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Denoising scanner effects from multimodal MRI data using linked independent component analysis.
- Source :
-
NeuroImage [Neuroimage] 2020 Mar; Vol. 208, pp. 116388. Date of Electronic Publication: 2019 Nov 23. - Publication Year :
- 2020
-
Abstract
- Pooling 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.<br /> (Copyright © 2019. Published by Elsevier Inc.)
- Subjects :
- Adult
Diffusion Tensor Imaging methods
Diffusion Tensor Imaging standards
Functional Neuroimaging methods
Functional Neuroimaging standards
Humans
Magnetic Resonance Imaging instrumentation
Magnetic Resonance Imaging standards
Multimodal Imaging
Neuroimaging instrumentation
Neuroimaging standards
Brain diagnostic imaging
Magnetic Resonance Imaging methods
Models, Statistical
Neuroimaging methods
Subjects
Details
- Language :
- English
- ISSN :
- 1095-9572
- Volume :
- 208
- Database :
- MEDLINE
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 31765802
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2019.116388