Sex matters: the effect of physical activity on brain perfusion
Bibliographic record
Abstract
Cerebral blood flow (CBF) declines consistently in aging, and this decline is a critical component of several late-life diseases. Understanding why this occurs in normal aging, prior to pathological changes, is crucial. Physical activity (PA) is a powerful preventative tool to improve vascular health and preserves CBF in both sexes, though females may benefit most throughout the lifespan. There is currently limited knowledge, however, about what intensity is needed to derive benefit, and if there are sex differences in this relationship with intensity. Here, CBF and PA were investigated according to sex and age. A total of 573 participants aged 36 to 90 years were included from the Human Connectome Lifespan Aging. Linear and quadratic regressions were utilized to investigate relationships among CBF and PA intensities in each of the 4 groups. Vigorous PA in middle-aged males was related to greater CBF (P < .05). Older females showed benefit at all intensities (P < .05). Middle-aged females were least sensitive to the effects of PA. In all groups except older males, hippocampal CBF was only dependent on vigorous PA (P < .05). These results highlight the sex-specific relationship between CBF and PA, and the importance of tailoring recommendations to sex and lifespan stage, including addressing and updating current public health guidelines to maximize adoption and benefit, specific to brain health.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".