Comparison of Tractography in Mouse Models of Multiple Sclerosis and Alzheimer’s Disease
Bibliographic record
Abstract
Tractography is a method that finds fiber tracts within a sample (e.g. a mouse brain), which allows users to better understand how different regions and structures of the brain are connected. The only animal magnetic resonance imaging (MRI) centre in Manitoba does not have the software to perform tractography on their images. This severely limits the variation of studies that can be performed in the centre. The goal of this project was to develop a robust tractography analysis method for the centre. The designed tractography analysis method was tested on known phantoms (objects which are meant to mimic tissue) such as celery, and then on animal brain samples from various mouse models of multiple sclerosis and Alzheimer’s disease. The first test of the tractography analysis method was to determine if the tracts within the corpus collosum in the brain of mice differ between mouse models. Tracts in the corpus callosum were measured using the developed tractography analysis method. Single factor ANOVA found no differences between the tractography parameters in tracts of the corpora callosa in a mouse model of multiple sclerosis (MS) and the corresponding wildtype mouse, nor between a mouse model of Alzheimer’s disease (AD) and its corresponding wildtype mouse. The tractography analysis method was successfully developed and is now ready for use in more complex models.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".