Evaluating the impact of cerebrovascular disease on cognition using quantitative MRI
Bibliographic record
Abstract
Brain atrophy and cerebrovascular disease are associated with cognitive dysfunction, increase in frequency with age and commonly co-occur. However, the relationships between these pathologies and their independent contributions to cognitive function remain unclear. This study quantified brain atrophy and multiple expressions of cerebrovascular disease in 205 individuals, including 34 normal elderly controls, 30 with cognitive impairment and 141 with dementia. Correlations between brain measures were identified and factor analysis was used to generate independent variables that could be used in multiple linear regression models of brain-behavior relationships. The results confirm and extend previous findings suggesting that brain atrophy is the strongest correlate of cognitive impairment. Atrophy was the only relevant factor in those under age 65. Diffuse and strategically located cerebrovascular disease contributed independently to cognitive status in those over age 65. Both the volume and location of cerebrovascular disease (e.g. anterior-medial thalamus) were important determinants of the effects of cerebrovascular disease on cognition. The concept of strategic location of cerebrovascular disease was extended to subcortical white matter pathways and possible specific effects of hyperintensities in acetylcholinergic white matter pathways were identified. Taken together, these in vivo studies demonstrate that cerebrovascular disease has small but independent effects on cognitive function and provide impetus to study interventions which might slow or halt the development of cerebrovascular disease with age.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".