Diffusion MRI tractography with along-tract profiling reveals subtle neurodevelopmental differences between moderate and late preterm infants
Bibliographic record
Abstract
PURPOSE: Moderately preterm (MP) infants (32-33 weeks' gestation) are at increased risk for developmental problems compared to late preterm (LP) infants (34-36 weeks' gestation). Fiber bundle tractography remains an unexplored avenue to understanding this risk-difference between MP and LP infants. This study aimed to examine along-tract profile differences between MP and LP infants at term-equivalent age (TEA). METHODS: (single shell). dMRI scans were preprocessed to reduce common artifacts. For all infants, 15 fiber bundles were reconstructed using TractSeg and along-tract profiles, expressed as fractional anisotropy (FA) and mean diffusivity (MD), and were compared between MP and LP infants using tractometry. RESULTS: Reconstructions with TractSeg demonstrated shape, position, and orientation of fiber bundles consistent with known neuroanatomy. FA and MD profiles were not significantly different between MP and LP infants. However, alternating trends towards along-tract profile differences between MP and LP infants were observed for multiple bundles. Wide 95% confidence intervals indicated substantial variability in fiber bundle organization within groups. CONCLUSION: Although not significant, along-tract differences between MP and LP infants suggest subtle alterations in white matter maturation. These findings indicate along-tract variability as potential focus for future research aimed at uncovering the mechanisms underlying early maturational differences and their potential role in later neurodevelopmental challenges encountered in moderate-late preterm infants.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".