Psilocybin Modulates TPJ Effective Connectivity during Out-of-Body Experiences
Bibliographic record
Abstract
Abstract Serotonergic psychedelics alter self-boundaries and can induce out-of-body experiences (OBEs)—the sense of being located outside one’s physical body. While OBEs also occur in clinical conditions and can be experimentally induced, their neural basis under psychedelics remains underexplored. In an open-label, baseline-controlled MRI study of 62 healthy adults administered psilocybin, we examined effective connectivity changes in regions implicated in clinical and induced OBEs. Spectral dynamic causal modelling (spDCM) was applied to resting-state and music-listening scans to estimate connectivity changes from baseline and assess their consistency across contexts. Participants were grouped by self-reported OBE symptom intensity at the end of the dosing day. In those reporting high-intensity OBEs, psilocybin reduced effective connectivity from the right to left anterior insula and between the right anterior insula and right temporoparietal junction (TPJ), inhibiting these connections across both scan types. These changes parallel known disruptions in TPJ–insula circuits linked to OBEs in clinical and experimental settings, particularly in the right hemisphere. Our findings highlight how psilocybin-induced disembodiment corresponds to altered effective connectivity and demonstrate the utility of spDCM for mapping causal neural dynamics underlying bodily self-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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".