The influence of head motion on intrinsic functional connectivity MRI

Abstract

Functional connectivity MRI (fcMRI) has been widely applied to explore group and individual differences. A confounding factor is head motion. Children move more than adults, older adults more than younger adults, and patients more than controls. Head motion varies considerably among individuals within the same population. Here we explored the influence of head motion on fcMRI estimates. Mean head displacement, maximum head displacement, the number of micro movements (> 0.1 mm), and head rotation were estimated in 1000 healthy, young adult subjects each scanned for two resting-state runs on matched 3T scanners. The majority of fcMRI variation across subjects was not linked to estimated head motion. However, head motion had significant, systematic effects on fcMRI network measures. Head motion was associated with decreased functional coupling in the default and frontoparietal control networks – two networks characterized by coupling among distributed regions of association cortex. Other network measures increased with motion including estimates of local functional coupling and coupling between left and right motor regions – a region pair sometimes used as a control in studies to establish specificity. Comparisons between groups of individuals with subtly different levels of head motion yielded difference maps that could be mistaken for neuronal effects in other contexts. These effects are important to consider when interpreting variation between groups and across individuals.

The emergence of functional architecture during early brain development

“…Head motion introduces spurious, yet systematic noise into rs-fMRI data, reducing long-range connectivity between distant brain regions (primarily along anterior-posterior and vertical axes) and increasing the strength of functional correlations between nearby voxels in the brain (predominantly left-right connectivity) (Power et al, 2012;van Dijk et al, 2012).…”

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

“…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). A standardized DVARS of 1.8 corresponds to the median plus 1.5 times interquartile range of the standardized DVARS data across all frames and runs.…”

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

“…However, physiological pulsations and motion (Van Dijk et al, 2012; showed moderate reliability, which might inflate the reliability of ALFF and FC of BOLD data. Our third goal is to compare the reliability of resting-state activity with and without data denoising during preprocessing.…”

“…The BOLD-ALFF maps were standardized by normalization with the mean BOLD-ALFF of the entire brain voxels (Zou et al, 2008;Zuo et al, 2010a). Prior to FC analysis, the residual BOLD time series were band-pass filtered (0.01-0.083 Hz) and a 10-mm diameter spherical seed located in the posterior cingulate cortex (PCC) was selected (Van Dijk et al, 2012;Yan et al, 2013b). We calculated the Pearson's correlation coefficient between the seed time series and every other voxel in the brain and transformed the correlation coefficient values into z values using Fisher's r-to-z transformation.…”