Abstract
Resting-state functional magnetic resonance imaging (MRI) has yielded seemingly disparate insights into large-scale organization of the human brain. The brain’s large-scale organization can be divided into two broad categories: zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. In this study, we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics. We showed that a range of empirical phenomena, including functional connectivity gradients, the task-positive/task-negative anti-correlation pattern, the global signal, time-lag propagation patterns, the quasiperiodic pattern and the functional connectome network structure, are manifestations of these three spatiotemporal patterns. These patterns account for much of the global spatial structure that underlies functional connectivity analyses and unifies phenomena in resting-state functional MRI previously thought distinct.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout






Similar content being viewed by others
Data availability
Data from the Human Connectome Project (HCP) are publicly available at http://www.humanconnectomeproject.org/data/. Instructions for accessing HCP data can be found at https://www.humanconnectome.org/. All metadata are provided at https://github.com/tsb46/BOLD_WAVES.
Code availability
All code for pre-processing and analysis is provided at https://github.com/tsb46/BOLD_WAVES.
References
Biswal, B., Yetkin, F. Z., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995).
Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl Acad. Sci. USA 113, 12574–12579 (2016).
Smith, S. M. et al. Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl Acad. Sci. USA 106, 13040–13045 (2009).
Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).
Liu, T. T., Nalci, A. & Falahpour, M. The global signal in fMRI: nuisance or information? Neuroimage 150, 213–229 (2017).
Fox, M. D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl Acad. Sci. USA 102, 9673–9678 (2005).
Mitra, A., Snyder, A. Z., Hacker, C. D. & Raichle, M. E. Lag structure in resting-state fMRI. J. Neurophysiol. 111, 2374–2391 (2014).
Raut, R. V. et al. Global waves synchronize the brain’s functional systems with fluctuating arousal. Sci. Adv. 7, eabf2709 (2021).
Yousefi, B. & Keilholz, S. Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain. Neuroimage 231, 117827 (2021).
Gu, Y. et al. Brain activity fluctuations propagate as waves traversing the cortical hierarchy. Cereb. Cortex 31, 3986–4005 (2021).
Tong, Y., Hocke, L. M., Licata, S. C. & deB. Frederick, B. Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals. J. Biomed. Opt. 17, 106004 (2012).
Abbas, A. et al. Quasi-periodic patterns contribute to functional connectivity in the brain. Neuroimage 191, 193–204 (2019).
Hannachi, A., Jolliffe, I. T. & Stephenson, D. B. Empirical orthogonal functions and related techniques in atmospheric science: a review. Int. J. Climatol. 27, 1119–1152 (2007).
Feeny, B. F. A complex orthogonal decomposition for wave motion analysis. J. Sound Vib. 310, 77–90 (2008).
Liu, X. & Duyn, J. H. Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proc. Natl Acad. Sci. USA 110, 4392–4397 (2013).
Smith, S. M. et al. Temporally-independent functional modes of spontaneous brain activity. Proc. Natl Acad. Sci. USA 109, 3131–3136 (2012).
Vidaurre, D., Smith, S. M. & Woolrich, M. W. Brain network dynamics are hierarchically organized in time. Proc. Natl Acad. Sci. USA 114, 12827–12832 (2017).
Majeed, W. et al. Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans. Neuroimage 54, 1140–1150 (2011).
Li, J. et al. Topography and behavioral relevance of the global signal in the human brain. Sci. Rep. 9, 14286 (2019).
Cattell, R. B. The description of personality: basic traits resolved into clusters. J. Abnorm. Soc. Psychol. 38, 476–506 (1943).
Ecker, C., Reynaud, E., Williams, S. C. & Brammer, M. J. Detecting functional nodes in large-scale cortical networks with functional magnetic resonance imaging: a principal component analysis of the human visual system. Hum. Brain Mapp. 28, 817–834 (2007).
Stetter, M. et al. Principal component analysis and blind separation of sources for optical imaging of intrinsic signals. Neuroimage 11, 482–490 (2000).
Liégeois, R., Laumann, T. O., Snyder, A. Z., Zhou, J. & Yeo, B. T. T. Interpreting temporal fluctuations in resting-state functional connectivity MRI. Neuroimage 163, 437–455 (2017).
Andersen, A. H., Gash, D. M. & Avison, M. J. Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework. Magn. Reson. Imaging 17, 795–815 (1999).
Vos de Wael, R. et al. BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun. Biol. 3, 103 (2020).
