- Magnetic resonance imaging
- OLS
- Ordinary least squares
- RAD
- Radial diffusivity
- RAVEL
- Removal of artificial voxel effect by linear regression
- RISH
- Rotation invariant spherical harmonic
- ROI
- Region of interest
- SVA
- Surrogate variable analysis
- SVD
- Singular value decomposition T1-
- w
- T1-weighted
- TBSS
- Tract-based spatial statistics
- WM
- White matter
- WMPM
- White matter parcellation map;