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Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity.

, , , , , , , , , , , , , and . NeuroImage, (2017)

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Sources and implications of whole-brain fMRI signals in humans., , , and . NeuroImage, (2017)Recent progress and outstanding issues in motion correction in resting state fMRI., , and . NeuroImage, (2015)Corrigendum to "Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion" NeuroImage 59 (3) (2012) 2142-2154., , , , and . NeuroImage, 63 (2): 999 (2012)Resting-state fMRI in the Human Connectome Project., , , , , , , , , and 15 other author(s). NeuroImage, (2013)Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project., , , , , , , , and . Brain Connectivity, 6 (9): 669-680 (2016)Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion., , , , and . NeuroImage, 59 (3): 2142-2154 (2012)Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity., , , , , , , , , and 4 other author(s). NeuroImage, (2017)Functional Brain Networks Develop from a "Local to Distributed" Organization., , , , , , , and . PLoS Comput. Biol., (2009)Methods to detect, characterize, and remove motion artifact in resting state fMRI., , , , , and . NeuroImage, (2014)A simple but useful way to assess fMRI scan qualities.. NeuroImage, (2017)