A Virtual Clinical Trial of Psychedelics to Treat Patients With Disorders of Consciousness
Bibliographic record
Abstract
Disorders of consciousness (DoC), including unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), have limited treatment options and are characterized by low complexity of brain activity. Recent research suggests that psychedelic drugs, which enhance the complexity of brain activity, could offer promising therapies. Here, individualized whole-brain computational models are developed for patients with DoC, optimized with empirical functional magnetic resonance imaging data and diffusion-weighted imaging data, upon which the administration of lysergic acid diethylamide (LSD) and psilocybin is simulated. An in silico perturbation protocol is applied to assess brain dynamics, first distinguishing between different states of consciousness, including DoC, anesthesia, and the psychedelic state. Then, brain dynamics are assessed before and after a simulation of psychedelic drugs on patients with DoC. Findings indicated that the simulation of LSD and psilocybin shifted the brain activity of patients with DoC closer to criticality (the point at a phase transition between order and chaos), with a greater effect in patients in the MCS. In patients with UWS, the treatment response correlated with structural connectivity, while in patients in the MCS, it aligned with baseline functional connectivity. These results offer a computational foundation for using psychedelics in DoC treatment and highlight the potential future role of computational modeling in drug discovery and personalized medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".