Reduction of motion-related artifacts in resting state fMRI using aCompCor
Male
Brain Mapping
Principal Component Analysis
Movement
Rest
Brain
Reproducibility of Results
Image Enhancement
Magnetic Resonance Imaging
Sensitivity and Specificity
Motion
03 medical and health sciences
0302 clinical medicine
Data Interpretation, Statistical
Image Interpretation, Computer-Assisted
Humans
Artifacts
Child
Algorithms
Software
DOI:
10.1016/j.neuroimage.2014.03.028
Publication Date:
2014-03-21T06:16:40Z
AUTHORS (6)
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.
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