Relationship between VMAT2-PET and DAT-SPECT caudate binding and Parkinson's disease cognitive decline
Bibliographic record
Abstract
INTRODUCTION: Dopamine transporter (DAT) imaging can help diagnose Parkinson's disease (PD), and decreased caudate binding predicts cognitive decline. Studies suggest vesicular monoamine transporter type 2 (VMAT2) imaging is more sensitive in detecting striatal changes associated with PD motor symptoms. Our hypothesis is that lower VMAT2 caudate binding is correlated with future cognitive decline in PD. METHODS: We utilized clinical and imaging data from the Parkinson's Progression Markers Initiative (PPMI). We evaluated the relationship between baseline VMAT2 and DAT caudate binding and future change in Montreal Cognitive Assessment (MoCA) score using linear regression. To evaluate localization of findings we performed similar analysis with VMAT2 and DAT putamen and most and least affected caudate binding. We also categorized patients into groups of normal and reduced VMAT2 and DAT caudate binding and compared baseline and follow up characteristics between groups. RESULTS: Among 54 subjects with follow up data, baseline VMAT2 caudate binding correlated with change in MoCA score, while DAT did not (r = 0.280, p = 0.0164 and r = 0.189, p = 0.410 respectively). Baseline VMAT2 putamen binding also correlated with change in MoCA score (p = 0.0236, r = 0.23). Reduced VMAT2 caudate binding groups had higher motor severity score at baseline and lower cognitive scores on follow-up (p = 0.0015), while DAT did not (p = 0.174). Patients with reduced VMAT2 caudate but not putamen binding had significantly greater decline in MoCA score (caudate p = 0.0015, putamen p = 0.179). CONCLUSION: Reduced caudate VMAT2 binding may predict future cognitive decline, and in our patient population, was more sensitive than caudate DAT binding in predicting the magnitude of decline.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".