Neurometabolic abnormalities in schizophrenia and depression observed with magnetic resonance spectroscopy at 7 T
Bibliographic record
Abstract
Background Examining neurometabolic abnormalities in critical brain areas in schizophrenia and major depressive disorder (MDD) may help guide future pharmacological interventions including glutamate-modulating treatments. Aims To measure metabolite concentrations within the anterior cingulate cortex (ACC) and thalamus of people with schizophrenia and people with MDD. Methods Spectra were acquired from 16 volunteers with schizophrenia, 17 with MDD and 18 healthy controls using magnetic resonance spectroscopy on a 7 Tesla scanner. Results In the thalamus, there were lower glycine concentrations in the schizophrenia group relative to control (P=0.017) and MDD groups (P=0.012), and higher glutamine concentrations relative to healthy controls (P=0.009). In the thalamus and the ACC, the MDD group had lower myo-inositol concentrations than the control (P=0.014, P=0.009, respectively) and schizophrenia (P=0.004, P=0.002, respectively) groups. Conclusion These results support the glutamatergic theory of schizophrenia and indicate a potential glycine deficiency in the thalamus. In addition, reduced myo-inositol concentrations in MDD suggest its involvement in the disorder.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".