Susceptibility-weighted imaging in the experimental autoimmune encephalomyelitis model of multiple sclerosis indicates elevated deoxyhemoglobin, iron deposition and demyelination
Bibliographic record
Abstract
BACKGROUND: Susceptibility-weighted imaging (SWI) is an iron-sensitive magnetic resonance imaging (MRI) method that has shown iron-related lesions in multiple sclerosis (MS) patients. The contribution of deoxyhemoglobin to the signals seen in SWI has not been well characterized in MS. OBJECTIVES: To determine if SWI lesions (seen as focal hypointensities) exist in the experimental autoimmune encephalomyelitis (EAE) animal model of MS, and to determine whether the lesions relate to iron deposits, inflammation, demyelination, and/or deoxyhemoglobin in the vasculature. METHODS: We performed SWI on the lumbar spinal cord and cerebellum of EAE and control mice (both complete Freund's adjuvant/pertussis toxin (CFA/PTX)-immunized and naive). We also performed SWI on mice before and after perfusion (to remove blood from vessels). SWI lesions were counted and their locations were compared to histology for iron, myelin and inflammation. RESULTS: SWI lesions were found to exist in the EAE model. Many lesions seen by SWI were not present after perfusion, especially at the grey/white matter boundary of the lumbar spinal cord and in the cerebellum, indicating that these lesion signals were associated with deoxyhemoglobin present in the lumen of vessels. We also observed SWI lesions in the white matter of the lumbar spinal cord that corresponded to iron deposition, inflammation and demyelination. In the cerebellum, SWI lesions were present in white matter tracts, where we found histological evidence of inflammatory perivascular cuffs. CONCLUSIONS: SWI lesions exist in EAE mice. Many lesions seen in SWI were a result of deoxyhemoglobin in the blood, and so may indicate areas of hypoxia. A smaller number of SWI lesions coincided with parenchymal iron, demyelination, and/or inflammation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".