Myelin Water Fraction Imaging of the Brain in Children with Prenatal Alcohol Exposure
Bibliographic record
Abstract
Background Prenatal alcohol exposure (PAE) is linked to alterations of cerebral white matter, including volume and nonspecific diffusion magnetic resonance imaging (MRI) indices of microstructure in humans. Some animal models of PAE have demonstrated myelination deficiencies, but myelin levels have not yet been evaluated in individuals with PAE. Multiecho T2 MRI offers a quantitative method to estimate myelin water fraction (MWF; related to myelin content) noninvasively, which was used here to evaluate brain myelination in children with PAE. Methods Participants with PAE (n = 10, 6 females, mean age 13.9 years, range 7 to 18 years) and controls (n = 14, 11 females, mean age 13.2 years, range 9 to 16 years) underwent 3T MRI of the brain. T2 images (15 minutes acquisition for 32 echoes) were used to create MWF maps from which mean MWF was measured in 12 regions of interest (ROIs) including 8 in white matter and 4 in deep gray matter. Results As expected, across the combined sample, MWF was highest for major white matter tracts such as the internal capsule and genu/splenium of the corpus callosum (10 to 18%) while the caudate and putamen had MWF less than 5%. Mean MWF was similar across 11/12 brain white and gray matter regions for the PAE and control groups (L/R internal capsule, major forceps, putamen, caudate nucleus, L minor forceps, genu and splenium of corpus callosum). In the PAE group, MWF was positively correlated with age in the genu of corpus callosum and right minor forceps, notably 2 frontal tracts. Conclusions Given comparable MRI‐derived myelination fraction measures in PAE relative to controls, white matter alterations shown in other imaging studies, such as diffusion tensor imaging, may reflect microstructural anomalies related to axon caliber and density.
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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 | 0.000 | 0.001 |
| 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".