Exploration of the correlation between superficial cerebral veins identified using susceptibility-weighted imaging findings and cognitive differences between sexes based on deep learning: a preliminary study
Bibliographic record
Abstract
Background: This study aimed to investigate the association of superficial cerebral veins (SCVs) with sex-related cognitive differences and the possible hemodynamic mechanisms underlying these associations. Methods: This investigation was a prospective case-control study. A total of 344 healthy volunteers were recruited. In all, 200 volunteers were included to establish the deep learning model, and 144 volunteers were used for the research, including 72 males (50%) and 72 females (50%). No significant differences in age (P=0.358) or education (P=0.779) were observed between the sexes. Cognitive functioning was evaluated using neuropsychological tests, including the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment-Basic (MOCA-B). Susceptibility-weighted imaging scans were acquired with a 3.0 T magnetic resonance imaging system using a 32-channel high-resolution phased array coil. Minimum intensity projection images were obtained by reconstructing susceptibility-weighted imaging data. A deep learning model was trained on the minimum intensity projection images to quantify the diameter, tortuosity index, length, and the number of SCVs in the bilateral cerebral hemispheres. Finally, the association between cognitive differences between males and females and the properties of the SCVs was analyzed. Results: The MMSE and MOCA-B scores of males were significantly higher than those of females (P<0.05). Males had more SCVs in the bilateral cerebral hemispheres than did females (right hemisphere: P<0.01; left hemisphere: P<0.05). The number of SCVs in the right cerebral hemisphere was significantly and positively correlated with the MMSE and MOCA-B scores (correlation coefficients: 0.246 and 0.201, respectively; P<0.05). The number of SCVs in the left cerebral hemisphere was positively correlated with the MMSE scores (correlation coefficient: 0.196; P<0.05) and the MOCA-B scores. In this study, no significant correlations were observed between cognition and the diameter, length, or tortuosity index of the SCVs in the bilateral cerebral hemispheres. Conclusions: The cognitive function of males was better than that of females, and the different numbers of SCVs may be one of the explanations for this phenomenon of sex-based differences in cognition.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".