Dynamical independence reveals anaesthetic specific fragmentation of emergent structure in neural dynamics
Bibliographic record
Abstract
Conscious experience depends on the coordinated activity of neural processes that span multiple scales--from synapses to whole-brain dynamics. A recently introduced measure, dynamical independence, identifies, characterises and quantifies these multi-scale relationships using an information-theoretic dimensionality-reduction approach. Here, we use DI to examine changes in emergent dynamical structure in the human brain under three pharmacologically-distinct anaesthetic interventions (propofol, xenon, ketamine). Applied to source-reconstructed EEG, our analysis reveals that propofol and xenon, anaesthetics that abolish conscious report, exhibit more emergent but highly variable dynamic structure, indicating fragmented macroscopic dynamical organisation. By contrast, ketamine, which preserves dream-like phenomenology, shows the opposite pattern: reduced overall emergence yet a partial preservation of the macroscopic structure, mirroring wake. Further exploratory analyses revealed spatially localised source-level contributions to emergent dynamical structure, highlighting regional variations. Together, our results highlight drug-specific reconfigurations of emergent dynamical structure under anaesthesia, dissociate the amount of emergence from the organisation of emergent dynamics, and caution against equating emergence with level of consciousness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".