Context-dependent structurally informed effective connectivity under psilocybin
Bibliographic record
Abstract
Abstract The extent to which anatomical connectivity constrains pharmacologically altered brain dynamics remains poorly understood. Here, we combined psilocybin administration with a structurally informed effective-connectivity model to examine how structural connectivity shapes directed inter-regional influences across experiential contexts. Using dynamic causal modeling embedded in a hierarchical empirical Bayes framework, we analyzed fMRI data acquired from a hippocampo–thalamo–cortical network during rest, guided meditation, music listening and movie viewing. Across contexts, psilocybin reorganized directed interactions while preserving structure-based scaling. Effects converged on efferents (outgoing influences) from the left hippocampus—a hub interfacing mnemonic and associative systems with the default-mode network and thalamus. Notably, the left-hippocampus-to-thalamus pathway showed a sign-reversed association with mystical-experience scores (downregulation during guided meditation and upregulation during music listening). In model-based leave-one-out cross-validation, left-hippocampal efferents predicted individual differences in mystical-experience intensity. A minimal model-free benchmark (hippocampal signal variability) also showed modest associations with mystical experience. Together, these findings link context-specific, structurally informed effective connectivity to individual differences in the acute psychedelic experience, providing a mechanistic bridge between anatomy, neurodynamics, and phenomenology.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".