Using Edge Voxel Information to Improve Motion Regression for rs-fMRI Connectivity Studies

Abstract

Recent fMRI studies have outlined the critical impact of in-scanner head motion, particularly on estimates of functional connectivity. Common strategies to reduce the influence of motion include realignment as well as the inclusion of nuisance regressors, such as the 6 realignment parameters, their first derivatives, time-shifted versions of the realignment parameters, and the squared parameters. However, these regressors have limited success at noise reduction. We hypothesized that using nuisance regressors consisting of the principal components (PCs) of edge voxel time series would be better able to capture slice-specific and nonlinear signal changes, thus explaining more variance, improving data quality (i.e., lower DVARS and temporal SNR), and reducing the effect of motion on default-mode network connectivity. Functional MRI data from 22 healthy adult subjects were preprocessed using typical motion regression approaches as well as nuisance regression derived from edge voxel time courses. Results were evaluated in the presence and absence of both global signal regression and motion censoring. Nuisance regressors derived from signal intensity time courses at the edge of the brain significantly improved motion correction compared to using only the realignment parameters and their derivatives. Of the models tested, only the edge voxel regression models were able to eliminate significant differences in default-mode network connectivity between high-and low-motion subjects regardless of the use of global signal regression or censoring.

Resting-state test–retest reliability of a priori defined canonical networks over different preprocessing steps

“…Probably the best-investigated source of spurious variance in RS time-series is head motion (Van Dijk et al 2012; Satterthwaite et al 2013; Griffanti et al 2014; Patriat et al 2015; Power et al 2015; Wong et al 2016). Satterthwaite et al, (2013), using a 24-parameter motion regression approach, found that the first derivative as well as the quadratic effects of both realignment parameters and derivatives could account for these effects.…”

“…Motion artifacts have been shown to produce spurious correlations in a systematic way (Van Dijk et al, 2012; Power et al, 2012; Satterthwaite et al, 2013), implying that the removal of motion related artifacts is a prerequisite for further analysis. Various approaches have been proposed for dealing with noise effects post-hoc, i.e., after the data have been acquired (Behzadi et al 2007; Fox et al 2009; Murphy et al 2009; Chai et al 2012; Griffanti et al 2014; Patriat et al 2015; Power et al 2015; Soltysik et al 2015; Wong et al 2016). Besides motion-related artifacts, one particular aspect that has received a lot of attention is the use of nuisance regressors reflecting global signals, either derived from the whole brain or from specific tissue types such as white-matter or cerebrospinal fluid.…”

Methods for cleaning the BOLD fMRI signal

“…Considering the average signals of white matter (WM) and ventricular cerebrospinal fluid (CSF) tissues as nuisance regressors has become fairly common in resting state and task-based fMRI approaches (Anderson et al, 2011; Hallquist et al, 2013; Jo et al, 2010, 2013; Power et al, 2012; Weissenbacher et al, 2009; Yan et al, 2013a). More elaborated approaches have also considered nuisance regressors defined from soft tissues (Anderson et al, 2011) or the edges of the brain (Birn et al, 1999; Patriat et al, 2015). In some cases, the temporal derivatives of each tissue-based regressor are also included (Fox et al, 2005; Power et al, 2014; Satterthwaite et al, 2013) or are shifted to maximize the impact of denoising (Anderson et al, 2011).…”

“…Nuisance regressors can also be determined by the PCs of voxel time series located on the outer edges of the brain (Birn et al, 1999; Patriat et al, 2015) in order to account for motion-related signal changes, respiration-induced fluctuations and system artefacts that are typically well represented in these voxels. Importantly, it is recommendable that the mask of the brain’s edge voxels is defined considering both functional and anatomical within-brain masks in order to minimize the possibility of removing any potential true neuronal activity-related BOLD signal of interest in voxels affected by susceptibility artefacts and signal dropouts in the functional image (Patriat et al, 2015).…”

“…This changes the spatial distribution of the local magnetic susceptibility gradients, and exacerbates distortions and signal dropouts in locations prone to these effects (Jiang et al, 1995). In general, the effect of head motion is predominantly seen in voxels at the edges of the brain, although it can also be particularly severe in voxels lying close to a tissue boundary due to the differences in proton density and relaxation parameters across brain tissues (Jo et al, 2010; Lemieux et al, 2007; Patriat et al, 2015). …”