Sequential <i>Versus</i> Nonsequential Measurement of Density and Affinity of Dopamine D <sub>2</sub> Receptors with [ <sup>11</sup> C]raclopride: Effect of Methamphetamine
Bibliographic record
Abstract
The multiple ligand concentration assays (MLCRA) method provides researchers with the ability to measure in vivo receptor characteristics in a stable condition. Measurements of the density and affinity of the dopamine D2 receptors with [11C]raclopride, using a sequential method (three scans throughout 1 day) or a nonsequential method (three scans spread over several weeks but at the same time of the day), yield similar values. However, after an acute challenge with drugs that affect dopamine neurotransmission, the concentration of endogenous ligand may vary over the course of the in vivo sequential MLCRA. Combined PET-microdialysis studies after acute amphetamine showed that during the imaging time frame the concentrations of extracellular dopamine vary widely, but that nonetheless the decrease in raclopride binding potential is sustained and nearly constant over time. These observations apparently contradict the simple competitive displacement model if the changes in extracellular concentration are taken to reflect necessarily comparable changes at the binding sites. To understand the effect of the delay between drug administration and start-to-end of data acquisition on the MLCRA results, we compared the outcomes of the sequential and nonsequential methods after methamphetamine. Comparison of the binding potential, density, and affinity of D2 receptors in both experimental conditions revealed good concordance between the data sets, suggesting that methamphetamine produces sustained and stable increases in synaptic dopamine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".