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Metastable brain waves

Author

Listed:
  • James A. Roberts

    (QIMR Berghofer Medical Research Institute
    QIMR Berghofer Medical Research Institute)

  • Leonardo L. Gollo

    (QIMR Berghofer Medical Research Institute
    QIMR Berghofer Medical Research Institute)

  • Romesh G. Abeysuriya

    (University of Oxford)

  • Gloria Roberts

    (University of New South Wales
    Black Dog Institute, Prince of Wales Hospital)

  • Philip B. Mitchell

    (University of New South Wales
    Black Dog Institute, Prince of Wales Hospital)

  • Mark W. Woolrich

    (University of Oxford)

  • Michael Breakspear

    (QIMR Berghofer Medical Research Institute
    QIMR Berghofer Medical Research Institute
    Royal Brisbane and Women’s Hospital
    University of Newcastle)

Abstract

Traveling patterns of neuronal activity—brain waves—have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human brain lacks a firm understanding. Here we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling waves, spiral waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.

Suggested Citation

  • James A. Roberts & Leonardo L. Gollo & Romesh G. Abeysuriya & Gloria Roberts & Philip B. Mitchell & Mark W. Woolrich & Michael Breakspear, 2019. "Metastable brain waves," Nature Communications, Nature, vol. 10(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-08999-0
    DOI: 10.1038/s41467-019-08999-0
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    Cited by:

    1. Manish Saggar & James M. Shine & Raphaël Liégeois & Nico U. F. Dosenbach & Damien Fair, 2022. "Precision dynamical mapping using topological data analysis reveals a hub-like transition state at rest," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    2. André, Morgan & Planche, Léo, 2021. "The effect of graph connectivity on metastability in a stochastic system of spiking neurons," Stochastic Processes and their Applications, Elsevier, vol. 131(C), pages 292-310.
    3. Chesebro, Anthony G. & Mujica-Parodi, Lilianne R. & Weistuch, Corey, 2023. "Ion gradient-driven bifurcations of a multi-scale neuronal model," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    4. Wojtusiak, A.M. & Balanov, A.G. & Savel’ev, S.E., 2021. "Intermittent and metastable chaos in a memristive artificial neuron with inertia," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

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