Putaminal hypermetabolism identifies Lewy body co‐pathology in Alzheimer's disease
Bibliographic record
Abstract
Abstract INTRODUCTION The clinical implications of brain hypermetabolism remain unexplored in Lewy body disease (LBD) co‐pathology in Alzheimer's disease (AD). METHODS We investigated cognition, 18 F‐fluorodeoxyglucose positron emission tomography (PET), and cerebrospinal fluid tau phosphorylated at threonine 181 (pTau 181 )/Aβ 42 plus α‐synuclein seeding amplification assays (SAA) in controls, 217 SAA‐negative AD (AD SAA− ), and 124 SAA‐positive AD (AD SAA+ ). Brain metabolism was assessed using subject residual profile (SRP) and standardized uptake value ratio (SUVR). RESULTS Compared to AD SAA− , AD SAA+ showed putamen SRP hypermetabolism and middle occipital gyrus (MOG) SUVR hypometabolism. SAA positivity correlated with putamen SRP hypermetabolism independently of pTau 181 /amyloid beta 42 (Aβ 42 ). Its interaction with pTau 181 /Aβ 42 influenced MOG SUVR, showing increased MOG SUVR with higher pTau 181 /Aβ 42 in AD SAA+ . Putamen SRP hypermetabolism predicted faster cognitive decline and greater variability in both groups. MOG SUVR hypometabolism correlated with them only in AD SAA− . Adding putamen SRP hypermetabolism to models, including SAA positivity and AD signature hypometabolism, improved the prediction of cognitive decline/variability, whereas MOG SUVR did not. DISCUSSION Putaminal hypermetabolism may serve as a robust metabolic marker of LBD co‐pathology in AD. Highlights LB co‐pathology in AD alters regional brain metabolism. SRP analyses capture putaminal hypermetabolism for SAA positivity. SUVR analyses emphasize occipital hypometabolism for SAA positivity. Occipital metabolism correlates positively with AD severity in mixed AD‐LB. Putaminal, not occipital, metabolism predicts cognitive change over AD‐related metabolism and SAA.
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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 | 0.000 | 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".