Methods to detect, characterize, and remove motion artifact in resting state fMRI

Abstract

Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10 seconds after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.

Differential patterns of age‐related cortical and subcortical functional connectivity in 6‐to‐10 year old children: A connectome‐wide association study

“…Functional connectivity studies, and particularly those involving pediatric populations, are frequently impacted by motion artifacts, which can erroneously increase long‐range connectivity and decrease short‐range connectivity (Fornito, Bullmore, & Zalesky, 2017; Di Martino et al., 2014; Power et al., 2014) Given that younger children tend to move more than older children, this can have an impact on developmental studies. In this study, we corrected for motion using the “scrubbing” method (Power et al., 2012, 2013), where corrupted volumes are removed.…”

“…In this study, we corrected for motion using the “scrubbing” method (Power et al., 2012, 2013), where corrupted volumes are removed. While this method significantly reduces the effect of motion (Power et al., 2014), it is but one of many strategies (Di Martino et al., 2014). Among the drawbacks of the scrubbing method are the loss of data within subjects, and the unequal degrees of freedom across subjects (Power et al., 2014).…”

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

“…To account for the differences in total number of frames (subsequently different degrees of freedom for correlation coefficients) after motion scrubbing across rsfMRI scans, all remaining frames were trimmed to the minimum length (121 frames; 242 s) across all rsfMRI scans as suggested in Power et al. (2014). …”

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.…”