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A parsimonious description of global functional brain organization in three spatiotemporal patterns

Nature Neuroscience volume 25, pages 1093–1103 (2022)Cite this article

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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.

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Fig. 1: Simulation to analyze standing and traveling wave oscillations.
Fig. 2: Form and properties of three prominent spatiotemporal patterns.
Fig. 3: Form and properties of three prominent FC topographies.
Fig. 4: Similar propagation patterns between average latency structure and pattern one.
Fig. 5: The task-positive/task-negative pattern, primary FC gradient and pattern two describe the same spatiotemporal pattern.
Fig. 6: The network structure of FC is explained by the three spatiotemporal patterns.

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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.

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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).

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Authors and Affiliations

  1. Emory University/Georgia Institute of Technology, Atlanta, GA, USA

    Taylor Bolt & Shella D. Keilholz

  2. Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA

    Taylor Bolt, Jason S. Nomi & Lucina Q. Uddin

  3. The Neuro (Montreal Neurological Institute), McGill University & Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada

    Danilo Bzdok

  4. Departments of Electrical and Computer Engineering, Computer Science, and Biomedical Engineering, Vanderbilt University, Nashville, TN, USA

    Jorge A. Salas & Catie Chang

  5. Department of Electrical & Computer Engineering, Centre for Translational MR Research, Centre for Sleep & Cognition, N.1 Institute for Health and Institute for Digital Medicine, National University of Singapore, Singapore, Singapore

    B. T. Thomas Yeo

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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.

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Correspondence to Taylor Bolt or Shella D. Keilholz.

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Nature Neuroscience thanks Janine Bijsterbosch, Javier Gonzalez-Castillo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Videos 1–3 captions, Supplementary Modeling Notes and Supplementary Figs. 1–13

Reporting Summary

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.

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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

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