bioRxiv (Cold Spring Harbor Laboratory)
January 13, 2026
Iván Mindlin, Carlos Coronel-Oliveros, Jacobo Sitt et al.
A biologically grounded inhibitory homeostatic plasticity rule embedded into the Dynamic Mean Field (DMF) model creates a Homeostatic Dynamic Mean Field (HDMF) model that dynamically tunes local excitation-inhibition balance. The HDMF reproduces statistical observables of brain activity as well as the original DMF, can sustain neuromodulatory perturbations without overhead computations, and generates unprecedented sleep-like slow-wave activity that can coexist with wake-like asynchronous dynamics, permitting modeling of dissociated states of consciousness such as parasomnias. A single homeostatic rule broadens the stability and expressiveness of the DMF, providing a unified platform for studying how local adaptive processes shape the diverse global dynamics of the human brain.
bioRxiv
January 29, 2024
Juan Ignacio Piccinini, Yonatan Sanz Perl, Carla Pallavicini et al.
preprint
The brain state induced by psychedelic drugs is often studied as a static snapshot, but this work focuses on the transition itself. Using a time-dependent whole-brain model and fMRI data from 15 volunteers given intravenous DMT, the authors show that the drug briefly pushes the brain near a critical point where it becomes maximally responsive to perturbations. This heightened reactivity is concentrated in fronto-parietal regions and visual cortices and correlates with serotonin 5HT2a receptor density. The findings suggest that even a short psychedelic episode can have a lasting influence because minimal perturbations during this transient achieve maximal effect, with the temporal evolution aligning with the drug's pharmacokinetics.
arXiv (Cornell University)
August 6, 2020
Rodrigo Cofré, Rubén Herzog, Pedro A. M. Mediano et al.
Altered states of consciousness provide a key opportunity to understand how global brain activity changes relate to different subjective experiences. This paper advocates a research program that bridges bottom-up generative models of whole-brain activity with top-down signatures proposed by theories of consciousness. It defines altered states, discusses relevant brain-activity signatures, and introduces whole-brain models to explore the mechanisms of altered consciousness from the bottom-up. The authors argue that systematic investigation of altered states via bottom-up modeling may help clarify the biophysical, informational, and dynamical foundations of consciousness.
bioRxiv Preprint Server
June 7, 2026
Andrea I. Luppi, Dragana Manasova, Justine Y. Hansen et al.
preprint
Functional connectivity in the awake human brain is shaped primarily by cognitive co-activation—the tendency of brain regions to work together during mental tasks—more than by structural or molecular constraints. This predominance is systematically lost across five datasets involving pharmacological and pathological perturbations of consciousness (chronic disorders of consciousness; anesthesia with sevoflurane, propofol, or ketamine), when cognition is disconnected from the environment or abolished. During such states, the predictors of functional architecture shift away from cognitive co-activation and toward anatomical and molecular constraints.
bioRxiv Preprint Server
December 9, 2025
Tomas Berjaga-Buisan, Juan Manuel Monti, Martina Cortada et al.
preprint
A non-invasive framework using generative whole-brain models of non-equilibrium dynamics reveals that violations of the Fluctuation-Dissipation Theorem (FDT) in spontaneous brain signals are reduced in unresponsive disorders of consciousness and anesthesia compared to conscious states, mirroring patterns seen with the Perturbational Complexity Index (PCI). This links PCI to fundamental physics principles and offers new objective, model-based tools for assessing consciousness loss and recovery.
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.
arXiv Preprint Archive
August 6, 2020
Rodrigo Cofré, Rubén Herzog, Pedro A. M. Mediano et al.
Altered states of consciousness, such as those experienced during dreaming or meditation, offer a way to study how large-scale brain activity relates to different subjective experiences. This paper advocates a research program that combines bottom-up generative models of whole-brain activity, based on known properties of neural tissue, with top-down signatures proposed by theories of consciousness. The authors define altered states, discuss relevant brain-activity signatures, and introduce whole-brain models to explore the mechanisms behind these states. They argue that systematically investigating altered states through bottom-up modeling can clarify the biophysical, informational, and dynamical foundations of consciousness.
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.