Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neuroimage.2019.116433
Title: Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI
Authors: Bolton, T.A.W.
Kebets, V. 
Glerean, E.
Zöller, D.
Li, J. 
Yeo, B.T.T. 
Caballero-Gaudes, C.
Van De Ville, D.
Keywords: Behaviour
Motion artefacts
Partial least squares analysis
Resting-state fMRI
Spatio-temporal motion
Issue Date: 2020
Publisher: Academic Press Inc.
Citation: Bolton, T.A.W., Kebets, V., Glerean, E., Zöller, D., Li, J., Yeo, B.T.T., Caballero-Gaudes, C., Van De Ville, D. (2020). Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI. NeuroImage 209 : 116433. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neuroimage.2019.116433
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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. © 2019
Source Title: NeuroImage
URI: https://scholarbank.nus.edu.sg/handle/10635/198164
ISSN: 1053-8119
DOI: 10.1016/j.neuroimage.2019.116433
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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