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An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data

NeuroImage · 2012 · Vol. 64 · pp. 240–256

Theodore D. Satterthwaite✉(University of Pennsylvania)Mark A. Elliott(University of Pennsylvania)Raphael T. Gerraty(University of Pennsylvania)Kosha Ruparel(University of Pennsylvania)James Loughead(University of Pennsylvania)Monica E. Calkins(University of Pennsylvania)Simon B. Eickhoff(RWTH Aachen University)Håkon Håkonarson(Children's Hospital of Philadelphia)Ruben C. Gur(University of Pennsylvania)Raquel E. Gur(University of Pennsylvania)Daniel H. Wolf(University of Pennsylvania)

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Functional Brain Connectivity Studies Advanced MRI Techniques and Applications Advanced Neuroimaging Techniques and ApplicationsArtifact (error)PreprocessorComputer scienceArtificial intelligenceResting state fMRIScannerRegressionMotion (physics)Pattern recognition (psychology)Computer vision

MeSH terms

AdolescentAlgorithmsBrainChildData Interpretation, StatisticalFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleRegression AnalysisRestSensitivity and SpecificityReproducibility of Results

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Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high‐motion data points