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Abstract #0601

Statistical harmonization of multi-site diffusion tensor imaging data with ComBat

Jean-Philippe Fortin1, Drew Parker2, Birkan Tunç2, Takanori Watanabe2, Mark A. Elliott2, Kosha Ruparel3, Ruben C. Gur3, Raquel E Gur3, Robert T. Schultz4, Russell T Shinohara1, and Ragini Verma2

1Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, United States, 2Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States, 3Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, United States, 4Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA, United States

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging technique to study microstructural changes in the white matter (WM). DTI images suffer from unwanted inter-scanner variability, which is problematic when combining datasets from different sites. In this work, we propose to use ComBat, a location-scale Empirical Bayes model largely used in genomics, to combine and harmonize multi-site DTI datasets. Using a study of 210 subjects with an age range of 8 to 18 years old from two imaging sites, we show that ComBat (1) removes unwanted variation associated with imaging site and (2) improves the power at detecting regions known to exhibit microstructural changes in this age range.

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