Regional Frontal Perfusion Deficits in Relapsing-Remitting Multiple Sclerosis with Cognitive Decline
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cortical dysfunction, quantifiable by cerebral perfusion techniques, is prevalent in patients with MS, contributing to cognitive impairment. We sought to localize perfusion distribution differences in patients with relapsing-remitting MS with and without cognitive impairment and healthy controls. MATERIALS AND METHODS: Thirty-nine patients with relapsing-remitting MS (20 cognitively impaired, 19 nonimpaired) and 19 age- and sex-matched healthy controls underwent a neurocognitive battery and MR imaging. Voxel-based analysis compared regional deep and cortical GM perfusion and volume among the cohorts. RESULTS: After we adjusted for localized volumetric differences in the right frontal, temporal, and occipital lobes, progressive CBF and CBV deficits were present in the left middle frontal cortex for all cohorts and in the left superior frontal gyrus for patients with cognitive impairment compared with patients without impairment and controls. Compared with healthy controls, reduced CBF was present in the limbic regions of patients with cognitive impairment, and reduced CBV was present in the right middle frontal gyrus in patients with cognitive impairment and in the temporal gyrus of relapsing-remitting MS patients without cognitive impairment. CONCLUSIONS: Consistent regional frontal cortical perfusion deficits are present in patients with relapsing-remitting MS, with more widespread hypoperfusion in those with cognitive impairment, independent of structural differences, indicating that cortical perfusion may be a useful biomarker of cortical dysfunction and cognitive impairment in 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 | 0.000 | 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".