Sex- and Brain Size–Related Small-World Structural Cortical Networks in Young Adults: A DTI Tractography Study
Bibliographic record
Abstract
The anatomical connectivity of the human cerebral cortex resembles a "small-world" architecture, which is characterized by the coexistence of structurally segregated and integrative connectivity patterns. However, organizational differences in networks among individuals remain largely unknown. Here, we utilize diffusion tensor imaging tractography and graph-theoretical approaches to investigate the effects of sex and brain size on the topological organization of human cortical anatomical network. Weighted cortical networks were constructed from 72 young healthy participants by measuring anatomical connection densities between 78 cortical regions. As expected, all participants showed a small-world topology (high local clustering and short paths between nodes), which suggests a highly efficient topological organization. Furthermore, we found that females had greater local efficiencies than males. Moreover, smaller brains showed higher local efficiency in females but not in males, suggesting an interaction between sex and brain size. Specifically, we show that several brain regions (e.g., the precuneus, precentral gyrus, and lingual gyrus) had significant associations between nodal centrality and sex or brain size. Our findings suggest that anatomical network organization in the human brain is associated with sex and brain size and provide insights into the understanding of the structural substrates that underlie individual differences in behavior and 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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".