Format

Send to

Choose Destination
Neuroimage. 2019 Dec 11;209:116433. doi: 10.1016/j.neuroimage.2019.116433. [Epub ahead of print]

Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI.

Author information

1
Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland. Electronic address: thomas.bolton@epfl.ch.
2
Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland; Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore.
3
Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland.
4
Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland; Developmental Imaging and Psychopathology Laboratory, Office Médico-Pédagogique, Department of Psychiatry, University of Geneva (UNIGE), Geneva, Switzerland.
5
Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore.
6
Basque Center on Cognition, Brain and Language, San Sebastian, Spain.
7
Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.

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.

KEYWORDS:

Behaviour; Motion artefacts; Partial least squares analysis; Resting-state fMRI; Spatio-temporal motion

Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center