Evaluation of denoising strategies to address motion-correlated artifacts in resting-state functional magnetic resonance imaging data from the human connectome project
Open accessAbstract
Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Global signal regression is arguably the most effective motion-denoising technique currently available.
“The most contentious debate probably concerns the use of global signal regression (GSR) – arguably the most effective motion-denoising technique currently available (Burgess et al., 2016; Power et al., 2015; Yan et al., 2013).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Burgess 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 Burgess et al., 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
Self-citationThe WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651 · see citing works
Self-citationThe minimal preprocessing pipelines for the Human Connectome Project
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.127 · Cited by 5,253 · see citing works
A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.042 · Cited by 4,502 · see citing works
Movement‐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 · see citing works
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 · see citing works
Self-citationMurphy, 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 · see citing works
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 · see citing works
Satterthwaite, T., Elliott, M., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M., Eickhoff, S., Hakonarson, H., Gur, R., Gur, R. E., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.052 · Cited by 1,900 · see citing works
The global signal and observed anticorrelated resting state brain networks
Fox, M. D., Zhang, D., Snyder, A. Z., & Raichle, M. E. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90777.2008 · Cited by 1,877 · see citing works
Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.046 · Cited by 1,770 · see citing works
Publication bias in the social sciences: Unlocking the file drawer
Franco, A., Malhotra, N., & Simonovits, G. (2014) · Science · doi:10.1126/science.1255484 · Cited by 1,542 · see citing works
Resting-state fMRI in the human connectome project
Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D. A., Griffanti, L., Harms, M. P., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.039 · Cited by 1,502 · see citing works
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 · see citing works
Self-citationYan, C.-G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., Li, Q., Zuo, X.-N., Castellanos, F. X., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.004 · Cited by 1,461 · see citing works
Birn, R. M., Diamond, J. B., Smith, M. A., & Bandettini, P. A. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.048 · Cited by 1,342 · see citing works
Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging
Feinberg, D. A., Moeller, S., Smith, S. M., Auerbach, E., Ramanna, S., Glasser, M. F., Miller, K. L., Ugurbil, K., & Yacoub, E. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015710 · Cited by 1,296 · see citing works
Anticorrelations in resting state networks without global signal regression
Chai, X. J., Nieto-Castañón, A., Öngür, D., & Whitfield, S. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.048 · Cited by 1,143 · see citing works
Satterthwaite, T. D., Wolf, D. H., Loughead, J., Ruparel, K., Elliott, M. A., Hakonarson, H., Gur, R. C., & Gur, R. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.063 · Cited by 1,125 · see citing works
Recent progress and outstanding issues in motion correction in resting state fMRI
Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.044 · Cited by 957 · see citing works
Self-citationNeural basis of global resting-state fMRI activity
Schölvinck, M. L., Maier, A., Ye, F. Q., Duyn, J. H., & Leopold, D. A. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0913110107 · Cited by 939 · see citing works
Saad, Z. S., Gotts, S. J., Murphy, K., Chen, G., Jo, H. J., Martin, A., & Cox, R. W. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0080 · Cited by 859 · see citing works
Uğurbil, K., Xu, J., Auerbach, E. J., Moeller, S., Vu, A. T., Duarte-Carvajalino, J. M., Lenglet, C., Wu, X., Schmitter, S., Van de Moortele, P. F., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.012 · Cited by 812 · see citing works
Temporally-independent functional modes of spontaneous brain activity
Smith, S. M., Miller, K. L., Moeller, S., Xu, J., Auerbach, E. J., Woolrich, M. W., Beckmann, C. F., Jenkinson, M., Andersson, J., Glasser, M. F., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121329109 · Cited by 702 · see citing works
Wise, R. G., Ide, K., Poulin, M. J., & Tracey, I. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.11.025 · Cited by 664 · see citing works
Birn, R. M., Smith, M. A., Jones, T. B., & Bandettini, P. A. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.11.059 · Cited by 645 · see citing works
Yan, C.-G., Craddock, R. C., Zuo, X.-N., Zang, Y.-F., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.081 · Cited by 440 · see citing works
Reduction of motion-related artifacts in resting state fMRI using aCompCor
Muschelli, J., Nebel, M. B., Caffo, B. S., Barber, A. D., Pekar, J. J., & Mostofsky, S. H. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.028 · Cited by 431 · see citing works
Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI
Chang, C. E., & Glover, G. H. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.04.048 · Cited by 292 · see citing works
Jo, H. J., Gotts, S. J., Reynolds, R. C., Bandettini, P. A., Martin, A. D., Cox, R. W., & Saad, Z. S. (2013) · Journal of Applied Mathematics · doi:10.1155/2013/935154 · Cited by 277 · see citing works
Addressing head motion dependencies for small-world topologies in functional connectomics
Yan, C., Craddock, R. C., Yong He, A., & Milham, M. P. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00910 · Cited by 191 · see citing works
Optimizing the order of operations for movement scrubbing: Comment on Power et al.
