Reduction of motion-related artifacts in resting state fMRI using aCompCor

John Muschelli; Mary Beth Nebel; Brian S Caffo; Anita D Barber; James J Pekar; Stewart H Mostofsky

doi:10.1016/j.neuroimage.2014.03.028

Reduction of motion-related artifacts in resting state fMRI using aCompCor

Neuroimage. 2014 Aug 1:96:22-35. doi: 10.1016/j.neuroimage.2014.03.028. Epub 2014 Mar 18.

Authors

John Muschelli¹, Mary Beth Nebel², Brian S Caffo¹, Anita D Barber³, James J Pekar⁴, Stewart H Mostofsky⁵

Affiliations

¹ Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street Baltimore, MD 21205, USA.
² Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, 716 North Broadway Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA. Electronic address: mb@jhmi.edu.
³ Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, 716 North Broadway Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA.
⁴ The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street Baltimore, MD 21287, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 North Broadway Baltimore, MD 21205, USA.
⁵ Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, 716 North Broadway Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA; Department of Psychiatry, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA.

Abstract

Recent studies have illustrated that motion-related artifacts remain in resting-state fMRI (rs-fMRI) data even after common corrective processing procedures have been applied, but the extent to which head motion distorts the data may be modulated by the corrective approach taken. We compare two different methods for estimating nuisance signals from tissues not expected to exhibit BOLD fMRI signals of neuronal origin: 1) the more commonly used mean signal method and 2) the principal components analysis approach (aCompCor: Behzadi et al., 2007). Further, we investigate the added benefit of "scrubbing" (Power et al., 2012) following both methods. We demonstrate that the use of aCompCor removes motion artifacts more effectively than tissue-mean signal regression. In addition, inclusion of more components from anatomically defined regions of no interest better mitigates motion-related artifacts and improves the specificity of functional connectivity estimates. While scrubbing further attenuates motion-related artifacts when mean signals are used, scrubbing provides no additional benefit in terms of motion artifact reduction or connectivity specificity when using aCompCor.

Keywords: Head motion; Nuisance regression; Resting state fMRI; Specificity.

Publication types

Comparative Study
Research Support, N.I.H., Extramural

MeSH terms

Algorithms
Artifacts*
Brain / physiology*
Brain Mapping / methods*
Child
Data Interpretation, Statistical
Humans
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods*
Male
Motion
Movement*
Principal Component Analysis
Reproducibility of Results
Rest / physiology
Sensitivity and Specificity
Software

Abstract

Publication types

MeSH terms

Grants and funding