Denoising scanner effects from multimodal MRI data using linked independent component analysis
Name:
1-s2.0-S1053811919309796-main.pdf
Size:
2.535Mb
Format:
PDF
Description:
Final Published Version
Author
Li, HuanjieSmith, Stephen M
Gruber, Staci
Lukas, Scott E
Silveri, Marisa M
Hill, Kevin P
Killgore, William D S
Nickerson, Lisa D
Affiliation
Univ Arizona, Dept PsychiatIssue Date
2020-03
Metadata
Show full item recordPublisher
ACADEMIC PRESS INC ELSEVIER SCIENCECitation
Li, 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.Journal
NEUROIMAGERights
Copyright © 2019. Published by Elsevier Inc.Collection Information
This 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 repository@u.library.arizona.edu.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.Note
Open access articleISSN
1053-8119EISSN
1095-9572PubMed ID
31765802Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1016/j.neuroimage.2019.116388
Scopus Count
Collections
Related articles
- Ground-truth "resting-state" signal provides data-driven estimation and correction for scanner distortion of fMRI time-series dynamics.
- Authors: Kumar R, Tan L, Kriegstein A, Lithen A, Polimeni JR, Mujica-Parodi LR, Strey HH
- Issue date: 2020 Dec 4
- Multi-site voxel-based morphometry--not quite there yet.
- Authors: Focke NK, Helms G, Kaspar S, Diederich C, Tóth V, Dechent P, Mohr A, Paulus W
- Issue date: 2011 Jun 1
- Interpreting scan data acquired from multiple scanners: a study with Alzheimer's disease.
- Authors: Stonnington CM, Tan G, Klöppel S, Chu C, Draganski B, Jack CR Jr, Chen K, Ashburner J, Frackowiak RS
- Issue date: 2008 Feb 1
- MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths.
- Authors: Jovicich J, Czanner S, Han X, Salat D, van der Kouwe A, Quinn B, Pacheco J, Albert M, Killiany R, Blacker D, Maguire P, Rosas D, Makris N, Gollub R, Dale A, Dickerson BC, Fischl B
- Issue date: 2009 May 15
- The power of sample size through a multi-scanner approach in MR neuroimaging regression analysis: evidence from Alzheimer's disease with and without depression.
- Authors: Karavasilis E, Parthimos TP, Papatriantafyllou JD, Christidi F, Papageorgiou SG, Kapsas G, Papanicolaou AC, Seimenis I
- Issue date: 2019 Jun