Carp, J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.061 · Cited by 175 · see citing works
Resting state functional connectivity of five neural networks in bipolar disorder and schizophrenia
Mamah, D. T., Barch, D. M., & Repovš, G. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2013.01.051 · Cited by 140 · see citing works
Self-citationIndividual Differences in Impulsivity Predict Head Motion during Magnetic Resonance Imaging
Kong, X., Zhen, Z., Li, X., Huan-hua, L., Ruosi, W., Liu, L., Yong He, A., Zang, Y., & Jia Liu, B. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104989 · Cited by 110 · see citing works
Epstein, J. N., Casey, B. J., Tonev, S. T., Davidson, M. C., Reiss, A. L., Garrett, A. S., Hinshaw, S. P., Greenhill, L. L., Vitolo, A., Kotler, L. A., et al. (2007) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2006.12.009 · Cited by 87 · see citing works
Pujol, J., Macià, D., Blanco‐Hinojo, L., Martínez‐Vilavella, G., Sunyer, J., de la Torre, R., Caixàs, A., Martín‐Santos, R., Deus, J., & Harrison, B. J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.065 · Cited by 57 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Ciric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., Shinohara, R. T., Elliott, M. A., Eickhoff, S. B., Davatzikos, C., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.020 · Cited by 1,042 · see citing works
Self-citationMethods for cleaning the BOLD fMRI signal
Caballero-Gaudes, C., & Reynolds, R. C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.018 · Cited by 542 · see citing works
Li, J., Kong, R., Liégeois, R., Orban, C., Tan, Y., Sun, N., Holmes, A. J., Sabuncu, M. R., Ge, T., & Yeo, B. T. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.016 · Cited by 380 · see citing works
Rudolph, M. D., Graham, A. M., Feczko, E., Miranda-Dominguez, O., Rasmussen, J. M., Nardos, R., Entringer, S., Wadhwa, P. D., Buss, C., & Fair, D. A. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0128-y · Cited by 374 · see citing works
He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116276 · Cited by 271 · see citing works
Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies
Satterthwaite, T. D., Ciric, R., Roalf, D. R., Davatzikos, C., Bassett, D. S., & Wolf, D. H. (2019) · Human Brain Mapping · doi:10.1002/hbm.23665 · Cited by 137 · see citing works
Regional GABA Concentrations Modulate Inter-network Resting-state Functional Connectivity
Chen, X., Fan, X., Hu, Y., Zuo, C., Whitfield-Gabrieli, S., Holt, D., Gong, Q., Yang, Y., Pizzagalli, D. A., Du, F., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhy059 · Cited by 62 · see citing works
Morin, E. L., Howell, B. R., Feczko, E., Earl, E., Pincus, M., Reding, K., Kovacs-Balint, Z. A., Meyer, J. S., Styner, M., Fair, D., et al. (2020) · Development and Psychopathology · doi:10.1017/s0954579420001133 · Cited by 37 · see citing works
- Authors
- Burgess, G. C., Kandala, S., Nolan, D., Laumann, T. O., Power, J. D., Adeyemo, B., Harms, M. P., Petersen, S. E., & Barch, D. M.
- Affiliations
- Washington University in St. Louis (US)
- National Institute of Mental Health (US)
- Corresponding author
- Burgess, Gregory C.
- ORCID
- Venue
- Brain Connectivity
- Publisher
- SAGE Publications
- Published
- 2016-11
- Volume
- 6
- Issue
- 9
- Pages
- 669-680
- ISSN
- 2158-0014 (ISSN-L)
- 2158-0022
- PMC
- PMC5105353
- OpenAlex
- W2535415557
- Cited by
- 216
- References
- 36
- Citations by year
- 2026: 8 · 2025: 17 · 2024: 19 · 2023: 14 · 2022: 27 · 2021: 21 · 2020: 30 · 2019: 32 · 2018: 24 · 2017: 22 · 2016: 2
- FWCI
- 16.97
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Brain Connectivity (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Digital Commons@Becker (Washington University School of Medicine) (submittedVersion)
- Funders
- National Institutes of Health (5u54mh091657-05, U54MH091657)
- McDonnell Center for Systems Neuroscience (U54MH091657)
- National Institute of Mental Health (U54-MH091657)
- NIH Blueprint for Neuroscience Research (U54MH091657)
- 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
- resting-state functional connectivity, noise reduction, head movement, Human Connectome Project, global signal regression
- MeSH terms
- Adult, Brain, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Motion, Signal Processing, Computer-Assisted, Reproducibility of Results, Databases, Factual, Artifacts, Young Adult, Connectome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
