The Influence of Head Motion on Intrinsic Functional Connectivity MRI

By K. V. Dijk, Mert Rory Sabuncu, Randy L. Buckner. NeuroImage, 2011

2,419 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/df6356460cfc0c293eb63e06c653ae777c0ce272

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Predicting brain-age from multimodal imaging data captures cognitive impairment (2016, positive): “However, since head motion has withinsubject stability (Van Dijk et al., 2012), we took head motion estimates based on functional scans as a proxy for head motion in structural scans, as also done by Alexander-Bloch et al. (2016).”
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion (2011, neutral): “Some similar effects related to motion in functional connectivity data have recently been described (Van Dijk et al., 2011).”
Neuroimaging of the Philadelphia Neurodevelopmental Cohort (2013, neutral): “Earlier, we (Satterthwaite et al., 2012b) and others (Power et al., 2011; Van Dijk et al., 2011) have demonstrated that subject motion is of particular concern for rsfc-MRI data.”
Dynamic functional connectivity of the default mode network tracks daydreaming (2014, methodology): “…2012; Van Dijk et al., 2012), so here we used partial correlation to test whether mean motion, calculated as mean absolute displacement of each brain volume compared to the previously acquired volume (Van Dijk et al., 2012), affected any identified significant relationships between DDF and FC/FCV.”
Resting state fMRI: A review of methods and clinical applications (2012, neutral): “correction for head motion can result in spurious correlations in RS-fMRI analysis.(30-32) Although head motion is less concerning”
A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (2013, methodology): “Several potential solutions to the challenges of overcoming motion in R-fMRI data have been proposed (e.g., Van Dijk et al., 2012; Fair et al., 2012).”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, methodology): “A recurring issue in the field of R-fMRI is head motion, which has complex relationships with age and various R-fMRI metrics at the group level (Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012).”
Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses (2017, background): “Many studies have shown that motion is a major source of variability in functional connectivity studies that can lead to erroneous results when comparing groups of subjects (Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012).”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, background): “Several studies suggest that such neurobiological correlates exist (Van Dijk et al., 2012; Couvy-Duchesne et al., 2014; Kong et al., 2014; Pujol et al., 2014; Zeng et al., 2014).”
Impact of In-Scanner Head Motion on Multiple Measures of Functional Connectivity: Relevance for Studies of Neurodevelopment in Youth (2012, methodology): “Van Dijk et al. (2011) compared local versus distant connectivity using a binary threshold of 12mm; our results suggest that motion transitions from causing increased connectivity to decreased connectivity at a much greater distance (96mm).”