Temporal dynamics in neuroimaging as correlates of therapeutic response to psilocybin in major depressive disorder: A systematic review and critical appraisal
Bibliographic record
Abstract
BACKGROUND: Psychedelics are emerging as promising treatments for major depressive disorder (MDD) and treatment-resistant depression (TRD). Functional magnetic resonance imaging (fMRI) offers a powerful tool to study neural mechanisms underlying therapeutic response. METHODS: This systematic review (PROSPERO #CRD42024557973) examined neuroimaging studies of psilocybin in MDD and TRD, with a focus on the temporal evolution of neuroimaging changes post-treatment. A secondary aim was to correlate imaging findings with validated clinical outcomes to assess their relevance in predicting treatment response. RESULTS: Eleven eligible studies were included, using diverse fMRI modalities such as resting-state functional connectivity, task-based BOLD imaging, amplitude of low-frequency fluctuations (ALFF), dynamic functional connectivity, and magnetic resonance spectroscopy. Early (0-4 weeks) post-treatment changes included reduced network modularity and increased global brain integration, alongside modulation of affective circuits involving the amygdala, default mode network, and prefrontal regions. These changes were significantly associated with reductions in BDI, QIDS, and SHAPS scores, reflecting improvements in mood and anhedonia. Longer-term changes (5+ weeks) involved sustained reorganization of large-scale networks, particularly increased connectivity between the prefrontal and parietal cortices and salience network. CONCLUSIONS: Although these findings suggest psilocybin is associated with dynamic and temporally distinct neuroplastic changes linked to clinical improvement, several limitations must be acknowledged. Many studies reused overlapping datasets with high exploratory flexibility and risk of bias. The generalizability of results is therefore constrained. Future research should emphasize independent datasets, pre-registered imaging endpoints, and longitudinal designs to clarify the mechanisms underlying psychedelic therapy for depression.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".