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Neuroimage. 2017 Nov 1;161:149-170. doi: 10.1016/j.neuroimage.2017.08.047. Epub 2017 Aug 18.

Harmonization of multi-site diffusion tensor imaging data.

Author information

1
Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, USA.
2
Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA.
3
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA.
4
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA.
5
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA.
6
Center for Autism Research, The Children's Hospital of Philadelphia, USA.
7
Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, USA. Electronic address: rshi@mail.med.upenn.edu.

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, may be 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.

KEYWORDS:

ComBat; DTI; Diffusion; Harmonization; Inter-scanner; Multi-site

PMID:
28826946
PMCID:
PMC5736019
DOI:
10.1016/j.neuroimage.2017.08.047
[Indexed for MEDLINE]
Free PMC Article

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