An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data

Abstract

Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed.

Dissociable relations between amygdala subregional networks and psychopathy trait dimensions in conduct‐disordered juvenile offenders

“…The maximum allowable mean displacement due to excessive head motion was set at 3 mm translation or 3º rotation in any direction. Additionally, to guard against the effects of in‐scanner micro‐motion on connectivity patterns we implemented motion‐censoring, also known as “scrubbing” [Power et al, 2012; Satterthwaite et al, 2013] (see Supporting Information for details).…”

Increased thalamic centrality and putamen–thalamic connectivity in patients with parkinsonian resting tremor

“…We quantified three head motion parameters that were used in previous studies (Plichta et al., 2012; Satterthwaite et al., 2013): the sum of the volume‐to‐volume translational excursions, the sum of the volume‐to‐volume rotational excursions, and the voxel‐level frame‐wise displacement. The first two measures calculate the sum of the root mean square of three translational and rotational motion vectors from the x , y , and z axes, respectively.…”

“…The third measure is a nonlinear combination of volume‐wise translations and rotations, reflecting the voxel‐specific distance compared to the previous image. Details of these measurements are described in previous literature (Plichta et al., 2012; Satterthwaite et al., 2013). For the purpose of quality control, we carefully checked several head motion parameters for each subject.…”

“…These raw time series were further corrected for white matter (WM) signal, cerebrospinal fluid (CSF) signal, and head motion nuisances. Because head motion is a potential detriment to graph analysis, which may induce spurious correlation estimates (Power, Barnes, Snyder, Schlaggar, & Petersen, 2012), we adopted a strict motion removal method, as reported in a previous study (Satterthwaite et al., 2013), to maximally reduce its influence on our results. Specifically, 24 head motion nuisances (i.e., 6 rigid‐body parameters generated from the realignment step, their first derivatives, and the squares of these 12 parameters) were calculated and regressed out from the time series.…”

Reprint of: Minimizing noise in pediatric task-based functional MRI; Adolescents with developmental disabilities and typical development

“…Further these nuisance regressors could be included in the design matrix either at the subject level, or at the group level to account for variance across subjects due to their motion (Beall and Lowe, 2014;Lund et al, 2005;Oakes et al, 2005). Some of these methods are discussed in detail in (Sattherthwaite et al, 2013), especially in the context of resting state connectivity where they are even more important (Fox et al, 2009;Sattherthwaite et al, 2013). However such motion parameter regression may also impact some of the task related signal when the motion is correlated with the experimental design, especially in block design tasks.…”