Proton Magnetic Resonance Spectroscopy Changes in Parkinson’s Disease With and Without Psychosis
Bibliographic record
Abstract
BACKGROUND: Psychosis prevalence in Parkinson's disease is estimated at 8-30%. Proton magnetic resonance spectroscopy measures specific metabolites as markers of cell functioning. OBJECTIVE: To study N-acetyl-aspartate and glutamate levels in the caudate and putamen nuclei in subjects with Parkinson's disease with and without psychosis. METHODS: We included 20 non-demented Parkinson's disease patients with psychosis and 20 Parkinson's disease patients without psychosis matched for age, sex, disease duration, and levodopa equivalent daily dose, all attended at an academic medical center. Proton magnetic resonance spectroscopy scans were performed in a 3T GE whole-body scanner. RESULTS: Decreased glutamate levels scaled to creatine were found in the dorsal caudate (p = 0.005) and putamen (p = 0.007) of the Parkinson's disease psychosis group compared with the without psychosis group. Glutamate plus glutamine levels scaled to creatine and N-acetyl-aspartate levels scaled to creatine were also significantly reduced in the dorsal caudate of the Parkinson's disease with psychosis group (p = 0.018 and p = 0.011, respectively). No group differences were found for any of the other metabolites in the two regions of interest. CONCLUSIONS: Our findings suggest that decreased metabolite levels in specific brain areas may be implicated in the development of psychosis in Parkinson's disease.
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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) | 0.002 | 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".