White Matter Microstructural Damage in Small Vessel Disease Is Associated With Montreal Cognitive Assessment But Not With Mini Mental State Examination Performances
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Montreal Cognitive Assessment (MoCA) has been proposed as a screening tool in vascular cognitive impairment. Diffusion tensor imaging is sensitive to white matter microstructural damage. We investigated if diffusion tensor imaging-derived indices are more strongly associated with performances on MoCA or on the widely used mini mental state examination in patients with mild cognitive impairment and small vessel disease. METHODS: Mild cognitive impairment patients with moderate/severe degrees of white matter hyperintensities on MRI were enrolled. Lacunar infarcts, cortical atrophy, medial temporal lobe atrophy and median values of mean diffusivity and fractional anisotropy of the cerebral white matter were studied and correlated with cognitive tests performances. RESULTS: Seventy-six patients (mean age 75.1±6.8 years, mean years of education 8.0±4.3) were assessed. In univariate analyses, a significant association of both MoCA and mini mental state examination scores with age, education, cortical atrophy, and medial temporal lobe atrophy was found, whereas mean diffusivity and fractional anisotropy were associated with MoCA. In partial correlation analyses, adjusting for all demographic and neuroimaging variables, both mean diffusivity and fractional anisotropy were associated only with MoCA (mean diffusivity: r= -0.275, P=0.023; fractional anisotropy: r=0.246, P=0.043). CONCLUSIONS: In patients with mild cognitive impairment and small vessel disease, diffusion tensor imaging-measured white matter microstructural damage is more related to MoCA than mini mental state examination performances. MoCA is suited for the cognitive screening of patients with small vessel disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".