Consciousness depends on the brain's ability to produce complex, variable patterns of activity after a perturbation, but measuring this directly is difficult. Using a whole-brain model, researchers found that such complexity only arises when spontaneous brain activity is highly fluid—meaning functional networks reorganize extensively. This fluid regime can be captured by a small set of dynamical systems metrics, which predict the effects of consciousness-altering drugs like Xenon, Propofol, and Ketamine. These predictions were validated in 15 subjects at different consciousness levels, showing agreement with established perturbational complexity measures but using a simpler, more accessible paradigm. The findings point to complexity properties underlying consciousness.
Brain states are often described on a single scale from full consciousness to unconsciousness, but this ignores the complex, high-dimensional nature of brain activity. By combining whole-brain modeling, data augmentation, and deep learning, researchers mapped states of consciousness into a low-dimensional space where distances reflect similarities between states. They found an orderly trajectory from wakefulness to brain-injured patients, with coordinates related to functional modularity and structure-function coupling, both increasing as consciousness is lost. Model perturbations provided a geometric interpretation of state stability and reversibility. The work suggests conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.