M157. A MULTICENTRE STUDY OF 1H-MRS BRAIN GLUTAMATE LEVELS IN SCHIZOPHRENIA; INVESTIGATING THE EFFECT OF ANTIPSYCHOTIC MEDICATION, SYMPTOM SEVERITY AND AGE
Bibliographic record
Abstract
Abstract Background Proton Magnetic Resonance Spectroscopy (1H-MRS) studies indicate that altered brain glutamate signalling contributes to the pathophysiology of schizophrenia and treatment response. However it is unclear whether clinical and demographic factors affect glutamate levels in the brain. Here we aim to determine the effects of age, antipsychotic medication, symptom severity and duration of illness on levels of glutamatergic metabolites and creatine, in a large multicentre dataset of patients with schizophrenia and healthy volunteers. Methods Authors of 1H-MRS studies in schizophrenia were contacted between January 2014 and August 2017 and asked to provide individual glutamate, Glx, glutamine and creatine values alongside demographic and clinical data. Forty-five 1H-MRS studies contributed data to the multicentre dataset, and data was available from 1194 healthy volunteers and 1526 patients with schizophrenia and those at high risk of developing psychosis. Results Age was associated with reduced glutamate levels in the medial frontal cortex, but the effect of aging was not accelerated in patients compared to healthy volunteers. Higher glutamate and Glx in the medial frontal and temporal lobes were associated with more severe symptoms, whereas Glx in subcortical regions, specifically the thalamus and striatum, did not relate to symptom severity. In the medial frontal cortex and striatum, exposure to antipsychotic medication was associated with lower levels of glutamatergic metabolites. Duration of illness was not associated with glutamatergic metabolites. Lastly, creatine in the medial frontal cortex and thalamus increased with age. Discussion These data suggest that future 1H-MRS studies should control for the effects of age, symptom severity, and antipsychotic medication exposure. Caution is needed when using creatine as a reference to scale metabolites.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".