<sup>1</sup> H-MRS metabolites in antipsychotic-responsive versus non-responsive psychosis: a meta- and mega-analysis
Bibliographic record
Abstract
Abstract Understanding the mechanisms underlying the response to antipsychotic medications is critical for refining targets for new interventions and predicting clinical outcomes. This study presents a mega-analysis of individual participant data (N = 1,189) from 18 ¹H-MRS datasets to examine differences in neurometabolites in antipsychotic non-responsive compared to antipsychotic-responsive psychosis, accompanied by complementary meta-analyses across the wider published literature (23 studies, N = 1,844). The mega-analysis revealed that antipsychotic non-response is associated with elevated levels of glutamate, Glx (the sum of glutamate and glutamine), choline, and myo-inositol (mI) in the medial frontal cortex (MFC) compared to individuals who showed a good antipsychotic response and healthy controls. Follow-up analyses revealed that elevated MFC Glx in antipsychotic non-responders, compared with responders, is already evident prospectively in first-episode psychosis, whereas elevated mI is most pronounced in individuals meeting criteria for treatment resistance following antipsychotic treatment. The elevations in MFC choline and mI associated with antipsychotic non-response were also detected in the meta-analysis. In both the meta- and mega-analysis, several metabolites were more variable in the patient than the healthy control groups. Collectively, these data provide the most robust evidence to date linking antipsychotic non-response in psychosis to elevations in medial frontal glutamate, choline and mI. The findings support the continued investigation of glutamate-acting and inflammatory pathway-associated interventions for psychosis and schizophrenia, and particularly for patients who have not responded to antipsychotic treatment.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.043 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".