Positron Emission Tomography Quantification of [<sup>11</sup>C]-Harmine Binding to Monoamine Oxidase-A in the Human Brain
Bibliographic record
Abstract
This article describes the kinetic modeling of [11C]-harmine binding to monoamine oxidase A (MAO-A) binding sites in the human brain using positron emission tomography (PET). Positron emission tomography studies were performed in healthy volunteers at placebo conditions and after treatment with clinical doses of moclobemide. In either condition, a two-tissue compartment model (2CM) provided better fits to the data than a one-tissue model. Estimates of k3/ k4 values from an unconstrained 2CM were highly variable. In contrast, estimates of the specifically bound radioligand distribution volume ( DVB) from an unconstrained 2CM were exceptionally stable, correlated well with the known distribution of MAO-A in the brain (cerebellum F + NS) or coupling DVF + NS between brain regions enabled more stable estimates of k3/ k4 as compared with an unconstrained 2CM. Moclobemide treatment leads to a 64% to 79% MAO-A blockade across brain regions, a result that supports the specificity of [11C]-harmine binding to MAO-A. The stability and reliability of DVB values obtained from an unconstrained 2CM, together with the computational simplicity associated with this method, support the use of DVB as an appropriate outcome measure for [11C]-harmine. These results indicate the suitability of using [11C]-harmine for quantitative evaluation of MAO-A densities using PET and should enable further studies of potential MAO-A dysregulation in several psychiatric and neurologic illnesses.
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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 | 0.000 | 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".