Dynamic neuroreceptor positron emission tomography in non-anesthetized rats using point source based motion correction: A feasibility study with [ <sup>11</sup> C]ABP688
Bibliographic record
Abstract
To prevent motion artifacts in small animal positron emission tomography (PET), animals are routinely scanned under anesthesia or physical restraint. Both may potentially alter metabolism and neurochemistry. This study investigates the feasibility of fully awake acquisition and subsequent absolute quantification of dynamic brain PET data via pharmacokinetic modelling in moving rats using the glutamate 5 receptor radioligand [ 11 C]ABP688 and point source based motion correction. Five male rats underwent three dynamic [ 11 C]ABP688 PET scans: two test-retest awake PET scans and one scan under anesthesia for comparison. Specific radioligand binding was determined via the simplified reference tissue model (reference: cerebellum) and outcome parameters BP ND and R 1 were evaluated in terms of stability and reproducibility. Test-retest measurements in awake animals gave reliable results with high correlations of BP ND (y = 1.08 × −0.2, r = 0.99, p < 0.01) and an acceptable variability (mean over all investigated regions 15.7 ± 2.4%). Regional [ 11 C]ABP688 BP ND s under awake and anesthetized conditions were comparable although in awake scans, absolute radioactive peak uptakes were lower and relative blood flow in terms of R 1 was higher. Awake small animal PET with absolute quantification of neuroreceptor availability is technically feasible and reproducible thereby providing a suitable alternative whenever effects of anesthesia are undesirable, e.g. in sleep research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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".