ORIGINAL RESEARCH article
Front. Comput. Neurosci.
Shifts in Brain Dynamics and Drivers of Consciousness State Transitions
Provisionally accepted
- 1University of Kansas, Lawrence, United States
- 2University of Southern California Ming Hsieh Department of Electrical and Computer Engineering, Los Angeles, United States
- 3Universidade de Lisboa, Lisbon, Portugal
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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.
Keywords: Consciousness states, dynamical systems, Linear time-invariant model, System idenfication, unknown inputs
Received: 24 Oct 2025; Accepted: 20 Jan 2026.
Copyright: © 2026 Bodenheimer, Bogdan, Pequito and Ashourvan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Arian Ashourvan
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