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Brain Connect. 2016 Nov;6(9):669-680. Epub 2016 Sep 30.

Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project.

Author information

1
1 Department of Neuroscience, Washington University School of Medicine , St. Louis, Missouri.
2
2 Department of Psychiatry, Washington University School of Medicine , St. Louis, Missouri.
3
3 Department of Neurology, Washington University School of Medicine , St. Louis, Missouri.
4
4 National Institute of Mental Health , Bethesda, Maryland.
5
5 Department of Radiology, Washington University School of Medicine , St. Louis, Missouri.
6
6 Department of Psychology, Washington University in St. Louis , St. Louis, Missouri.

Abstract

Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR.

KEYWORDS:

Human Connectome Project; artifact; denoising; fMRI; functional connectivity; independent component analysis; motion; resting state

PMID:
27571276
PMCID:
PMC5105353
DOI:
10.1089/brain.2016.0435
[Indexed for MEDLINE]
Free PMC Article

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