Reframing “Paradoxical” Excitation: Disentangling EEG Complexity and Entropy Reveals Resting State Dynamics Associated with Propofol Susceptibility
Bibliographic record
Abstract
Abstract Background Propofol exposure can produce heterogenous neural responses, from the expected suppression to transient paradoxical excitation. EEG measures of signal complexity and entropy have emerged as reliable markers of consciousness, but different types of complexity and entropy measures are often conflated. We used Type I and II complexity measures on the Complexity–Entropy Causal Plane (CECP) to characterize divergent neural trajectories during propofol-induced loss of consciousness. We hypothesized that paradoxical excitation is reflected in both Type I and Type II complexity; that divergent trajectories on the CECP separate paradoxical excitation from suppression; and that baseline EEG complexity is associated with susceptibility to propofol. Methods We analyzed EEG data from two independent cohorts of healthy adults receiving propofol: the Chennu dataset (n = 20), which included resting-state baseline, mild, and moderate sedation, followed by a recovery period; and the RecCognition dataset (n = 8), which used escalating infusions from baseline to deep sedation. For each participant and sedation level, we extracted Lempel–Ziv Complexity (LZC; Type I) and Statistical Complexity (SC; Type II) and projected them onto the CECP. Pearson correlations related baseline SC to changes in SC during moderate sedation; behavioral responsiveness; effect-site propofol concentration; and time-to-loss-of-consciousness. Results At moderate sedation, participants who remained responsive showed paradoxical increases in LZC and decreases in SC, whereas unresponsive participants exhibited the opposite pattern. Baseline SC correlated negatively with both the change in SC (r = –0.88) and behavioural responsiveness, indicating that intrinsic brain dynamics influence individual susceptibility to sedation. CECP trajectories revealed a reproducible inflection point demarcating paradoxical excitation from suppression. Conclusions Mapping EEG trajectories on the CECP bridges anesthetic state transitions with underlying neural dynamics. Baseline neural complexity indexes individual sensitivity to propofol, determining whether brain dynamics transiently enter excitation or direct suppression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".