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Non-hallucinogenic psychedelics for mood and anxiety disorders: A systematic review

2025· review· en· W4410200405 on OpenAlexaff
Margery J.Q. Chen, David Chen‐Li, Noah Chisamore, Muhammad Ishrat Husain, Joshua D. Di Vincenzo, Rodrigo B. Mansur, Lee Phan, Danica E. Johnson, Joshua D. Rosenblat

Bibliographic record

VenuePsychiatry Research · 2025
Typereview
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsCentre for Addiction and Mental HealthBrain and Cognition Discovery FoundationUniversity Health Network
Fundersnot available
KeywordsHallucinogenAnxietyMoodPsychologyPsilocybinClinical psychologyPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Psychedelics have re-emerged as promising treatments for mood disorders. The current model provides a moderate-to-high dose of a psychedelic agent (e.g., psilocybin) to reliably induce an altered state of consciousness. Unfortunately, the hallucinatory effects limit the treatment's potential scalability given patients' vulnerability and extensive monitoring costs, leading to growing interest in non-hallucinatory psychedelics (NHPs). This review's objective was to identify, summarize and synthesize all published pre-clinical and clinical studies evaluating NHPs for mood and anxiety disorders. We included five animal studies demonstrating antidepressant-like effects through assessments like forced swim test (FST) and open field test (OFT) without observing head-twitch response (HTR), and one case report that identified a patient who inadvertently combined trazodone and psilocybin and experienced potent antidepressant effects without psychedelic effects. These preliminary findings provide a strong impetus for further investigation in human samples with rigorously designed clinical trials that may delineate the potential antidepressant effects of psychedelics without hallucinatory effects.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.422
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.117
GPT teacher head0.508
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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