Visual functional magnetic resonance imaging of preterm infants
Bibliographic record
Abstract
AIM: The aim of this study was to determine the feasibility of undertaking visual functional magnetic resonance imaging (fMRI) in very preterm children. METHOD: Forty-seven infants born at less than 32 weeks gestational age (25 males, 22 females; mean (SD) age at birth 28.8 wks [1.9]) were scanned using 1.5 T MRI as part of a longitudinal neuroimaging study. These infants were scanned at preterm age (within 2 wks of birth) and at term-equivalent age. Quantitative T2* data and fMRI in response to visual stimuli (flashing strobe) were acquired in this population. T2* values were compared at preterm age and at term-equivalent age using a two-tailed t-test. A general linear model was used to evaluate occipital lobe response to visual stimuli. RESULTS: T2* values were significantly higher at preterm age than at term-equivalent age in both the medial and lateral occipital lobes (preterm infants: 187.2 ms and 198.4 ms respectively; term infants: 110.9 ms and 133.2 ms respectively; p<0.002). Significant positive occipital lobe activation (q<0.01) was found in 3 out of 65 (5%) fMRIs carried out at preterm age and in 19 out of 26 (73%) scans carried out at term-equivalent age. INTERPRETATION: Visual stimuli do not elicit a reliable blood oxygen level-dependent (BOLD) response in very preterm infants during the preterm period. This suggests that BOLD fMRI may not be the appropriate modality for investigating occipital lobe function in very 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 | 0.003 |
| 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".