Increase in Dopamine Turnover Occurs Early in Parkinson's Disease: Evidence from a New Modeling Approach to PET <sup>18</sup> F-Fluorodopa Data
Bibliographic record
Abstract
An increase in dopamine turnover has been hypothesized to occur early in Parkinson's disease (PD) as a compensatory mechanism for dopaminergic neuronal loss. A new approach to the determination of dopamine turnover was developed using 4-hour-long 18 F-fluorodopa (FD) positron emission tomography (PET) data. An effective dopamine turnover, an estimate of dopamine turnover, has been measured using its inverse, the effective dopamine distribution volume (EDV). This new method is based on a reversible tracer approach and determines the EDV using a graphical method. Six healthy subjects and 10 subjects with very early PD underwent a 4-hour-long FD scan. The EDV and the plasma uptake rate constant K(i), a marker of dopamine synthesis and storage, were compared according to their ability to separate the PD group from the healthy group. The EDV was the better discriminator (93.8% correct classification versus 81.3% for K(i)). Effective dopamine distribution volume decreased by 65% in the PD group relative to the healthy group, whereas the decrease in K(i) was 39%. These results show that changes in EDV are measurable with PET earlier than changes in the dopamine synthesis and storage rate, indicating that EDV is a sensitive marker for early PD and that a dopamine turnover increase likely serves as an early compensatory mechanism.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".