Tensor decomposition reveals discrete cortical reorganization under graded propofol anesthesia
4 affiliations
- 1Department of Anesthesiology, Huashan Hospital, Fudan University , ,
- 2Department of Cardiothoracic Surgery, Huashan Hospital, Fudan University , ,
- 3Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine , ,
- 4Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences , ,
Abstract
Background: Although propofol-induced anesthesia is known to suppress consciousness by promoting low-frequency frontal oscillations through thalamocortical circuits, a comprehensive understanding of its whole-brain coupling feature set remains elusive. Specifically, the global reorganization of nonlinear cross-frequency coupling remains poorly characterized due to analytical limitations.
Methods: We analyzed 64-channel EEG data from 17 participants across five states (awake and target propofol concentrations of 1, 2, 3, and 4 mcg/ml, defined as States 1 to 5). After computing time-frequency representation (TFR) and bicoherence, Parallel Factor Analysis (PARAFAC) was used to extract unbiased spatio-spectral components from both linear and nonlinear measures.
Results: PARAFAC decomposition isolated distinct linear and nonlinear neurodynamic components driving the anesthetic transition. While linear spectral features and signal complexity transitioned in a gradual, monotonic manner. PARAFAC tensor decomposition of high-dimensional whole-brain bicoherence identified two robust, intrinsically decoupled nonlinear components with distinct spatial-spectral profiles. Model comparison demonstrated that frontal slow-wave self-coupling (Component 1) undergoes a discrete, stepwise state-space reconfiguration during loss of consciousness, whereas posterior alpha coupling (Component 2) exhibits a smooth, continuous decay with propofol titration. Phase-space kinetic trajectories confirmed a highly directed transition at unconsciousness onset, followed by confined local oscillations during deep anesthesia.
Conclusion: This multi-dimensional decomposition framework captures discrete versus continuous neural dynamics, providing objective, state-sensitive biomarkers for precisely monitoring anesthesia depth and cortical state transitions.
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BibTeX
@article{tong2026tensor,
title = {{Tensor decomposition reveals discrete cortical reorganization under graded propofol anesthesia}},
author = {Chuan-Di Tong and Yun Wang and Zhou-Jing Yang and Jie-Lu Yu and Jie-Chun Huang and Dong-Li Li and Meng-Fan Hu and Chen Li and Ruo-Yu Xia and Xue-Ying Li and Yi-Qi Wu and Ruo-Ying Xie},
journal = {Frontiers in neuroscience},
year = {2026},
month = oct,
doi = {10.3389/fnins.2026.1950745},
url = {https://europepmc.org/article/PMC/PMC13672537},
note = {CC BY},
}
RIS
TY - JOUR
TI - Tensor decomposition reveals discrete cortical reorganization under graded propofol anesthesia
AU - Chuan-Di Tong
AU - Yun Wang
AU - Zhou-Jing Yang
AU - Jie-Lu Yu
AU - Jie-Chun Huang
AU - Dong-Li Li
AU - Meng-Fan Hu
AU - Chen Li
AU - Ruo-Yu Xia
AU - Xue-Ying Li
AU - Yi-Qi Wu
AU - Ruo-Ying Xie
PY - 2026
DA - 2026/10/02
JO - Frontiers in neuroscience
DO - 10.3389/fnins.2026.1950745
UR - https://europepmc.org/article/PMC/PMC13672537
AB - Background: Although propofol-induced anesthesia is known to suppress consciousness by promoting low-frequency frontal oscillations through thalamocortical circuits, a comprehensive understanding of its whole-brain coupling feature set remains elusive. Specifically, the global reorganization of nonlinear cross-frequency coupling remains poorly characterized due to analytical limitations. Methods: We analyzed 64-channel EEG data from 17 participants across five states (awake and target propofol concentrations of 1, 2, 3, and 4 mcg/ml, defined as States 1 to 5). After computing time-frequency representation (TFR) and bicoherence, Parallel Factor Analysis (PARAFAC) was used to extract unbiased spatio-spectral components from both linear and nonlinear measures. Results: PARAFAC decomposition isolated distinct linear and nonlinear neurodynamic components driving the anesthetic transition. While linear spectral features and signal complexity transitioned in a gradual, monotonic manner. PARAFAC tensor decomposition of high-dimensional whole-brain bicoherence identified two robust, intrinsically decoupled nonlinear components with distinct spatial-spectral profiles. Model comparison demonstrated that frontal slow-wave self-coupling (Component 1) undergoes a discrete, stepwise state-space reconfiguration during loss of consciousness, whereas posterior alpha coupling (Component 2) exhibits a smooth, continuous decay with propofol titration. Phase-space kinetic trajectories confirmed a highly directed transition at unconsciousness onset, followed by confined local oscillations during deep anesthesia. Conclusion: This multi-dimensional decomposition framework captures discrete versus continuous neural dynamics, providing objective, state-sensitive biomarkers for precisely monitoring anesthesia depth and cortical state transitions.
SN - 1662-4548
SN - 1662-453X
ER -