Cortical signatures linked to behavior quantitatively track arousal levels.
Sijia Gao, Yelena Bibineyshvili, Seyed A Safavynia, Juan Calderón-Martínez, Zachary M Grinspan, Diany P Calderon
Proceedings of the National Academy of Sciences of the United States of America May 13, 2025 DOI: 10.1073/pnas.2413789122 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Prospective validation study Peer reviewed |
|---|---|
| Population | Rodents, neonatal humans with static hypoxic injuries, and senior patients emerging from anesthesia |
| Intervention | anesthesia |
| Keywords | Brain injury Cortical patterns Disorders of consciousness Monitoring arousal recovery Motor behavior Consciousness research Brain monitoring Medical diagnostics Patient recovery |
| Key points | A repeated, temporally discrete dynamical pattern called an Arousal Unit (AU) lawfully links spectral power and breathing frequency changes, reliably associates with motor changes, and generalizes across species and brain-injured states. |
Abstract
While current arousal level assessments in patients with disorders of consciousness discriminate altered states of consciousness, there are significant limitations in characterizing the transition from one state to another or quantifying the frequent arousal level fluctuations observed in a patient. Here, we identified a repeated, temporally discrete, dynamical pattern evident in the recovery of consciousness from anesthesia and brain injury coma models in rodents. We prospectively validated these features we label "Arousal Units" (AU) in neonatal humans recovering from static hypoxic injuries and senior patients emerging from anesthesia indicating their generalizability. The AUs lawfully link changes in spectral power and breathing frequency and reliably associate with motor changes. Distinctive cortical patterns within AUs can be transformed into arousal indices, determining arousal levels. The reliability of these events is demonstrated across intact and brain-injured states and translates to the human brain; extracting these stereotyped dynamics could aid anesthesia monitoring, tracking coma recovery, and identifying cognitive motor dissociation.