Neurobiological basis of head motion in brain imaging

Abstract

Individual differences in brain metrics, especially connectivity measured with functional MRI, can correlate with differences in motion during data collection. The assumption has been that motion causes artifactual differences in brain connectivity that must and can be corrected. Here we propose that differences in brain connectivity can also represent a neurobiological trait that predisposes to differences in motion. We support this possibility with an analysis of intra-versus intersubject differences in connectivity comparing high-to low-motion subgroups. Intersubject analysis identified a correlate of head motion consisting of reduced distant functional connectivity primarily in the default network in individuals with high head motion. Similar connectivity differences were not found in analysis of intrasubject data. Instead, this correlate of head motion was a stable property in individuals across time. These findings suggest that motion-associated differences in brain connectivity cannot fully be attributed to motion artifacts but rather also reflect individual variability in functional organization.resting-state fMRI | motion correction | connectome H ead motion has long been known as a confounding factor in brain imaging including MRI (1, 2), PET (3, 4), singlephoton emission computerized tomography (5, 6), and near infrared spectroscopy (7), but has raised particular concerns recently following the growing prominence of resting-state functional connectivity MRI. Studies found that head motion can vary considerably across individuals and often demonstrates systematic group effects when contrasting different populations, especially in neurodevelopmental (8-10), aging (11, 12), and neuropsychiatric studies (13). Some recent work reported that head motion augmented local coupling of the blood oxygenation level-dependent (BOLD) signal but reduced distant coupling (14-16). These correlations between connectivity measures and head motion have raised appropriate concern that previously observed differences in connectivity are due to artifact induced by differences in head motion. For example, developmental changes in functional connectivity might also be predicted by head motion (15). The assumption has been that head motion causes distorted connectivity measurements that must be addressed through improved motion-correction techniques (15). However, this correlation could be driven by causal factors in the other direction. Specifically, individual differences in brain connectivity could determine how well a subject can lie still in the scanner. This is not unreasonable as individual differences in structural connectivity can predict trait anxiety and can be related to attention deficits (17, 18) and individual differences in resting-state functional MRI (fMRI) measures may relate to various behavioral differences, including impulsivity (19)(20)(21)(22). In such a scenario, certain intersubject differences in connectivity measures could persist even after the most rigorous motion correction, as has been suggested ...

Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion

“…For instance, variation in the tendency to move during MRI might reflect a broader phenotype that is triggered in part by certain patterns of cortical thinning. One recent study reported that group‐based differences in the resting‐state correlations of the default mode network might distinguish high‐motion scans of high‐moving individuals from high‐motion scans of low‐moving individuals [Zeng et al, 2014]. While we cannot rule out a comparable scenario here, given that excessive motion has been shown to systematically bias structural measures in within‐subject longitudinal study designs [Reuter et al, 2015], we suspect that the reduced morphometric estimates found in the structural images flagged by the present report are a consequence of excessive movement during T1w scans rather than a cause.…”

“…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.…”

Children with well controlled epilepsy possess different spatio-temporal patterns of causal network connectivity during a visual working memory task

“…However, it is currently appeared to be a more complicated issue than was previously thought. As shown in the recent study of Zeng and colleagues (Zeng et al 2014), there is a bidirectional relationship between head motion and brain connectivity. Namely, the connectivity in a distributed set of cortical regions, primarily in the default network, was stronger in subjects with lower head motion than in those with high head motion.…”

Functional connectivity changes in the entorhinal cortex of taxi drivers

“…The preprocessing of the resting‐state fMRI data was carried out using the statistical parametric mapping software package (SPM8, http://www.fil.ion.ucl.ac.uk/spm), as described in our previous studies (Zeng, Shen, Liu, & Hu, 2014a; Zeng et al., 2014b, 2018). The first 10 volumes of each scan were discarded to avoid the magnetic saturation effect.…”