Motion correction and the use of motion covariates in multiple‐subject fMRI analysis

Abstract

The impact of using motion estimates as covariates of no interest was examined in general linear modeling (GLM) of both block design and rapid event-related functional magnetic resonance imaging (fMRI) data. The purpose of motion correction is to identify and eliminate artifacts caused by task-correlated motion while maximizing sensitivity to true activations. To optimize this process, a combination of motion correction approaches was applied to data from 33 subjects performing both a block-design and an event-related fMRI experiment, including analysis: (1) without motion correction; (2) with motion correction alone; (3) with motion-corrected data and motion covariates included in the GLM; and (4) with non-motion-corrected data and motion covariates included in the GLM. Inclusion of covariates was found to be generally useful for increasing the sensitivity of GLM results in the analysis of event-related data. When motion parameters were included in the GLM for event-related data, it made little difference if motion correction was actually applied to the data. For the block design, inclusion of motion covariates had a deleterious impact on GLM sensitivity when even moderate correlation existed between motion and the experimental design. Based on these results, we present a general strategy for block designs, event-related designs, and hybrid designs to identify and eliminate probable motion artifacts while maximizing sensitivity to true activations.

Strategy‐based reasoning training modulates cortical thickness and resting‐state functional connectivity in adults with chronic traumatic brain injury

“…At the motion correction stage, the six rigid body motion profiles were obtained for the linear regression. In the linear regression, the rsfMRI time series were third‐order detrended, and several sources of signal fluctuation unlikely to be of neuronal origin were regressed out as nuisance variables: (1) six parameters for rigid body head motion acquired from the motion correction (Johnstone et al., 2006), (2) the signal averaged over the lateral ventricles (Fox et al., 2005), (3) the signal averaged over a region centered in the deep cerebral white matter (Fox et al., 2005), and (4) the first temporal derivatives of the aforementioned parameters. After the linear regression, motion ‘scrubbing’ (Power, Barnes, Snyder, Schlaggar, & Petersen, 2012) was performed with a frame‐wise displacement (FD) of 0.5 mm and a standardized DVARS (http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols/scripts/fsl/DVARS.sh) of 1.8 to prevent potential motion artifacts (van Dijk, Sabuncu, & Buckner, 2012; Power et al., 2012; Satterthwaite et al., 2012).…”

The neural substrates for atypical planning and execution of word production in stuttering

“…After the estimated motion parameters were visually inspected, participants with extreme motion (N4 mm translation, 5°rotation) were eliminated. These values were based on their match with the voxel size with consideration also for expectations of the spatial resolution of BOLD responses and the inherent variability between participants in brain anatomy (Johnstone et al, 2006). Two non-stuttering and three stuttering participants were discarded during this process.…”

Prospective motion correction improves the sensitivity of fMRI pattern decoding

“…Historically, the use of RMC has been more widespread due to convenience and limited ability to acquire time‐linked motion data in the MRI scanner. In RMC, rigid body translations and rotations are applied to each volume postscan to align all acquired volumes to the same scan (Ashburner & Friston, 2003; Johnstone et al, 2006). Although this works well for slow motion between acquisitions, RMC is unable to correct for spin history effects and k‐space distortion due to intravolume motion (Goebel, Esposito, & Formisano, 2006; Penny, Friston, Ashburner, Kiebel, & Nichols, 2011).…”