Complexity of MRI White Matter Hyperintensity Assessments in Relation to Cognition in Aging and Dementia from the Sunnybrook Dementia Study
Bibliographic record
Abstract
PURPOSE: Quantification methods for white matter hyperintensities (WMH) on Magnetic Resonance Imaging are heterogeneous, deterring their application. This study compared three WMH rating scales, varying in complexity, and a volumetric method, to evaluate trade-offs between complexity and clinical utility in differentiating dementia subgroups and in correlating with cognition. METHODS: WMH were rated using the Fazekas, Age-Related White Matter Changes (ARWMC) and Scheltens scales, and segmented by computational volumetry in 108 patients with Alzheimer's Disease (AD), 23 with Mild Cognitive Impairment (MCI) and 34 normal controls (NC). Global and hippocampal atrophy, age and education, were accounted for in correlations of WMH with cognitive domains. RESULTS: Intra- and inter-rater reliability were high (intraclass correlation coefficients = 0.88-0.97) across rating scales. WMH scores of all scales were highly correlated with volumes (Spearman r = 0.78-0.90, Ps < 0.001), as well as with each other (Spearman r = 0.86-0.91, Ps < 0.001). The Fazekas scale showed significant separation between AD, MCI and NC using non-parametric analysis, while the ARWMC and Scheltens' scales, and WMH volumes demonstrated significant correlations (standardized β = -0.19 to -0.24, Ps < 0.05) with cognitive domain scores using multivariate regression analysis, controlling for age, education, global and hippocampal atrophy in patients with AD. CONCLUSIONS: This study suggests that the degree of complexity of WMH rating scales did not affect validation against WMH volumes, but did vary in validation against cognition. The simplest scale performed best in separating cognitive subgroups, but the more complex scales and quantification correlated better with cognitive measures, especially executive function. Hence the best choice of scale depends on the particular application.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".