Association Between Periventricular and Subcortical White Matter Hyperintensities and Cognition in a Local Population
Bibliographic record
Abstract
ABSTRACT Background: White matter hyperintensities (WMH) on fluid-attenuated inversion recovery MRI sequence are regions where fluid from supplying vessels leaks into brain tissue. Some studies have demonstrated an association between WMH and cognitive decline. Given the common WMH risk factors in our local population, the aim of this study is to examine the relationship of overall and regional WMH with cognition in Hamilton, Canada. Methods: Adults presenting to Hamilton General Hospital in 2020 with a head MRI and cognitive assessment within 6 months of the MRI were included in our cross-sectional study. MRIs were reviewed, assigning a periventricular (PV), a subcortical (SC) and an overall severity score to each based on the Fazekas scale, ranging from 0 to 3. Montreal Cognitive Assessment (MoCA) scores were used as a measure of cognitive function. Patients with confounding diagnoses were excluded. Multiple regression analyses were conducted between WMH and cognitive scores, adjusting for hypertension, diabetes and smoking. Results: Multiple regression models revealed R 2 values of 0.097, 0.050 and 0.036 for overall, PV and SC WMH with MoCA, respectively. There were negative associations between overall Fazekas scores and MoCA ( B = −2.11, p < 0.001), PV scores and MoCA ( B = −1.46, p < 0.001) and SC scores and MoCA ( B = −1.21, p = 0.002). Conclusion: The association between MRI WMH and cognition supports prognostic use for cognitive decline to limit/delay deterioration. Specifically, stronger PV associations prompt research and perhaps development of revised scales prioritizing PV changes. Implementing this into the field of radiology whereby WMH severity and location assessment becomes a standard within brain MRI reports could improve patient outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".