White matter alterations and cognitive outcomes in children born very low birth weight
Bibliographic record
Abstract
BACKGROUND: Very low birth weight (VLBW) infants are at risk for disrupted white matter maturation, yet little is known about the contributing factors, particularly at preschool-age when cognitive difficulties begin to emerge. We examined white matter microstructure in five-year-old VLBW and full-term (FT) children, and its association with cognitive outcomes and birth weight. METHODS: Multi-shell diffusion and MR images were obtained for 41 VLBW (mean birth weight: 1028.6 ± 256.8 g) and 26 FT (3295.4 ± 493.9 g) children. Fractional anisotropy (FA), radial diffusivity (RD), neurite orientation dispersion index (ODI) and density index (NDI) were estimated using diffusion tensor and neurite orientation dispersion and density imaging models. Between-group analyses used a general linear model with group and sex as explanatory variables. Within-group associations between white matter microstructure, cognitive outcomes and birth weight were also investigated. RESULTS: VLBW compared to FT children showed lower FA and NDI across widespread white matter regions. Smaller clusters of atypical ODI were also found in VLBW children. Within-group analyses in FT children revealed that lower RD and higher NDI were associated with vocabulary acquisition and working memory. In VLBW children, higher FA and NDI, and lower RD and ODI, were associated with improved processing speed. In both groups, FA was positively associated with birth weight. CONCLUSIONS: Our findings demonstrate white matter alterations in young VLBW children, including widespread reductions in axon density that may reflect sustained myelination disruptions. The associations with cognitive outcomes may also highlight which of the VLBW children are at higher risk for later cognitive difficulties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".