Low-dimensional organization of global brain states of reduced consciousness
Yonatan Sanz Perl, Carla Pallavicini, Juan Piccinini, Athena Demertzi, Vincent Bonhomme, Charlotte Martial, Rajanikant Panda, Naji Alnagger, Jitka Annen, Olivia Gosseries, Agustin Ibañez, Helmut Laufs, Jacobo Sitt, Viktor Jirsa, Morten Kringelbach, Steven Laureys, Gustavo Deco, Enzo Tagliazucchi
bioRxiv Preprint Server September 28, 2022 preprint DOI: 10.1101/2022.09.28.509817 via bioRxiv
Summary
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.
Study at a glance
| Characteristics | Observational cohort |
|---|---|
| Population | Patients with brain injury and healthy individuals |
| Key finding | Conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations, with functional modularity and structure-function coupling increasing alongside loss of consciousness. |
Abstract
Brain states are frequently represented using a unidimensional scale measuring the richness of subjective experience (level of consciousness). This description assumes a mapping between the high-dimensional space of whole-brain configurations and the trajectories of brain states associated with changes in consciousness, yet this mapping and its properties remain unknown. We combined whole-brain modelling, data augmentation and deep learning for dimensionality reduction to determine a mapping representing states of consciousness in a low-dimensional space, where distances parallel similarities between states. An orderly trajectory from wakefulness to brain injured patients is revealed in a latent space whose coordinates represent metrics related to functional modularity and structure-function coupling, both increasing alongside loss of consciousness. Finally, we investigated the effects of model perturbations, providing geometrical interpretation for the stability and reversibility of states. We conclude that conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.