White Matter Imaging Correlates of Early Cognitive Impairment Detected by the Montreal Cognitive Assessment After Transient Ischemic Attack and Minor Stroke
Bibliographic record
Abstract
Background and Purpose— Among screening tools for cognitive impairment in large cohorts, the Montreal Cognitive Assessment (MoCA) seems to be more sensitive to early cognitive impairment than the Mini-Mental State Examination (MMSE), particularly after transient ischemic attack or minor stroke. We reasoned that if MoCA-detected early cognitive impairment is pathologically significant, then it should be specifically associated with the presence of white matter hyperintensities (WMHs) and reduced fractional anisotropy (FA) on magnetic resonance imaging. Methods— Consecutive eligible patients with transient ischemic attack or minor stroke (Oxford Vascular Study) underwent magnetic resonance imaging and cognitive assessment. We correlated MoCA and MMSE scores with WMH and FA, then specifically studied patients with low MoCA and normal MMSE. Results— Among 400 patients, MoCA and MMSE scores were significantly correlated (all P <0.001) with WMH volumes ( r MoCA =−0.336; r MMSE =−0.297) and FA ( r MoCA =0.409; r MMSE =0.369) and—on voxel-wise analyses—with WMH in frontal white matter and reduced FA in almost all white matter tracts. However, only the MoCA was independently correlated with WMH volumes ( r =−0.183; P <0.001), average FA values ( r =0.218; P <0.001), and voxel-wise reduced FA in anterior tracts after controlling for the MMSE. In addition, patients with low MoCA but normal MMSE (n=57) had higher WMH volumes ( t =3.1; P =0.002), lower average FA ( t =−4.0; P <0.001), and lower voxel-wise FA in almost all white matter tracts than those with normal MoCA and MMSE (n=238). Conclusions— In patients with transient ischemic attack or minor stroke, early cognitive impairment detected with the MoCA but not with the MMSE was independently associated with white matter damage on magnetic resonance imaging, particularly reduced FA.
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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 | 0.001 |
| 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 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".