Metrics to track dopamine release magnitude using PET: Comparison of Residual Space Detection and linear parametric neurotransmitter PET
Bibliographic record
Abstract
Residual Space Detection (RSD) has been previously shown to improve upon traditional methods for detecting low-magnitude dopamine (DA) release in the human brain by use of [11C]raclopride PET scanning. This work aims to extend this methodology, by examining RSD’s ability to track DA release-induced changes in synaptic DA levels, with the goal of identifying a suitable metric that can be related to behavioural and clinical measures of interest to various research studies. We compare RSD outputs with the traditional metric from linear parametric neurotransmitter PET (lp-ntPET) by means of simulation. Both methods track transient changes in synaptic DA concentration well, exhibiting near-linear scaling of their respective metric with simulated levels of DA release and achieving coefficients of variation between 20-40%. For simulated DA concentration increases from 50 to 275 pmol, RSD’s metric exhibited a 3 to 4x increase compared to lp-ntPET’s 1.5 to 2x increase, along with a y-intercept of close to zero, providing a more sensitive and interpretable measure to track single-task DA release.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".