White Matter Hyperintensities in Mild Cognitive Impairment and Lower Risk of Cognitive Decline
Bibliographic record
Abstract
BACKGROUND: White matter hyperintensities (WMH) may have a different impact on cognitive decline depending on strategic localization. OBJECTIVE: The goal of this study is to assess the impact of global and cholinergic WMH on cognitive decline of mild cognitive impairment (MCI) patients in the ADNI-1 dataset. METHODS: This is a retrospective analysis of data from a natural history study. MRI scans (T2 and PD sequences) were assessed with two visual scales: 1) The Cholinergic Pathways HyperIntensities Scale (CHIPS) score, designed to assess WMH in the cholinergic tracts, and 2) the Age-Related White Matter Changes Scale (ARWMC), a scale to assess the global WMH burden. All subjects underwent standardized neuropsychological testing. RESULTS: Subjects included 310 individuals with MCI. Analysis showed no association between WMH at baseline and conversion from MCI to Alzheimer's disease (AD), either for the global WMH burden or WMH within the cholinergic pathways. However, ARWMC scores had a significant confounding effect (p = 0.03) on conversion to dementia (hazard ratio of 0.37) among MCI subjects with low executive functions. CONCLUSION: We found no association between the burden of WMH at baseline in MCI and conversion to AD over 3 years. However, a higher global WMH burden appears to reduce the risk of conversion to AD in subjects with low executive functions. These results suggest that higher WMH burden in MCI individuals may be associated with a more gradual cognitive decline or stabilization, compared to a low WMH burden.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".