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Abstract #0659

Combining Multi-Site/Study MRI Data: A Novel Linked-ICA Denoising Method for Removing Scanner and Site Variability from Multi-Modal MRI Data

Huanjie Li1,2, Staci Gruber1, Stephen M Smith3, Scott E Lukas1, Marisa Silveri1, Kevin P Hill4, William D. S Killgore5, and Lisa D Nickerson1

1Imaging Center, Harvard Medical School, McLean Hospital, Belmont, MA, United States, 2Dalian University of Technology, Dalian, China, 3Oxford University, Oxford, United Kingdom, 4Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, United States, 5University of Arizona, Tucson, AZ, United States

Large multi-site studies that pool magnetic resonance imaging (MRI) data across research sites present exceptional opportunities to advance neuroscience and enhance reproducibility of neuroimaging research. However, inconsistent MRI data collection platforms and scanning sequences both introduce systematic variability that can confound the true effect of interest and make the interpretation of results obtained from combined data difficult. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach for multi-site, multi-modal MRI data that implements a data-driven linked independent component analysis to efficiently identify scanner/site-related effects for removal.

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