Deep Gray Matter Maturation in Very Preterm Neonates: Regional Variations and Pathology-related Age-dependent Changes in Magnetization Transfer Ratio
Bibliographic record
Abstract
PURPOSE: To elucidate the relationship between gestational age, pathologic findings, and magnetic resonance (MR) imaging measures of tissue maturation-myelination in deep gray matter areas in very preterm neonates imaged at birth. MATERIALS AND METHODS: The study was approved by the research ethics board. Written informed consent was given by the infants' parents. Forty-two preterm neonates (19 boys; median gestational age, 28.7 weeks) with normal-appearing gray matter structures at presentation underwent MR imaging within 2 weeks of birth that included T1- and T2-weighted, magnetization transfer, and T1 relaxometry sequences. Neonates were separated into the following groups: those with normal findings (n = 23), those with white matter injury (WMI) (n = 9), those with grade I germinal matrix hemorrhage (GMH) (n = 3), and those with grade II GMH and WMI (n = 7). Analysis of covariance was used to determine regional effects of age and pathologic findings on magnetization transfer ratio (MTR) and to assess the relationship between MTR and T1. RESULTS: MTR increased linearly with age (P ≤ .0265), with a similar rate of change of 0.32% per week (95% confidence interval [CI]: 0.16, 0.49) in the basal ganglia (BG) and thalami. A lower trend (0.11% per week; 95% CI: -0.05, 0.28) was seen in the pons. Higher MTRs were seen in the thalami and pons than in the BG (P < .05), indicating earlier maturation. Accordingly, higher T1 values were observed in the BG relative to the thalami (P < .0001). Higher MTRs in the BG were observed in the group of neonates with normal findings at presentation than in the group with WMI (P = .02). CONCLUSION: MTR measurements can be used to monitor early myelination in the developing brain and to help detect changes in tissue that are not shown on T1- and T2-weighted MR images.
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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 | 0.006 |
| 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 | 0.000 |
| 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".