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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".