A structural MRI study of cholinergic pathways and cognition in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: White matter hyperintensities (WMH) in the cholinergic pathways are associated with cognitive performance in Alzheimer's disease. This study aimed to evaluate the relationship between the volume reduction of cholinergic pathways and cognitive function in patients with multiple sclerosis (MS). METHODS: Thirty-two MS patients underwent a brain MRI and cognitive measurements including the Mini-Mental State Examination (MMSE) and the Japanese version of the Montreal Cognitive Assessment (MoCA-J). The extent of WMH within the cholinergic pathways was assessed using the Cholinergic Pathways Hyperintensities Scale (CHIPS). Computerized WMH volumes were also obtained. FreeSurfer was used to measure regional volumes including the cortical and subcortical volumes. The correlations among the CHIPS, the WMH volume, and the clinical data were assessed, in addition to the correlations between the cognitive scores and regional volumes measured by FreeSurfer. RESULTS: The CHIPS score and the WMH volume were strongly positively correlated with each other (r = 0.87, P < 0.001). The CHIPS score had significantly negative correlations with the MMSE (r = - 0.49, P = 0.003) and the MoCA-J (r = - 0.47, P = 0.005) results. The WMH volume had significantly negative correlations with the MMSE (r = - 0.54, P = 0.001) and the MoCA-J (r = - 0.57, P < 0.001) results. In the analysis by FreeSurfer, both the MMSE and MoCA-J scores had significant positive correlations only with the volume of the corpus callosum. CONCLUSIONS: The CHIPS score tended to be less sensitive to the WMH volume in cognitive function evaluation, although the difference did not reach the level of statistical significance. Thus the CHIPS method may not be as effective in MS patients.
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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".