Cerebral focal WMH co‐locate with transcerebral intramedullary vessels and can vary over time
Bibliographic record
Abstract
Abstract Background The origin of MRI focal white matter hyperintensities (fWMH) is not fully understood. Recent evidence suggests the perivascular surface of arterioles and venules may serve as cerebral lymphatics for homeostasis of interstitial fluid and toxic metabolite clearance. This system could be injured by age‐related vascular wall damage, particularly venous collagenosis in Alzheimer's disease (AD), where fWMH could be an early marker of this injury. We investigated whether fWMH spatially co‐localize with deep medullary vessels (DMV), and change dynamically reflecting vasogenic edema. Method 107 AD and 30 controls (age=74) were included with baseline and follow‐up MRI. fWMH were foci <10mm on T2/FLAIR. DMVs were linear visible streaks on T1, invert‐T2 or SWI. T2/FLAIR was co‐registered to T1 space. The spatial relationship of each fWMH with DMV was classified as either 'perivascular positive' if an fWMH was overlapped/centered by a DMV or otherwise as 'perivascular negative'. Each fWMH was followed for change over time in 70 AD and 19 controls. We reasoned that fWMH would exhibit dynamic change if they reflect vasogenic edema. Result At the baseline, 1630 fWMH were identified, with 91.6% perivascular positive. They distributed mostly in the frontal (60.2%) and occipitoparietal (32.2%) region, along the angles of the lateral ventricles, areas with highest distribution of intramedullary venules, suggesting perivenular distribution. In follow‐up, 1098 fWMH showed change over time. 1019 (92.8%) of 1098 fWMH were perivascular positive with 8.5% decreased, 31.9% increased and 59.6% unchanged over 1.5 years. AD had a higher rate of fWMH increase (χ 2 =6.23, p = 0.012), while controls had a higher rate of fWMH unchanged over time (χ 2 =5.16, p = 0.023). Most DMVs connected to lateral ventricles and had trans‐cerebral features, consistent with intramedullary veins. Small venular infarctions were sometimes observed. Conclusion Most fWMH were distributed along DMVs, particularly. Their dynamic progression was compatible with being fluid in nature and not necessarily indicative of ischemiaas previously thought. fWMH could relate to multiple underlying pathologies, but venous insufficiency of deep intramedullary venules may be an important substrate of fWMH in AD and aging.
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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 | 0.001 |
| 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 | 0.000 | 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".