Nonequilibrium brain dynamics elicited as the origin of perturbative complexity.

A person's level of consciousness can be assessed by how the brain reacts to stimulation, but this study shows that the brain's unperturbed activity already contains that information. Using personalized whole-brain models fitted to resting-state fMRI data from people in altered states of consciousness (deep sleep, disorders of consciousness), the researchers measured the brain's out-of-equilibrium dynamics—specifically, the asymmetry of effective connections and time irreversibility. They found that states with lower arousal or awareness had less asymmetric connectivity, less irreversibility, and lower complexity in simulated responses compared to controls. The asymmetry in connections drives the nonequilibrium state and, in turn, the differences in complexity.

Study at a glance

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.