A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics

Abstract

Functional connectomics is one of the most rapidly expanding areas of neuroimaging research. Yet, concerns remain regarding the use of resting-state fMRI (R-fMRI) to characterize inter-individual variation in the functional connectome. In particular, recent findings that “micro” head movements can introduce artifactual inter-individual and group-related differences in R-fMRI metrics have raised concerns. Here, we first build on prior demonstrations of regional variation in the magnitude of framewise displacements associated with a given head movement, by providing a comprehensive voxel-based examination of the impact of motion on the BOLD signal (i.e., motion-BOLD relationships). Positive motion-BOLD relationships were detected in primary and supplementary motor areas, particularly in low motion datasets. Negative motion-BOLD relationships were most prominent in prefrontal regions, and expanded throughout the brain in high motion datasets (e.g., children). Scrubbing of volumes with FD > 0.2 effectively removed negative but not positive correlations; these findings suggest that positive relationships may reflect neural origins of motion while negative relationships are likely to originate from motion artifact. We also examined the ability of motion correction strategies to eliminate artifactual differences related to motion among individuals and between groups for a broad array of voxel-wise R-fMRI metrics. Residual relationships between motion and the examined R-fMRI metrics remained for all correction approaches, underscoring the need to covary motion effects at the group-level. Notably, global signal regression reduced relationships between motion and inter-individual differences in correlation-based R-fMRI metrics; Z-standardization (mean-centering and variance normalization) of subject-level maps for R-fMRI metrics prior to group-level analyses demonstrated similar advantages. Finally, our test-retest (TRT) analyses revealed significant motion effects on TRT reliability for R-fMRI metrics. Generally, motion compromised reliability of R-fMRI metrics, with the exception of those based on frequency characteristics – particularly, amplitude of low frequency fluctuations (ALFF). The implications of our findings for decision-making regarding the assessment and correction of motion are discussed, as are insights into potential differences among volume-based metrics of motion.

Reliability comparison of spontaneous brain activities between BOLD and CBF contrasts in eyes-open and eyes-closed resting states

“…After denoising, test-retest reliability of BOLD-ALFF decreased (Table 3 and Fig. 8), which was consistent with prior work based on BOLD emphasizing the susceptibility of ALFF to physiological noises Zuo et al, 2010a) as well as previous findings that motion regression decreased reliability of ALFF (Yan et al, 2013a). Similar to our findings of ALFF, test-retest reliability of BOLD-FC was also decreased with denoising (Table 3 and Fig.…”

Intranasal Oxytocin Normalizes Amygdala Functional Connectivity in Posttraumatic Stress Disorder

“…Additionally, we calculated the mean frame-wise displacement (FD) for each individual, for each session, using the Data Processing Assistant Resting-State fMRI toolbox (Chao-Gan and Yu-Feng, 2010). Mean FD values were added as a covariate to all second-level models, to account for variance in relative displacement between scans (Yan et al, 2013).…”

Bridging the Gap between the Human and Macaque Connectome: A Quantitative Comparison of Global Interspecies Structure-Function Relationships and Network Topology

“…Several steps were also taken to prepare the data for connectivity analyses (Fox et al, 2005). These steps included spatial smoothing (6 mm full width at half maximum smoothing for the human group and 3 mm full width at half maximum for the macaque), regression of 24 motion parameters obtained by rigid body head motion correction (Satterthwaite et al, 2013;Yan et al, 2013;Power et al, 2014), regression of nuisance signals (ventricular, white matter, and whole-brain signal) and their first order derivatives, and temporal band-pass filtering (0.009 Hz Ͻ f Ͻ 0.08 Hz). For macaques, ventricular and white matter masks were based on their corresponding regions in the INIA19 atlas (Rohlfing et al, 2012); human masks were applied as per Fox et al (2005.…”