Litcius/Paper detail

Dynamical structure-function correlations provide robust and generalizable signatures of consciousness in humans

Pablo Castro, Andrea I. Luppi, Enzo Tagliazucchi, Yonatan Sanz Perl, Lorina Naçi, Adrian M. Owen, Jacobo Sitt, Alain Destexhe, Rodrigo Cofré

2024Communications Biology21 citationsDOIOpen Access PDF

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.

Topics & Concepts

UnconsciousnessConsciousnessDynamic functional connectivityDefault mode networkPsychologyFunctional magnetic resonance imagingCognitionUnconscious mindCognitive psychologyNeuroscienceNeural correlates of consciousnessResting state fMRIBrain activity and meditationBrain functionNetwork dynamicsCognitive scienceElectroencephalographyMathematicsPsychiatryPsychoanalysisDiscrete mathematicsFunctional Brain Connectivity StudiesNeural dynamics and brain functionEEG and Brain-Computer Interfaces