The construction of MRI brain/head templates for Chinese children from 7 to 16 years of age
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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 it