ArticleFrontiers in neuroscience2026
Tensor decomposition reveals discrete cortical reorganization under graded propofol anesthesia.
Article in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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