Abstract
The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-of-the-art guidelines advise to scrub out excessively corrupted frames as assessed by a composite framewise displacement (FD) score, to regress out models of nuisance variables, and to include average FD as a covariate in group-level analyses.Here, we studied individual motion time courses at time points typically retained in fMRI analyses. We observed that even in this set of putatively clean time points, motion exhibited a very clear spatio-temporal structure, so that we could distinguish subjects into separate groups of movers with varying characteristics.Then, we showed that this spatio-temporal motion cartography tightly relates to a broad array of anthropometric and cognitive factors. Convergent results were obtained from two different analytical perspectives: univariate assessment of behavioural differences across mover subgroups unraveled defining markers, while subsequent multivariate analysis broadened the range of involved factors and clarified that multiple motion/behaviour modes of covariance overlap in the data.Our results demonstrate that even the smaller episodes of motion typically retained in fMRI analyses carry structured, behaviourally relevant information. They call for further examinations of possible biases in current regression-based motion correction strategies.
Highlights
Resting-state functional magnetic resonance imaging (RS fMRI) has been a vibrant and flourishing research topic
Since time points linked to excessive displacement are typically removed from RS fMRI analyses, we only considered non-scrubbed motion instances according to Power’s framewise displacement (FD) definition (Power et al, 2012) at a threshold of 0.3 mm
We considered the matrix of behavioural scores on the one hand, and the matrix of spatio-temporal motion features on the other
Summary
Resting-state functional magnetic resonance imaging (RS fMRI) has been a vibrant and flourishing research topic. It was discovered that even short-lived episodes of motion might greatly bias FC analyses (Power et al, 2012; Van Dijk et al, 2012; Satterthwaite et al, 2012), and lead to erroneous interpretations in clinical or developmental studies (Deen and Pelphrey, 2012; Makowski et al, 2019). These observations of motion-biased results further fueled the development of robust post-processing strategies to free fMRI time courses from confounding motion effects
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