Rewiring the extremely preterm brain: Altered structural connectivity relates to language function
Bibliographic record
Abstract
Children born preterm are at increased risk for cognitive impairment, with higher-order functions such as language being especially vulnerable. Previously, we and others have reported increased interhemispheric functional connectivity in children born extremely preterm; the finding appears at odds with literature showing decreased integrity of the corpus callosum, the primary commissural bundle, in preterm children. We address the apparent discrepancy by obtaining advanced measures of structural connectivity in twelve school-aged children born extremely preterm (<28 weeks) and ten term controls. We hypothesize increased extracallosal structural connectivity might support the functional hyperconnectivity we had previously observed. Participants were aged four to six years at time of study and groups did not differ in age, sex, race, ethnicity, or socioeconomic status. Whole-brain and language-network-specific (functionally-constrained) connectometry analyses were performed. At the whole-brain level, preterm children had decreased connectivity in the corpus callosum and increased connectivity in the cerebellum versus controls. Functionally-constrained analyses revealed significantly increased extracallosal connectivity between bilateral temporal regions in preterm children (FDRq <0.05). Connectivity within these extracallosal pathways was positively correlated with performance on standardized language assessments in children born preterm (FDRq <0.001), but unrelated to performance in controls. This is the first study to identify anatomical substrates for increased interhemispheric functional connectivity in children born preterm; increased reliance on an extracallosal pathway may represent a biomarker for resiliency following extremely preterm 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 | 0.002 |
| 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 | 0.001 |
| 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".