Mapping Human Brain Fatty Acid Amide Hydrolase Activity with PET
Bibliographic record
Abstract
Endocannabinoid tone has recently been implicated in a number of prevalent neuropsychiatric conditions. [(11)C]CURB is the first available positron emission tomography (PET) radiotracer for imaging fatty acid amide hydrolase (FAAH), the enzyme which metabolizes the prominent endocannabinoid anandamide. Here, we sought to determine the most suitable kinetic modeling approach for quantifying [(11)C]CURB that binds selectively to FAAH. Six healthy volunteers were scanned with arterial blood sampling for 90 minutes. Kinetic parameters were estimated regionally using a one-tissue compartment model (TCM), a 2-TCM with and without irreversible trapping, and an irreversible 3-TCM. The 2-TCM with irreversible trapping provided the best identifiability of PET outcome measures among the approaches studied (coefficient of variation (COV) of the net influx constant K(i) and the composite parameter λk(3) (λ=K(1)/k(2)) <5%, and COV(k(3))<10%). Reducing scan time to 60 minutes did not compromise the identifiability of rate constants. Arterial spin labeling measures of regional cerebral blood flow were only slightly correlated with K(i), but not with k(3) or λk(3). Our data suggest that λk(3) is sensitive to changes in FAAH activity, therefore, optimal for PET quantification of FAAH activities with [(11)C]CURB. Simulations showed that [(11)C]CURB binding in healthy subjects is far from a flow-limited uptake.
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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 | 0.001 | 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".