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Intrinsic brain dynamics in the Default Mode Network predict involuntary fluctuations of visual awareness

Author

Listed:
  • Dian Lyu

    (University of Cambridge, Addenbrooke’s Hospital
    University of Cambridge, Addenbrooke’s Hospital)

  • Shruti Naik

    (Universite´ Paris-Saclay)

  • David K. Menon

    (University of Cambridge, Addenbrooke’s Hospital
    University of Cambridge, Cambridge Biomedical Campus (Box 65))

  • Emmanuel A. Stamatakis

    (University of Cambridge, Addenbrooke’s Hospital
    University of Cambridge, Addenbrooke’s Hospital)

Abstract

Brain activity is intrinsically organised into spatiotemporal patterns, but it is still not clear whether these intrinsic patterns are functional or epiphenomenal. Using a simultaneous fMRI-EEG implementation of a well-known bistable visual task, we showed that the latent transient states in the intrinsic EEG oscillations can predict upcoming involuntarily perceptual transitions. The critical state predicting a dominant perceptual transition was characterised by the phase coupling between the precuneus (PCU), a key node of the Default Mode Network (DMN), and the primary visual cortex (V1). The interaction between the lifetime of this state and the PCU- > V1 Granger-causal effect is correlated with the perceptual fluctuation rate. Our study suggests that the brain’s endogenous dynamics are phenomenologically relevant, as they can elicit a diversion between potential visual processing pathways, while external stimuli remain the same. In this sense, the intrinsic DMN dynamics pre-empt the content of consciousness.

Suggested Citation

  • Dian Lyu & Shruti Naik & David K. Menon & Emmanuel A. Stamatakis, 2022. "Intrinsic brain dynamics in the Default Mode Network predict involuntary fluctuations of visual awareness," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34410-6
    DOI: 10.1038/s41467-022-34410-6
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    References listed on IDEAS

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    4. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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