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 D. Sitt, Viktor K. Jirsa, Morten L. Kringelbach, Steven Laureys, Gustavo Deco, Enzo Tagliazucchi

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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 unclear. We combine whole-brain modeling, 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 patients with brain injury is revealed in a latent space whose coordinates represent metrics related to functional modularity and structure-function coupling, increasing alongside loss of consciousness. Finally, we investigate 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.

Original languageEnglish
Article number112491
JournalCell Reports
Volume42
Issue number5
DOIs
StatePublished - 30 May 2023
Externally publishedYes

Keywords

  • CP: Neuroscience
  • deep learning
  • fMRI
  • low-dimensional brain dynamics
  • reduced consciousness
  • variational autoencoders
  • whole-brain modeling

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