Axonal Degeneration Across the Alzheimer’s Disease Spectrum: A Longitudinal MRI and Fluid Biomarker Study
Bibliographic record
Abstract
With global dementia rates rising sharply, there is an urgent need for sensitive biomarkers to detect cognitive changes and predict dementia risk. White matter degeneration, especially axonal loss, is increasingly recognized as an early hallmark of Alzheimer's disease (AD), but its temporal trajectory and its relationship with cognition have not been established. We utilized a novel MRI-derived Axonal Density Index (ADI) to longitudinally investigate axonal degeneration and cognitive decline in the ADNI cohort. Linear mixed-effects models showed cognitively impaired individuals had lower baseline ADI and faster axonal degeneration compared to cognitively normal subjects. In cognitively impaired individuals, higher baseline ADI predicted slower prospective cognitive deterioration and lower dementia risk, while greater longitudinal ADI declines correlated with cognitive worsening and increased dementia risk. Notably, ADI outperformed cerebrospinal fluid biomarkers of AD pathology in predicting cognitive outcomes. Our original findings position axonal degeneration as an early AD feature and ADI as a promising biomarker for early detection, disease phenotyping and monitoring, and intervention targets.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".