Typical and disrupted small-world architecture and regional communication in full-term and preterm infants
Bibliographic record
Abstract
Abstract Understanding the emergence of complex cognition in the neonate is one of the great frontiers of cognitive neuroscience. In the adult brain, small-world organization enables efficient information segregation and integration and dynamic adaptability to cognitive demands. It remains unknown, however, when functional small-world architecture emerges in development, whether it is present by birth and how prematurity affects it. We leveraged the world's largest fMRI neonatal dataset—Developing Human Connectome Project—to include full-term neonates (n = 278), and preterm neonates scanned at term-equivalent age (TEA; n = 72), or before TEA (n = 70), and the Human Connectome Project for a reference adult group (n = 176). Although different from adults', the small-world architecture was developed in full-term neonates at birth. The key novel finding was that premature neonates before TEA showed dramatic underdevelopment of small-world organization and regional communication in 9/11 networks, with disruption in 32% of brain nodes. The somatomotor and dorsal attention networks carry the largest spatial effect, and visual network the smallest. Significant prematurity-related disruption of small-world architecture and reduced efficiency of regional communication in networks related to high-order cognition, including language, persisted at TEA. Critically, at full-term birth or by TEA, infants exhibited functional small-world architecture, which facilitates differentiated and integrated neural processes that support complex cognition. Conversely, this brain infrastructure is significantly underdeveloped before infants reach TEA. These findings improve understanding of the ontogeny of functional small-world architecture and efficiency of neural communication, and of their disruption by premature birth.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".