Correlation of White Matter Hyperintensities and Perivascular Spaces With Montreal Cognitive Assessment (MoCA) Scores in Patients Evaluated for Anti-amyloid Therapy
Bibliographic record
Abstract
Objective This study aimed to investigate the relationship between white matter hyperintensities (WMH) and perivascular spaces (PVS) on magnetic resonance imaging (MRI) with Montreal Cognitive Assessment (MoCA) scores in patients referred for possible lecanemab therapy based on clinical suspicion of Alzheimer's disease (AD) prior to biomarker confirmation. Materials and methods In this retrospective review, 149 consecutive patients with suspected AD between November 2023 and June 2024 who were evaluated for possible lecanemab therapy were identified. All underwent brain MRI and had valid MoCA scores. WMH were graded using the Fazekas scale (0-3). PVS were visually graded (1-4) in the basal ganglia and centrum semiovale on T2-weighted images. Generalized linear models assessed the association between imaging markers and MoCA, adjusting for age, sex, hypertension (HTN), hyperlipidemia (HLD), and diabetes mellitus (DM). Results The mean MoCA score was 19.56, reflecting mild to moderate cognitive impairment. The mean Fazekas score was 1.37, indicating mild to moderate WMH burden, while the mean PVS scores for basal ganglia and centrum semiovale were 1.99 and 2.37, respectively. There is no significant correlation between age and MoCA scores in our patient population. A negative association of -0.1 between the Fazekas score and MoCA score was observed after controlling for the effects of PVS. In contrast, PVS did not significantly correlate with MoCA score. Conclusion In patients evaluated for possible lecanemab therapy, a higher WMH burden was negatively associated with global cognition, whereas PVS demonstrated no significant relationship with MoCA scores. These findings suggest that WMH may be an imaging marker of vascular pathology in those suspected of AD.
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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 | 0.003 |
| 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 | 0.001 | 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".