A miR-137-related biological pathway of risk for Schizophrenia is associated with human brain emotion processing
Bibliographic record
Abstract
Abstract Genome-Wide-Association studies have involved miR-137 in schizophrenia. However, the biology underlying this statistical evidence is unclear. Statistical polygenic risk for schizophrenia is associated with working memory, while other biological evidence involves miR-137 in emotion processing. We investigated the function of miR-137 target schizophrenia risk genes in humans. We identified a prefrontal co-expression pathway of schizophrenia-associated miR-137 targets and validated the association with miR-137 expression in neuroblastoma cells. Alleles predicting greater co-expression of this pathway were associated with greater prefrontal activation during emotion processing in two independent cohorts of healthy volunteers (N 1 =222; N 2 =136). Statistical polygenic risk for schizophrenia was instead associated with prefrontal activation during working memory. A co-expression pathway links miR-137 and its target genes to emotion processing and risk for schizophrenia. Low prefrontal miR-137 expression may be related with SCZ risk via increased expression of target risk genes, itself associated with increased prefrontal activation during emotion processing.
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 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) | 0.000 | 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) | 0.004 | 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".