Shifts in Brain Dynamics and Drivers of Consciousness State Transitions

TL;DR

This study uses linear dynamical models to analyze fMRI data, revealing how brain dynamics and external drivers change during transitions between consciousness states, aiding in understanding and diagnosing consciousness disorders.

Contribution

It introduces a novel LTI model-based approach to identify brain dynamics and external drivers across consciousness levels, including during naturalistic stimulation.

Findings

Distinct spectral changes in brain oscillations during consciousness transitions

External drivers influence large-scale brain activity differently across states

LTI models effectively capture complex brain dynamics in naturalistic paradigms

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.