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

Abstract

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.

Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement

“…For instance, one of the first EEG-fMRI studies correlating continuous alpha power with BOLD changes excluded all scans with motion exceeding a threshold of one standard deviation above the mean (Goldman et al, 2002). A similar approach of censoring motion scans has been demonstrated as a useful approach in resting state fMRI studies and event related fMRI designs (Siegel et al, 2014). Our results suggest this is not stringent enough when considering EEG-BOLD signal correlations when motion may be correlated with the task due to anything from voluntary movements to a change in breathing.…”

Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion

“…Substantial emphasis has been placed on characterizing how motion‐induced artifacts affect echo‐planar imaging (EPI): both in functional MRI [fMRI; Power et al, 2014; Satterthwaite et al, 2012; Siegel et al, 2014; Van Dijk et al, 2012; Zeng et al, 2014] and diffusion weighted imaging [DWI; Koldewyn et al, 2014; Thomas et al, 2014; Yendiki et al, 2013]. There has been less focus on characterizing how spurious motion‐related biases impact high‐resolution T1‐weighted (T1w) images.…”

Tourette Syndrome research highlights 2014

“…Even with these relatively stringent requirements, frame-by-frame motion censoring excluded an additional 15–20% of the data. However, this approach bought cleaner signal; motion censoring performed better than all forms of motion regression 23 .…”