Entropy production of Multivariate Ornstein-Uhlenbeck processes correlates with consciousness levels in the human brain
arXiv:2207.05197 · doi:10.1103/PhysRevE.107.024121
Abstract
Consciousness is supported by complex patterns of brain activity which are indicative of irreversible non-equilibrium dynamics. While the framework of stochastic thermodynamics has facilitated the understanding of physical systems of this kind, its application to infer the level of consciousness from empirical data remains elusive. We faced this challenge by calculating entropy production in a multivariate Ornstein-Uhlenbeck process fitted to fMRI brain activity recordings. To test this approach, we focused on the transition from wakefulness to deep sleep, revealing a monotonous relationship between entropy production and the level of consciousness. Our results constitute robust signatures of consciousness while also advancing our understanding of the link between consciousness and complexity from the fundamental perspective of statistical physics.
Cited by in corpus (11)
- Does the brain behave like a (complex) network? I. Dynamics
- Broken detailed balance and entropy production in directed networks
- Conceptual and practical approaches for investigating irreversible processes
- The Fluctuation-Dissipation Relations: Growth, Diffusion, and Beyond
- Decomposing Thermodynamic Dissipation of Linear Langevin Systems via Oscillatory Modes and Its Application to Neural Dynamics
- Macroscopic fluctuation-response theory and its use for gene regulatory networks
- State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilibrium neuronal dynamics
- Ergodicity bounds for stable Ornstein-Uhlenbeck systems in Wasserstein distance with applications to cutoff stability
- Functional Decomposition and Estimation of Irreversibility in Time Series via Machine Learning
- Entropy production and irreversibility in the linearized stochastic Amari neural model
- Response function as a quantitative measure of consciousness in brain dynamics