Distribution of Vesicular Monoamine Transporter 2 Protein in Human Brain: Implications for Brain Imaging Studies
Bibliographic record
Abstract
The choice of reference region in positron emission tomography (PET) human brain imaging of the vesicular monoamine transporter 2 (VMAT2), a marker of striatal dopamine innervation, has been arbitrary, with cerebellar, whole cerebral, frontal, or occipital cortices used. To establish whether levels of VMAT2 are in fact low in these cortical areas, we measured VMAT2 protein distribution by quantitative immunoblotting in autopsied normal human brain (n=6). Four or five species of VMAT2 immunoreactivity (75, 55, 52, 45, 35 kDa) were detected, which were all markedly reduced in intensity in nigrostriatal regions of patients with parkinsonian conditions versus matched controls (n=9 to 10 each). Using the intact VMAT2 immunoreactivity, cerebellar and cerebral neocortices had levels of the transporter >100-fold lower than the VMAT2-rich striatum and with no significant differences among the cortical regions. We conclude that human cerebellar and cerebral cortices contain negligible VMAT2 protein versus the striatum and, in this respect, all satisfy a criterion for a useful reference region for VMAT2 imaging. The slightly lower PET signal for VMAT2 binding in occipital (the currently preferred reference region) versus cerebellar cortex might not therefore be explained by differences in VMAT2 protein itself but possibly by other imaging variables, for example, partial volume effects.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".