Brain serotonin transporter binding in non‐depressed patients with Parkinson's disease
Bibliographic record
Abstract
Early post-mortem data suggest that damage to brain serotonin neurones might play a role in some features (e.g., depression) of Parkinson's disease (PD). However, it is not known whether such damage is a typical characteristic of living patients with PD or whether the changes are regionally widespread. To address this question we measured, by positron emission tomography imaging, levels of the brain serotonin transporter (SERT), a marker for serotonin neurones, as inferred from binding of [11C]-3-amino-4-(2-dimethylaminomethyl-phenylsulfanyl)-benzonitrile (DASB), a second generation SERT radioligand, in subcortical and cerebral cortical brain areas of clinically advanced non-depressed (confirmed by structured psychiatric interview) patients with PD. SERT binding levels in PD were lower than those in controls in all examined brain areas, with the changes statistically significant in orbitofrontal cortex (-22%), caudate (-30%), putamen (-26%), and midbrain (-29%). However, only a slight non-significant reduction (-7%) was observed in dorsolateral pre-frontal cortex, an area implicated in major depression. Our imaging data suggests that a modest, regionally widespread loss of brain serotonergic innervation might be a common feature of advanced PD. Further investigation will be required to establish whether SERT binding is more or less decreased in those patients with PD who also have major depressive disorder.
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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) | 0.001 | 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".