Increased Perfusion in Normal Appearing White Matter in High Inflammatory Multiple Sclerosis Patients
Bibliographic record
Abstract
PURPOSE: Although cerebral perfusion alterations have long been acknowledged in multiple sclerosis (MS), the relationship between measurable perfusion changes and the status of highly active MS has not been examined. We hypothesized that alteration of perfusion can be detected in normal appearing white matter and is increased in high inflammatory patients. MATERIALS AND METHODS: Thirty-three patients with relapsing-remitting MS underwent four monthly 3T MRI scans including dynamic susceptibility contrast perfusion-weighted MRI. Cerebral blood flow (CBF) and cerebral blood volume (CBV) were measured in normal appearing white matter. Patients were stratified in a high- and low-inflammatory group according to the number of new contrast enhancing lesions. RESULTS: Thirteen patients were classified as high-inflammatory. Compared to low-inflammatory patients, the high-inflammatory group demonstrated significantly higher CBV (p = 0.001) and CBF (p = 0.014) values. A mixed model analysis to assess independent variables associated with CBV and CBF revealed that white matter lesion load and atrophy measurements had no significant influence on CBF and CBV. CONCLUSION: This work provides evidence that high inflammatory lesion load is associated with increased CBV and CBF, underlining the role of global modified microcirculation prior to leakage of the blood-brain barrier in the pathophysiology of MS. Perfusion changes might therefore be sensitive to active inflammation apart from lesion development without local blood-brain barrier breakdown, and could be utilized to further assess the metabolic aspect of current inflammation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".