Gray matter blood‐brain barrier water exchange dynamics are reduced in progressive multiple sclerosis
Bibliographic record
Abstract
Abstract Background and Purpose To compare transcapillary wall water exchange, a putative marker of cerebral metabolic health, in brain T2 white matter (WM) lesions and normal appearing white and gray matter (NAWM and NAGM, respectively) in individuals with progressive multiple sclerosis (PMS) and healthy controls (HC). Methods Dynamic‐contrast‐enhanced 7T MRI data were obtained from 19 HC and 23 PMS participants. High‐resolution pharmacokinetic parametric maps representing tissue microvascular and microstructural properties were created by shutter‐speed (SS) paradigm modeling to obtain estimates of blood volume fraction (vb), water molecule capillary efflux rate constant (kpo), and the water capillary wall permeability surface area product (PwS ≡ vb*kpo). Linear regression models were used to investigate differences in (i) kpo and PwS between groups in NAWM and NAGM, and (ii) between WM lesions and NAWM in PMS. Results High‐resolution parametric maps were produced to visualize tissue classes and resolve individual WM lesions. Normal‐appearing gray matter kpo and PwS were significantly decreased in PMS compared to HC (p ≤ .01). Twenty‐one T2 WM lesions were analyzed in 10 participants with PMS. kpo was significantly decreased in WM lesions compared to PMS NAWM (p < .0001). Conclusions Transcapillary water exchange is reduced in PMS NAGM compared to HC and is further reduced in PMS WM lesions, suggesting pathologically impaired brain metabolism. kpo provides a sensitive measure of cerebral metabolic activity and/or coupling, and can be mapped at higher spatial resolution than conventional imaging techniques assessing metabolic activity.
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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 | 0.000 |
| 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) | 0.001 | 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".