  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.
