A pediatric structural MRI analysis of healthy brain development from newborns to young adults
Bibliographic record
Abstract
Assessment of healthy brain maturation can be useful toward better understanding natural patterns of brain growth and toward the characterization of a variety of neurodevelopmental disorders as deviations from normal growth trajectories. Structural magnetic resonance imaging (MRI) provides excellent soft-tissue contrast, which allows for the assessment of gray and white matter in the developing brain. We performed a large-scale retrospective analysis of 993 pediatric structural brain MRI examinations of healthy subjects (n = 988, aged 0-32 years) imaged clinically at 3 T, and extracted a wide variety of measurements such as white matter volumes, cortical thickness, and gyral curvature localized to subregions of the brain. All extracted structural biomarkers were tested for their correlation with subject age at time of imaging, providing measurements that may assist in the assessment of neurological maturation. Additional analyses were also performed to assess gender-based differences in the brain at a variety of developmental stages, and to assess hemispheric asymmetries. Results add to the literature by analyzing a realistic distribution of healthy participants imaged clinically, a useful cohort toward the investigation and creation of diagnostic tests for a variety of pathologies as aberrations from healthy growth trajectories. The next generation of diagnostic tests will be responsible for identifying pathological conditions from populations of healthy clinically imaged individuals. Hum Brain Mapp 38:5931-5942, 2017. © 2017 Wiley Periodicals, Inc.
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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 | 0.002 | 0.001 |
| 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".