Cerebrovascular reactivity and implications for understanding the pathophysiology of multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is a severe neurological disease commonly diagnosed in young adults. Recent controversial publications attribute the inflammation, sclerosis, and degenerative lesions to venous insufficiency. We recently found characteristic reductions in cerebrovascular reactivity (CVR) in the presence of venous congestion caused by other neurological disorders. We therefore hypothesize that CVR may also be reduced in patients with MS. A computer‐controlled gas blender (RespirAct ™ , TRI, Canada) was used to precisely target normoxic P ET CO2 changes between 40 to 50 mmHg in 7 MS subjects with varying type and severity. BOLD MRI was performed simultaneously as an indirect measure of changes in cerebral blood flow. CVR, defined on a voxel‐wise basis as %ΔBOLD signal/ΔP ET CO2, was color coded and superimposed on anatomical scans to form CVR maps. Each CVR map was compared voxel‐by‐voxel with a CVR atlas generated from 37 healthy subjects. Contrary to our hypothesis, CVR in MS subjects was greater than the healthy cohort. The degree of hyper‐reactivity correlated with the severity of MS. Regions of increased CVR may reflect the confounding dependency of BOLD MRI on cerebral blood volume which could be increased due to insufficient venous drainage. However, it may actually reflect an increased flow response to hypercapnia which overwhelms the altered venous draining system leading to venous congestion.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".