A Three-dimensional MRI Atlas of the Mouse Brain with Estimates of the Average and Variability
Bibliographic record
Abstract
Although there is growing interest in finding mouse models of human disease, no technique for quickly and quantitatively determining anatomical mutants currently exists. Magnetic resonance imaging (MRI) is ideally suited to probe fine structures in mice. This technology is three-dimensional, non-destructive and rapid compared to histopathology; hence MRI scientists have been able to create detailed three-dimensional images of 60 mum resolution or better. The data is digital which lends itself to sophisticated image processing algorithms. Here we show a variational MRI atlas constructed from nine excised brains of 8 week old 129S1/SvImJ male mice. This new type of atlas is comprised of an unbiased average brain--created from alignment of the individual brains--and the mathematical descriptors of anatomical variation across the individuals. We found that the majority of internal points in the individuals never varied more than 117 microm from equivalent points in the atlas. A three-dimensional annotation of the average image was performed and used to estimate the mean and standard deviation of volumes in a variety of structures across the individual brains; these volumes never differed by more than 5%. Our results indicate that variational atlases of inbred strains represent a well-defined basis against which mutant outliers can be readily compared.
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.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".