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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
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“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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Emotions amplify speaker–listener neural alignment
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Functional Connectivity of the Striatum in Schizophrenia and Psychotic Bipolar Disorder
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“Images were bandpass filtered to retain frequencies between 0.008 and 0.1 Hz, and volumes exceeding 0.25 mm frame-wise displacement or 1.55 standardized DVARS (Power et al., 2014; Nichols, 2017) were censored.”
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Reconfiguration of Cortical Networks in MDD Uncovered by Multiscale Community Detection with fMRI
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Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space
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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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