Brain Dynamics of Classical Psychedelics Show Paradoxical Hierarchical Flattening With Increased Complexity
Jakub Vohryzek, Elvira Garcia Guzman, Morten L. Kringelbach, Edmundo Lopez-Sola, Christopher Timmermann, Leor Roseman, Enzo Tagliazucchi, Giulio Ruffini, Robin Carhart-Harris, Gustavo Deco, Yonatan Sanz Perl
SSRN Electronic Journal 2026 preprint DOI: 10.2139/ssrn.6137001 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study |
|---|---|
| Keywords | Flattening Consciousness Hierarchy Complement music Object grammar Statistical mechanics Process computing Functional connectivity Probabilistic logic Cognitive science Statistical physics Artificial intelligence Cognitive psychology Sequence biology Abstraction |
| Citations | 1 |
| Key findings | Psychedelics reduce the brain's functional hierarchy and displace it toward thermodynamic equilibrium while increasing neural activity complexity, distinguishing this state from the flattening observed during loss of consciousness. |
Abstract
Despite divergent behavioral and phenomenological profiles, both psychedelic states and reduced states of consciousness have been associated with a flattening of the brain's functional hierarchy. To address this apparent paradox, we developed a more specific definition of hierarchy based on the proximity of the brain to thermodynamic equilibrium and then applied it to investigate the changes to the functional hierarchy elicited by three classical serotonergic psychedelics: psilocybin, lysergic acid diethylamide, and dimethyltryptamine. We found that all three psychedelics consistently induced a global reduction in the functional hierarchy. In contrast to the flattening of the functional hierarchy observed during loss of consciousness, psychedelics displaced the brain towards equilibrium while simultaneously increasing the complexity of neural activity, indicating a unique mechanism linked to specific changes in the configuration and differentiation of resting-state networks. This work showcases how metrics based on statistical mechanics can be used for the specific characterization of different global brain states, contributing to the understanding of consciousness as a collective process emerging from complex neural interactions.