White matter microstructural differences identified using multi-shell diffusion imaging in six-year-old children born very preterm
Bibliographic record
Abstract
INTRODUCTION: The underlying microstructural properties of white matter differences in children born very preterm (<32 weeks gestational age) can be investigated in depth using multi-shell diffusion imaging. The present study compared white matter across the whole brain using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics in children born very preterm and full-term children at six years of age. We also investigated associations between white matter microstructure with early brain injury and developmental outcomes. METHOD: Multi-shell diffusion imaging, T1-weighted anatomical MR images and developmental assessments were acquired in 23 children born very preterm (16 males; mean scan age: 6.57 ± 0.34 years) and 24 full-term controls (10 males, mean scan age: 6.62 ± 0.37 years). DTI metrics were obtained and neurite orientation dispersion index (ODI) and density index (NDI) were estimated using the NODDI diffusion model. FSL's tract-based spatial statistics were performed on traditional DTI metrics and NODDI metrics. Voxel-wise comparisons were performed to test between-group differences and within-group associations with developmental outcomes (intelligence and visual motor abilities) as well as early white matter injury and germinal matrix/intraventricular haemorrhage (GMH/IVH). RESULTS: In comparison to term-born children, the children born very preterm exhibited lower fractional anisotropy (FA) across many white matter regions as well as higher mean diffusivity (MD), radial diffusivity (RD), and ODI. Within-group analyses of the children born very preterm revealed associations between higher FA and NDI with higher IQ and VMI. Lower ODI was found within the corona radiata in those with a history of white matter injury. Within the full-term group, associations were found between higher NDI and ODI with lower IQ. CONCLUSION: Children born very preterm exhibit lower FA and higher ODI than full-term children. NODDI metrics provide more biologically specific information beyond DTI metrics as well as additional information of the impact of prematurity and white matter microstructure on cognitive outcomes at six years of age.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".