The Asymmetry of White Matter Hyperintensity Burden Between Hemispheres Is Associated With Intracranial Atherosclerotic Plaque Enhancement Grade
Bibliographic record
Abstract
Purpose: The contribution of intracranial atherosclerotic stenosis (ICAS) to the development of white matter hyperintensities (WMHs) has not been fully elucidated. We aimed to retrospectively assess the relationship between WMH burden and unilateral ICAS by combined examination of lumen stenosis, plaque enhancement, and cerebral perfusion. Materials and Methods: A cross-sectional study of 41 patients with symptomatic unilateral ICAS (mean age 57 ± 10 years; 26 males) was conducted. Detailed clinical data, including vascular risk factors, were obtained. WMH volume was derived from 3D-fluid-attenuated inversion recovery (3D-FLAIR) and was assessed by using a validated semiautomated protocol. Lumen stenosis, plaque enhancement, and cerebral perfusion (assessed on time-to-peak parameter using the Alberta Stroke Program Early CT score (TTP-ASPECTS) scale) were evaluated. The WMH volumes of periventricular (PWMH) and deep (DWMH) white matter were calculated separately and compared between hemispheres. Associations between WMH volume (interhemispheric volume difference, ipsilateral and contralateral to the ICAS site separately), unilateral ICAS imaging metrics, and vascular risk factors were assessed by using linear regression. Results: The DWMH volume ipsilateral to the ICAS site (ipsilateral DWMH volume) was significantly greater than that of the contralateral site (P < 0.001), while the PWMH volume difference between hemispheres did not reach statistical significance. The interhemispheric DWMH volume difference was significantly associated with a higher plaque enhancement grade (β = 0.436, P = 0.005) and inversely associated with cerebral hypoperfusion (lower TTP-ASPECTS) (β = -0.613, P < 0.001). In the between-subjects multivariable regression analysis, while older age (β = 0.323, P = 0.025), hypoperfusion (β = -0.394, P = 0.007), and hypertension (β = 0.378, P = 0.011) were independently associated with ipsilateral DWMH volume, plaque enhancement did not show an association with ipsilateral DWMH volume. The association between ipsilateral DWMH volume and lumen stenosis approached statistical significance (β = 0.274, P = 0.084). Conclusion: The DWMH was attributed to chronic hypoperfusion secondary to atherosclerotic stenosis. The association between the asymmetry of deep white matter lesions and plaque enhancement might suggest that increased deep white matter lesions are those ischemic lesions, which are more prone to the development of stroke.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".