Multiple-mouse magnetic resonance imaging with cryogenic radiofrequency probes for evaluation of brain development
Bibliographic record
Abstract
Multiple-mouse magnetic resonance imaging (MRI) increases scan throughput by imaging several mice simultaneously in the same magnet bore, enabling multiple images to be obtained in the same time as a single scan. This increase in throughput enables larger studies than otherwise feasible and is particularly advantageous in longitudinal study designs where frequent imaging time points result in high demand for MRI resources. Cryogenically-cooled radiofrequency probes (CryoProbes) have been demonstrated to have significant signal-to-noise ratio benefits over comparable room temperature coils for in vivo mouse imaging. In this work, we demonstrate implementation of a multiple-mouse MRI system using CryoProbes, achieved by mounting four such coils in a 30-cm, 7-Tesla magnet bore. The approach is demonstrated for longitudinal quantification of brain structure from infancy to early adulthood in a mouse model of Sanfilippo syndrome (mucopolysaccharidosis type III), generated by knockout of the Hgsnat gene. We find that Hgsnat−/− mice have regionally increased growth rates compared to Hgsnat+/+ mice in a number of brain regions, notably including the ventricles, amygdala and superior colliculus. A strong sex dependence was also noted, with the lateral ventricle volume growing at an accelerated rate in males, but several structures in the brain parenchyma growing faster in females. This approach is broadly applicable to other mouse models of human disease and the increased throughput may be particularly beneficial in studying mouse models of neurodevelopmental disorders.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".