# Shifts in brain dynamics and drivers of consciousness state transitions

## TL;DR

This study explores how brain dynamics change during different states of consciousness using fMRI and a new modeling approach.

## Contribution

The paper introduces a novel model-based method using LTI systems under unknown inputs to analyze brain dynamics during consciousness transitions.

## Key findings

- Distinct spectral changes in brain dynamics were observed during transitions between consciousness states.
- External drivers influencing brain activity were identified during naturalistic auditory stimulation.
- LTI models under UI proved effective in capturing brain dynamic changes in complex 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 toward conscious states, holding promise for developing more accurate biomarkers of consciousness recovery in disorders of consciousness.