The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Movement‐Related effects in fMRI time‐series
Abstract
This paper concerns the spatial and intensity transformations that are required to adjust for the confounding effects of subject movement during functional MRI (fMRI) activation studies. An approach is presented that models, and removes, movement‐related artifacts from fMRI time‐series. This approach is predicated on the observation that movement‐related effects are extant even after perfect realignment. Movement‐related effects can be divided into those that are a function of position of the object in the frame of reference of the scanner and those that are due to movement in previous scans. This second component depends on the history of excitation experienced by spins in a small volume and consequent differences in local saturation. The spin excitation history thus will itself be a function of previous positions, suggesting an autoregression‐moving average model for the effects of previous displacements on the current signal. A model is described as well as the adjustments for movement‐related components that ensue. The empirical analyses suggest that (in extreme situations) over 90% of fMRI signal can be attributed to movement, and that this artifactual component can be successfully removed.
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“Subjects with a mean/maximum frame-wise displacement29, 69 exceeding 0.6/3.0 mm were excluded.”
From Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity (2017) · cites this paper as [69]
Citations
Cites
Spatial registration and normalization of images
Friston, K. J., Ashburner, J., Frith, C., Poline, J.-B., Heather, J. D., & Frackowiak, R. S. J. (1995) · Human Brain Mapping · doi:10.1002/hbm.460030303 · Cited by 3,744
Self-citationFunctional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets
Friston, K. J., Frith, C., Liddle, P. F., & Frackowiak, R. S. J. (1993) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.1993.4 · Cited by 1,997
Self-citationAnalysis of functional MRI time‐series
Friston, K. J., Jezzard, P., & Turner, R. (1994) · Human Brain Mapping · doi:10.1002/hbm.460010207 · Cited by 1,835
Self-citationArtifacts due to stimulus correlated motion in functional imaging of the brain
Hajnal, J. V., Myers, R., Oatridge, A., Schwieso, J. E., YOUNG, I. R., & Bydder, G. M. (1994) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910310307 · Cited by 486
Nuclear Magnetic Resonance Imaging in Medicine and Biology
Morris, P. G. (1986) · Medical Entomology and Zoology · Cited by 277
Nuclear magnetic resonance imaging in medicine and biology
Baker, R. R. (1986) · Trends in Biochemical Sciences · doi:10.1016/0968-0004(86)90163-5 · Cited by 275
Some computational and statistical tools for paired comparisons of digital images
LANGE, N. D. (1994) · Statistical Methods in Medical Research · doi:10.1177/096228029400300103 · Cited by 11
Cited by
Sorted by citations
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
Methods to detect, characterize, and remove motion artifact in resting state fMRI
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.048 · Cited by 3,748
Gusnard, D. A., Akbudak, E., Shulman, G. L., & Raichle, M. E. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.071043098 · Cited by 2,997
Sridharan, D., Levitin, D. J., & Menon, V. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0800005105 · Cited by 2,892
Metadata
- Authors
- Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S. J., & Turner, R.
- Affiliations
- UCL Queen Square Institute of Neurology (GB)
- National Hospital for Neurology and Neurosurgery (GB)
- Corresponding author
- Friston, Karl J.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 1996-03
- Volume
- 35
- Issue
- 3
- Pages
- 346-355
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2005238835
- Cited by
- 3,774
- References
- 7
- Citations by year
- 2026: 216 · 2025: 313 · 2024: 286 · 2023: 287 · 2022: 328 · 2021: 294 · 2020: 286 · 2019: 228 · 2018: 194 · 2017: 188 · 2016: 143 · 2015: 110 · 2014: 75 · 2013: 70 · 2012: 55
- FWCI
- 40.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Magnetic Resonance in Medicine (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- subject motion, fMRI time series, motion correction, realignment, local saturation, ARMA models, artifact removal
- MeSH terms
- Brain, Humans, Magnetic Resonance Imaging, Male, Movement, Photic Stimulation, Psychomotor Performance, Artifacts
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