Deep medullary veins disruption in cerebral small vessel disease: links to AI-quantified lesions and cognitive decline
Bibliographic record
Abstract
Background The relationship between cerebral small vessel diseases (CSVD) and deep medullary veins (DMV) has been the subject of only a limited number of studies to date, with the majority of research focusing on the correlation between DMV and cognitive function in patients with CSVD. The present study aims to quantitatively evaluate the relationship between DMV and CSVD imaging markers, utilizing an artificial intelligence neuroimaging system to achieve this objective. Furthermore, an investigation was conducted into the correlation between DMV disruption and cognitive impairment. Methods This cross-sectional study enrolled patients with CSVD imaging markers who were admitted to the Department of Neurology at our hospital. The quantitative assessment of CSVD imaging markers, encompassing white matter hyperintensities (WMH), lacunar infarction (LI), and cerebral microbleeds (CMB), was conducted employing the AccuBrain® neuroimaging system. The DMV was evaluated using susceptibility-weighted imaging (SWI) with a semi-quantitative scale. The Montreal Cognitive Assessment (MoCA) score was evaluated for each patient. Results The study’s sample population comprised 171 patients. Multivariate ordinal logistic regressions indicated that DMV score was significantly associated with WMH volume, LI count, and CMB count (p < 0.05). In the context of multivariate linear regression analysis, a significant negative correlation was observed between the DMV score and the MoCA score, with the latter being adjusted for confounding variables such as age, education, gender, and smoking history (p = 0.003). The application of path analysis revealed a significant correlation between the DMV score and the MoCA score, which indicates that WMH volume plays a mediating role in this relationship, thereby offering a novel perspective on cognitive function and neurodegenerative processes. Conclusion DMV score is associated with the severity of WMH, CMB, and LI, as well as cognitive performance decline, implicating that cerebral venule damage may play a role in the development of CSVD and related 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 | 0.001 | 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 | 0.001 | 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".