Magnetic resonance imaging as a tool for in vivo and ex vivo anatomical phenotyping in experimental genetic models
Bibliographic record
Abstract
This article describes a suite of computational approaches suitable for deriving various quantitative phenotypes from structural magnetic resonance (MR) images obtained in rodents and used subsequently in genetic studies of complex traits. We begin by introducing the basic principles of genetic studies of complex traits in experimental models. We then illustrate the use of MR-based computational anatomy in vivo and ex vivo, and in combination with histology. This work was carried out in two inbred strains of rats, namely spontaneously hypertensive rats and Brown Norway rats; these are parental strains of the only existing panel of recombinant inbred strains of rats. The rats were scanned in vivo at two time points (at 8 and 12 weeks of age) and ex vivo (at 12 weeks of age). We describe between-strain differences and across-time changes in brain and kidney volumes, as well as regional variations in brain structure using surface- and deformation-based approaches. We conclude by discussing the power of the population-based computational analysis of MR images, and their fusion with histology, in studies of complex traits.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".