Microdosing Psychedelics to Restore Synaptic Density in Schizophrenia
Bibliographic record
Abstract
Schizophrenia is a highly polygenic disease, and several genetic variants associated with the disease converge on altered synaptic homeostasis. In particular, the gene encoding complement component 4 (C4) showed the strongest association with schizophrenia, and this protein is involved in complement-dependent and microglia-mediated synaptic pruning. As a matter of fact, microglia are overactive in schizophrenia, and reduced synaptic arborization, especially in the prefrontal cortex (PFC), is an established hallmark of schizophrenia, likely associated with gray matter loss, cortical thinning, hypofrontality, and deficit syndrome. The recent development of a new radioligand targeting the synaptic vesicle glycoprotein 2A (SV2A) demonstrated in vivo lower synaptic density at the PFC level in individuals with schizophrenia, corroborating the synaptic hypothesis of thedisease first proposed by Feinberg in 1982. Interestingly, robust preclinical evidence (in vitro and animal models) showed the ability of psychedelics to promote neuroplasticity and synaptogenesis, potentially counteracting the excessive synaptic loss, restoring volume loss, and possibly explaining improvements in negative and cognitive symptoms described by old clinical studies. Overall, microdoses should be explored first as a possible treatment in a selected sample of patients affected by deficit schizophrenia, followed by low and full doses if encouraging results were to emerge.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".