Head motion and correction methods in resting-state functional MRI
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Resting-state functional magnetic resonance imaging (RS-fMRI) is used to investigate brain functional connectivity at rest. However, noise from human physiological motion is an unresolved problem associated with this technique. Following the unexpected previous result that group differences in head motion between control and patient groups caused group differences in the resting-state network with RS-fMRI, we reviewed the effects of human physiological noise caused by subject motion, especially motion of the head, on functional connectivity at rest detected with RS-fMRI. The aim of the present study was to review head motion artifact with RS-fMRI, individual and patient population differences in head motion, and correction methods for head motion artifact with RS-fMRI. Numerous reports have described new methods [e.g., scrubbing, regional displacement interaction (RDI)] for motion correction on RS-fMRI, many of which have been successful in reducing this negative influence. However, the influence of head motion could not be entirely excluded by any of these published techniques. Therefore, in performing RS-fMRI studies, head motion of the participants should be quantified with measurement technique (e.g., framewise displacement). Development of a more effective correction method would improve the accuracy of RS-fMRI analysis.
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There is strong consensus about the importance of preventing head motion during acquisition and rigorously quantifying motion effects in study data.
“there is strong consensus about the importance of preventing head motion during acquisition and rigorously quantifying motion effects in study data (Goto et al., 2016; Power et al., 2015; Siegel et al., 2016).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Goto et al., 2016
Some of the techniques currently being used to remove motion artifacts are non-optimal.
“Some of the techniques currently being used to remove these artifacts are non-optimal (Burgess et al., 2016; Goto et al., 2016).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Goto et al., 2016
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- Authors
- GOTO, M., ABE, O., MIYATI, T., YAMASUE, H., GOMI, T., & TAKEDA, T.
- Affiliations
- Kitasato University (JP)
- Nihon University (JP)
- Kanazawa University (JP)
- The University of Tokyo (JP)
- Corresponding author
- GOTO, Masami
- Venue
- Magnetic Resonance in Medical Sciences
- Published
- 2016
- Volume
- 15
- Issue
- 2
- Pages
- 178-186
- ISSN
- 1347-3182 (ISSN-L)
- 1880-2206
- PMC
- PMC5600054
- OpenAlex
- W2266374336
- Cited by
- 82
- References
- 54
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 14 · 2023: 8 · 2022: 8 · 2021: 6 · 2020: 7 · 2019: 11 · 2018: 6 · 2017: 6 · 2016: 4
- FWCI
- 0.53
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Magnetic Resonance in Medical Sciences (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- head movement, resting-state fMRI, motion correction, functional connectivity, physiological noise, scrubbing, resting-state networks
- MeSH terms
- Brain Mapping, Head, Humans, Magnetic Resonance Imaging, Artifacts, Head Movements
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