Reply to Spreng et al.: Multiecho fMRI denoising does not remove global motion-associated respiratory signals
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[1] R Nathan Spreng,et al. Take a deep breath: Multiecho fMRI denoising effectively removes head motion artifacts, obviating the need for global signal regression , 2019, Proceedings of the National Academy of Sciences.
[2] Timothy O. Laumann,et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.
[3] Noah D. Brenowitz,et al. Integrated strategy for improving functional connectivity mapping using multiecho fMRI , 2013, Proceedings of the National Academy of Sciences.
[4] Timothy O. Laumann,et al. On Global fMRI Signals and Simulations , 2017, Trends in Cognitive Sciences.
[5] Jonathan D. Power. Temporal ICA has not properly separated global fMRI signals: A comment on Glasser et al. (2018) , 2019, NeuroImage.
[6] Christos Davatzikos,et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity , 2017, NeuroImage.
[7] Timothy O. Laumann,et al. Sources and implications of whole-brain fMRI signals in humans , 2017, NeuroImage.
[8] Hang Joon Jo,et al. Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression , 2012, Brain Connect..
[9] 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.
[10] Jonathan D. Power. A simple but useful way to assess fMRI scan qualities , 2017, NeuroImage.
[11] Stephen J. Gotts,et al. Identifying task-general effects of stimulus familiarity in the parietal memory network , 2019, Neuropsychologia.