Noradrenergic Deficits in Parkinson Disease Imaged with<sup>11</sup>C-MeNER
Bibliographic record
Abstract
Degeneration of noradrenergic neurons may underlie the disabling nonmotor symptoms in patients with Parkinson disease (PD). Quantification of the loss of noradrenergic neurons by means of neuroimaging has been limited by the lack of radioligands that are selective for noradrenergic neurotransmission. The radioligand (S,S)-11C-2-(α-(2-methoxyphenoxy)benzyl)morpholine (11C-MeNER) is a highly selective inhibitor of noradrenaline transporters, and PET studies suggest that this radioligand is suitable for quantitative neuroimaging of noradrenergic deficits in human brain in vivo. In the present investigation, we used PET with 11C-MeNER to map the density of noradrenaline transporters in groups of patients with PD and age-matched healthy controls. Methods: After administration of 11C-MeNER, 15 nondemented patients with PD and 10 healthy subjects underwent 90-min dynamic PET. We determined 11C-MeNER binding potential relative to nondisplaceable binding potential (BPND) by multilinear analysis, simplified reference tissue model 2, and multilinear reference tissue model 2. Results: Metabolism of 11C-MeNER did not differ between groups. The simplified reference tissue model 2 and the multilinear reference tissue model 2 were used to determine 11C-MeNER BPND. 11C-MeNER BPND was reduced in the PD group compared with the control subjects, with regionally significant declines in the thalamus and nucleus ruber. Tremor was associated with higher tracer binding in the PD group on multivariate regression analysis. Conclusion: To our knowledge, this was the first specific quantification of noradrenergic denervation in PD patients in vivo. In agreement with predictions from determinations in vitro, we discovered a decline of noradrenergic projections in vivo in brain of PD patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".