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.
This abstract and the presentation materials are available to members only; a login is required.