Contribution of alpha‐synuclein pathology to cerebral glucose metabolism in patients with amnestic MCI
Bibliographic record
Abstract
INTRODUCTION: The in vivo detection of mixed Alzheimer's disease (AD) and α-synuclein (αSyn) pathology is important for clinical management and prognostic stratification. We investigated the contribution of αSyn pathology, detected by cerebrospinal fluid (CSF) seed amplification assay (αSyn SAA), on [18F]-fluorodeoxyglucose positron emission tomography (FDG PET) pattern in subjects with amnestic mild cognitive impairment (aMCI). METHODS: We included 562 aMCI participants and 204 cognitively normal controls (CN) with available αSyn SAA and cerebral metabolic rate for glucose utilization (rCMRgl) data. RESULTS: 24% of aMCI cases were positive (+) for CSF αSyn SAA. Compared to CN, both αSyn+ and negative (-) aMCI participants showed reductions in rCMRgl within AD typical regions. αSyn+ aMCI had lower rCMRgl within AD and dementia with Lewy bodies (DLB) typical regions compared to αSyn- aMCI, even after stratification according to the CSF AT(N) system. DISCUSSION: αSyn pathology contributes to a distinct FDG PET pattern in aMCI. HIGHLIGHTS: αSyn pathology can be detected in vivo by CSF αSyn SAA. We investigated the FDG PET pattern in aMCI patients with CSF αSyn SAA positivity. αSyn+ aMCI showed a marked brain hypometabolism in AD and DLB typical regions.
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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 | 0.000 |
| 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) | 0.001 | 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".