Rightward White Matter Disease is Correlated to Intraplaque Hemorrhage
Bibliographic record
Abstract
BACKGROUND: Cerebrovascular disease (CVD) is a leading cause of mortality with a strong link to cognitive impairment and dementia. White matter lesions (WML) are prevalent in CVD and are early markers of vascular compromise, particularly in relation to intraplaque hemorrhage (IPH), an indicator of carotid artery plaque instability. As vascular disease represents a possible treatment window for dementia subjects, this study explores the relationship between hemispheric WML asymmetry and IPH utilizing a large multicenter cohort to find novel biomarkers of disease. METHOD: FLAIR MRI scans of 264 subjects from the Canadian Atherosclerosis Imaging Network were categorized as IPH positive (IPH+) or IPH negative (IPH-) and WML biomarkers were automatically computed (Figure 1). Biomarkers related to WML prevalence (volume) and WML ischemia and progression (intensity) were extracted: ICV-normalized WML volume (WML-ICV), WML mean intensity (WML-Intensity), and WML intensity ratio (WML-IR). WML asymmetry was assessed via an asymmetry index measure (AIM). Linear mixed models and regression analyses were conducted, with adjustments for age, sex, scanner manufacturer, and stenosis, to evaluate associations between WML biomarkers and IPH status. RESULT: IPH+ patients exhibited significant rightward asymmetry in WML-ICV (0.0032 ± 0.002, p < 0.05), WML-Intensity (7.26 ± 5.41, p < 0.05), and WML-IR (0.0271 ± 0.0204, p < 0.05); Table 1. IPH+ subjects (left, right or bilateral) had more lesions that were brighter in the right hemisphere. This trend was most pronounced in younger male patients (<65 years), suggesting a high-risk demographic. Regression analysis revealed IPH as a significant predictor of WML asymmetry, with stronger effects observed in subjects with IPH in the right carotid artery. CONCLUSION: Previous studies suggest more injury in the right hemisphere for subjects with small vessel disease, and this work supports this finding. With rightward WML asymmetry being strongly associated with IPH, this could be reflecting a surrogate marker for overall vascular disease and its contribution to brain health and dementia. Automated WML biomarkers can be used to identify these high-risk patients and guide early interventions for subjects with vascular disease and dementia. Future work should validate these findings in larger, longitudinal datasets to enhance clinical applications.
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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 | 0.003 |
| 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) | 0.003 | 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".