Statistical Improvements in Functional Magnetic Resonance Imaging Analyses Produced by Censoring High-Motion Data Points

By J. Siegel, Jonathan D. Power, J. Dubis, Alecia C. Vogel, Jessica A. Church, Bradley L. Schlaggar, Steven E. Petersen. Human Brain Mapping, 2013

555 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8c44c8b556c812958d4454c398af79aef10a3c93

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Recent progress and outstanding issues in motion correction in resting state fMRI (2014, unknown): “For example, two studies have investigated the impact of censoring high-motion volumes in task fMRI, both finding that statistical power can be increased by censoring (Christodoulou et al., 2013; Siegel et al., 2014).”
Image processing and analysis methods for the Adolescent Brain Cognitive Development Study (2018, methodology): “Censoring of individual degraded frames and/or slices is one approach used in the literature to minimize contamination of dMRI and fMRI measures (Hagler et al., 2009; Power et al., 2014; Siegel et al., 2014).”
Data Quality Influences Observed Links Between Functional Connectivity and Behavior (2017, methodology): “(frame-wise displacement; FD) has long been recognized as a source of artifact in task-based magnetic resonance imaging (Friston et al. 1996; Siegel et al. 2014).”
Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses (2017, methodology): “All other networks show no group differences in amplitude.”
Methods for cleaning the BOLD fMRI signal (2016, unknown): “Censoring is also known as scrubbing (Carp, 2013; Jones et al., 2010; Kennedy and Courchesne, 2008; Power et al., 2012, 2013, 2014; Siegel et al., 2014), and is equivalent to adding scan nulling regressors in the model (Lemieux et al., 2007; Satterthwaite et al., 2013; Yan et al., 2013a).”