Abstract
AbstractWe systematically presented various nuisance signals in resting-state fMRI (R-fMRI) dataset. First, we examined the impact of different extraction methods on the default-mode pattern seeded by posterior cingulate cortex (PCC). Second, we compared their differences in test-retest reliability. Finally, we speculated the equivalence of removing global mean signal to removing gray matter mean signal.
Highlights
Given: Numerous approaches exist for the extraction of time-series data for nuisance signals in RSFC analyses
Do they highly affect the RSFC results w/o them in the model? Do nuisance signals significantly correlated with each other? Who is more important? What should we use? High reliability? What is the meaning of global mean signal?
WM: three extracting methods, 1) participant-specific tissue segmentation where tissue probability is more than 50% (SEG), 2) tissue seed (26, -12, 35) in Chang’s et al (2009) (SEED); 3) TC-GICA combing dual regression (ICADR) in Zuo et al (NeuroImage, 2010); CSF: three extracting methods, 1) participant-specific tissue segmentation where tissue probability is more than 50% (SEG), 2) tissue seed (19, -33, 18) in Chang’s et al (2009) (SEED); 3) TC-GICA combing dual regression (ICADR) in Zuo et al (NeuroImage, in press); Noise regions defined by our previous amplitude study (NOISE) (Zuo et al, NeuroImage, 2010); SEG SEED ICA-DR
Summary
Given: Numerous approaches exist for the extraction of time-series data for nuisance signals in RSFC analyses. Do they highly affect the RSFC results w/o them in the model? Do nuisance signals significantly correlated with each other? What is the meaning of global mean signal? Do they highly affect the RSFC results w/o them in the model? Do nuisance signals significantly correlated with each other? Who is more important? What should we use? High reliability? What is the meaning of global mean signal?
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