Sexual dimorphism in the cerebrovascular network: Brain MRI shows lower arterial density in women
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Accumulating evidence suggests that there is a sexual dimorphism in brain health, with women exhibiting greater disability following strokes of comparable size and having a higher prevalence of cognitive impairment later in life. Despite the critical implication of the cerebrovascular architecture in brain perfusion and brain health, it remains unclear whether structural differences in vessel density exist across the sexes. METHODS: In this study, we used high-density MRI imaging to characterize the intracerebral arterial and venous density of 28 (14 women) sex-matched healthy young volunteers in vivo. Using an in-house vessel segmentation algorithm, we quantified and compared these vascular features across the cortical and subcortical deep gray matter, white matter, and periventricular white matter. RESULTS: We found that, on average, women have reduced intracerebral arterial density in comparison to men (F 2.34 ± 0.48%, M 2.67 ± 0.39%; p<.05). This difference was most pronounced in the subcortical deep gray matter (F 1.78 ± 0.53%, M 2.38 ± 0.82%; p<.05) and periventricular white matter (F 0.68 ± 0.15%, M 1.14 ± 0.33%; p<.0005), indicating a potential sex-specific vulnerability to hypoperfusion in areas critical to core cerebral functions. In contrast, venous density did not exhibit a significant difference between sexes. CONCLUSIONS: While this research remains exploratory, it raises important pathophysiological considerations for brain health, adverse cerebrovascular events, and dementia across the sexes. Our findings also highlight the need to take into account sex differences when investigating cerebral characteristics in humans.
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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 | 0.000 | 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) | 0.003 | 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".