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Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high‐motion data points
Author(s) -
Siegel Joshua S.,
Power Jonathan D.,
Dubis Joseph W.,
Vogel Alecia C.,
Church Jessica A.,
Schlaggar Bradley L.,
Petersen Steven E.
Publication year - 2014
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.22307
Subject(s) - censoring (clinical trials) , functional magnetic resonance imaging , general linear model , artificial intelligence , statistics , regression , computer science , regression analysis , linear regression , mathematics , pattern recognition (psychology) , psychology , neuroscience
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 .

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