From Structure to Function: Social and Cognitive Networks in Children Born Very Low Birth Weight
Bibliographic record
Abstract
Infants born very low birth weight (VLBW, <1500 g) have an increased risk of social and cognitive impairments, which can affect their social and academic success at school. However, little is known about the functional and structural underpinnings of these impairments at preschool-age, an important transitional period in development, as well as the early modifiable predictors of outcome, such as postnatal nutrition. The present thesis used a multi-modal approach to investigate altered white matter microstructure and functional connectivity in five-year-old VLBW children. Using multi-shell diffusion imaging, studies 1 & 2 assessed white matter microstructure in VLBW compared to full-term (FT) children and associations with early nutrition (during postnatal days 9-29) and developmental outcome. Fractional anisotropy (FA), radial diffusivity (RD), neurite orientation dispersion index (ODI) and density index (NDI) were estimated using diffusion tensor and NODDI models. Using magnetoencephalography, social and cognitive networks were assessed during a resting-state (Study 3) and an emotional face processing task (Study 4). Studies 1 & 2 demonstrated disrupted white matter maturation in VLBW compared to FT children. These findings suggest that lower FA in VLBW children may be partly due to increases in axon dispersion, reflecting less coherent organization of axons. We also found that among the macronutrients studied, greater protein intake contributed most to the beneficial effect of nutrition, showing increases in FA and reductions in RD at preschool-age. Study 3 demonstrated increased resting-state functional connectivity in VLBW compared to FT children in the gamma (65-80 Hz) frequency band, anchored in frontal regions known to underlie social-cognitive functions. Further, significant associations between macronutrient/energy intakes with functional connectivity at preschool-age were found. Study 4 demonstrated that VLBW compared to FT children show atypical recruitment of emotional face processing networks in theta (4–7 Hz), specific to angry faces. This hypo-connected network involved key emotion face processing regions with links to inhibitory control. In conclusion, VLBW children showed alterations in structural and functional connectivity underlying social and cognitive functioning at preschool-age. Most importantly, our study relates these findings to early postnatal nutrition, demonstrating the long-term impacts of nutrition on preterm brain development.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".