Understanding variability in brain MRI templates: Optimal sample sizes for representative population averages
Bibliographic record
Abstract
Average anatomical brain templates are routinely used in neuroanatomical and functional studies. Several widely used anatomical models were historically constructed with different algorithms and a limited number of subjects. We performed an experiment to determine the number of subjects required to achieve a stable population average and to estimate variability in individual subjects’ registration. We used a random subset of 2000 subjects from the UK Biobank (between 40 and 60 years of age) to generate a “silver standard” population average and then ran a template generation process with a variable number of subjects from 10 to 320, repeating each draw 50 times in a bootstrapping fashion. We compared two methods which are widely used in the literature to generate population averages (ANIMAL and ANTs). Our results showed that 160 subjects are enough to generate a stable population average, and both methods achieve comparable results, with ANTs having advantage over ANIMAL when a smaller number of subjects are available.
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How this classification was reachedexpand
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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".