Denoising scanner effects from multimodal MRI data using linked independent component analysis
Smith, Stephen M
Lukas, Scott E
Silveri, Marisa M
Hill, Kevin P
Killgore, William D S
Nickerson, Lisa D
AffiliationUniv Arizona, Dept Psychiat
MetadataShow full item record
PublisherACADEMIC PRESS INC ELSEVIER SCIENCE
CitationLi, H., Smith, S. M., Gruber, S., Lukas, S. E., Silveri, M. M., Hill, K. P., ... & Nickerson, L. D. (2020). Denoising scanner effects from multimodal MRI data using linked independent component analysis. NeuroImage, 208, 116388.
RightsCopyright © 2019. Published by Elsevier Inc.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractPooling magnetic resonance imaging (MRI) data across research studies, or utilizing shared data from imaging repositories, presents exceptional opportunities to advance and enhance reproducibility of neuroscience research. However, scanner confounds hinder pooling data collected on different scanners or across software and hardware upgrades on the same scanner, even when all acquisition protocols are harmonized. These confounds reduce power and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach that implements a data-driven linked independent component analysis (LICA) to identify scanner-related effects for removal from multimodal MRI to denoise scanner effects. We utilized multi-study data to test our proposed method that were collected on a single 3T scanner, pre- and post-software and major hardware upgrades and using different acquisition parameters. Our proposed denoising method shows a greater reduction of scanner-related variance compared with standard GLM confound regression or ICA-based single-modality denoising. Although we did not test it here, for combining data across different scanners, LICA should prove even better at identifying scanner effects as between-scanner variability is generally much larger than within-scanner variability. Our method has great promise for denoising scanner effects in multi-study and in large-scale multi-site studies that may be confounded by scanner differences.
NoteOpen access article
VersionFinal published version
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