The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Methods to detect, characterize, and remove motion artifact in resting state fMRI
Open accessAbstract
Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.
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“DVAR values, which are based in BOLD signal intensity, differ across datasets for a variety of reasons (e.g. blurring kernel size, frequency filter or data acquisition characteristics) (Power et al., 2014), effectively eliminating the need for a standard threshold.”
From Developmental sex differences in resting state functional connectivity of amygdala sub-regions (2015) · cites this paper as Power et al., 2014
In the citing paper
“we tested the robustness of our findings by adjusting the functional connectivity processing steps and employing a different method of motion censoring in a supplementary analysis (see Supplementary Methods), as suggested by prior reports (Hallquist et al., 2013, Power et al., 2014).”
From Developmental sex differences in resting state functional connectivity of amygdala sub-regions (2015) · cites this paper as Power et al., 2014
In the citing paper
“Due to concerns associated with the alteration of the temporal structure of the images, the data used in the main analysis were not ‘scrubbed’ (Power et al., 2014), however we did compare the results of our experiment with scrubbed data (missing values corrected using interpolation) and found strong correspondence between the outcome measures of the two studies (see Validation).”
From The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2016) · cites this paper as Power et al., 2014
In the citing paper
“Following standard Statistical Parametric Mapping (SPM12; https://www.fil.ion.ucl.ac.uk/spm12 ) pre-processing steps, additional motion correction was applied in the form of censoring, i.e. identifying outliers based on framewise displacement and regressing them from the fMRI timeseries ( Power et al., 2012 , Power et al., 2014 ) (see Supplementary Methods), to compensate for using fairly lenient”
From Self-blame-selective hyper-connectivity between anterior temporal and subgenual cortices predicts prognosis in major depressive disorder (2023) · cites this paper as Power et al., 2014
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Citations
Cites
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
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
Self-citationThe organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
Least-squares frequency analysis of unequally spaced data
Lomb, N. (1976) · Astrophysics and Space Science · doi:10.1007/bf00648343 · Cited by 5,724
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150
Functional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124
Self-citationMovement‐Related effects in fMRI time‐series
Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S. J., & Turner, R. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910350312 · Cited by 3,774
Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.036 · Cited by 2,387
The influence of head motion on intrinsic functional connectivity MRI
Van Dijk, K. R. A., Sabuncu, M. R., & Buckner, R. L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.044 · Cited by 2,344
Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., Eickhoff, S. B., Hakonarson, H. H., Gur, R. C., Gur, R. E., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.052 · Cited by 1,982
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fMRIPrep: A robust preprocessing pipeline for functional MRI
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., et al. (2019) · Nature Methods · doi:10.1038/s41592-018-0235-4 · Cited by 3,905
ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data
Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.064 · Cited by 1,684
Intrinsic and task-evoked network architectures of the human brain
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.014 · Cited by 1,564
Self-citationGeneration and evaluation of a cortical area parcellation from resting-state correlations
Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhu239 · Cited by 1,463
Self-citationPrecision Functional Mapping of Individual Human Brains
Gordon, E. M., Laumann, T. O., Gilmore, A. W., Newbold, D. J., Greene, D. J., Berg, J. J., Ortega, M., Hoyt-Drazen, C., Gratton, C., Sun, H., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.07.011 · Cited by 1,335
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Metadata
- Authors
- Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Power, Jonathan D.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 84
- Pages
- 320-341
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3849338
- OpenAlex
- W1973776237
- Cited by
- 3,748
- References
- 38
- Citations by year
- 2026: 347 · 2025: 458 · 2024: 444 · 2023: 367 · 2022: 368 · 2021: 374 · 2020: 328 · 2019: 284 · 2018: 249 · 2017: 186 · 2016: 160 · 2015: 114 · 2014: 60
- FWCI
- 61.98
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (F30 MH940322, R21NS061144, 5R01 HD057076-03-S1, R01HD057076, U54MH091657, P30 NS048056)
- McDonnell Foundation Collaborative Action Award
- Simons Foundation Award (95177)
- Intellectual and Developmental Disabilities Research Center (NIH/NICHD P30 HD062171)
- 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, motion artifacts, resting-state functional connectivity, global signal regression, motion correction, signal intensity changes, data quality metrics
- MeSH terms
- Adolescent, Adult, Algorithms, Brain, Brain Mapping, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Motion, Pattern Recognition, Automated, Rest, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results, Artifacts, Head Movements, Young Adult
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