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

• Published in: Human brain mapping Vol. 35; no. 5; pp. 1981 - 1996
• Main Authors: Siegel, Joshua S., Power, Jonathan D., Dubis, Joseph W., Vogel, Alecia C., Church, Jessica A., Schlaggar, Bradley L., Petersen, Steven E.
• Format: Journal Article
• Language: English
• Published: New York, NY Blackwell Publishing Ltd 01.05.2014; Wiley-Liss; John Wiley & Sons, Inc; John Wiley and Sons Inc
• Subjects: Adolescent; Adult; Algorithms; Biological and medical sciences; Brain - blood supply; Brain - physiology; Child; Child Development - physiology; Cohort Studies; data quality; Electrodiagnosis. Electric activity recording; Female; fMRI; general linear model; GLM; head movement; Head Movements - physiology; Humans; Image Processing, Computer-Assisted; Investigative techniques, diagnostic techniques (general aspects); Magnetic Resonance Imaging; Male; Medical sciences; Motion; Nervous system; noise; Oxygen - blood; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Regression Analysis; scrubbing; Sensory Thresholds - physiology; task; Young Adult; Nervous system diseases; Radiodiagnosis; motion; Noise; head movement; GLM; Functional analysis; Nuclear magnetic resonance imaging; Linear model; general linear model; data quality; fMRI; Improvement; task; Body movement; scrubbing; Functional imaging; noise
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.22307

Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring (“motion scrubbing”). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal‐to‐noise ratio. Motion censoring decreased variance in parameter estimates within‐ and across‐subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement. Hum Brain Mapp 35:1981–1996, 2014. © 2013 Wiley Periodicals, Inc.