Microvessel Stenosis and Density Analysis of Post Mortem WMH Detected Using Ultra High Field MRI in Aging, Cerebrovascular and Alzheimer Disease
Bibliographic record
Abstract
Cerebral small vessel disease (SVD) affects the microvasculature of the brain, increasing in prevalence with age and associating with cognitive decline and dementia. SVD can be visualized on MR imaging by markers such as hyperintensities within the white matter (WMH). Periventricular WMH within the frontal lobe specifically may have a negative effect on executive functions. Executive dysfunction represents a set of cognitive changes, including processing speed and attention, that can precede memory impairment in Alzheimer disease. The etiology and development of cerebral small vessel disease, including WMH, and it's relation to executive dysfunction is not well understood. Previous work has characterized the presence of stenosis of the arterioles, but little work has focused on the venules. In this study we sought to characterize alterations in microvascular stenosis and density including small and medium venules and arterioles within the frontal white matter. Post mortem 7‐Tesla MR imaging of formalin‐fixed coronal brain sections was performed on 20 brains with a neuropathological diagnosis of normal, cerebrovascular disease or Alzheimer disease. We calculated the percent stenosis within small, medium and large venules and arterioles within both the periventricular and subcortical white matter. Stenosis of the small (<50 um diameter) arterioles and venules within the periventricular white matter specifically was associated with increased severity of periventricular WMH. Small vein and artery stenosis was also associated with the presence of periventricular infarction identified on T1‐weighted imaging. This highlights the utility of multiple sequences when interpreting MRI findings within the white matter. Further study on markers of small vessel disease that affect executive functioning, such as frontal WMH, can help elucidate mechanisms that lead to cognitive decline prior to the onset of memory impairments and dementia. Support or Funding Information Canada First Research Excellence Fund to BrainsCAN Canadian Institutes of Health Research Canadian Consortium on Neurodegeneration in Aging This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".