The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Sources and implications of whole-brain fMRI signals in humans
Open accessAbstract
Abstract Whole‐brain fMRI signals are a subject of intense interest: variance in the global fMRI signal (the spatial mean of all signals in the brain) indexes subject arousal, and psychiatric conditions such as schizophrenia and autism have been characterized by differences in the global fMRI signal. Further, vigorous debates exist on whether global signals ought to be removed from fMRI data. However, surprisingly little research has focused on the empirical properties of whole‐brain fMRI signals. Here we map the spatial and temporal properties of the global signal, individually, in 1000+ fMRI scans. Variance in the global fMRI signal is strongly linked to head motion, to hardware artifacts, and to respiratory patterns and their attendant physiologic changes. Many techniques used to prepare fMRI data for analysis fail to remove these uninteresting kinds of global signal fluctuations. Thus, many studies include, at the time of analysis, prominent global effects of yawns, breathing changes, and head motion, among other signals. Such artifacts will mimic dynamic neural activity and will spuriously alter signal covariance throughout the brain. Methods capable of isolating and removing global artifactual variance while preserving putative “neural” variance are needed; this paper adopts no position on the topic of global signal regression. HighlightsDemonstrates brain‐wide (global) fMRI signals, individually, in 1000+ scans from 8 sites.Global signals often reflect artifact caused by head motion, respiration, or hardware problems.Most existing fMRI denoising methods do not adequately remove global artifacts.Global artifacts mimic dynamic neural activity and modulate signal correlations.Studies reporting global fMRI signal effects must carefully account for artifact.
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Cites
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
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-citationFunctional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
The 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
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-citationMethods 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
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
Fox, P. T., & Raichle, M. E. (1986) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.83.4.1140 · Cited by 2,056
Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., Eickhoff, S. B., Hakonarson, H., Gur, R. C., Gur, R. E., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.052 · Cited by 1,881
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
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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
Self-citationYan, C.-G., Chen, X., Li, L., Castellanos, F. X., Bai, T.-J., Bo, Q.-J., Cao, J., Chen, G.-M., Chen, N.-X., Chen, W., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900390116 · Cited by 895
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Metadata
- Authors
- Power, J. D., Plitt, M., Laumann, T. O., & Martin, A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 146
- Pages
- 609-625
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2537382818
- Cited by
- 552
- References
- 59
- FWCI
- 43.89
- Type
- journal article
- Language
- en
- Open access
- green
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