Robust Perfusion Deficits in Cognitively Impaired Patients with Secondary-Progressive Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cognitive impairment is a common, disabling symptom of MS. We investigated the impact of cerebral perfusion and brain and lesion volumetry on cognitive performance in 45 patients with SPMS by using MR imaging. MATERIALS AND METHODS: Cognition was assessed by using a standard battery, the Minimal Assessment of Cognitive Function in Multiple Sclerosis. qCBF and qCBV maps were analyzed by using SPM and PLS. SPM was also used to conduct the GM, WM, and WML volumetric analyses. RESULTS: Both SPM and PLS demonstrated significantly reduced qCBV in the superior medial frontal cortex of impaired patients. PLS also revealed significantly lower qCBV in the bilateral thalami and caudate nuclei of impaired patients and identified a pattern of significantly attenuated qCBF similar to that of qCBV. Performance on the Symbol Digit Modalities Test, which assesses information-processing speed, correlated most strongly overall with cerebral perfusion. Focal (ie, voxelwise) analyses of GM, WM, and WML volume revealed no significant differences between patients with and without cognitive impairment, though global GM volume was significantly decreased and global WML volume was significantly increased in impaired patients. CONCLUSIONS: These results suggest that cognitively impaired patients with SPMS exhibit robust perfusion deficits in cortical and subcortical GM and impaired processing speed.
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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) | 0.002 | 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".