Understanding disease processes in multiple sclerosis through magnetic resonance imaging studies in animal models
Bibliographic record
Abstract
There are exciting new advances in multiple sclerosis (MS) resulting in a growing understanding of both the complexity of the disorder and the relative involvement of grey matter, white matter and inflammation. Increasing need for preclinical imaging is anticipated, as animal models provide insights into the pathophysiology of the disease. Magnetic resonance (MR) is the key imaging tool used to diagnose and to monitor disease progression in MS, and thus will be a cornerstone for future research. Although gadolinium-enhancing and T2 lesions on MRI have been useful for detecting MS pathology, they are not correlative of disability. Therefore, new MRI methods are needed. Such methods require validation in animal models. The increasing necessity for MRI of animal models makes it critical and timely to understand what research has been conducted in this area and what potential there is for use of MRI in preclinical models of MS. Here, we provide a review of MRI and magnetic resonance spectroscopy (MRS) studies that have been carried out in animal models of MS that focus on pathology. We compare the MRI phenotypes of animals and patients and provide advice on how best to use animal MR studies to increase our understanding of the linkages between MR and pathology in patients. This review describes how MRI studies of animal models have been, and will continue to be, used in the ongoing effort to understand MS.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".