A New Visual Rating Scale to Assess Strategic White Matter Hyperintensities Within Cholinergic Pathways in Dementia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: One possible mechanism of cognitive decline in individuals with subcortical vascular disease is disruption of cholinergic fibers by ischemic lesions, such as strategically located white matter hyperintensities (WMH). The authors applied a new MRI visual rating scale to assess WMH within cholinergic pathways in patients with Alzheimer Disease (AD) and subcortical ischemic microvascular disease. METHODS: Subjects included 60 AD patients with and without WMH, matched for age, as well as 15 control subjects. A visual rating scale was developed based on published immunohistochemical tracings of the cholinergic pathways in humans. On 4 selected axial images, the severity of WMH in the cholinergic pathways was rated on a 3-point scale for ten regions, identified with major anatomical landmarks. A published, consensus-derived, general WMH scale was also applied. All subjects underwent standardized neuropsychological testing. RESULTS: The Cholinergic Pathways HyperIntensities Scale showed reliability and was validated with volumetry of strategic WMH. After accounting for age and education in a multiple linear regression model, The Cholinergic Pathways HyperIntensities Scale ratings were associated with impaired performance on the Mattis Dementia Rating Scale (r=0.40; P=0.02) and accounted for 12% of the variance (corrected r2). A similar model was not significant for general WMH scores. CONCLUSIONS: The new MRI rating scale for WMH in cholinergic pathways is reliable and shows stronger correlations with cognitive performance than a general WMH rating scale in AD with WMH. This new rating scale provides indirect evidence that localization of WMH within neurotransmitter systems may contribute to cognitive decline.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".