Comparison of Metabolite Concentrations in the Left Dorsolateral Prefrontal Cortex, the Left Frontal White Matter, and the Left Hippocampus in Patients in Stable Schizophrenia Treated with Antipsychotics with or without Antidepressants. 1H-NMR Spectroscopy Study
Bibliographic record
Abstract
Managing affective, negative, and cognitive symptoms remains the most difficult therapeutic problem in stable phase of schizophrenia. Efforts include administration of antidepressants. Drugs effects on brain metabolic parameters can be evaluated by means of proton nuclear magnetic resonance (¹H-NMR) spectroscopy. We compared spectroscopic parameters in the left prefrontal cortex (DLPFC), the left frontal white matter (WM) and the left hippocampus and assessed the relationship between treatment and the spectroscopic parameters in both groups. We recruited 25 patients diagnosed with schizophrenia (DSM-IV-TR), with dominant negative symptoms and in stable clinical condition, who were treated with antipsychotic and antidepressive medication for minimum of three months. A group of 25 patients with schizophrenia, who were taking antipsychotic drugs but not antidepressants, was matched. We compared metabolic parameters (N-acetylaspartate (NAA), myo-inositol (mI), glutamatergic parameters (Glx), choline (Cho), and creatine (Cr)) between the two groups. All patients were also assessed with the Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS). In patients receiving antidepressants we observed significantly higher NAA/Cr and NAA/Cho ratios within the DLPFC, as well as significantly higher mI/Cr within the frontal WM. Moreover, we noted significantly lower values of parameters associated with the glutamatergic transmission--Glx/Cr and Glx/Cho in the hippocampus. Doses of antipsychotic drugs in the group treated with antidepressants were also significantly lower in the patients showing similar severity of psychopathology.
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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".