Sex & menopause related differences in vascular health and rsFMRI connectivity in middle‐aged adults
Bibliographic record
Abstract
Abstract Background Menopause is associated with hormonal changes that can impact vascular health and brain function, including resting‐state functional connectivity (rsFC). Declines in estrogen have been linked to memory disruptions and altered connectivity in networks supporting cognition 1 . Vascular risk (VR) factors, such as cholesterol, blood pressure (BP), and BMI, may exacerbate these effects, particularly in postmenopausal females 2,3 . Understanding how menopause and VR influence rsFC is crucial for identifying mechanisms underlying memory decline and informing strategies to support cognitive health in aging females. Method We conducted two complementary analyses to (1) investigate how VR factors influence rsFC between males vs. females and (2) examine whether menopause status in middle‐aged females influences these associations. Rs‐fMRI data was collected from 42 premenopausal, 41 postmenopausal and 39 male participants. FC matrices were computed using 200 cortical ROIs 4 and 4 hippocampal ROIs 5 . VR was assessed using measures of BMI, exercise, education, age, cholesterol, and systolic BP. VR factors were entered as behavioral vectors in two‐group B‐PLS analyses (Analysis #1: males vs. females; Analysis #2: premenopausal vs. postmenopausal females) to identify group‐specific rsFC patterns. Result Analysis 1 identified both similarities and differences in the effect of VR on rsFC in males and females. Both sexes exhibited increased FC between hippocampus (HC) and other cortical networks, and decreased FC between Dorsal and Ventral Attention networks (DAN, VAN) for individuals with higher cholesterol, who also exercised regularly. In males this pattern was also associated with BMI. In females this pattern of FC was also associated with higher BP, and older age. In addition, females uniquely showed increased FC between DAN and Control (CON) and Default Mode (DMN) networks, respectively, which was also related to increased cholesterol, BP, age and regular exercise. Analysis 2 indicated the effects observed in females in Analysis 1 were driven by postmenopausal females. Conclusion Sex and menopause status shape the relationship between VR and rsFC. Females appear more vulnerable to VR‐related rsFC changes, particularly postmenopausal females, where cholesterol, age, and BP affected rsFC involving the hippocampus, DMN, DAN, and Control networks. Interestingly, BMI was not associated with rsFC in females, but was in males.
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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".