Microvessel stenosis, enlarged perivascular spaces, and fibrinogen deposition are associated with ischemic periventricular white matter hyperintensities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Periventricular white matter hyperintensities (pvWMH) are neuroimaging abnormalities surrounding the lateral ventricles that are apparent on magnetic resonance imaging (MRI). They are associated with age, neurodegenerative disease, and cerebrovascular risk factors. While pvWMH ultimately represent a loss of white matter structural integrity, the pathological causes are heterogeneous in nature, and currently, cannot be distinguished using neuroimaging alone. pvWMH could occur because of a combination of small vessel disease (SVD), ependymal loss, blood-brain barrier dysfunction, and microgliosis. In this study we aimed to characterize microvascular stenosis, fibrinogen extravasation, and microgliosis within pvWMH with and without imaging evidence of periventricular infarction. Using postmortem neuroimaging of human brains (n = 20), we identified pvWMH with and without periventricular infarcts (PVI). We performed histological analysis of microvessel stenosis, perivascular spaces, microgliosis, and immunohistochemistry against fibrinogen as a measure of serum protein extravasation. Herein, we report distinctions between pvWMH with and without periventricular infarcts based on associations with microvessel stenosis, enlarged perivascular spaces, and fibrinogen IHC. Microvessel stenosis was significantly associated with PVI and with cellular deposition of fibrinogen in the white matter. The presence of fibrinogen was associated with PVI and increased number of microglia. These findings suggest that neuroimaging-based detection of infarction within pvWMH may help distinguish more severe lesions, associated with underlying microvascular disease and BBB dysfunction, from milder pvWMH that are a highly frequent finding on MRI.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it