Shifts in Brain Dynamics and Drivers of Consciousness State Transitions

The brain's large-scale dynamics change in distinct ways as people move between wakefulness, light sedation, deep sedation, and recovery. Using a model that treats the brain as a linear time-invariant system with unknown inputs, the authors show that the stability and frequency of oscillatory modes shift across these states. The same model identifies external drivers that shape brain activity during naturalistic auditory stimulation, revealing how stimulus-induced co-activity propagation differs across consciousness levels. The approach captures brain-wide changes that conventional methods miss, and these findings may help develop better biomarkers for consciousness recovery in disorders of consciousness.

Study at a glance

Abstract

Understanding the neural mechanisms underlying the transitions between different states of consciousness is a fundamental challenge in neuroscience. Thus, we investigate the underlying drivers of changes during the resting-state dynamics of the human brain, as captured by functional magnetic resonance imaging (fMRI) across varying levels of consciousness (awake, light sedation, deep sedation, and recovery). We deploy a model-based approach relying on linear time-invariant (LTI) dynamical systems under unknown inputs (UI). Our findings reveal distinct changes in the spectral profile of brain dynamics - particularly regarding the stability and frequency of the system's oscillatory modes during transitions between consciousness states. These models further enable us to identify external drivers influencing large-scale brain activity during naturalistic auditory stimulation. Our findings suggest that these identified inputs delineate how stimulus-induced co-activity propagation differs across consciousness states. Notably, our approach showcases the effectiveness of LTI models under UI in capturing large-scale brain dynamic changes and drivers in complex paradigms, such as naturalistic stimulation, which are not conducive to conventional general linear model analysis. Importantly, our findings shed light on how brain-wide dynamics and drivers evolve as the brain transitions towards conscious states, holding promise for developing more accurate biomarkers of consciousness recovery in disorders of consciousness.