Psilocybin’s Potential Mechanisms in the Treatment of Depression: A Systematic Review
Bibliographic record
Abstract
Evidence suggests that psilocybin has therapeutic benefit for treating depression. However, there is little consensus regarding the mechanism by which psilocybin elicits antidepressant effects. This systematic review summarizes existing evidence. Ovid MEDLINE, EMBASE, psychINFO, and Web of Science were searched, for both human and animal studies, using a combination of MeSH Terms and free-text keywords in September 2021. No other mood disorders or psychiatric diagnoses were included. Original papers in English were included. The PRISMA framework was followed for the screening of papers. Two researchers screened the retrieved articles from the literature search, and a third researcher resolved any conflicts. Of 2,193 papers identified, 49 were selected for full-text review. 14 articles were included in the qualitative synthesis. Six supported psilocybin's mechanism of antidepressant action via changes to serotonin or glutamate receptor activity and three papers found an increase in synaptogenesis. Thirteen papers investigated changes in non-receptor or pathway-specific brain activity. Five papers found changes in functional connectivity or neurotransmission, most commonly in the hippocampus or prefrontal cortex. Several neuroreceptors, neurotransmitters, and brain areas are thought to be involved in psilocybin's ability to mitigate depressive symptoms. Psilocybin appears to alter cerebral blood flow to the amygdala and prefrontal cortex, but the evidence on changes in functional connectivity and specific receptor activity remains sparse. The lack of consensus between studies suggests that psilocybin's mechanism of action may involve a variety of pathways, demonstrating the need for more studies on psilocybin's mechanism of action as an antidepressant.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".