Take a deep breath: Multiecho fMRI denoising effectively removes head motion artifacts, obviating the need for global signal regression
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R Nathan Spreng | Sara Fernández-Cabello | Gary R Turner | W Dale Stevens | R. N. Spreng | W. Stevens | G. Turner | S. Fernández-Cabello | Sara Fernández-Cabello
[1] Hang Joon Jo,et al. Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression , 2012, Brain Connect..
[2] Hang Joon Jo,et al. The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders , 2013, Front. Hum. Neurosci..
[3] Timothy O. Laumann,et al. Sources and implications of whole-brain fMRI signals in humans , 2017, NeuroImage.
[4] Martin Bland,et al. An Introduction to Medical Statistics , 1987 .
[5] Kevin Murphy,et al. Towards a consensus regarding global signal regression for resting state functional connectivity MRI , 2017, NeuroImage.
[6] Brian A. Nosek,et al. Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.
[7] Kevin Murphy,et al. The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.
[8] T. Yarkoni. Big Correlations in Little Studies: Inflated fMRI Correlations Reflect Low Statistical Power—Commentary on Vul et al. (2009) , 2009, Perspectives on psychological science : a journal of the Association for Psychological Science.
[9] Stephen M. Smith,et al. Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power , 2019, NeuroImage.
[10] Jonathan D. Power,et al. Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data , 2018, Proceedings of the National Academy of Sciences.