Bibliographic record
Abstract
It has been well known that gender plays a critical role in the anatomy and function of the human brain, as well as human behaviors. Recent neuroimaging studies have demonstrated gender effects on not only focal brain areas but also the connectivity between areas. Specifically, structural MRI and diffusion MRI data have revealed substantial gender differences in white matter-based anatomical connectivity. Structural MRI data further demonstrated gender differences in the connectivity revealed by morphometric correlation among brain areas. Functional connectivity derived from functional neuroimaging (e.g., functional MRI and PET) data is also modulated by gender. Moreover, male and female human brains display differences in the network topology that represents the organizational patterns of brain connectivity across the entire brain. In this review, the authors summarize recent findings in the multimodal brain connectivity/network research with gender, focusing on large-scale data sets derived from modern neuroimaging techniques. The literature provides convergent evidence for a substantial gender difference in brain connectivity within the human brain that possibly underlies gender-related cognitive differences. Therefore, it should be mandatory to take gender into account when designing experiments or interpreting results of brain connectivity/network in health and disease. Future studies will likely be conducted to explore the interdependence between gender-related brain connectivity/network and the gender-specific nature of brain diseases as well as to investigate gender-related characteristics of multimodal brain connectivity/network in the normal brain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".