Astrocyte activation imaging with 11C-acetate and amyloid PET in mild cognitive impairment due to Alzheimer pathology
Bibliographic record
Abstract
BACKGROUND: Neuroinflammation is a well-known feature of early Alzheimer disease (AD) yet astrocyte activation has not been extensively evaluated with in vivo imaging in mild cognitive impairment (MCI) due to amyloid plaque pathology. Unlike neurons, astrocytes metabolize acetate, which has potential as a glial biomarker in neurodegeneration in response to AD pathologic features. Since the medial temporal lobe (MTL) is a hotspot for AD neurodegeneration and inflammation, we assessed astrocyte activity in the MTL and compared it to amyloid and cognition. METHODS: We evaluate spatial patterns of in vivo astrocyte activation and their relationships to amyloid deposition and cognition in a cross-sectional pilot study of six participants with MCI and five cognitively normal participants. We measure 11C-acetate and 18F-florbetaben amyloid standardized uptake values ratios (SUVRs) and kinetic flux compared to the cerebellum on PET, with MRI and neurocognitive testing. RESULTS: MTL 11C-acetate SUVR was significantly elevated in MCI compared to cognitively normal participants (P = 0.03; Cohen d = 1.76). Moreover, MTL 11C-acetate SUVR displayed significant associations with global and regional amyloid burden in MCI. Greater MTL 11C-acetate retention was significantly related with worse neurocognitive measures including the Montreal Cognitive Assessment (P = 0.001), word list recall memory (P = 0.03), Boston naming test (P = 0.04) and trails B test (P = 0.04). CONCLUSIONS: While further validation is required, this exploratory pilot study suggests a potential role for 11C-acetate PET as a neuroinflammatory biomarker in MCI and early AD to provide clinical and translational insights into astrocyte activation as a pathological response to amyloid.
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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 | 0.001 |
| 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 | 0.000 | 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".