Harmonization of multi-site diffusion tensor imaging data.

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TitleHarmonization of multi-site diffusion tensor imaging data.
Publication TypeJournal Article
Year of Publication2017
AuthorsFortin, J-P, Parker, D, Tunc, B, Watanabe, T, Elliott, MA, Ruparel, K, Roalf, DR, Satterthwaite, TD, Gur, RC, Gur, RE, Schultz, RT, Verma, R, Shinohara, RT
JournalNeuroimage
Volume161
Pagination149-170
Date Published2017 11 01
ISSN1095-9572
KeywordsAdolescent, Adult, Autism Spectrum Disorder, Child, Cohort Studies, Diffusion Tensor Imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Multicenter Studies as Topic, White Matter, Young Adult
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.

DOI10.1016/j.neuroimage.2017.08.047
Alternate JournalNeuroimage
PubMed ID28826946
PubMed Central IDPMC5736019
Grant ListR01 MH092862 / MH / NIMH NIH HHS / United States
R01 HD089390 / HD / NICHD NIH HHS / United States
R21 NS093349 / NS / NINDS NIH HHS / United States
RC1 MH088791 / MH / NIMH NIH HHS / United States
U54 HD086984 / HD / NICHD NIH HHS / United States
R01 NS085211 / NS / NINDS NIH HHS / United States
R01 MH107703 / MH / NIMH NIH HHS / United States