High white matter hyperintensity burden in strategic white matter tracts relates to worse global cognitive performance in community-dwelling individuals
Bibliographic record
Abstract
BACKGROUND: White matter hyperintensities (WMH) are associated with cognitive impairment. The impact of WMH on cognitive domains (e.g. processing speed, executive functioning) depends on location. We determined whether the relevance of WMH location also applies to global cognitive functioning by testing if WMH in strategic white matter tracts are associated with global cognitive functioning independent of total WMH burden. METHODS: We included 830 community-dwelling individuals. WMH volume within two a priori specified strategic white matter tracts (forceps minor and anterior thalamic radiation) were entered in a linear regression model with the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) as outcome variables and corrected for total WMH volume and other MRI markers for vascular injury and neurodegenerations (i.e. brain parenchymal fraction, and the presence of lacunes and microbleeds). RESULTS: WMH in the forceps minor and left anterior thalamic radiation inversely correlated with MoCA, and WMH in the forceps minor inversely correlated with MMSE, independent of total WMH volume and other MRI markers. CONCLUSION: The impact of WMH on global cognitive functioning depends on location. Whether this reflects accumulated impairment in isolated cognitive domains or disruption of a network that is crucially involved in global cognitive performance remains to be determined.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".