Calhoun, V. D., Adali, T., Pearlson, G. D. & Pekar, J. J. A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14, 140–151 (2001).
Alexander-Bloch, A. F. et al. On testing for spatial correspondence between maps of human brain structure and function. Neuroimage 178, 540–551 (2018).
Yousefi, B., Shin, J., Schumacher, E. H. & Keilholz, S. D. Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal. Neuroimage 167, 297–308 (2018).
Fransson, P. Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum. Brain Mapp. 26, 15–29 (2005).
Espadoto, M., Martins, R. M., Kerren, A., Hirata, N. S. T. & Telea, A. C. Toward a quantitative survey of dimension reduction techniques. IEEE Trans. Vis. Comput. Graph. 27, 2153–2173 (2021).
Esfahlani, F. Z. et al. High-amplitude cofluctuations in cortical activity drive functional connectivity. Proc. Natl Acad. Sci. USA 117, 28393–28401 (2020).
Betzel, R. F., Cutts, S. A., Greenwell, S., Faskowitz, J. & Sporns, O. Individualized event structure drives individual differences in whole-brain functional connectivity. Neuroimage 252, 118993 (2022).
Lurie, D. J. et al. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw. Neurosci. 4, 30–69 (2020).
Tong, Y., Hocke, L. M. & Frederick, B. B. Low frequency systemic hemodynamic ‘noise’ in resting state BOLD fMRI: characteristics, causes, implications, mitigation strategies, and applications. Front. Neurosci. 13, 787 (2019).
Erdoğan, S. B., Tong, Y., Hocke, L. M., Lindsey, K. P. & deB Frederick, B. Correcting for blood arrival time in global mean regression enhances functional connectivity analysis of resting state fMRI-BOLD signals. Front. Hum. Neurosci. 10, 311 (2016).
Tong, Y., Yao, J. F., Chen, J. J. & deB Frederick, B. The resting-state fMRI arterial signal predicts differential blood transit time through the brain. J. Cereb. Blood Flow. Metab. 39, 1148–1160 (2019).
Schölvinck, M. L., Maier, A., Ye, F. Q., Duyn, J. H. & Leopold, D. A. Neural basis of global resting-state fMRI activity. Proc. Natl Acad. Sci. USA 107, 10238–10243 (2010).
Turchi, J. et al. The basal forebrain regulates global resting-state fMRI fluctuations. Neuron 97, 940–952 (2018).
Özbay, P. S. et al. Sympathetic activity contributes to the fMRI signal. Commun. Biol. 2, 421 (2019).
Colenbier, N. et al. Disambiguating the role of blood flow and global signal with partial information decomposition. Neuroimage 213, 116699 (2020).
Raichle, M. E. et al. A default mode of brain function. Proc. Natl Acad. Sci. USA 98, 676–682 (2001).
Grooms, J. K. et al. Infraslow electroencephalographic and dynamic resting state network activity. Brain Connect. 7, 265–280 (2017).
Thompson, G. J., Pan, W.-J., Magnuson, M. E., Jaeger, D. & Keilholz, S. D. Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity. Neuroimage 84, 1018–1031 (2014).
Liang, Y. et al. Cortex-wide dynamics of intrinsic electrical activities: propagating waves and their interactions. J. Neurosci. 41, 3665–3678 (2021).
Takeda, Y., Hiroe, N. & Yamashita, O. Whole-brain propagating patterns in human resting-state brain activities. Neuroimage 245, 118711 (2021).
Gonzalez-Castillo, J., Kam, J. W. Y., Hoy, C. W. & Bandettini, P. A. How to interpret resting-state fMRI: ask your participants. J. Neurosci. 41, 1130–1141 (2021).
Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. Neuroimage 80, 62–79 (2013).
Smith, S. M. et al. Resting-state fMRI in the Human Connectome Project. Neuroimage 80, 144–168 (2013).
Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).
Horel, J. D. Complex principal component analysis: theory and examples. J. Clim. Appl. Meteor. 23, 1660–1673 (1984).
Reid, A. T. et al. Advancing functional connectivity research from association to causation. Nat. Neurosci. 22, 1751–1760 (2019).
Bzdok, D. et al. Formal models of the network co-occurrence underlying mental operations. PLoS Comput. Biol. 12, e1004994 (2016).
Schaefer, A. et al. Local–global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2018).
Mitra, A., Snyder, A. Z., Blazey, T. & Raichle, M. E. Lag threads organize the brain’s intrinsic activity. Proc. Natl Acad. Sci. USA 112, E2235–E2244 (2015).
