[18F]SynVest-1 PET imaging in people with Parkinson’s disease
Bibliographic record
Abstract
Abstract The [18F]SynVest-1 radiotracer targets the synaptic vesicle glycoprotein 2A (SV2A) and is a proxy of presynaptic density. Parkinson’s disease is associated with synaptic dysfunction. Here we investigated synaptic density via the [18F]SynVest-1 radiotracer in people with PD compared with healthy controls, with reference to how it compares to the previous SV2A radiotracer, [11C]UCB-J. Ten Parkinson’s patients and 12 healthy subjects underwent a [18F]SynVest-1 PET scan. We compared non-displaceable binding potential via voxel-wise and volume of interest analysis to investigate group differences. Volume-of-interest-analyses reported lower non-displaceable binding potential in key a priori regions associated with Parkinson’s disease, namely the substantia nigra and caudate nucleus (P < 0.05). Follow-up exploratory volume-of-interest-analyses reported widespread reduction in non-displaceable binding potential within all brain lobes, cerebellum, hippocampus, thalamus and insula; however, these findings did not survive correction for multiple comparisons (P < 0.004). In addition, voxel-wise analyses with family-wise error correction, highlighted significantly lower non-displaceable binding potential in the PD cohort within the putamen and cerebellum. We did not observe any relationships between clinical metrics and non-displaceable binding potential. The results are in line with differences observed using the [11C]UCB-J radiotracer. The [18F]SynVest-1 radiotracer confirmed lower synaptic density in the Parkinson’s disease cohort and adds to the growing evidence of synaptic dysfunction in Parkinson’s disease pathology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".