Associations between fluid biomarkers and PET imaging ([11C]UCB‐J) of synaptic pathology in Alzheimer's disease
Bibliographic record
Abstract
Abstract INTRODUCTION Positron emission tomography (PET) imaging with ligands for synaptic vesicle glycoprotein 2A (SV2A) has emerged as a promising methodology for measuring synaptic density in Alzheimer's disease (AD). We investigated the relationship between SV2A PET and CSF synaptic protein changes of AD patients. METHOD Twenty‐one participants with early AD and seven cognitively normal (CN) individuals underwent [ 11 C]UCB‐J PET. We used mass spectrometry to measure a panel of synaptic proteins in cerebrospinal fluid (CSF). RESULTS In the AD group, higher levels of syntaxin‐7 and PEBP‐1 were associated with lower global synaptic density. In the total sample, lower global synaptic density was associated with higher levels of AP2B1, neurogranin, γ‐synuclein, GDI‐1, PEBP‐1, syntaxin‐1B, and syntaxin‐7 but not with the levels of the neuronal pentraxins or 14‐3‐3 zeta/delta. CONCLUSION Reductions of synaptic density found in AD compared to CN participants using [ 11 C]UCB‐J PET were observed to be associated with CSF biomarker levels of synaptic proteins. Highlights A panel of synaptic proteins was quantified in the CSF using mass spectrometry. SV2A ([ 11 C]UCB‐J) PET was used to quantify synaptic density. Reductions of synaptic density were associated with CSF synaptic biomarker levels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".