Total Cerebral Small Vessel Disease Burden on MRI Correlates With Medial Temporal Lobe Atrophy and Cognitive Performance in Patients of a Memory Clinic
Bibliographic record
Abstract
Background: Cerebral small vessel disease (cSVD) and neurodegeneration are the two main causes of dementia and are considered distinct pathological processes, while studies have shown overlaps and interactions between the two pathological pathways. Medial temporal atrophy (MTA) is considered a classic marker of neurodegeneration. We aimed to investigate the relationship of total cSVD burden and MTA on MRI using a total cSVD score and to explore the impact of the two MRI features on cognition. Methods: Patients in a memory clinic were enrolled, who underwent brain MRI scan and cognitive evaluation within 7 days after the first visit. MTA and total cSVD score were rated using validated visual scales. Cognitive function was assessed by using Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scales. Spearman's correlation and regression models were used to test (i) the association between MTA and total cSVD score as well as each cSVD marker and (ii) the correlation of the MRI features and cognitive status. Results: A total of 312 patients were finally enrolled, with a median age of 75.0 (66.0–80.0) years and 40.7% (127/312) males. All of them finished MRI and MMSE, and 293 subjects finished MoCA. Of note, 71.8% (224/312) of the patients had at least one of the cSVD markers, and 48.7% (152/312) of them had moderate–severe MTA. The total cSVD score was independently associated with MTA levels, after adjusting for age, gender, years of education, and other vascular risk factors (OR 1.191, 95% CI 1.071–1.324, P = 0.001). In regard to individual markers, a significant association existed only between white matter hyperintensities and MTA after adjusting for the factors mentioned above (OR 1.338, 95% CI 1.050–1.704, P = 0.018). Both MTA and total cSVD score were independent risk factors for MMSE ≤ 26 (MTA: OR 1.877, 95% CI 1.407–2.503, P < 0.001; total cSVD score: OR 1.474, 95% CI 1.132–1.921, P = 0.004), and MoCA < 26 (MTA: OR 1.629, 95% CI 1.112–2.388, P = 0.012; total cSVD score: OR 1.520, 95% CI 1.068–2.162, P = 0.020). Among all the cSVD markers, microbleed was found significantly associated with MMSE ≤ 26, while no marker was demonstrated a relationship with MoCA < 26. Conclusion: Cerebral small vessel disease was related to MTA in patients of a memory clinic, and both the MRI features had a significant association with cognitive impairment.
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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 | 0.001 | 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) | 0.002 | 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".