Nonequilibrium brain dynamics elicited as the origin of perturbative complexity

Wiep Stikvoort, Eider Pérez-Ordoyo, Iván Mindlin, Anira Escrichs, Jacobo D Sitt, Morten L Kringelbach, Gustavo Deco and Yonatan Sanz Perl

PLOS Computational Biology, 2025, vol. 21, issue 6, 1-19

Abstract: Assessing someone’s level of consciousness is a complex matter, and attempts have been made to aid clinicians in these assessments through metrics based on neuroimaging data. Many studies have empirically investigated measures related to the complexity elicited after the brain is stimulated to quantify the level of consciousness across different states. Here we hypothesized that the level of non-equilibrium dynamics of the unperturbed brain already contains the information needed to know how the system will react to an external stimulus. We created personalized whole-brain models fitted to resting state fMRI data recorded in participants in altered states of consciousness (e.g., deep sleep, disorders of consciousness) to infer the effective connections underlying their brain dynamics. We then measured the out-of-equilibrium nature of the unperturbed brain by evaluating the level of asymmetry of the inferred connectivity, the time irreversibility in each model and compared this with the elicited complexity generated after in silico perturbations, using a simulated fMRI-based version of the Perturbational Complexity Index, a measure that has been shown to distinguish different levels of consciousness in in vivo settings. Crucially, we found that states of consciousness involving lower arousal and/or lower awareness had a lower level of asymmetry in their effective connectivities, a lower level of irreversibility in their simulated dynamics, and a lower complexity compared to control subjects. We show that the asymmetry in the underlying connections drives the nonequilibrium state of the system and in turn the differences in complexity as a response to the external stimuli.Author summary: The Perturbational Complexity Index (PCI) is a measure that was created to distinguish different states of consciousness. By introducing a perturbation in the brain, the brain’s response can be compared to its resting state dynamics. It has been shown that the PCI makes this distinction with high accuracy in altered states of consciousness such as sleep, anesthesia, and disorders of consciousness. In this work we looked at what causes the difference in this measure for different states. We used fMRI data from people in wakefulness and deep sleep, and disorders of consciousness. By using computational models and simulating the dynamics of each subject from the dataset, we were able to investigate the relationship between the simulated fMRI-based PCI (sfPCI) values and the underlying dynamics of each model by calculating the asymmetry of connections between brain regions. This asymmetry is known to cause hierarchy and to be present in healthy awake subjects. Here we found that the asymmetry in connections drives the dynamics into an out-of-equilibrium state, which in turn causes the difference in sfPCI values. We conclude that the information to know how the brain will react to such stimulations could already be found in the unperturbed dynamics.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1013150

DOI: 10.1371/journal.pcbi.1013150

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