Michael M Craig

This paper proposes a synergistic global workspace model of consciousness using network science and information theory, showing that loss of consciousness correlates with reduced information integration in specific brain networks.

This study quantifies neural emergence in brain injury patients using functional MRI and shows reduced emergent dynamics correlate with structural connectome disruptions.

The paper identifies harmonic patterns of structure-function dependence in the human connectome that correlate with different states of consciousness.

This study uses resting-state fMRI and a deep learning model to predict task-related activation in DOC patients, finding that frontoparietal control network connectivity is key for volitional capacity classification.

This study identifies common neural mechanisms underlying anaesthesia and disorders of consciousness by simulating brain dynamics with a whole-brain model incorporating GABA receptor density and structural connectomes.

The paper measures fractal dimension in brain activity to predict consciousness severity in disorders of consciousness, finding lower fractal dimensions correlate with reduced consciousness.