Cerebral Microbleeds Are Associated with Impairments in Executive Function and Processing Speed
Bibliographic record
Abstract
BACKGROUND: Cerebral microbleed (CMB) is an increasingly important risk factor for cognitive impairment due to population aging. Controversies, however, remain regarding the exact association between CMB and cognitive dysfunction. OBJECTIVE: We aimed to determine the relationship between CMB burden and cognitive impairment, and also explore the characteristics of cognitive decline in CMB patients for middle-aged and elderly people. METHODS: The present cross-sectional study included 174 participants (87 CMB patients and 87 controls) who underwent brain magnetic resonance imaging and a battery of neuropsychological test. Global cognitive function was measured using Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Compound z-scores were calculated for three cognitive subdomains: memory, executive function and processing speed. RESULTS: CMB patients had lower scores of MMSE (p < 0.001) and MoCA (p < 0.001). Patients at each category of CMB count had worse performance in global cognitive function and all three cognitive subdomains (p < 0.001). In multiple linear regression models, CMB patients had significantly greater declines in executive function (p < 0.001), processing speed (p < 0.001), and MoCA (p = 0.003) with increasing number of CMB. We found no relationship between CMB location and cognition (p > 0.05). CONCLUSION: CMB is associated with impairment in global cognition as well as for all tested subdomains. Strongest effect sizes were seen for tests which rely on executive functioning, where performance deficits increased in proportion to degree of CMB burden. Prospective studies are needed to evaluate whether the association between CMB and executive dysfunction is causal.
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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 | 0.002 |
| 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".