Recent advances in neuroimaging of Alzheimer's disease and related dementias
Bibliographic record
Abstract
This review covers recent advances (2023-2024) in neuroimaging research into the pathophysiology, progression, and treatment of Alzheimer's disease (AD) and related dementias (ADRD). Despite the rapid emergence of blood-based biomarkers, neuroimaging continues to be a vital area of research in ADRD. Here, we discuss neuroimaging as a powerful tool to topographically visualize and quantify amyloid, tau, neurodegeneration, inflammation, and vascular disease in the brain. We examine the utility of neuroimaging for (1) tracking the spatiotemporal progression of pathology, (2) serving as the reference standard for validating novel fluid biomarkers, (3) characterizing disease heterogeneity, (4) exploring the role of brain networks in ADRD progression, and (5) evaluating biomarkers for better individualized estimates of treatment benefit. Finally, we discuss advances in radiotracer development and AD risk factors. By reviewing the most promising breakthroughs in the neuroimaging field, we hope to spark new ideas for future discoveries that will deepen our understanding of ADRD. HIGHLIGHTS: The diagnostic and staging criteria for Alzheimer's disease (AD) were updated in 2024. Despite robust harmonization methods for amyloid beta positron emission tomography (PET), parallel efforts for tau PET remain challenging. Larger anti-amyloid drug effects were seen at lower levels of amyloid and tau PET. Phosphorylated tau217 (p-tau217) is currently the most promising plasma biomarker to detect AD pathology. There are new tracer developments for alpha-synuclein, primary tauopathies, and inflammation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".