Parity and APOEε4 genotype contribute distinct changes to functional connectivity across the middle-aged brain
Bibliographic record
Abstract
Abstract Cognition and its underlying neurobiology change throughout the trajectory of aging, with prominent sex differences and influences of sex-specific factors. Research has shown that parity (pregnancy and parenthood) uniquely altered various biomarkers of brain health in middle age depending on presence of Alzheimer’s disease (AD) risk. The present study builds on prior work by providing a comprehensive view of functional connectivity changes and elucidating how network-level dynamics contribute to cognitive outcomes depending on primiparity and APOEε4 genotype, the top genetic risk factor for late-onset sporadic AD risk. We assessed neural activation in middle-aged wildtype and hAPOEε4 rats that were either nulliparous (0 litters) or primiparous (1 litter). Activation of the immediate early gene zif268 was quantified across 19 brain regions implicated in memory and AD. Primiparous hAPOEε4 rats exhibited widespread reductions in neural activation, particularly in the dorsal striatum, nucleus accumbens, frontal cortex, and retrosplenial cortex. Network analyses further revealed that primiparous wildtype rats had the most cohesive and efficient functional connectivity networks. Notably, the hierarchy of influence of brain regions within the neural network shifted based on parity and hAPOEε4 genotype. Activation of hippocampal new-born neurons in conjunction with subregions of the dorsal striatum, frontal cortex, and retrosplenial cortex dynamically predicted cognitive performance in a parity- and genotype-dependent manner. These findings underscore the lasting impact of reproductive history on brain health and cognitive aging, highlighting the need to consider sex-specific experiences in aging and AD research.
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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 | 0.000 |
| 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) | 0.001 | 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".