White matter injury predicts disrupted functional connectivity and microstructure in very preterm born neonates
Bibliographic record
Abstract
OBJECTIVE: To determine whether the spatial extent and location of early-identified punctate white matter injury (WMI) is associated with regionally-specific disruptions in thalamocortical-connectivity in very-preterm born neonates. METHODS: 37 very-preterm born neonates (median gestational age: 28.1 weeks; interquartile range [IQR]: 27-30) underwent early MRI (median age 32.9 weeks; IQR: 32-35), and WMI was identified in 13 (35%) neonates. Structural T1-weighted, resting-state functional Magnetic Resonance Imaging (rs-fMRI, n = 34) and Diffusion Tensor Imaging (DTI, n = 31) sequences were acquired using 3 T-MRI. A probabilistic map of WMI was developed for the 13 neonates demonstrating brain injury. A neonatal atlas was applied to the WMI maps, rs-fMRI and DTI analyses to extract volumetric, functional and microstructural data from regionally-specific brain areas. Associations of thalamocortical-network strength and alterations in fractional anisotropy (FA, a measure of white-matter microstructure) with WMI volume were assessed in general linear models, adjusting for age at scan and cerebral volumes. RESULTS: WMI volume in the superior (β = -0.007; p = .02) and posterior corona radiata (β = -0.01; p = .01), posterior thalamic radiations (β = -0.01; p = .005) and superior longitudinal fasciculus (β = -0.02; p = .001) was associated with reduced connectivity strength between thalamus and parietal resting-state networks. WMI volume in the left (β = -0.02; p = .02) and right superior corona radiata (β = -0.03; p = .008), left posterior corona radiata (β = -0.03; p = .01), corpus callosum (β = -0.11; p < .0001) and right superior longitudinal fasciculus (β = -0.02; p = .02) was associated with functional connectivity strength between thalamic and sensorimotor networks. Increased WMI volume was also associated with decreased FA values in the corpus callosum (β = -0.004, p = .015). CONCLUSIONS: Regionally-specific alterations in early functional and structural network complexity resulting from WMI may underlie impaired outcomes.
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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 | 0.001 |
| 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".