bioRxiv Preprint Server
September 28, 2022
Yonatan Sanz Perl, Carla Pallavicini, Juan Piccinini et al.
preprint
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.
arXiv Preprint Archive
December 19, 2020
Yonatan Sanz Perl, Hernan Bocaccio, Ignacio Perez-Ipina et al.
Consciousness depends on brain activity that is far from thermodynamic equilibrium. Analyzing electrocorticography data from non-human primates during sleep and various anesthetics, and fMRI data from humans during deep sleep and propofol anesthesia, all states of reduced consciousness showed dynamics closer to equilibrium than conscious wakefulness. This was measured by entropy production and the curl of probability flux in phase space. Non-equilibrium macroscopic brain dynamics therefore serve as a robust signature of consciousness, offering a statistical mechanics approach to studying cognition and awareness.
bioRxiv Preprint Server
July 2, 2020
Yonatan Sanz Perl, Carla Pallavicini, Ignacio Pérez Ipiña et al.
preprint
The level of consciousness—how conscious someone is—is often measured by how similar their brain activity is to normal wakefulness. However, this approach misses important information about how stable that state is. Using computer models of the whole brain, the authors show that the stability of a conscious state—how easily it can be disrupted—provides additional, complementary information. They propose a new framework that sorts brain states by both their similarity to wakefulness and their stability, which helps distinguish between different types of unconsciousness: natural sleep, anesthesia, and brain injury. This framework offers a more complete way to characterize and differentiate states of consciousness.