Dopamine transporter relation to dopamine turnover in Parkinson's disease: a positron emission tomography study
Bibliographic record
Abstract
OBJECTIVE: To investigate the role of the dopamine transporter (DAT) in the regulation of synaptic dopamine (DA) levels in Parkinson's disease and its role in the preservation of DA in presynaptic terminals. METHODS: Ten Parkinson's disease patients (age, 62.9 +/- 9.5 years; Unified Parkinson's Disease Rating Scale motor score in "off" state, 28.5 +/- 8.2) underwent positron emission tomography with (11)C-methylphenidate (MP, a DAT marker), (11)C-dihydrotetrabenazine (a vesicular monoamine transporter 2 marker), and (18)F-fluorodopa, leading to the determination of the MP and (11)C-dihydrotetrabenazine binding potentials (BPs) and the effective distribution volume for (18)F-fluorodopa, the inverse of DA turnover. Seven patients also underwent positron emission tomography with (11)C-raclopride before and 1 hour after levodopa administration to estimate levodopa-induced changes in synaptic DA concentration. RESULTS: We found a significant positive correlation between effective distribution volume and BP(MP) (r = 0.93; p < 0.001) and a significant negative correlation between changes in synaptic DA concentration and BP(MP) (r = -0.93; p = 0.04), independent of disease severity and duration. INTERPRETATION: These data show that in Parkinson's disease, greater DAT levels are directly associated with lower DA turnover and lower changes in synaptic DA concentration. This implies that an important functional role of DAT is to maintain relatively constant synaptic DA levels and to preserve DA in nerve terminals. A decrease in DAT, although potentially serving as a compensatory mechanism in early disease, may ultimately result in increased DA turnover and higher oscillations in synaptic DA concentration, thereby possibly predisposing toward the occurrence of motor complications as disease progresses.
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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 | 0.000 | 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".