Shifts in brain dynamics and drivers of consciousness state transitions.

Brain dynamics change in distinct ways across levels of consciousness—awake, light sedation, deep sedation, and recovery—as measured by fMRI. Using linear time-invariant dynamical systems with unknown inputs, the authors show that the stability and frequency of the brain's oscillatory modes shift during transitions between consciousness states. The same models identify external drivers that influence large-scale brain activity during naturalistic auditory stimulation, and these drivers differ across consciousness states. The approach captures brain-wide dynamic changes not amenable to conventional analysis, suggesting potential 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 toward conscious states, holding promise for developing more accurate biomarkers of consciousness recovery in disorders of consciousness.