Increased Vesicular Monoamine Transporter Binding during Early Abstinence In Human Methamphetamine Users: Is VMAT2 a Stable Dopamine Neuron Biomarker?
Bibliographic record
Abstract
Animal data indicate that methamphetamine can damage striatal dopamine terminals. Efforts to document dopamine neuron damage in living brain of methamphetamine users have focused on the binding of [(11)C]dihydrotetrabenazine (DTBZ), a vesicular monoamine transporter (VMAT2) positron emission tomography (PET) radioligand, as a stable dopamine neuron biomarker. Previous PET data report a slight decrease in striatal [(11)C]DTBZ binding in human methamphetamine users after prolonged (mean, 3 years) abstinence, suggesting that the reduction would likely be substantial in early abstinence. We measured striatal VMAT2 binding in 16 recently withdrawn (mean, 19 d; range, 1-90 d) methamphetamine users and in 14 healthy matched-control subjects during a PET scan with (+)[(11)C]DTBZ. Unexpectedly, striatal (+)[(11)C]DTBZ binding was increased in methamphetamine users relative to controls (+22%, caudate; +12%, putamen; +11%, ventral striatum). Increased (+)[(11)C]DTBZ binding in caudate was most marked in methamphetamine users abstinent for 1-3 d (+41%), relative to the 7-21 d (+15%) and >21 d (+9%) groups. Above-normal VMAT2 binding in some drug users suggests that any toxic effect of methamphetamine on dopamine neurons might be masked by an increased (+)[(11)C]DTBZ binding and that VMAT2 radioligand binding might not be, as is generally assumed, a "stable" index of dopamine neuron integrity in vivo. One potential explanation for increased (+)[(11)C]DTBZ binding is that VMAT2 binding is sensitive to changes in vesicular dopamine storage levels, presumably low in drug users. If correct, (+)[(11)C]DTBZ might be a useful imaging probe to correlate changes in brain dopamine stores and behavior in users of methamphetamine.
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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 | 0.001 | 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".