Agito ergo sum: correlates of spatiotemporal motion characteristics during fMRI

TL;DR

This study reveals that even minimal motion during fMRI scans exhibits structured spatiotemporal patterns that relate to individual behavioral and clinical factors, challenging current correction methods.

Contribution

It uncovers the structured nature of motion during fMRI and links it to behavioral and anthropometric factors, highlighting potential biases in existing correction strategies.

Findings

Motion exhibits clear spatiotemporal structure at retained time points.

Motion patterns are associated with behavioral, anthropometric, and clinical factors.

Multiple motion/behavior modes of covariance overlap in the data.

Abstract

The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-ofthe-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 spatiotemporal structure, so that we could distinguish subjects into four groups of movers with varying characteristics Then, we showed that this spatiotemporal motion cartography tightly relates to a broad array of anthropometric, behavioral and clinical factors. Convergent results were obtained from two different analytical perspectives: univariate assessment of behavioral differences across mover subgroups unraveled defining markers, while subsequent multivariate analysis broadened the range of involved factors and clarified that multiple motion/behavior modes of covariance overlap in the data. Our results demonstrate that even the smaller episodes of motion typically retained in fMRI analyses carry structured, behaviorally relevant information. They call for further examinations of possible biases in current regression-based motion correction strategies.