Acknowledgements
This work was supported by grants from the Canadian Institute for Advanced Research, a Gabelli Senior Scholar Award from the University of Miami, R01MH107549 from the National Institute of Mental Health (NIMH) (to L.Q.U.), an NIMH award (R03MH121668) and a National Alliance for Research on Schizophrenia & Depression Young Investigator Award (to J.S.N.). B.T.T.Y. was supported by the Singapore National Research Foundation Fellowship (Class of 2017), the NUS Yong Loo Lin School of Medicine (NUHSRO/2020/124/TMR/LOA), the Singapore National Medical Research Council Large Collaborative Grant (OFLCG19May-0035) and NMRC STaR (STaR20nov-0003). S.D.K was supported by RO1MH111416 and R01NS078095 from the National Institutes of Health (NIH).
Author information
Authors and Affiliations
Contributions
T.B. performed all analyses. S.D.K developed the original QPP algorithm used in this study. L.Q.U., S.D.K., B.T.T.Y., D.B., J.N. and C.C assisted in the interpretation of analyses, conceptualization of the project and writing of the manuscript. J.S. assisted in the development and testing of the publicly available GitHub repository that documents and stores analysis code.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Neuroscience thanks Janine Bijsterbosch, Javier Gonzalez-Castillo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary Information
Supplementary Videos 1–3 captions, Supplementary Modeling Notes and Supplementary Figs. 1–13
Supplementary Video 1
Supplementary Movie 1. Visualization of three spatiotemporal patterns. Temporal reconstruction of all three spatiotemporal patterns displayed as movies in the following order: pattern one, pattern two and pattern three. The timepoints are equally spaced samples (n = 30) of the spatiotemporal patterns. The seconds since the beginning of the spatiotemporal pattern are displayed in the top left. In the bottom of the panel, the timepoints of the spatiotemporal pattern are displayed in three-dimensional PC space (Fig. 2). Two-dimensional slices of the three-PC space (Fig. 3) are displayed as the three two-dimensional plots. The progression of timepoints in the PC space is illustrated by a cyclical color map (light to dark to light). The movement of the spatiotemporal pattern through this space is illustrated by a moving red dot from timepoint to timepoint in synchronization with the temporal reconstruction in the movie.
Supplementary Video 2
Supplementary Movie 2. Dynamic visualization of the qpp, pattern one and global signal. The 30 timepoints (TR = 0.72 seconds) of the global QPP, pattern one and peak-average global signal displayed as a movie (in that order). The time index of each sequence is displayed in the top left. The timepoints of pattern one are equally spaced phase samples (n = 30) of the timepoint reconstruction (see above). The timepoints of the global QPP are derived from the spatiotemporal template computed from the repeated-template-averaging procedure on non-global signal regressed data. The global signal visualization concatenates the left and right windows (w = 15 TRs) of the global signal peak-average. The timepoints of the global signal visualization begin at TR = −15, corresponding to 15 TRs pre-peak, and proceed to TR = 15, corresponding to 15 TRs post-peak.
Supplementary Video 3
Supplementary Movie 3. Dynamic visualization of the anti-correlated QPP and pattern two. The 30 timepoints (TR = 0.72 seconds) of the anti-correlated QPP and pattern two. The time index of each sequence is displayed in the top left. The timepoints of pattern two are equally spaced phase samples (n = 30) of the timepoint reconstruction (Methods). The timepoints of the anti-correlated QPP are derived from the spatiotemporal template computed from the repeated-template-averaging procedure on global signal regressed data.
Rights and permissions
About this article
Cite this article
Bolt, T., Nomi, J.S., Bzdok, D. et al. A parsimonious description of global functional brain organization in three spatiotemporal patterns. Nat Neurosci 25, 1093–1103 (2022). https://doi.org/10.1038/s41593-022-01118-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41593-022-01118-1
This article is cited by
-
Prognostic model for predicting Alzheimer’s disease conversion using functional connectome manifolds
Alzheimer's Research & Therapy (2024)
-
Association of body-mass index with physiological brain pulsations across adulthood – a fast fMRI study
International Journal of Obesity (2024)
-
Human connectome topology directs cortical traveling waves and shapes frequency gradients
Nature Communications (2024)
-
Evolutionarily conserved fMRI network dynamics in the mouse, macaque, and human brain
Nature Communications (2024)
-
Global spatiotemporal synchronizing structures of spontaneous neural activities in different cell types
Nature Communications (2024)