Tensor decomposition reveals discrete cortical reorganization under graded propofol anesthesia

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.

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.

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.

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.