<i>In Vivo</i>Magnetic Resonance Imaging and Semiautomated Image Analysis Extend the Brain Phenotype for<i>cdf/cdf</i>Mice
Bibliographic record
Abstract
Magnetic resonance imaging and computer image analysis in human clinical studies effectively identify abnormal neuroanatomy in disease populations. As more mouse models of neurological disorders are discovered, such an approach may prove useful for translational studies. Here, we demonstrate the effectiveness of a similar strategy for mouse neuroscience studies by phenotyping mice with the cerebellar deficient folia (cdf) mutation. Using in vivo multiple-mouse magnetic resonance imaging for increased throughput, we imaged groups of cdf mutant, heterozygous, and wild-type mice and made an atlas-based segmentation of the structures in 15 individual brains. We then performed computer automated volume measurements on the structures. We found a reduced cerebellar volume in the cdf mutants, which was expected, but we also found a new phenotype in the inferior colliculus and the olfactory bulbs. Subsequent local histology revealed additional cytoarchitectural abnormalities in the olfactory bulbs. This demonstrates the utility of anatomical magnetic resonance imaging and semiautomated image analysis for detecting abnormal neuroarchitecture in mutant mice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".