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Abstract
Resting-state functional magnetic resonance imaging evolves through a repertoire of functional connectivity patterns which might reflect ongoing cognition, as well as the contents of conscious awareness. We investigated whether the dynamic exploration of these states can provide robust and generalizable markers for the state of consciousness in human participants, across loss of consciousness induced by general anaesthesia or slow wave sleep. By clustering transient states of functional connectivity, we demonstrated that brain activity during unconsciousness is dominated by a recurrent pattern primarily mediated by structural connectivity and with a reduced capacity to transition to other patterns. Our results provide evidence supporting the pronounced differences between conscious and unconscious brain states in terms of whole-brain dynamics; in particular, the maintenance of rich brain dynamics measured by entropy is a critical aspect of conscious awareness. Collectively, our results may have significant implications for our understanding of consciousness and the neural basis of human awareness, as well as for the discovery of robust signatures of consciousness that are generalizable among different brain conditions.
Dynamical analysis of resting-state fMRI networks reveals a consistent increase in structure-function correlation and entropy decrease upon both general anesthesia-induced and deep sleep-induced loss of consciousness in humans.
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1 Centre National de la Recherche Scientifique (CNRS), Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Gif-sur-Yvette, France (GRID:grid.457079.8); NeuroSpin Center, Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, Gif-sur-Yvette, France (GRID:grid.7429.8) (ISNI:0000000121866389)
2 University of Cambridge, Division of Anaesthesia and Department of Clinical Neurosciences, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934); McGill University, Montreal Neurological Institute, Montreal, Canada (GRID:grid.14709.3b) (ISNI:0000 0004 1936 8649)
3 University of Buenos Aires, Buenos Aires Physics Institute and Physics Department, Buenos Aires, Argentina (GRID:grid.7345.5) (ISNI:0000 0001 0056 1981); CABA, National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina (GRID:grid.423606.5) (ISNI:0000 0001 1945 2152); Universidad Adolfo Ibáñez, Latin American Brain Health Institute (BrainLat), Santiago, Chile (GRID:grid.440617.0) (ISNI:0000 0001 2162 5606)
4 University of Buenos Aires, Buenos Aires Physics Institute and Physics Department, Buenos Aires, Argentina (GRID:grid.7345.5) (ISNI:0000 0001 0056 1981); CABA, National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina (GRID:grid.423606.5) (ISNI:0000 0001 1945 2152); CNRS, Sorbonne Université, Institut du Cerveau—Paris Brain Institute—ICM, Inserm, Paris, France (GRID:grid.4444.0) (ISNI:0000 0001 2112 9282); Universitat Pompeu Fabra, Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Barcelona, Spain (GRID:grid.5612.0) (ISNI:0000 0001 2172 2676)
5 Trinity College Institute of Neuroscience Trinity College Dublin, Dublin, Ireland (GRID:grid.8217.c) (ISNI:0000 0004 1936 9705); Trinity College Dublin, Global Brain Health Institute, Dublin, Ireland (GRID:grid.8217.c) (ISNI:0000 0004 1936 9705)
6 Western University, Departments of Physiology and Pharmacology and Psychology, London, Canada (GRID:grid.39381.30) (ISNI:0000 0004 1936 8884)
7 CNRS, Sorbonne Université, Institut du Cerveau—Paris Brain Institute—ICM, Inserm, Paris, France (GRID:grid.4444.0) (ISNI:0000 0001 2112 9282)
8 Centre National de la Recherche Scientifique (CNRS), Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Gif-sur-Yvette, France (GRID:grid.457079.8)