The construction of MRI brain/head templates for Chinese children from 7 to 16 years of age
Bibliographic record
Abstract
Population-specific brain templates that provide detailed brain information are beneficial to both structural and functional neuroimaging research. However, age-specific MRI templates have not been constructed for Chinese or any Asian developmental populations. This study developed novel T1-weighted average brain and head templates for Chinese children from 7 to 16 years of age in two-year increments using high quality magnetic resonance imaging (MRI) and well-validated image analysis techniques. A total of 138 Chinese children (51 F/87 M) were included in this study. The internally and externally validated registrations show that these Chinese age-specific templates fit Chinese children's MR images significantly better than age-specific templates created from U.S. children, or adult templates based on either Chinese or North American adults. It implies that age-inappropriate (e.g., the Chinese56 template, the US20-24 template) and nationality-inappropriate brain templates (e.g., U.S. children's templates, the US20-24 template) do not provide optimal reference MRIs for processing MR brain images of Chinese pediatric populations. Thus, our age-specific MRI templates are the first of the kind and should be useful in neuroimaging studies with children from Chinese or other Asian populations. These templates can also serve as the foundations for the construction of more comprehensive sets of nationality-specific templates for Asian developmental populations. These templates are available for use in our database.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".