Amyloid Imaging in Aging and Dementia: Testing the Amyloid Hypothesis<i>In Vivo</i>
Bibliographic record
Abstract
Amyloid imaging represents a major advance in neuroscience, enabling the detection and quantification of pathologic protein aggregations in the brain. In this review we survey current amyloid imaging techniques, focusing on positron emission tomography (PET) with ^{11}carbon-labelled Pittsburgh Compound-B (11C-PIB), the most extensively studied and best validated tracer. PIB binds specifically to fibrillar beta-amyloid (Aβ) deposits, and is a sensitive marker for Aβ pathology in cognitively normal older individuals and patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). PIB-PET provides us with a powerful tool to examine in vivo the relationship between amyloid deposition, clinical symptoms, and structural and functional brain changes in the continuum between normal aging and AD. Amyloid imaging studies support a model in which amyloid deposition is an early event on the path to dementia, beginning insidiously in cognitively normal individuals, and accompanied by subtle cognitive decline and functional and structural brain changes suggestive of incipient AD. As patients progress to dementia, clinical decline and neurodegeneration accelerate and proceed independently of amyloid accumulation. In the future, amyloid imaging is likely to supplement clinical evaluation in selecting patients for anti-amyloid therapies, while MRI and FDG-PET may be more appropriate markers of clinical progression.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".