White matter hyperintensity burden predicts domain-specific cognitive decline across the Alzheimer’s disease continuum
Bibliographic record
Abstract
White matter hyperintensity (WMH), indicative of cerebral small vessel disease, has emerged as a potential biomarker for cognitive decline in Alzheimer's disease (AD). However, their predictive role across specific cognitive domains within the AD spectrum remains unclear. This study investigates the relationship between WMH volume and cognitive performance in memory, executive function, and language across the AD continuum. A cross-sectional analysis was conducted using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), comprising 557 participants categorized into cognitively normal (CN; n = 158), mild cognitive impairment (MCI; n = 334), and Alzheimer's dementia (AD; n = 65) groups. Cognitive function was assessed using composite scores for memory (ADNI-MEM), executive function (ADNI-EF), and language (ADNI-LAN). WMH volume was quantified through validated Bayesian segmentation of MRI data. Associations between cognitive scores and WMH volume, adjusted for age, gender, APOE ε4 status, and vascular risk factors, were evaluated via multiple linear regression analyses. WMH volume showed numerically progressive increases from CN to MCI and AD groups; however, between-group differences did not reach statistical significance. Within the MCI group, significant negative associations emerged between WMH volume and memory (β=-0.13, adjusted p = 0.045) and language scores (β=-0.12, adjusted p = 0.045). Conversely, these relationships were absent in both the CN and AD groups. WMH volume relates specifically to declines in memory and language abilities, particularly in individuals with MCI. These results support using WMH measurements as early markers to identify cognitive decline in AD, potentially helping to guide earlier diagnosis and treatment decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".