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

Published version
Peer-reviewed

Type

Article

Change log

Authors

Lu, Dian 
Shruti, Naik 
Menon, david 
Stamatakis, Emmanuel Andreas 

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.

Description

Keywords

Brain Mapping, Default Mode Network, Brain, Magnetic Resonance Imaging, Visual Perception

Journal Title

Nature Communications

Conference Name

Journal ISSN

2041-1723
2041-1723

Volume Title

13

Publisher

Nature Research
Sponsorship
Medical Research Council (MR/M009041/1)
Canadian Institute for Advanced Research The National Institute for Health Research, Cambridge Biomedical Research Centre and NIHR Senior Investigator Awards The British Oxygen Professorship of the Royal College of Anaesthetists The China Scholarship Council and Cambridge Commonwealth, European & International Trust The Stephen Erskine Fellowship, Queens’ College, University of Cambridge Computing infrastructure at the WBIC High Performance Hub for Clinical Informatics was funded by Medical Research Council research infrastructure Award No. MR/M009041/1.